diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index eb0d7df83c..899821a340 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -19,14 +19,14 @@ repos:
language: system
types_or: [python, pyi]
args: [--fix]
- exclude: ^docs/docs/API-Reference/python-examples/
+ exclude: ^docs/.*/API-Reference/python-examples/
- id: ruff-format
name: ruff format
entry: uv run ruff format
language: system
types_or: [python, pyi]
args: [--config, pyproject.toml]
- exclude: ^docs/docs/API-Reference/python-examples/
+ exclude: ^docs/.*/API-Reference/python-examples/
- id: validate-migrations
name: Validate Alembic Migrations (Expand-Contract)
entry: python src/backend/base/langflow/alembic/migration_validator.py
diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js
index ac0a628fe0..2e716892c5 100644
--- a/docs/docusaurus.config.js
+++ b/docs/docusaurus.config.js
@@ -111,15 +111,19 @@ const config = {
sidebarPath: require.resolve("./sidebars.js"), // Use sidebars.js file
sidebarCollapsed: true,
// Versioning configuration
- lastVersion: "1.8.0",
+ lastVersion: "1.9.0",
versions: {
current: {
- label: "1.9.x (Next)",
+ label: "1.10.x (Next)",
path: "next",
},
+ "1.9.0": {
+ label: "1.9.x",
+ path: "",
+ },
"1.8.0": {
label: "1.8.x",
- path: "",
+ path: "1.8.0",
},
},
beforeDefaultRemarkPlugins: [
diff --git a/docs/sidebars.js b/docs/sidebars.js
index 020a83d037..29b97bee19 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -491,10 +491,6 @@ module.exports = {
id: "API-Reference/api-flows-run",
label: "Flow trigger endpoints",
},
- "API-Reference/api-reference-api-examples",
- "API-Reference/typescript-client",
- "API-Reference/flow-devops-sdk",
- "API-Reference/api-flows-run",
{
type: "category",
label: "Developer API (Beta)",
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/README.md b/docs/versioned_docs/version-1.9.0/API-Reference/README.md
new file mode 100644
index 0000000000..72500fe345
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/README.md
@@ -0,0 +1,25 @@
+# API Examples (Local Test Harness)
+
+Run all API example suites against a local Langflow server:
+
+```bash
+make api_examples_local
+```
+
+Run one test suite:
+
+```bash
+make api_examples_local suites=python
+make api_examples_local suites=javascript
+make api_examples_local suites=curl
+```
+
+The following examples are not executed in this harness:
+
+- `api-build/build-flow-and-stream-events-2.py`
+- `api-build/build-flow-and-stream-events-3.py`
+- `api-flows-run/stream-llm-token-responses.py`
+- `api-openai-responses/example-streaming-request.py`
+- `api-logs/stream-logs.py`
+- `api-logs/retrieve-logs-with-optional-parameters.py`
+- `api-users/reset-password.py`
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-build.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-build.mdx
new file mode 100644
index 0000000000..de826d955f
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-build.mdx
@@ -0,0 +1,198 @@
+---
+title: Build endpoints
+slug: /api-build
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiBuildBuildFlowAndStreamEvents from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-build/build-flow-and-stream-events.sh';
+import resultApiBuildResultBuildFlowAndStreamEvents from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-build/result-build-flow-and-stream-events.json';
+import exampleApiBuildBuildFlowAndStreamEvents2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-build/build-flow-and-stream-events-2.sh';
+import resultApiBuildResultBuildFlowAndStreamEvents2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-build/result-build-flow-and-stream-events-2.json';
+import exampleApiBuildBuildFlowAndStreamEvents3 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-build/build-flow-and-stream-events-3.sh';
+import exampleApiBuildSetStartAndStopPoints from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-build/set-start-and-stop-points.sh';
+import exampleApiBuildOverrideFlowParameters from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-build/override-flow-parameters.sh';
+import resultApiBuildResultOverrideFlowParameters from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-build/result-override-flow-parameters.json';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import examplePythonApiBuildBuildFlowAndStreamEvents from '!!raw-loader!@site/docs/API-Reference/python-examples/api-build/build-flow-and-stream-events.py';
+import exampleJavascriptApiBuildBuildFlowAndStreamEvents from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-build/build-flow-and-stream-events.js';
+import examplePythonApiBuildBuildFlowAndStreamEvents2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-build/build-flow-and-stream-events-2.py';
+import exampleJavascriptApiBuildBuildFlowAndStreamEvents2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-build/build-flow-and-stream-events-2.js';
+import examplePythonApiBuildBuildFlowAndStreamEvents3 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-build/build-flow-and-stream-events-3.py';
+import exampleJavascriptApiBuildBuildFlowAndStreamEvents3 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-build/build-flow-and-stream-events-3.js';
+import examplePythonApiBuildSetStartAndStopPoints from '!!raw-loader!@site/docs/API-Reference/python-examples/api-build/set-start-and-stop-points.py';
+import exampleJavascriptApiBuildSetStartAndStopPoints from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-build/set-start-and-stop-points.js';
+import examplePythonApiBuildOverrideFlowParameters from '!!raw-loader!@site/docs/API-Reference/python-examples/api-build/override-flow-parameters.py';
+import exampleJavascriptApiBuildOverrideFlowParameters from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-build/override-flow-parameters.js';
+
+
+
+:::info
+The `/build` endpoints are used by Langflow's frontend visual editor code.
+These endpoints are part of the internal Langflow codebase.
+
+Don't use these endpoints to run flows in applications that use your Langflow flows.
+To run flows in your apps, see [Flow trigger endpoints](/api-flows-run).
+:::
+
+The `/build` endpoints support Langflow's frontend code for building flows in the Langflow visual editor.
+You can use these endpoints to build vertices and flows, as well as execute flows with streaming event responses.
+You might need to use or understand these endpoints when contributing to the Langflow codebase.
+
+## Build flow and stream events
+
+This endpoint builds and executes a flow, returning a job ID that can be used to stream execution events.
+
+1. Send a POST request to the `/build/$FLOW_ID/flow` endpoint:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Result
+
+
+
+
+
+2. After receiving a job ID from the build endpoint, use the `/build/$JOB_ID/events` endpoint to stream the execution results:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Result
+
+
+
+
+
+The `/build/$FLOW_ID/events` endpoint has a `stream` query parameter that defaults to `true`.
+To disable streaming and get all events at once, set `?stream=false`.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Build headers
+
+| Header | Info | Example |
+|--------|------|---------|
+| Content-Type | Required. Specifies the JSON format. | "application/json" |
+| accept | Optional. Specifies the response format. | "application/json" |
+| x-api-key | Optional. Required only if authentication is enabled. | "sk-..." |
+
+## Build parameters
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| inputs | object | Optional. Input values for flow components. |
+| data | object | Optional. Flow data to override stored configuration. |
+| files | array[string] | Optional. List of file paths to use. |
+| start_component_id | string | Optional. ID of the component where the execution should start. Component `id` values can be found in [Langflow JSON files](/concepts-flows-import#langflow-json-file-contents) |
+| stop_component_id | string | Optional. ID of the component where the execution should stop. Component `id` values can be found in [Langflow JSON files](/concepts-flows-import#langflow-json-file-contents).|
+| log_builds | Boolean | Whether to record build logs. Default: Enabled (`true`). |
+
+### Set start and stop points
+
+The `/build` endpoint accepts optional values for `start_component_id` and `stop_component_id` to control where the flow run starts and stops.
+Setting `stop_component_id` for a component triggers the same behavior as clicking **Run component** on that component in the visual editor: The specified component and all dependent components leading up to that component will run.
+
+The following example stops flow execution at an **OpenAI** component:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+### Override flow parameters
+
+The `/build` endpoint also accepts inputs for `data` directly, instead of using the values stored in the Langflow database.
+This is useful for running flows without having to pass custom values through the visual editor.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## See also
+
+- [Get Vertex builds](/api-monitor#get-vertex-builds)
+- [Delete Vertex builds](/api-monitor#delete-vertex-builds)
+- [Session ID](/session-id)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-files.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-files.mdx
new file mode 100644
index 0000000000..558bc4b395
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-files.mdx
@@ -0,0 +1,576 @@
+---
+title: Files endpoints
+slug: /api-files
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiFilesUploadFileV1 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/upload-file-v1.sh';
+import resultApiFilesResultUploadFileV1 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-upload-file-v1.json';
+import exampleApiFilesUploadImageFilesV1 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/upload-image-files-v1.sh';
+import exampleApiFilesUploadImageFilesV12 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/upload-image-files-v1-2.sh';
+import exampleApiFilesListFilesV1 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/list-files-v1.sh';
+import resultApiFilesResultListFilesV1 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-list-files-v1.json';
+import exampleApiFilesDownloadFileV1 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/download-file-v1.sh';
+import resultApiFilesResultDownloadFileV1 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-download-file-v1.txt';
+import exampleApiFilesDeleteFileV1 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/delete-file-v1.sh';
+import resultApiFilesResultDeleteFileV1 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-delete-file-v1.json';
+import exampleApiFilesUploadFileV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/upload-file-v2.sh';
+import resultApiFilesResultUploadFileV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-upload-file-v2.txt';
+import exampleApiFilesUploadFileV22 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/upload-file-v2-2.sh';
+import exampleApiFilesSendFilesToYourFlowsV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/send-files-to-your-flows-v2.sh';
+import exampleApiFilesSendFilesToYourFlowsV22 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/send-files-to-your-flows-v2-2.sh';
+import exampleApiFilesListFilesV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/list-files-v2.sh';
+import resultApiFilesResultListFilesV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-list-files-v2.json';
+import exampleApiFilesDownloadFileV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/download-file-v2.sh';
+import resultApiFilesResultDownloadFileV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-download-file-v2.txt';
+import exampleApiFilesEditFileNameV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/edit-file-name-v2.sh';
+import resultApiFilesResultEditFileNameV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-edit-file-name-v2.json';
+import exampleApiFilesDeleteFileV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/delete-file-v2.sh';
+import resultApiFilesResultDeleteFileV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-delete-file-v2.json';
+import exampleApiFilesDeleteAllFilesV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/delete-all-files-v2.sh';
+import resultApiFilesResultDeleteAllFilesV2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-files/result-delete-all-files-v2.json';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import examplePythonApiFilesUploadFileV1 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/upload-file-v1.py';
+import exampleJavascriptApiFilesUploadFileV1 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/upload-file-v1.js';
+import examplePythonApiFilesUploadImageFilesV1 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/upload-image-files-v1.py';
+import exampleJavascriptApiFilesUploadImageFilesV1 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/upload-image-files-v1.js';
+import examplePythonApiFilesUploadImageFilesV12 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/upload-image-files-v1-2.py';
+import exampleJavascriptApiFilesUploadImageFilesV12 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/upload-image-files-v1-2.js';
+import examplePythonApiFilesListFilesV1 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/list-files-v1.py';
+import exampleJavascriptApiFilesListFilesV1 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/list-files-v1.js';
+import examplePythonApiFilesDownloadFileV1 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/download-file-v1.py';
+import exampleJavascriptApiFilesDownloadFileV1 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/download-file-v1.js';
+import examplePythonApiFilesDeleteFileV1 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/delete-file-v1.py';
+import exampleJavascriptApiFilesDeleteFileV1 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/delete-file-v1.js';
+import examplePythonApiFilesUploadFileV2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/upload-file-v2.py';
+import exampleJavascriptApiFilesUploadFileV2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/upload-file-v2.js';
+import examplePythonApiFilesUploadFileV22 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/upload-file-v2-2.py';
+import exampleJavascriptApiFilesUploadFileV22 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/upload-file-v2-2.js';
+import examplePythonApiFilesSendFilesToYourFlowsV2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/send-files-to-your-flows-v2.py';
+import exampleJavascriptApiFilesSendFilesToYourFlowsV2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/send-files-to-your-flows-v2.js';
+import examplePythonApiFilesSendFilesToYourFlowsV22 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/send-files-to-your-flows-v2-2.py';
+import exampleJavascriptApiFilesSendFilesToYourFlowsV22 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/send-files-to-your-flows-v2-2.js';
+import examplePythonApiFilesListFilesV2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/list-files-v2.py';
+import exampleJavascriptApiFilesListFilesV2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/list-files-v2.js';
+import examplePythonApiFilesDownloadFileV2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/download-file-v2.py';
+import exampleJavascriptApiFilesDownloadFileV2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/download-file-v2.js';
+import examplePythonApiFilesEditFileNameV2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/edit-file-name-v2.py';
+import exampleJavascriptApiFilesEditFileNameV2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/edit-file-name-v2.js';
+import examplePythonApiFilesDeleteFileV2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/delete-file-v2.py';
+import exampleJavascriptApiFilesDeleteFileV2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/delete-file-v2.js';
+import examplePythonApiFilesDeleteAllFilesV2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-files/delete-all-files-v2.py';
+import exampleJavascriptApiFilesDeleteAllFilesV2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-files/delete-all-files-v2.js';
+
+
+
+Use the `/files` endpoints to move files between your local machine and Langflow.
+
+All `/files` endpoints (both `/v1/files` and `/v2/files`) require authentication with a Langflow API key.
+You can only access files that belong to your own user account, even as a superuser.
+
+## Differences between `/v1/files` and `/v2/files`
+
+There are two versions of the `/files` endpoints.
+
+`/v2/files` offers the following improvements over `/v1/files`:
+
+- `/v2` files are organized by `user_id` instead of `flow_id`.
+ This means files are owned by users, and they aren't attached to specific flows.
+ You can upload a file to Langflow one time, and use it with multiple flows.
+- `/v2` files are tracked in the Langflow database.
+- `/v2` supports bulk upload and delete.
+- `/v2` responses contain more descriptive metadata.
+
+However, `/v2/files` doesn't support image files.
+To send image files to your flows through the API, use [Upload image files (v1)](#upload-image-files-v1).
+
+## Files/V1 endpoints
+
+Use the `/files` endpoints to move files between your local machine and Langflow.
+
+### Upload file (v1)
+
+Upload a file to the `v1/files/upload/$FLOW_ID` endpoint:
+Replace **FILE_NAME** with the uploaded file name.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Replace `FILE_NAME.txt` with the name and extension of the file you want to upload.
+Not all file types are supported.
+
+
+Result
+
+
+
+
+
+### Upload image files (v1)
+
+Send image files to Langflow to use them in flows.
+
+The default file limit is 1024 MB.
+To change this limit, set the `LANGFLOW_MAX_FILE_SIZE_UPLOAD` [environment variable](/environment-variables).
+
+1. Attach the image to a `POST /v1/files/upload/$FLOW_ID` request with `--form` (`-F`) and the file path:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ A successful request returns the `file_path` for the image in the Langflow file management system in the format `FLOW_ID/TIMESTAMP_FILENAME.TYPE`.
+ For example:
+
+ ```json
+ {
+ "flowId": "a430cc57-06bb-4c11-be39-d3d4de68d2c4",
+ "file_path": "a430cc57-06bb-4c11-be39-d3d4de68d2c4/2024-11-27_14-47-50_image-file.png"
+ }
+ ```
+
+2. Use the returned `file_path` to send the image file to other components that can accept file input. Where you specify the file path depends on the component type.
+
+ The following example runs the **Basic Prompting** template flow, passing the image file and the query `describe this image` as input for the **Chat Input** component.
+ In this case, the file path is specified in `tweaks`.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ :::tip
+ For help with tweaks, use the **Input Schema** in a flow's [**API access** pane](/concepts-publish#api-access).
+ Setting tweaks with **Input Schema** also automatically populates the required component IDs.
+ :::
+
+### List files (v1)
+
+List all files associated with a specific flow.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Download file (v1)
+
+Download a specific file from a flow.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Delete file (v1)
+
+Delete a specific file from a flow.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Files/V2 endpoints
+
+Use the `/files` endpoints to move files between your local machine and Langflow.
+
+The `/v2/files` endpoints can be authenticated by an API key or JWT.
+To create a Langflow API key and export it as an environment variable, see [Get started with the Langflow API](/api-reference-api-examples).
+
+### Upload file (v2)
+
+Upload a file to your user account. The file can be used across multiple flows.
+
+The file is uploaded in the format `USER_ID/FILE_ID.FILE_EXTENSION`, such as `07e5b864-e367-4f52-b647-a48035ae7e5e/d44dc2e1-9ae9-4cf6-9114-8d34a6126c94.pdf`.
+
+1. To retrieve your current `user_id`, call the `/whoami` endpoint:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Result
+
+
+
+
+
+2. In the POST request to `v2/files`, replace **@FILE_NAME.EXTENSION** with the uploaded file name and its extension.
+You must include the ampersand (`@`) in the request to instruct curl to upload the contents of the file, not the string `FILE_NAME.EXTENSION`.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ The file is uploaded in the format `USER_ID/FILE_ID.FILE_EXTENSION`, and the API returns metadata about the uploaded file:
+
+ ```json
+ {
+ "id":"d44dc2e1-9ae9-4cf6-9114-8d34a6126c94",
+ "name":"engine_manual",
+ "path":"07e5b864-e367-4f52-b647-a48035ae7e5e/d44dc2e1-9ae9-4cf6-9114-8d34a6126c94.pdf",
+ "size":851160,
+ "provider":null
+ }
+ ```
+
+### Send files to your flows (v2)
+
+:::info
+The `/v2/files` endpoint can't send image files to flows.
+To send image files to your flows through the API, see [Upload image files (v1)](#upload-image-files-v1).
+:::
+
+This endpoint uploads files to your Langflow server's file management system.
+To use an uploaded file in a flow, send the file path to a flow with a [**Read File** component](/read-file).
+
+The default file limit is 1024 MB. To configure this value, change the `LANGFLOW_MAX_FILE_SIZE_UPLOAD` [environment variable](/environment-variables).
+
+1. To send a file to your flow with the API, POST the file to the `/api/v2/files` endpoint.
+
+ Replace **FILE_NAME.EXTENSION** with the name and extension of the file you want to upload.
+ This is the same step described in [Upload file (v2)](#upload-file-v2), but since you need the filename to upload to your flow, it is included here.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ The file is uploaded in the format `USER_ID/FILE_ID.FILE_EXTENSION`, and the API returns metadata about the uploaded file:
+
+ ```json
+ {
+ "id":"d44dc2e1-9ae9-4cf6-9114-8d34a6126c94",
+ "name":"engine_manual",
+ "path":"07e5b864-e367-4f52-b647-a48035ae7e5e/d44dc2e1-9ae9-4cf6-9114-8d34a6126c94.pdf",
+ "size":851160,
+ "provider": null
+ }
+ ```
+
+2. To use this file in your flow, add a **Read File** component to your flow.
+This component loads files into flows from your local machine or Langflow file management.
+
+3. Run the flow, passing the `path` to the `Read-File` component in the `tweaks` object:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ To get the `Read-File` component's ID, call the [Read flow](/api-flows#read-flow) endpoint or inspect the component in the visual editor.
+
+ If the file path is valid, the flow runs successfully.
+
+### List files (v2)
+
+List all files associated with your user account.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Download file (v2)
+
+Download a specific file by its ID and file extension.
+
+You must specify the file type you expect in the `--output` value.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Edit file name (v2)
+
+Change a file name.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Delete file (v2)
+
+Delete a specific file by its ID.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Delete all files (v2)
+
+Delete all files associated with your user account.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Create upload file (Deprecated)
+
+This endpoint is deprecated. Use the `/files` endpoints instead.
+
+## See also
+
+* [Manage files](/concepts-file-management)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-flows-run.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-flows-run.mdx
new file mode 100644
index 0000000000..f3ce210aae
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-flows-run.mdx
@@ -0,0 +1,274 @@
+---
+title: Flow trigger endpoints
+slug: /api-flows-run
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiFlowsRunRunFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows-run/run-flow.sh';
+import exampleApiFlowsRunStreamLlmTokenResponses from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows-run/stream-llm-token-responses.sh';
+import exampleApiFlowsRunRequestExampleWithAllHeadersAndParameters from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows-run/request-example-with-all-headers-and-parameters.sh';
+import exampleApiFlowsRunPassGlobalVariablesInRequestHeaders from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows-run/pass-global-variables-in-request-headers.sh';
+import exampleApiFlowsRunWebhookRunFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows-run/webhook-run-flow.sh';
+import resultApiFlowsRunResultWebhookRunFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows-run/result-webhook-run-flow.json';
+import examplePythonApiFlowsRunRunFlow from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows-run/run-flow.py';
+import exampleJavascriptApiFlowsRunRunFlow from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows-run/run-flow.js';
+import examplePythonApiFlowsRunStreamLlmTokenResponses from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows-run/stream-llm-token-responses.py';
+import exampleJavascriptApiFlowsRunStreamLlmTokenResponses from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows-run/stream-llm-token-responses.js';
+import examplePythonApiFlowsRunRequestExampleWithAllHeadersAndParameters from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows-run/request-example-with-all-headers-and-parameters.py';
+import exampleJavascriptApiFlowsRunRequestExampleWithAllHeadersAndParameters from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows-run/request-example-with-all-headers-and-parameters.js';
+import examplePythonApiFlowsRunPassGlobalVariablesInRequestHeaders from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows-run/pass-global-variables-in-request-headers.py';
+import exampleJavascriptApiFlowsRunPassGlobalVariablesInRequestHeaders from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows-run/pass-global-variables-in-request-headers.js';
+import examplePythonApiFlowsRunWebhookRunFlow from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows-run/webhook-run-flow.py';
+import exampleJavascriptApiFlowsRunWebhookRunFlow from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows-run/webhook-run-flow.js';
+
+
+
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Use the `/run` and `/webhook` endpoints to run flows.
+
+To create, read, update, and delete flows, see [Flow management endpoints](/api-flows).
+
+## Run flow
+
+:::tip
+Langflow automatically generates Python, JavaScript, and curl code snippets for the `/v1/run/$FLOW_ID` endpoint for all flows.
+For more information, see [Generate API code snippets](/concepts-publish#generate-api-code-snippets).
+:::
+
+Execute a specified flow by ID or name.
+Flow IDs can be found on the code snippets on the [**API access** pane](/concepts-publish#api-access) or in a flow's URL.
+
+The following example runs the **Basic Prompting** template flow with flow parameters passed in the request body.
+This flow requires a chat input string (`input_value`), and uses default values for all other parameters.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+The response from `/v1/run/$FLOW_ID` includes metadata, inputs, and outputs for the run.
+
+
+Result
+
+The following example illustrates a response from a Basic Prompting flow:
+
+```json
+{
+ "session_id": "chat-123",
+ "outputs": [{
+ "inputs": {
+ "input_value": "Tell me about something interesting!"
+ },
+ "outputs": [{
+ "results": {
+ "message": {
+ "text": "Sure! Have you ever heard of the phenomenon known as \"bioluminescence\"? It's a fascinating natural occurrence where living organisms produce and emit light. This ability is found in various species, including certain types of jellyfish, fireflies, and deep-sea creatures like anglerfish.\n\nBioluminescence occurs through a chemical reaction in which a light-emitting molecule called luciferin reacts with oxygen, catalyzed by an enzyme called luciferase. The result is a beautiful glow that can serve various purposes, such as attracting mates, deterring predators, or luring prey.\n\nOne of the most stunning displays of bioluminescence can be seen in the ocean, where certain plankton emit light when disturbed, creating a mesmerizing blue glow in the water. This phenomenon is often referred to as \"sea sparkle\" and can be seen in coastal areas around the world.\n\nBioluminescence not only captivates our imagination but also has practical applications in science and medicine, including the development of biosensors and imaging techniques. It's a remarkable example of nature's creativity and complexity!",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "session_id": "chat-123",
+ "timestamp": "2025-03-03T17:17:37+00:00",
+ "flow_id": "d2bbd92b-187e-4c84-b2d4-5df365704201",
+ "properties": {
+ "source": {
+ "id": "OpenAIModel-d1wOZ",
+ "display_name": "OpenAI",
+ "source": "gpt-4o-mini"
+ },
+ "icon": "OpenAI"
+ },
+ "component_id": "ChatOutput-ylMzN"
+ }
+ }
+ }]
+ }]
+}
+```
+
+
+If you are parsing the response in an application, you most likely need to extract the relevant content from the response, rather than pass the entire response back to the user.
+For an example of a script that extracts data from a Langflow API response, see the [Quickstart](/get-started-quickstart).
+
+### Stream LLM token responses
+
+With `/v1/run/$FLOW_ID`, the flow is executed as a batch with optional LLM token response streaming.
+
+To stream LLM token responses, append the `?stream=true` query parameter to the request:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+LLM chat responses are streamed back as `token` events, culminating in a final `end` event that closes the connection.
+
+
+Result
+
+The following example is truncated to illustrate a series of `token` events as well as the final `end` event that closes the LLM's token streaming response:
+
+```text
+{"event": "add_message", "data": {"timestamp": "2025-03-03T17:20:18", "sender": "User", "sender_name": "User", "session_id": "chat-123", "text": "Tell me about something interesting!", "files": [], "error": false, "edit": false, "properties": {"text_color": "", "background_color": "", "edited": false, "source": {"id": null, "display_name": null, "source": null}, "icon": "", "allow_markdown": false, "positive_feedback": null, "state": "complete", "targets": []}, "category": "message", "content_blocks": [], "id": "0103a21b-ebf7-4c02-9d72-017fb297f812", "flow_id": "d2bbd92b-187e-4c84-b2d4-5df365704201"}}
+
+{"event": "add_message", "data": {"timestamp": "2025-03-03T17:20:18", "sender": "Machine", "sender_name": "AI", "session_id": "chat-123", "text": "", "files": [], "error": false, "edit": false, "properties": {"text_color": "", "background_color": "", "edited": false, "source": {"id": "OpenAIModel-d1wOZ", "display_name": "OpenAI", "source": "gpt-4o-mini"}, "icon": "OpenAI", "allow_markdown": false, "positive_feedback": null, "state": "complete", "targets": []}, "category": "message", "content_blocks": [], "id": "27b66789-e673-4c65-9e81-021752925161", "flow_id": "d2bbd92b-187e-4c84-b2d4-5df365704201"}}
+
+{"event": "token", "data": {"chunk": " Have", "id": "27b66789-e673-4c65-9e81-021752925161", "timestamp": "2025-03-03 17:20:18 UTC"}}
+
+{"event": "token", "data": {"chunk": " you", "id": "27b66789-e673-4c65-9e81-021752925161", "timestamp": "2025-03-03 17:20:18 UTC"}}
+
+{"event": "token", "data": {"chunk": " ever", "id": "27b66789-e673-4c65-9e81-021752925161", "timestamp": "2025-03-03 17:20:18 UTC"}}
+
+{"event": "token", "data": {"chunk": " heard", "id": "27b66789-e673-4c65-9e81-021752925161", "timestamp": "2025-03-03 17:20:18 UTC"}}
+
+{"event": "token", "data": {"chunk": " of", "id": "27b66789-e673-4c65-9e81-021752925161", "timestamp": "2025-03-03 17:20:18 UTC"}}
+
+{"event": "token", "data": {"chunk": " the", "id": "27b66789-e673-4c65-9e81-021752925161", "timestamp": "2025-03-03 17:20:18 UTC"}}
+
+{"event": "token", "data": {"chunk": " phenomenon", "id": "27b66789-e673-4c65-9e81-021752925161", "timestamp": "2025-03-03 17:20:18 UTC"}}
+
+{"event": "end", "data": {"result": {"session_id": "chat-123", "message": "Sure! Have you ever heard of the phenomenon known as \"bioluminescence\"?..."}}}
+```
+
+
+
+### Run endpoint headers
+
+| Header | Info | Example |
+|--------|------|---------|
+| Content-Type | Required. Specifies the JSON format. | "application/json" |
+| accept | Optional. Specifies the response format. Defaults to JSON if not specified. | "application/json" |
+| x-api-key | Required. Your Langflow API key for authentication. Can be passed as a header or query parameter. | "sk-..." |
+| `X-LANGFLOW-GLOBAL-VAR-*` | Optional. Pass global variables to the flow. Variable names are automatically converted to uppercase. These variables take precedence over OS environment variables and are only available during this specific request execution. | `"X-LANGFLOW-GLOBAL-VAR-API_KEY: sk-..."` |
+
+### Run endpoint parameters
+
+| Parameter | Type | Info |
+|-----------|------|------|
+| flow_id | UUID/string | Required. Part of URL: `/run/$FLOW_ID` |
+| stream | Boolean | Optional. Query parameter: `/run/$FLOW_ID?stream=true` |
+| input_value | string | Optional. JSON body field. Main input text/prompt. Default: `null` |
+| input_type | string | Optional. JSON body field. Input type ("chat" or "text"). Default: `"chat"` |
+| output_type | string | Optional. JSON body field. Output type ("chat", "any", "debug"). Default: `"chat"` |
+| output_component | string | Optional. JSON body field. Target component for output. Default: `""` |
+| tweaks | object | Optional. JSON body field. Component adjustments. Default: `null` |
+| session_id | string | Optional. JSON body field. Conversation context ID. See [Session ID](/session-id). Default: `null` |
+
+### Request example with all headers and parameters
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+### Pass global variables in request headers {#pass-global-variables-in-headers}
+
+You can pass global variables to your flow using HTTP headers with the format `X-LANGFLOW-GLOBAL-VAR-{VARIABLE_NAME}`.
+
+Variables passed in headers take precedence over OS environment variables. If a variable is provided in both a header and an environment variable, the header value is used. Variables are only available during this specific request execution and aren't persisted.
+
+Variable names are automatically converted to uppercase. For example, `X-LANGFLOW-GLOBAL-VAR-api-key` becomes `API_KEY` in your flow.
+
+You don't need to create these variables in Langflow's Global Variables section first. Pass any variable name using this header format.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+If your flow components reference variables that aren't provided in headers or your Langflow database, the flow fails by default. To avoid this, you can set `LANGFLOW_FALLBACK_TO_ENV_VAR=True` in your `.env` file, which allows the flow to use values from OS environment variables if they aren't otherwise specified.
+
+
+## Webhook run flow
+
+Use the `/webhook` endpoint to start a flow by sending an HTTP `POST` request.
+
+:::tip
+After you add a [**Webhook** component](/webhook) to a flow, open the [**API access** pane](/concepts-publish), and then click the **Webhook curl** tab to get an automatically generated `POST /webhook` request for your flow.
+For more information, see [Trigger flows with webhooks](/webhook).
+:::
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Deprecated flow trigger endpoints
+
+The following endpoints are deprecated and replaced by the `/run` endpoint:
+
+* `/process`
+* `/predict`
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-flows.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-flows.mdx
new file mode 100644
index 0000000000..a778bb53a9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-flows.mdx
@@ -0,0 +1,330 @@
+---
+title: Flow management endpoints
+slug: /api-flows
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiFlowsCreateFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/create-flow.sh';
+import resultApiFlowsResultCreateFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/result-create-flow.json';
+import exampleApiFlowsCreateFlows from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/create-flows.sh';
+import exampleApiFlowsReadFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/read-flow.sh';
+import resultApiFlowsResultReadFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/result-read-flow.json';
+import exampleApiFlowsReadFlows from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/read-flows.sh';
+import exampleApiFlowsReadFlows2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/read-flows-2.sh';
+import exampleApiFlowsReadSampleFlows from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/read-sample-flows.sh';
+import exampleApiFlowsUpdateFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/update-flow.sh';
+import resultApiFlowsResultUpdateFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/result-update-flow.json';
+import exampleApiFlowsDeleteFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/delete-flow.sh';
+import resultApiFlowsResultDeleteFlow from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/result-delete-flow.json';
+import exampleApiFlowsExportFlows from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/export-flows.sh';
+import resultApiFlowsResultExportFlows from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/result-export-flows.txt';
+import exampleApiFlowsImportFlows from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-flows/import-flows.sh';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import examplePythonApiFlowsCreateFlow from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/create-flow.py';
+import exampleJavascriptApiFlowsCreateFlow from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/create-flow.js';
+import examplePythonApiFlowsCreateFlows from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/create-flows.py';
+import exampleJavascriptApiFlowsCreateFlows from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/create-flows.js';
+import examplePythonApiFlowsReadFlow from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/read-flow.py';
+import exampleJavascriptApiFlowsReadFlow from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/read-flow.js';
+import examplePythonApiFlowsReadFlows from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/read-flows.py';
+import exampleJavascriptApiFlowsReadFlows from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/read-flows.js';
+import examplePythonApiFlowsReadFlows2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/read-flows-2.py';
+import exampleJavascriptApiFlowsReadFlows2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/read-flows-2.js';
+import examplePythonApiFlowsReadSampleFlows from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/read-sample-flows.py';
+import exampleJavascriptApiFlowsReadSampleFlows from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/read-sample-flows.js';
+import examplePythonApiFlowsUpdateFlow from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/update-flow.py';
+import exampleJavascriptApiFlowsUpdateFlow from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/update-flow.js';
+import examplePythonApiFlowsDeleteFlow from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/delete-flow.py';
+import exampleJavascriptApiFlowsDeleteFlow from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/delete-flow.js';
+import examplePythonApiFlowsExportFlows from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/export-flows.py';
+import exampleJavascriptApiFlowsExportFlows from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/export-flows.js';
+import examplePythonApiFlowsImportFlows from '!!raw-loader!@site/docs/API-Reference/python-examples/api-flows/import-flows.py';
+import exampleJavascriptApiFlowsImportFlows from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-flows/import-flows.js';
+
+
+
+Use the `/flows` endpoint to create, read, update, and delete flows.
+
+If you want to use the Langflow API to run a flow, see [Flow trigger endpoints](/api-flows-run).
+
+## Create flow
+
+Creates a new flow.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Create flows
+
+Creates multiple new flows, returning an array of flow objects.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Read flow
+
+Retrieves a specific flow by its ID.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Read flows
+
+Returns a JSON object containing a list of flows.
+
+Retrieve all flows with pagination:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+To retrieve flows from a specific project, use the `project_id` query parameter:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Read sample flows
+
+Retrieves a list of sample flows:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Update flow
+
+Updates an existing flow by its ID.
+
+This example changes the value for `endpoint_name` from a random UUID to `my_new_endpoint_name`.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Delete flow
+
+Deletes a specific flow by its ID.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Export flows
+
+Exports specified flows to a ZIP file.
+
+This endpoint downloads a ZIP file containing [Langflow JSON files](/concepts-flows-import#langflow-json-file-contents) for each flow ID listed in the request body.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Import flows
+
+Imports flows by uploading a [Langflow-compatible JSON file](/concepts-flows-import#langflow-json-file-contents).
+
+To specify a target project for the flow, include the query parameter `folder_id`.
+The target `folder_id` must already exist before uploading a flow. Call the [/api/v1/projects/](/api-projects#read-projects) endpoint for a list of available folders and projects.
+
+This example uploads a local file named `agent-with-astra-db-tool.json` to a folder specified by a `FOLDER_ID` variable:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+```json
+[
+ {
+ "name": "agent-with-astra-db-tool",
+ "description": "",
+ "icon": null,
+ "icon_bg_color": null,
+ "gradient": null,
+ "data": {}
+ ...
+ }
+]
+```
+
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-logs.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-logs.mdx
new file mode 100644
index 0000000000..ed7f3e3eaf
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-logs.mdx
@@ -0,0 +1,96 @@
+---
+title: Logs endpoints
+slug: /api-logs
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiLogsStreamLogs from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-logs/stream-logs.sh';
+import resultApiLogsResultStreamLogs from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-logs/result-stream-logs.txt';
+import exampleApiLogsRetrieveLogsWithOptionalParameters from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-logs/retrieve-logs-with-optional-parameters.sh';
+import resultApiLogsResultRetrieveLogsWithOptionalParameters from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-logs/result-retrieve-logs-with-optional-parameters.txt';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import examplePythonApiLogsStreamLogs from '!!raw-loader!@site/docs/API-Reference/python-examples/api-logs/stream-logs.py';
+import exampleJavascriptApiLogsStreamLogs from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-logs/stream-logs.js';
+import examplePythonApiLogsRetrieveLogsWithOptionalParameters from '!!raw-loader!@site/docs/API-Reference/python-examples/api-logs/retrieve-logs-with-optional-parameters.py';
+import exampleJavascriptApiLogsRetrieveLogsWithOptionalParameters from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-logs/retrieve-logs-with-optional-parameters.js';
+
+
+
+Retrieve logs for your Langflow flows and server.
+
+## Enable log retrieval
+
+The `/logs` endpoint requires log retrieval to be enabled in your Langflow instance.
+
+To enable log retrieval, set the following [environment variables](/environment-variables) in your Langflow `.env` file, and then start Langflow with `uv run langflow run --env-file .env`:
+
+```text
+LANGFLOW_ENABLE_LOG_RETRIEVAL=True
+LANGFLOW_LOG_RETRIEVER_BUFFER_SIZE=10000 // Must be greater than 0
+LANGFLOW_LOG_LEVEL=DEBUG // Can be DEBUG, ERROR, INFO, WARNING, or CRITICAL
+```
+
+## Stream logs
+
+Stream logs in real-time using Server Sent Events (SSE):
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Retrieve logs with optional parameters
+
+Retrieve logs with optional query parameters:
+
+- `lines_before`: The number of logs before the timestamp or the last log.
+- `lines_after`: The number of logs after the timestamp.
+- `timestamp`: The timestamp to start getting logs from.
+
+The default values for all three parameters is `0`.
+With default values, the endpoint returns the last 10 lines of logs.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-monitor.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-monitor.mdx
new file mode 100644
index 0000000000..9b0b639a10
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-monitor.mdx
@@ -0,0 +1,429 @@
+---
+title: Monitor endpoints
+slug: /api-monitor
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiMonitorGetVertexBuilds from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/get-vertex-builds.sh';
+import resultApiMonitorResultGetVertexBuilds from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/result-get-vertex-builds.json';
+import exampleApiMonitorDeleteVertexBuilds from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/delete-vertex-builds.sh';
+import resultApiMonitorResultDeleteVertexBuilds from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/result-delete-vertex-builds.txt';
+import exampleApiMonitorGetMessages from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/get-messages.sh';
+import exampleApiMonitorGetMessages2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/get-messages-2.sh';
+import resultApiMonitorResultGetMessages2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/result-get-messages-2.json';
+import exampleApiMonitorDeleteMessages from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/delete-messages.sh';
+import resultApiMonitorResultDeleteMessages from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/result-delete-messages.txt';
+import exampleApiMonitorUpdateMessage from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/update-message.sh';
+import resultApiMonitorResultUpdateMessage from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/result-update-message.json';
+import exampleApiMonitorUpdateSessionId from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/update-session-id.sh';
+import resultApiMonitorResultUpdateSessionId from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/result-update-session-id.json';
+import exampleApiMonitorDeleteMessagesBySession from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/delete-messages-by-session.sh';
+import resultApiMonitorResultDeleteMessagesBySession from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/result-delete-messages-by-session.txt';
+import exampleApiMonitorExampleRequest from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/example-request.sh';
+import exampleApiMonitorGetTransactions from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/get-transactions.sh';
+import resultApiMonitorResultGetTransactions from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-monitor/result-get-transactions.json';
+import examplePythonApiMonitorExampleRequest from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/example-request.py';
+import examplePythonApiMonitorGetVertexBuilds from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/get-vertex-builds.py';
+import exampleJavascriptApiMonitorGetVertexBuilds from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-monitor/get-vertex-builds.js';
+import examplePythonApiMonitorDeleteVertexBuilds from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/delete-vertex-builds.py';
+import exampleJavascriptApiMonitorDeleteVertexBuilds from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-monitor/delete-vertex-builds.js';
+import examplePythonApiMonitorGetMessages from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/get-messages.py';
+import exampleJavascriptApiMonitorGetMessages from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-monitor/get-messages.js';
+import examplePythonApiMonitorGetMessages2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/get-messages-2.py';
+import exampleJavascriptApiMonitorGetMessages2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-monitor/get-messages-2.js';
+import examplePythonApiMonitorDeleteMessages from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/delete-messages.py';
+import exampleJavascriptApiMonitorDeleteMessages from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-monitor/delete-messages.js';
+import examplePythonApiMonitorUpdateMessage from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/update-message.py';
+import exampleJavascriptApiMonitorUpdateMessage from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-monitor/update-message.js';
+import examplePythonApiMonitorUpdateSessionId from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/update-session-id.py';
+import exampleJavascriptApiMonitorUpdateSessionId from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-monitor/update-session-id.js';
+import examplePythonApiMonitorDeleteMessagesBySession from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/delete-messages-by-session.py';
+import exampleJavascriptApiMonitorDeleteMessagesBySession from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-monitor/delete-messages-by-session.js';
+import examplePythonApiMonitorGetTransactions from '!!raw-loader!@site/docs/API-Reference/python-examples/api-monitor/get-transactions.py';
+import exampleJavascriptApiMonitorGetTransactions from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-monitor/get-transactions.js';
+
+
+
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+The `/monitor` endpoints are for internal Langflow functionality, primarily related to running flows in the **Playground**, storing chat history, and generating flow logs.
+
+This information is primarily for those who are building custom components or contributing to the Langflow codebase in a way that requires calling or understanding these endpoints.
+
+For typical application development with Langflow, there are more appropriate options for monitoring, debugging, and memory management.
+For more information, see the following:
+
+* [Logs](/logging): Langflow log storage locations, customization options, and where to view logs in the visual editor
+* [Test flows in the Playground](/concepts-playground): Run flows and inspect message history
+* [Memory management options](/memory): Langflow storage locations and options, including the database, cache, and chat history
+
+## Vertex builds
+
+The Vertex build endpoints (`/monitor/builds`) are exclusively for **Playground** functionality.
+
+When you run a flow in the **Playground**, Langflow calls the `/build/$FLOW_ID/flow` endpoint in [chat.py](https://github.com/langflow-ai/langflow/blob/main/src/backend/base/langflow/api/v1/chat.py#L130). This call retrieves the flow data, builds a graph, and executes the graph. As each component (or node) is executed, the `build_vertex` function calls `build_and_run`, which may call the individual components' `def_build` method, if it exists. If a component doesn't have a `def_build` function, the build still returns a component.
+
+The `build` function allows components to execute logic at runtime. For example, the [**Recursive Character Text Splitter** component](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/components/langchain_utilities/recursive_character.py) is a child of the `LCTextSplitterComponent` class. When text needs to be processed, the parent class's `build` method is called, which creates a `RecursiveCharacterTextSplitter` object and uses it to split the text according to the defined parameters. The split text is then passed on to the next component. This all occurs when the component is built.
+
+### Get Vertex builds
+
+Retrieve Vertex builds for a specific flow.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Delete Vertex builds
+
+Delete Vertex builds for a specific flow.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Messages endpoints
+
+The `/monitor/messages` endpoints store, retrieve, edit, and delete records in the message table in [`langflow.db`](/memory)
+Typically, these are called implicitly when running flows that produce message history, or when inspecting and modifying **Playground** memories.
+
+### Get messages
+
+Retrieve a list of all messages:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+To filter messages, use the `flow_id`, `session_id`, `sender`, and `sender_name` query parameters.
+
+To sort the results, use the `order_by` query parameter.
+
+This example retrieves messages sent by `Machine` and `AI` in a given chat session (`session_id`) and orders the messages by timestamp.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Delete messages
+
+Delete specific messages by their IDs.
+
+This example deletes the message retrieved in the previous `GET /messages` example.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Update message
+
+Update a specific message by its ID.
+
+This example updates the `text` value of message `3ab66cc6-c048-48f8-ab07-570f5af7b160`.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Update session ID
+
+Update the session ID for messages.
+
+This example updates the `session_ID` value `01ce083d-748b-4b8d-97b6-33adbb6a528a` to `different_session_id`.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Delete messages by session
+
+Delete all messages for a specific session.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Get traces
+
+ Retrieve trace metadata and span trees for a specific flow.
+
+### Example request
+
+Use `GET /monitor/traces` and filter by `flow_id`:
+
+
+
+
+
+
+
+
+
+```ts
+const baseUrl = process.env.LANGFLOW_SERVER_URL ?? "http://localhost:7860";
+const apiKey = process.env.LANGFLOW_API_KEY!;
+const flowId = "YOUR_FLOW_ID";
+
+async function listTraces() {
+ const url = new URL("/api/v1/monitor/traces", baseUrl);
+ url.searchParams.set("flow_id", flowId);
+ url.searchParams.set("page", "1");
+ url.searchParams.set("size", "50");
+
+ const res = await fetch(url.toString(), {
+ headers: {
+ accept: "application/json",
+ "x-api-key": apiKey,
+ },
+ });
+
+ if (!res.ok) {
+ throw new Error(`Request failed with status ${res.status}`);
+ }
+
+ const data = await res.json();
+ console.log(data);
+}
+
+listTraces().catch(console.error);
+```
+
+
+
+
+
+
+
+
+
+### Example response
+
+```json
+{
+ "traces": [
+ {
+ "id": "426656db-fc3c-4a3a-acf8-c60acf099543",
+ "name": "Simple Agent - 9e774f60-857b-44b4-bbcd-87bd23848ee8",
+ "status": "ok",
+ "startTime": "2026-03-03T19:13:30.692628Z",
+ "totalLatencyMs": 18693,
+ "totalTokens": 2050,
+ "flowId": "9e774f60-857b-44b4-bbcd-87bd23848ee8",
+ "sessionId": "9e774f60-857b-44b4-bbcd-87bd23848ee8",
+ "input": {
+ "input_value": "Use tools to teach me about vertex graphs"
+ },
+ "output": {
+ "message": {
+ "text_key": "text",
+ "data": {
+ "timestamp": "2026-03-03 19:13:30 UTC",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "session_id": "9e774f60-857b-44b4-bbcd-87bd23848ee8",
+ "text": "I can teach you the concept, but I couldn’t pull the Wikipedia pages with the tool ... (truncated)"
+ }
+ }
+ }
+ }
+ ],
+ "total": 1,
+ "pages": 1
+}
+```
+
+## Get transactions
+
+Retrieve all transactions, which are interactions between components, for a specific flow.
+This information is also available in [flow logs](/logging).
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## See also
+
+- [Use voice mode](/concepts-voice-mode)
+- [Session ID](/session-id)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-openai-responses.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-openai-responses.mdx
new file mode 100644
index 0000000000..01e3c21f88
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-openai-responses.mdx
@@ -0,0 +1,529 @@
+---
+title: OpenAI Responses API
+slug: /api-openai-responses
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiOpenaiResponsesExampleRequest from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/example-request.sh';
+import exampleApiOpenaiResponsesExampleStreamingRequest from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/example-streaming-request.sh';
+import resultApiOpenaiResponsesResultExampleStreamingRequest from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/result-example-streaming-request.json';
+import exampleApiOpenaiResponsesContinueConversationsWithResponseAndSessionIds from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/continue-conversations-with-response-and-session-ids.sh';
+import resultApiOpenaiResponsesResultContinueConversationsWithResponseAndSessionIds from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/result-continue-conversations-with-response-and-session-ids.json';
+import exampleApiOpenaiResponsesContinueConversationsWithResponseAndSessionIds2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.sh';
+import resultApiOpenaiResponsesResultContinueConversationsWithResponseAndSessionIds2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/result-continue-conversations-with-response-and-session-ids-2.json';
+import exampleApiOpenaiResponsesContinueConversationsWithResponseAndSessionIds3 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-3.sh';
+import exampleApiOpenaiResponsesRetrieveToolCallResults from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/retrieve-tool-call-results.sh';
+import resultApiOpenaiResponsesResultRetrieveToolCallResults from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/result-retrieve-tool-call-results.json';
+import exampleApiOpenaiResponsesPassGlobalVariablesToYourFlowsInHeaders from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/pass-global-variables-to-your-flows-in-headers.sh';
+import exampleApiOpenaiResponsesTokenUsageTracking from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-openai-responses/token-usage-tracking.sh';
+import examplePythonApiOpenaiResponsesAdditionalConfigurationForOpenaiClientLibraries from '!!raw-loader!@site/docs/API-Reference/python-examples/api-openai-responses/additional-configuration-for-openai-client-libraries.py';
+import exampleJavascriptApiOpenaiResponsesAdditionalConfigurationForOpenaiClientLibraries from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-openai-responses/additional-configuration-for-openai-client-libraries.ts';
+import examplePythonApiOpenaiResponsesTokenUsageTracking from '!!raw-loader!@site/docs/API-Reference/python-examples/api-openai-responses/token-usage-tracking.py';
+import examplePythonApiOpenaiResponsesExampleRequest from '!!raw-loader!@site/docs/API-Reference/python-examples/api-openai-responses/example-request.py';
+import exampleJavascriptApiOpenaiResponsesExampleRequest from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-openai-responses/example-request.js';
+import examplePythonApiOpenaiResponsesExampleStreamingRequest from '!!raw-loader!@site/docs/API-Reference/python-examples/api-openai-responses/example-streaming-request.py';
+import exampleJavascriptApiOpenaiResponsesExampleStreamingRequest from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-openai-responses/example-streaming-request.js';
+import examplePythonApiOpenaiResponsesContinueConversationsWithResponseAndSessionIds from '!!raw-loader!@site/docs/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids.py';
+import exampleJavascriptApiOpenaiResponsesContinueConversationsWithResponseAndSessionIds from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids.js';
+import examplePythonApiOpenaiResponsesContinueConversationsWithResponseAndSessionIds2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.py';
+import exampleJavascriptApiOpenaiResponsesContinueConversationsWithResponseAndSessionIds2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.js';
+import examplePythonApiOpenaiResponsesContinueConversationsWithResponseAndSessionIds3 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-3.py';
+import exampleJavascriptApiOpenaiResponsesContinueConversationsWithResponseAndSessionIds3 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-3.js';
+import examplePythonApiOpenaiResponsesRetrieveToolCallResults from '!!raw-loader!@site/docs/API-Reference/python-examples/api-openai-responses/retrieve-tool-call-results.py';
+import exampleJavascriptApiOpenaiResponsesRetrieveToolCallResults from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-openai-responses/retrieve-tool-call-results.js';
+import examplePythonApiOpenaiResponsesPassGlobalVariablesToYourFlowsInHeaders from '!!raw-loader!@site/docs/API-Reference/python-examples/api-openai-responses/pass-global-variables-to-your-flows-in-headers.py';
+import exampleJavascriptApiOpenaiResponsesPassGlobalVariablesToYourFlowsInHeaders from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-openai-responses/pass-global-variables-to-your-flows-in-headers.js';
+import exampleJavascriptApiOpenaiResponsesTokenUsageTracking from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-openai-responses/token-usage-tracking.js';
+
+
+
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Langflow includes an endpoint that is compatible with the [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses).
+It is available at `POST /api/v1/responses`.
+
+This endpoint allows you to use existing OpenAI client libraries with minimal code changes.
+You only need to replace the `model` name, such as `gpt-4`, with your `flow_id`.
+You can find Flow IDs in the code snippets on the [**API access** pane](/concepts-publish#api-access) or in a flow's URL.
+
+## Prerequisites
+
+To be compatible with Langflow's OpenAI Responses API endpoint, your flow and request must adhere to the following requirements:
+
+- **Chat Input**: Your flow must contain a **Chat Input** component.
+Flows without this component return an error when passed to this endpoint.
+The component types `ChatInput` and `Chat Input` are recognized as chat inputs.
+- **Tools**: The `tools` parameter isn't supported, and returns an error if provided.
+- **Model Names**: In your request, the `model` field must contain a valid flow ID or endpoint name.
+- **Authentication**: All requests require an API key passed in the `x-api-key` header.
+For more information, see [API keys and authentication](/api-keys-and-authentication).
+
+### Additional configuration for OpenAI client libraries
+
+This endpoint is compatible with OpenAI's API, but requires special configuration when using OpenAI client libraries.
+Langflow uses `x-api-key` headers for authentication, while OpenAI uses `Authorization: Bearer` headers.
+When sending requests to Langflow with OpenAI client libraries, you must configure custom headers and include an `api_key` configuration.
+The `api_key` parameter can have any value, such as `"dummy-api-key"` in the client examples, as the actual authentication is handled through the `default_headers` configuration.
+
+In the following examples, replace the values for `LANGFLOW_SERVER_URL`, `LANGFLOW_API_KEY`, and `FLOW_ID` with values from your deployment.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Response
+```text
+Here are the event dates for the second Wednesday of each month in 2026:
+- January 14, 2026
+- February 11, 2026
+- March 11, 2026
+- April 8, 2026
+- May 13, 2026
+- June 10, 2026
+- July 8, 2026
+- August 12, 2026
+- September 9, 2026
+- October 14, 2026
+- November 11, 2026
+- December 9, 2026
+If you need these in a different format or want a downloadable calendar, let me know!
+```
+
+
+## Example request
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+### Headers
+
+| Header | Required | Description | Example |
+|--------|----------|-------------|---------|
+| `x-api-key` | Yes | Your Langflow API key for authentication | `"sk-..."` |
+| `Content-Type` | Yes | Specifies the JSON format | `"application/json"` |
+| `X-LANGFLOW-GLOBAL-VAR-*` | No | Global variables for the flow | `"X-LANGFLOW-GLOBAL-VAR-API_KEY: sk-..."` For more, see [Pass global variables to your flows in headers](#global-var). |
+
+### Request body
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `model` | `string` | Yes | - | The flow ID or endpoint name to execute. |
+| `input` | `string` | Yes | - | The input text to process. |
+| `stream` | `boolean` | No | `false` | Whether to stream the response. |
+| `background` | `boolean` | No | `false` | Whether to process in background. |
+| `tools` | `list[Any]` | No | `null` | Tools are not supported yet. |
+| `previous_response_id` | `string` | No | `null` | ID of previous response to continue conversation. For more, see [Continue conversations with response and session IDs](#response-id). |
+| `include` | `list[string]` | No | `null` | Additional response data to include, such as `['tool_call.results']`. For more, see [Retrieve tool call results](#tool-call-results). |
+
+## Example response
+
+```json
+{
+ "id": "e5e8ef8a-7efd-4090-a110-6aca082bceb7",
+ "object": "response",
+ "created_at": 1756837941,
+ "status": "completed",
+ "model": "ced2ec91-f325-4bf0-8754-f3198c2b1563",
+ "output": [
+ {
+ "type": "message",
+ "id": "msg_e5e8ef8a-7efd-4090-a110-6aca082bceb7",
+ "status": "completed",
+ "role": "assistant",
+ "content": [
+ {
+ "type": "output_text",
+ "text": "Hello! I'm here and ready to help. How can I assist you today?",
+ "annotations": []
+ }
+ ]
+ }
+ ],
+ "parallel_tool_calls": true,
+ "previous_response_id": null,
+ "reasoning": {"effort": null, "summary": null},
+ "store": true,
+ "temperature": 1.0,
+ "text": {"format": {"type": "text"}},
+ "tool_choice": "auto",
+ "tools": [],
+ "top_p": 1.0,
+ "truncation": "disabled",
+ "usage": null,
+ "user": null,
+ "metadata": {}
+}
+```
+
+### Response body
+
+The response contains fields that Langflow sets dynamically and fields that use OpenAI-compatible defaults.
+
+The OpenAI-compatible default values shown above are currently fixed and cannot be modified via the request.
+They are included to maintain API compatibility and provide a consistent response format.
+
+For your requests, you will only be setting the dynamic fields.
+The default values are documented here for completeness and to show the full response structure.
+
+Fields set dynamically by Langflow:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | `string` | Unique response identifier. |
+| `created_at` | `int` | Unix timestamp of response creation. |
+| `model` | `string` | The flow ID that was executed. |
+| `output` | `list[dict]` | Array of output items (messages, tool calls, etc.). |
+| `previous_response_id` | `string` | ID of previous response if continuing conversation. |
+| `usage` | `dict` | Token usage statistics if the `usage` field is available. Contains `prompt_tokens`, `completion_tokens`, and `total_tokens`. |
+
+
+Fields with OpenAI-compatible default values
+
+| Field | Type | Default Value | Description |
+|-------|------|---------------|-------------|
+| `object` | `string` | `"response"` | Always `"response"`. |
+| `status` | `string` | `"completed"` | Response status: `"completed"`, `"in_progress"`, or `"failed"`. |
+| `error` | `dict` | `null` | Error details (if any). |
+| `incomplete_details` | `dict` | `null` | Incomplete response details (if any). |
+| `instructions` | `string` | `null` | Response instructions (if any). |
+| `max_output_tokens` | `int` | `null` | Maximum output tokens (if any). |
+| `parallel_tool_calls` | `boolean` | `true` | Whether parallel tool calls are enabled. |
+| `reasoning` | `dict` | `{"effort": null, "summary": null}` | Reasoning information with effort and summary. |
+| `store` | `boolean` | `true` | Whether response is stored. |
+| `temperature` | `float` | `1.0` | Temperature setting. |
+| `text` | `dict` | `{"format": {"type": "text"}}` | Text format configuration. |
+| `tool_choice` | `string` | `"auto"` | Tool choice setting. |
+| `tools` | `list[dict]` | `[]` | Available tools. |
+| `top_p` | `float` | `1.0` | Top-p setting. |
+| `truncation` | `string` | `"disabled"` | Truncation setting. |
+| `usage` | `dict` | `null` | Token usage statistics. Set dynamically when available from flow components, otherwise `null`. See [Token usage tracking](#token-usage-tracking). |
+| `user` | `string` | `null` | User identifier (if any). |
+| `metadata` | `dict` | `{}` | Additional metadata. |
+
+
+
+## Example streaming request
+
+When you set `"stream": true` with your request, the API returns a stream where each chunk contains a small piece of the response as it's generated. This provides a real-time experience where users can see the AI's output appear word by word, similar to ChatGPT's typing effect.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Streaming response body
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | `string` | Unique response identifier. |
+| `object` | `string` | Always `"response.chunk"`. |
+| `created` | `int` | Unix timestamp of chunk creation. |
+| `model` | `string` | The flow ID that was executed. |
+| `delta` | `dict` | The new content chunk. |
+| `status` | `string` | Response status: `"completed"`, `"in_progress"`, or `"failed"` (optional). |
+
+The stream continues until a final chunk with `"status": "completed"` indicates the response is finished.
+
+
+Final completion chunk
+
+```
+{
+ "id": "f7fcea36-f128-41c4-9ac1-e683137375d5",
+ "object": "response.chunk",
+ "created": 1756838094,
+ "model": "ced2ec91-f325-4bf0-8754-f3198c2b1563",
+ "delta": {},
+ "status": "completed"
+}
+```
+
+
+## Continue conversations with response and session IDs {#response-id}
+
+Conversation continuity allows you to maintain context across multiple API calls, enabling multi-turn conversations with your flows. This is essential for building chat applications where users can have ongoing conversations.
+
+When you make a request, the API returns a response with an `id` field. You can use this `id` as the `previous_response_id` in your next request to continue the conversation from where it left off.
+
+First Message:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+Follow-up message:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+Optionally, you can use your own session ID values for the `previous_response_id`:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+This example uses the same flow as the other `previous_response_id` examples, but the LLM had not yet been introduced to Alice in the specified session:
+
+```json
+{
+ "id": "session-alice-1756839048",
+ "object": "response",
+ "created_at": 1756839048,
+ "status": "completed",
+ "model": "ced2ec91-f325-4bf0-8754-f3198c2b1563",
+ "output": [
+ {
+ "type": "message",
+ "id": "msg_session-alice-1756839048",
+ "status": "completed",
+ "role": "assistant",
+ "content": [
+ {
+ "type": "output_text",
+ "text": "I don't have access to your name unless you tell me. If you'd like, you can share your name, and I'll remember it for this conversation!",
+ "annotations": []
+ }
+ ]
+ }
+ ],
+ "previous_response_id": "session-alice-1756839048"
+}
+```
+
+
+
+## Retrieve tool call results {#tool-call-results}
+
+When you send a request to the `/api/v1/responses` endpoint to run a flow that includes tools or function calls, you can retrieve the raw tool execution details by adding `"include": ["tool_call.results"]` to the request payload.
+
+Without the `include` parameter, tool calls return basic function call information, but not the raw tool results.
+For example:
+
+```json
+{
+ "id": "fc_1",
+ "type": "function_call",
+ "status": "completed",
+ "name": "evaluate_expression",
+ "arguments": "{\"expression\": \"15*23\"}"
+},
+```
+
+To get the raw `results` of each tool execution, add `include: ["tool_call.results"]` to the request payload:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+The response now includes the tool call's results.
+For example:
+
+```json
+{
+ "id": "evaluate_expression_1",
+ "type": "tool_call",
+ "tool_name": "evaluate_expression",
+ "queries": ["15*23"],
+ "results": {"result": "345"}
+}
+```
+
+
+Result
+
+
+
+
+
+Variables passed with `X-LANGFLOW-GLOBAL-VAR-{VARIABLE_NAME}` are always available to your flow, regardless of whether they exist in the database.
+
+If your flow components reference variables that aren't provided in headers or your Langflow database, the flow fails by default.
+
+To avoid this, you can set the `FALLBACK_TO_ENV_VARS` environment variable is `true`, which allows the flow to use values from the `.env` file if they aren't otherwise specified.
+
+In the above example, `OPENAI_API_KEY` will fall back to the database variable if not provided in the header.
+`USER_ID` and `ENVIRONMENT` will fall back to environment variables if `FALLBACK_TO_ENV_VARS` is enabled.
+Otherwise, the flow fails.
+
+## Token usage tracking {#token-usage-tracking}
+
+The OpenAI Responses API endpoint tracks token usage when your flow uses language model components that provide token usage information. The `usage` field in the response contains statistics about the number of tokens used for the request and response.
+
+Token usage is automatically extracted from the flow execution results when the `usage` field is available.
+The `usage` field follows OpenAI's format with `prompt_tokens`, `completion_tokens`, and `total_tokens` fields.
+If token usage information is not available from the flow components, the `usage` field is `null`.
+
+The `usage` field is always present in the response, either with token counts or as `null`. The conditional checks shown in the examples below are optional defensive programming to handle cases where usage might not be available.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Response with token usage
+
+```json
+{
+ "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
+ "object": "response",
+ "created_at": 1756837941,
+ "status": "completed",
+ "model": "ced2ec91-f325-4bf0-8754-f3198c2b1563",
+ "output": [
+ {
+ "type": "message",
+ "id": "msg_a1b2c3d4-e5f6-7890-abcd-ef1234567890",
+ "status": "completed",
+ "role": "assistant",
+ "content": [
+ {
+ "type": "output_text",
+ "text": "Quantum computing is a type of computing that uses quantum mechanical phenomena...",
+ "annotations": []
+ }
+ ]
+ }
+ ],
+ "usage": {
+ "prompt_tokens": 12,
+ "completion_tokens": 145,
+ "total_tokens": 157
+ },
+ "previous_response_id": null
+}
+```
+
+
+
+
+
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-projects.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-projects.mdx
new file mode 100644
index 0000000000..e4945aff97
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-projects.mdx
@@ -0,0 +1,259 @@
+---
+title: Projects endpoints
+slug: /api-projects
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiProjectsReadProjects from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/read-projects.sh';
+import resultApiProjectsResultReadProjects from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/result-read-projects.json';
+import exampleApiProjectsCreateProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/create-project.sh';
+import resultApiProjectsResultCreateProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/result-create-project.json';
+import exampleApiProjectsCreateProject2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/create-project-2.sh';
+import exampleApiProjectsReadProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/read-project.sh';
+import resultApiProjectsResultReadProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/result-read-project.json';
+import exampleApiProjectsUpdateProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/update-project.sh';
+import resultApiProjectsResultUpdateProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/result-update-project.json';
+import exampleApiProjectsDeleteProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/delete-project.sh';
+import resultApiProjectsResultDeleteProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/result-delete-project.txt';
+import exampleApiProjectsExportAProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/export-a-project.sh';
+import exampleApiProjectsImportAProject from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-projects/import-a-project.sh';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import examplePythonApiProjectsReadProjects from '!!raw-loader!@site/docs/API-Reference/python-examples/api-projects/read-projects.py';
+import exampleJavascriptApiProjectsReadProjects from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-projects/read-projects.js';
+import examplePythonApiProjectsCreateProject from '!!raw-loader!@site/docs/API-Reference/python-examples/api-projects/create-project.py';
+import exampleJavascriptApiProjectsCreateProject from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-projects/create-project.js';
+import examplePythonApiProjectsCreateProject2 from '!!raw-loader!@site/docs/API-Reference/python-examples/api-projects/create-project-2.py';
+import exampleJavascriptApiProjectsCreateProject2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-projects/create-project-2.js';
+import examplePythonApiProjectsReadProject from '!!raw-loader!@site/docs/API-Reference/python-examples/api-projects/read-project.py';
+import exampleJavascriptApiProjectsReadProject from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-projects/read-project.js';
+import examplePythonApiProjectsUpdateProject from '!!raw-loader!@site/docs/API-Reference/python-examples/api-projects/update-project.py';
+import exampleJavascriptApiProjectsUpdateProject from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-projects/update-project.js';
+import examplePythonApiProjectsDeleteProject from '!!raw-loader!@site/docs/API-Reference/python-examples/api-projects/delete-project.py';
+import exampleJavascriptApiProjectsDeleteProject from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-projects/delete-project.js';
+import examplePythonApiProjectsExportAProject from '!!raw-loader!@site/docs/API-Reference/python-examples/api-projects/export-a-project.py';
+import exampleJavascriptApiProjectsExportAProject from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-projects/export-a-project.js';
+import examplePythonApiProjectsImportAProject from '!!raw-loader!@site/docs/API-Reference/python-examples/api-projects/import-a-project.py';
+import exampleJavascriptApiProjectsImportAProject from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-projects/import-a-project.js';
+
+
+
+Use the `/projects` endpoint to create, read, update, and delete [Langflow projects](/concepts-flows#projects).
+
+## Read projects
+
+Get a list of Langflow projects, including project IDs, names, and descriptions.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Create project
+
+Create a new project.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+To add flows and components at project creation, retrieve the `components_list` and `flows_list` values from the [`/all`](/api-reference-api-examples#get-all-components) and [/flows/read](/api-flows#read-flows) endpoints and add them to the request body.
+
+Adding a flow to a project moves the flow from its previous location. The flow isn't copied.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Read project
+
+Retrieve details of a specific project.
+
+To find the UUID of your project, call the [read projects](#read-projects) endpoint.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Update project
+
+Update the information of a specific project with a `PATCH` request.
+
+Each PATCH request updates the project with the values you send.
+Only the fields you include in your request are updated.
+If you send the same values multiple times, the update is still processed, even if the values are unchanged.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Delete project
+
+Delete a specific project.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Export a project
+
+Download all flows from a project as a zip file.
+
+The `--output` flag is optional.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Import a project
+
+Import a project and its flows by uploading a Langflow project zip file:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-reference-api-examples.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-reference-api-examples.mdx
new file mode 100644
index 0000000000..2d41039c8a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-reference-api-examples.mdx
@@ -0,0 +1,440 @@
+---
+title: Get started with the Langflow API
+slug: /api-reference-api-examples
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiReferenceApiExamplesFormLangflowApiRequests from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-reference-api-examples/form-langflow-api-requests.sh';
+import exampleApiReferenceApiExamplesSetEnvironmentVariables from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-reference-api-examples/set-environment-variables.sh';
+import exampleApiReferenceApiExamplesHealthCheck from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-reference-api-examples/health-check.sh';
+import resultApiReferenceApiExamplesResultHealthCheck from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-reference-api-examples/result-health-check.json';
+import exampleApiReferenceApiExamplesGetVersion from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-reference-api-examples/get-version.sh';
+import resultApiReferenceApiExamplesResultGetVersion from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-reference-api-examples/result-get-version.txt';
+import exampleApiReferenceApiExamplesGetConfiguration from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-reference-api-examples/get-configuration.sh';
+import resultApiReferenceApiExamplesResultGetConfiguration from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-reference-api-examples/result-get-configuration.json';
+import exampleApiReferenceApiExamplesGetAllComponents from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-reference-api-examples/get-all-components.sh';
+import examplePythonApiReferenceApiExamplesFormLangflowApiRequests from '!!raw-loader!@site/docs/API-Reference/python-examples/api-reference-api-examples/form-langflow-api-requests.py';
+import exampleJavascriptApiReferenceApiExamplesFormLangflowApiRequests from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-reference-api-examples/form-langflow-api-requests.js';
+import examplePythonApiReferenceApiExamplesHealthCheck from '!!raw-loader!@site/docs/API-Reference/python-examples/api-reference-api-examples/health-check.py';
+import exampleJavascriptApiReferenceApiExamplesHealthCheck from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-reference-api-examples/health-check.js';
+import examplePythonApiReferenceApiExamplesGetVersion from '!!raw-loader!@site/docs/API-Reference/python-examples/api-reference-api-examples/get-version.py';
+import exampleJavascriptApiReferenceApiExamplesGetVersion from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-reference-api-examples/get-version.js';
+import examplePythonApiReferenceApiExamplesGetConfiguration from '!!raw-loader!@site/docs/API-Reference/python-examples/api-reference-api-examples/get-configuration.py';
+import exampleJavascriptApiReferenceApiExamplesGetConfiguration from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-reference-api-examples/get-configuration.js';
+import examplePythonApiReferenceApiExamplesGetAllComponents from '!!raw-loader!@site/docs/API-Reference/python-examples/api-reference-api-examples/get-all-components.py';
+import exampleJavascriptApiReferenceApiExamplesGetAllComponents from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-reference-api-examples/get-all-components.js';
+
+
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+You can use the Langflow API for programmatic interactions with Langflow, such as the following:
+
+* Create and edit flows, including file management for flows.
+* Develop applications that use your flows.
+* Develop custom components.
+* Build Langflow as a dependency of a larger application, codebase, or service.
+* Contribute to the overall Langflow codebase.
+
+To view and test all available endpoints, you can access the Langflow API's OpenAPI specification at your Langflow deployment's `/docs` endpoint, such as `http://localhost:7860/docs`.
+
+:::tip Try it
+For an example of the Langflow API in a script, see the [Langflow quickstart](/get-started-quickstart).
+
+The quickstart demonstrates how to get automatically generated code snippets for your flows, use a script to run a flow, and extract data from the Langfow API response.
+:::
+
+## Form Langflow API requests
+
+While individual options vary by endpoint, all Langflow API requests share some commonalities, like a URL, method, parameters, and authentication.
+
+As an example of a Langflow API request, the following curl command calls the `/v1/run` endpoint, and it passes a runtime override (`tweaks`) to the flow's **Chat Output** component:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+### Base URL
+
+By default, local deployments serve the Langflow API at `http://localhost:7860/api`.
+
+Remotely hosted Langflow deployments are available at the domain set by the hosting service, such as `http://IP_OR_DNS/api` or `http://IP_OR_DNS:LANGFLOW_PORT/api`.
+
+You can configure the Langflow port number in the `LANGFLOW_PORT` [environment variable](/environment-variables).
+
+* `https://UUID.ngrok.app/api`
+* `http://IP_OR_DNS/api`
+* `http://IP_OR_DNS:LANGFLOW_PORT/api`
+
+### Authentication
+
+In Langflow versions 1.5 and later, most API endpoints require authentication with a Langflow API key in either an `x-api-key` header or query parameter.
+For more information, see [API keys and authentication](/api-keys-and-authentication).
+
+As with any API, follow industry best practices for storing and referencing sensitive credentials.
+For example, you can [set environment variables](#set-environment-variables) for your API keys, and then reference those environment variables in your API requests.
+
+### Methods, paths, and parameters
+
+Langflow API requests use various methods, paths, path parameters, query parameters, and body parameters.
+The specific requirements and options depend on the endpoint that you want to call.
+
+For example, to create a flow, you pass a JSON-formatted flow definition to `POST /v1/flows`.
+Then, to run your flow, you call `POST /v1/run/$FLOW_ID` with optional run parameters in the request body.
+
+### API versions
+
+The Langflow API serves `/v1` and `/v2` endpoints.
+
+Some endpoints only exist under a single version and some exist under both the `/v1` and `/v2` versions.
+
+If a request fails or has an unexpected result, make sure your endpoint path has the correct version.
+
+## Set environment variables
+
+You can store commonly used values in environment variables to facilitate reuse, simplify token rotation, and securely reference sensitive values.
+
+You can use any method you prefer to set environment variables, such as `export`, `.env`, `zshrc`, or `.curlrc`.
+Then, reference those environment variables in your API requests.
+For example:
+
+
+
+Commonly used values in Langflow API requests include your [Langflow server URL](#base-url), [Langflow API keys](#authentication), flow IDs, and [project IDs](/api-projects#read-projects).
+
+You can retrieve flow IDs from the [**API access** pane](/concepts-publish#api-access), in a flow's URL, and with [`GET /flows`](/api-flows#read-flows).
+
+## Try some Langflow API requests
+
+Once you have your Langflow server URL, try calling these endpoints that return Langflow metadata.
+
+### Health check
+
+Returns the health status of the Langflow database and chat services:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+Langflow provides an additional `GET /health` endpoint.
+This endpoint is served by uvicorn before Langflow is fully initialized, so it's not reliable for checking Langflow service health.
+
+### Get version
+
+Returns the current Langflow API version:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Get configuration
+
+Returns configuration details for your Langflow deployment.
+Requires a [Langflow API key](/api-keys-and-authentication).
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+### Get all components
+
+Returns a dictionary of all Langflow components.
+Requires a [Langflow API key](/api-keys-and-authentication).
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+## Available endpoints
+
+Because you can run Langflow as either an IDE (frontend and backend) or a runtime (headless, backend-only), it serves endpoints that support frontend and backend operations.
+Many endpoints are for orchestration between the frontend and backend, reading and writing to the Langflow database, or enabling frontend functionality, like the **Playground**.
+Unless you are contributing to the Langflow codebase, you won't directly call most of the Langflow endpoints.
+
+For application development, the most commonly used endpoints are the `/run` and `/webhook` [flow trigger endpoints](/api-flows-run).
+For some use cases, you might use some other endpoints, such as the `/files` endpoints to use files in flows.
+
+To help you explore the available endpoints, the following lists are sorted by primary use case, although some endpoints might support multiple use cases.
+
+
+
+
+The following endpoints are useful for developing applications with Langflow and administering Langflow deployments with one or more users.
+You will most often use the flow trigger endpoints.
+Other endpoints are helpful for specific use cases, such as administration and flow management in runtime deployments that don't have a visual editor.
+
+* [Flow trigger endpoints](/api-flows-run):
+ * POST `/v1/run/{flow_id_or_name}`: Run a flow.
+ * POST `/v1/run/advanced/{flow_id}`: Advanced run with explicit `inputs`, `outputs`, `tweaks`, and optional `session_id`.
+ * POST `/v1/webhook/{flow_id_or_name}`: Trigger a flow via webhook payload.
+
+* [OpenAI Responses API](/api-openai-responses):
+ * POST `/v1/responses`: Execute flows using an OpenAI-compatible request format.
+
+* Deployment details:
+ * GET `/v1/version`: Return Langflow version. See [Get version](/api-reference-api-examples#get-version).
+ * GET `/v1/config`: Return deployment configuration. See [Get configuration](/api-reference-api-examples#get-configuration).
+ * GET `/health_check`: Health check endpoint that validates database and chat service connectivity. Returns 500 status if any service is unavailable.
+
+* [Projects endpoints](/api-projects):
+ * POST `/v1/projects/`: Create a project.
+ * GET `/v1/projects/`: List projects.
+ * GET `/v1/projects/{project_id}`: Read a project (with paginated flows support).
+ * PATCH `/v1/projects/{project_id}`: Update project info and membership.
+ * DELETE `/v1/projects/{project_id}`: Delete a project.
+ * GET `/v1/projects/download/{project_id}`: Export all flows in a project as ZIP.
+ * POST `/v1/projects/upload/`: Import a project ZIP (creates project and flows).
+ * GET `/v1/starter-projects/`: Return a list of templates.
+
+* [Files endpoints](/api-files):
+ * Files (v1)
+ * POST `/v1/files/upload/{flow_id}`: Upload a file to a specific flow.
+ * GET `/v1/files/download/{flow_id}/{file_name}`: Download a file from a flow.
+ * GET `/v1/files/images/{flow_id}/{file_name}`: Stream an image from a flow.
+ * GET `/v1/files/profile_pictures/{folder_name}/{file_name}`: Get a profile picture asset.
+ * GET `/v1/files/profile_pictures/list`: List available profile picture assets.
+ * GET `/v1/files/list/{flow_id}`: List files for a flow.
+ * DELETE `/v1/files/delete/{flow_id}/{file_name}`: Delete a file from a flow.
+ * Files (v2)
+ * POST `/v2/files` (alias `/v2/files/`): Upload a file owned by the current user.
+ * GET `/v2/files` (alias `/v2/files/`): List files owned by the current user.
+ * DELETE `/v2/files/batch/`: Delete multiple files by IDs.
+ * POST `/v2/files/batch/`: Download multiple files as a ZIP by IDs.
+ * GET `/v2/files/{file_id}`: Download a file by ID (or return raw content internally).
+ * PUT `/v2/files/{file_id}`: Edit a file name by ID.
+ * DELETE `/v2/files/{file_id}`: Delete a file by ID.
+ * DELETE `/v2/files` (alias `/v2/files/`): Delete all files for the current user.
+
+* [API keys and authentication](/api-keys-and-authentication):
+ * GET `/v1/api_key/`: List API keys for the current user.
+ * POST `/v1/api_key/`: Create a new API key.
+ * DELETE `/v1/api_key/{api_key_id}`: Delete an API key.
+ * POST `/v1/api_key/store`: Save an encrypted Store API key (cookie set).
+
+* [Flow management endpoints](/api-flows):
+ * POST `/v1/flows/`: Create a flow.
+ * GET `/v1/flows/`: List flows (supports pagination and filters).
+ * GET `/v1/flows/{flow_id}`: Read a flow by ID.
+ * GET `/v1/flows/public_flow/{flow_id}`: Read a public flow by ID.
+ * PATCH `/v1/flows/{flow_id}`: Update a flow.
+ * DELETE `/v1/flows/{flow_id}`: Delete a flow.
+ * POST `/v1/flows/batch/`: Create multiple flows.
+ * POST `/v1/flows/upload/`: Import flows from a JSON file.
+ * DELETE `/v1/flows/`: Delete multiple flows by IDs.
+ * POST `/v1/flows/download/`: Export flows to a ZIP file.
+ * GET `/v1/flows/basic_examples/`: List basic example flows.
+
+* [Users endpoints](/api-users):
+ * POST `/v1/users/`: Add a user (superuser required when auth enabled).
+ * GET `/v1/users/whoami`: Return the current authenticated user.
+ * GET `/v1/users/`: List all users (superuser required).
+ * PATCH `/v1/users/{user_id}`: Update a user (with role checks).
+ * PATCH `/v1/users/{user_id}/reset-password`: Reset own password.
+ * DELETE `/v1/users/{user_id}`: Delete a user (cannot delete yourself).
+
+* Custom components: You might use these endpoints when developing custom Langflow components for your own use or to share with the Langflow community:
+ * GET `/v1/all`: Return all available Langflow component types. See [Get all components](/api-reference-api-examples#get-all-components).
+ * POST `/v1/custom_component`: Build a custom component from code and return its node.
+ * POST `/v1/custom_component/update`: Update an existing custom component's build config and outputs.
+ * POST `/v1/validate/code`: Validate a Python code snippet for a custom component.
+ * POST `/v1/validate/prompt`: Validate a prompt payload.
+
+
+
+
+The following endpoints are most often used when contributing to the Langflow codebase, and you need to understand or call endpoints that support frontend-to-backend orchestration or other internal functionality.
+
+* Base (metadata):
+ * GET `/v1/all`: Return all available Langflow component types. See [Get all components](/api-reference-api-examples#get-all-components).
+ * GET `/v1/version`: Return Langflow version. See [Get version](/api-reference-api-examples#get-version).
+ * GET `/v1/config`: Return deployment configuration. See [Get configuration](/api-reference-api-examples#get-configuration).
+ * GET `/v1/starter-projects/`: Return a list of templates.
+
+* [Build endpoints](/api-build) (internal editor support):
+ * POST `/v1/build/{flow_id}/flow`: Start a flow build and return a job ID.
+ * GET `/v1/build/{job_id}/events`: Stream or fetch build events.
+ * POST `/v1/build/{job_id}/cancel`: Cancel a build job.
+ * POST `/v1/build_public_tmp/{flow_id}/flow`: Build a public flow without auth.
+ * POST `/v1/validate/prompt`: Validate a prompt payload.
+
+* [API keys and authentication](/api-keys-and-authentication):
+ * POST `/v1/login`: Login and set tokens as cookies.
+ * GET `/v1/auto_login`: Auto-login (if enabled) and set tokens.
+ * POST `/v1/refresh`: Refresh tokens using refresh cookie.
+ * POST `/v1/logout`: Logout and clear cookies.
+
+* [Monitor endpoints](/api-monitor):
+ * GET `/v1/monitor/builds`: Get vertex builds for a flow.
+ * DELETE `/v1/monitor/builds`: Delete vertex builds for a flow.
+ * GET `/v1/monitor/messages/sessions`: List message session IDs (auth required).
+ * GET `/v1/monitor/messages`: List messages with optional filters.
+ * DELETE `/v1/monitor/messages`: Delete messages by IDs (auth required).
+ * PUT `/v1/monitor/messages/{message_id}`: Update a message.
+ * PATCH `/v1/monitor/messages/session/{old_session_id}`: Change a session ID for all messages in that session.
+ * DELETE `/v1/monitor/messages/session/{session_id}`: Delete messages by session.
+ * GET `/v1/monitor/transactions`: List transactions for a flow (paginated).
+
+* Variables:
+ * POST `/v1/variables/`: Create a variable, such as an API key, for the user.
+ * GET `/v1/variables/`: List variables for the user.
+ * PATCH `/v1/variables/{variable_id}`: Update a variable.
+ * DELETE `/v1/variables/{variable_id}`: Delete a variable.
+
+* [Use voice mode](/concepts-voice-mode):
+ * WS `/v1/voice/ws/flow_as_tool/{flow_id}`: Bi-directional voice session exposing the flow as a tool.
+ * WS `/v1/voice/ws/flow_as_tool/{flow_id}/{session_id}`: Same as above with explicit session ID.
+ * WS `/v1/voice/ws/flow_tts/{flow_id}`: Voice-to-text session that runs a flow and returns TTS.
+ * WS `/v1/voice/ws/flow_tts/{flow_id}/{session_id}`: Same as above with explicit session ID.
+ * GET `/v1/voice/elevenlabs/voice_ids`: List available ElevenLabs voice IDs for the user.
+
+* MCP servers: The following endpoints are for managing Langflow MCP servers and MCP server connections.
+They aren't typically called directly; instead, they are used to drive internal functionality in the Langflow frontend and when running flows that call MCP servers.
+Langflow MCP servers support both streamable HTTP and SSE transport.
+ * HEAD `/v1/mcp/streamable`: Health check for streamable HTTP MCP.
+ * GET `/v1/mcp/streamable`: Open streamable HTTP connection for MCP server.
+ * POST `/v1/mcp/streamable`: Post messages to the MCP server via streamable HTTP.
+ * DELETE `/v1/mcp/streamable`: Close streamable HTTP connection.
+ * HEAD `/v1/mcp/sse` (LEGACY): Health check for MCP SSE.
+ * GET `/v1/mcp/sse` (LEGACY): Open SSE stream for MCP server events.
+ * POST `/v1/mcp/` (LEGACY): Post messages to the MCP server.
+ * GET `/v1/mcp/project/{project_id}`: List MCP-enabled tools and project auth settings.
+ * HEAD `/v1/mcp/project/{project_id}/streamable`: Health check for project streamable HTTP MCP.
+ * GET `/v1/mcp/project/{project_id}/streamable`: Open project-scoped streamable HTTP connection.
+ * POST `/v1/mcp/project/{project_id}/streamable`: Post messages to project MCP server via streamable HTTP.
+ * DELETE `/v1/mcp/project/{project_id}/streamable`: Close project streamable HTTP connection.
+ * HEAD `/v1/mcp/project/{project_id}/sse` (LEGACY): Health check for project SSE.
+ * GET `/v1/mcp/project/{project_id}/sse` (LEGACY): Open project-scoped MCP SSE.
+ * POST `/v1/mcp/project/{project_id}` (LEGACY): Post messages to project MCP server.
+ * PATCH `/v1/mcp/project/{project_id}`: Update MCP settings for flows and project auth settings.
+ * POST `/v1/mcp/project/{project_id}/install`: Install MCP client config for Cursor/Windsurf/Claude (local only).
+ * GET `/v1/mcp/project/{project_id}/installed`: Check which clients have MCP config installed.
+
+* Custom components: You might use these endpoints when developing custom Langflow components for your own use or to share with the Langflow community:
+ * GET `/v1/all`: Return all available Langflow component types. See [Get all components](/api-reference-api-examples#get-all-components).
+ * POST `/v1/custom_component`: Build a custom component from code and return its node.
+ * POST `/v1/custom_component/update`: Update an existing custom component's build config and outputs.
+ * POST `/v1/validate/code`: Validate a Python code snippet for a custom component.
+ * POST `/v1/validate/prompt`: Validate a prompt payload.
+
+
+
+
+The following endpoints are deprecated:
+
+* POST `/v1/predict/{flow_id}`: Use [`/v1/run/{flow_id}`](/api-flows-run) instead.
+* POST `/v1/process/{flow_id}`: Use [`/v1/run/{flow_id}`](/api-flows-run) instead.
+* GET `/v1/task/{task_id}`: Deprecated functionality.
+* POST `/v1/upload/{flow_id}`: Use [`/files`](/api-files) instead.
+* POST `/v1/build/{flow_id}/vertices`: Replaced by [`/monitor/builds`](/api-monitor).
+* POST `/v1/build/{flow_id}/vertices/{vertex_id}`: Replaced by [`/monitor/builds`](/api-monitor).
+* GET `/v1/build/{flow_id}/{vertex_id}/stream`: Replaced by [`/monitor/builds`](/api-monitor).
+* GET `/v1/store/check/`: Return whether the Store feature is enabled.
+* GET `/v1/store/check/api_key`: Check if a Store API key exists and is valid.
+* POST `/v1/store/components/`: Share a component to the Store.
+* PATCH `/v1/store/components/{component_id}`: Update a shared component.
+* GET `/v1/store/components/`: List available Store components (filters supported).
+* GET `/v1/store/components/{component_id}`: Download a component from the Store.
+* GET `/v1/store/tags`: List Store tags.
+* GET `/v1/store/users/likes`: List components liked by the current user.
+* POST `/v1/store/users/likes/{component_id}`: Like a component.
+
+
+
+
+## Next steps
+
+* Use the Langflow API to [run a flow](/api-flows-run).
+* Use the Langflow API to [upload files](/api-files).
+* Use the Langflow API to [get flow logs](/api-logs).
+* Explore all endpoints in the [Langflow API specification](/api).
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/api-users.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/api-users.mdx
new file mode 100644
index 0000000000..5da898cd67
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/api-users.mdx
@@ -0,0 +1,225 @@
+---
+title: Users endpoints
+slug: /api-users
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleApiUsersAddUser from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/add-user.sh';
+import resultApiUsersResultAddUser from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/result-add-user.json';
+import exampleApiUsersGetCurrentUser from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/get-current-user.sh';
+import resultApiUsersResultGetCurrentUser from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/result-get-current-user.json';
+import exampleApiUsersListAllUsers from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/list-all-users.sh';
+import resultApiUsersResultListAllUsers from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/result-list-all-users.json';
+import exampleApiUsersUpdateUser from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/update-user.sh';
+import resultApiUsersResultUpdateUser from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/result-update-user.json';
+import exampleApiUsersResetPassword from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/reset-password.sh';
+import resultApiUsersResultResetPassword from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/result-reset-password.json';
+import exampleApiUsersDeleteUser from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/delete-user.sh';
+import resultApiUsersResultDeleteUser from '!!raw-loader!@site/docs/API-Reference/curl-examples/api-users/result-delete-user.json';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import examplePythonApiUsersAddUser from '!!raw-loader!@site/docs/API-Reference/python-examples/api-users/add-user.py';
+import exampleJavascriptApiUsersAddUser from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-users/add-user.js';
+import examplePythonApiUsersGetCurrentUser from '!!raw-loader!@site/docs/API-Reference/python-examples/api-users/get-current-user.py';
+import exampleJavascriptApiUsersGetCurrentUser from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-users/get-current-user.js';
+import examplePythonApiUsersListAllUsers from '!!raw-loader!@site/docs/API-Reference/python-examples/api-users/list-all-users.py';
+import exampleJavascriptApiUsersListAllUsers from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-users/list-all-users.js';
+import examplePythonApiUsersUpdateUser from '!!raw-loader!@site/docs/API-Reference/python-examples/api-users/update-user.py';
+import exampleJavascriptApiUsersUpdateUser from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-users/update-user.js';
+import examplePythonApiUsersResetPassword from '!!raw-loader!@site/docs/API-Reference/python-examples/api-users/reset-password.py';
+import exampleJavascriptApiUsersResetPassword from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-users/reset-password.js';
+import examplePythonApiUsersDeleteUser from '!!raw-loader!@site/docs/API-Reference/python-examples/api-users/delete-user.py';
+import exampleJavascriptApiUsersDeleteUser from '!!raw-loader!@site/docs/API-Reference/javascript-examples/api-users/delete-user.js';
+
+
+
+Use the `/users` endpoint to manage user accounts in Langflow.
+
+## Add user
+
+Create a new user account with a given username and password.
+
+Requires authentication as a superuser if the Langflow server has authentication enabled.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+The request returns an object describing the new user.
+The user's UUID is stored in `user_id` in the Langflow database, and returned as `id` in the `/users` API response.
+This `user_id` key is specifically for Langflow user management.
+
+
+Result
+
+
+
+
+
+## Get current user
+
+Retrieve information about the authenticated user.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## List all users
+
+Get a paginated list of all users in the system.
+
+Requires authentication as a superuser if the Langflow server has authentication enabled.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Update user
+
+Modify an existing user's information with a PATCH request.
+
+Requires authentication as a superuser if the Langflow server has authentication enabled.
+
+This example activates the specified user's account and makes them a superuser:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Reset password
+
+Change the specified user's password to a new secure value.
+
+Requires authentication as the target user.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
+
+## Delete user
+
+Remove a user account from the system.
+
+Requires authentication as a superuser if the Langflow server has authentication enabled.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Result
+
+
+
+
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/build-flow-and-stream-events-2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/build-flow-and-stream-events-2.sh
new file mode 100644
index 0000000000..465dff08ef
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/build-flow-and-stream-events-2.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/build/123e4567-e89b-12d3-a456-426614174000/events" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/build-flow-and-stream-events-3.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/build-flow-and-stream-events-3.sh
new file mode 100644
index 0000000000..1cb3f245e0
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/build-flow-and-stream-events-3.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/build/123e4567-e89b-12d3-a456-426614174000/events?stream=false" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/build-flow-and-stream-events.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/build-flow-and-stream-events.sh
new file mode 100644
index 0000000000..e256df56e9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/build-flow-and-stream-events.sh
@@ -0,0 +1,10 @@
+curl -X POST \
+ "$LANGFLOW_URL/api/v1/build/$FLOW_ID/flow" \
+ -H "accept: application/json" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "inputs": {
+ "input_value": "Tell me a story"
+ }
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/override-flow-parameters.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/override-flow-parameters.sh
new file mode 100644
index 0000000000..0ad3e28cfe
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/override-flow-parameters.sh
@@ -0,0 +1,15 @@
+curl -X POST \
+ "$LANGFLOW_URL/api/v1/build/$FLOW_ID/flow" \
+ -H "accept: application/json" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "data": {
+ "nodes": [],
+ "edges": []
+ },
+ "inputs": {
+ "input_value": "Your custom input here",
+ "session": "session_id"
+ }
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/result-build-flow-and-stream-events-2.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/result-build-flow-and-stream-events-2.json
new file mode 100644
index 0000000000..ece8751026
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/result-build-flow-and-stream-events-2.json
@@ -0,0 +1,7 @@
+{"event": "vertices_sorted", "data": {"ids": ["ChatInput-XtBLx"], "to_run": ["Prompt-x74Ze", "ChatOutput-ylMzN", "ChatInput-XtBLx", "OpenAIModel-d1wOZ"]}}
+
+{"event": "add_message", "data": {"timestamp": "2025-03-03T17:42:23", "sender": "User", "sender_name": "User", "session_id": "d2bbd92b-187e-4c84-b2d4-5df365704201", "text": "Tell me a story", "files": [], "error": false, "edit": false, "properties": {"text_color": "", "background_color": "", "edited": false, "source": {"id": null, "display_name": null, "source": null}, "icon": "", "allow_markdown": false, "positive_feedback": null, "state": "complete", "targets": []}, "category": "message", "content_blocks": [], "id": "28879bd8-6a68-4dd5-b658-74d643a4dd92", "flow_id": "d2bbd92b-187e-4c84-b2d4-5df365704201"}}
+
+// ... Additional events as the flow executes ...
+
+{"event": "end", "data": {}}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/result-build-flow-and-stream-events.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/result-build-flow-and-stream-events.json
new file mode 100644
index 0000000000..91ecc694d9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/result-build-flow-and-stream-events.json
@@ -0,0 +1,3 @@
+{
+ "job_id": "123e4567-e89b-12d3-a456-426614174000"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/result-override-flow-parameters.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/result-override-flow-parameters.json
new file mode 100644
index 0000000000..ecf947abcc
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/result-override-flow-parameters.json
@@ -0,0 +1 @@
+{ "job_id": "0bcc7f23-40b4-4bfa-9b8a-a44181fd1175" }
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/set-start-and-stop-points.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/set-start-and-stop-points.sh
new file mode 100644
index 0000000000..484d0357c4
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-build/set-start-and-stop-points.sh
@@ -0,0 +1,6 @@
+curl -X POST \
+ "$LANGFLOW_URL/api/v1/build/$FLOW_ID/flow" \
+ -H "accept: application/json" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{"stop_component_id": "OpenAIModel-Uksag"}'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/delete-all-files-v2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/delete-all-files-v2.sh
new file mode 100644
index 0000000000..0c443c5b8e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/delete-all-files-v2.sh
@@ -0,0 +1,4 @@
+curl -X DELETE \
+ "$LANGFLOW_URL/api/v2/files" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/delete-file-v1.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/delete-file-v1.sh
new file mode 100644
index 0000000000..a3ec4c1a64
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/delete-file-v1.sh
@@ -0,0 +1,4 @@
+curl -X DELETE \
+ "$LANGFLOW_URL/api/v1/files/delete/$FLOW_ID/2024-12-30_15-19-43_your_file.txt" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/delete-file-v2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/delete-file-v2.sh
new file mode 100644
index 0000000000..c897b79d42
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/delete-file-v2.sh
@@ -0,0 +1,4 @@
+curl -X DELETE \
+ "$LANGFLOW_URL/api/v2/files/$FILE_ID" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/download-file-v1.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/download-file-v1.sh
new file mode 100644
index 0000000000..a75a55bee3
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/download-file-v1.sh
@@ -0,0 +1,5 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/files/download/$FLOW_ID/2024-12-30_15-19-43_your_file.txt" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ --output downloaded_file.txt
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/download-file-v2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/download-file-v2.sh
new file mode 100644
index 0000000000..20c868fc49
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/download-file-v2.sh
@@ -0,0 +1,5 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v2/files/c7b22c4c-d5e0-4ec9-af97-5d85b7657a34" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ --output downloaded_file.txt
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/edit-file-name-v2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/edit-file-name-v2.sh
new file mode 100644
index 0000000000..5a831da3c8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/edit-file-name-v2.sh
@@ -0,0 +1,4 @@
+curl -X PUT \
+ "$LANGFLOW_URL/api/v2/files/$FILE_ID?name=new_file_name" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/list-files-v1.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/list-files-v1.sh
new file mode 100644
index 0000000000..040d72230d
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/list-files-v1.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/files/list/$FLOW_ID" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/list-files-v2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/list-files-v2.sh
new file mode 100644
index 0000000000..c99d450573
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/list-files-v2.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v2/files" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-delete-all-files-v2.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-delete-all-files-v2.json
new file mode 100644
index 0000000000..90d8bd5c44
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-delete-all-files-v2.json
@@ -0,0 +1,3 @@
+{
+ "message": "All files deleted successfully"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-delete-file-v1.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-delete-file-v1.json
new file mode 100644
index 0000000000..9ba62fe3da
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-delete-file-v1.json
@@ -0,0 +1,3 @@
+{
+ "message": "File 2024-12-30_15-19-43_your_file.txt deleted successfully"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-delete-file-v2.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-delete-file-v2.json
new file mode 100644
index 0000000000..0d5d491f18
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-delete-file-v2.json
@@ -0,0 +1,3 @@
+{
+ "message": "File deleted successfully"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-download-file-v1.txt b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-download-file-v1.txt
new file mode 100644
index 0000000000..4921b2a3b5
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-download-file-v1.txt
@@ -0,0 +1 @@
+File contents downloaded to downloaded_file.txt
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-download-file-v2.txt b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-download-file-v2.txt
new file mode 100644
index 0000000000..4921b2a3b5
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-download-file-v2.txt
@@ -0,0 +1 @@
+File contents downloaded to downloaded_file.txt
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-edit-file-name-v2.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-edit-file-name-v2.json
new file mode 100644
index 0000000000..c9fbe8f494
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-edit-file-name-v2.json
@@ -0,0 +1,7 @@
+{
+ "id": "76543e40-f388-4cb3-b0ee-a1e870aca3d3",
+ "name": "new_file_name",
+ "path": "6f17a73e-97d7-4519-a8d9-8e4c0be411bb/76543e40-f388-4cb3-b0ee-a1e870aca3d3.png",
+ "size": 2728251,
+ "provider": null
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-list-files-v1.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-list-files-v1.json
new file mode 100644
index 0000000000..3e6abd62a9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-list-files-v1.json
@@ -0,0 +1,3 @@
+{
+ "files": ["2024-12-30_15-19-43_your_file.txt"]
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-list-files-v2.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-list-files-v2.json
new file mode 100644
index 0000000000..d95c1f19de
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-list-files-v2.json
@@ -0,0 +1,9 @@
+[
+ {
+ "id": "c7b22c4c-d5e0-4ec9-af97-5d85b7657a34",
+ "name": "your_file",
+ "path": "6f17a73e-97d7-4519-a8d9-8e4c0be411bb/c7b22c4c-d5e0-4ec9-af97-5d85b7657a34.txt",
+ "size": 1234,
+ "provider": null
+ }
+]
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-upload-file-v1.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-upload-file-v1.json
new file mode 100644
index 0000000000..4e17a96826
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-upload-file-v1.json
@@ -0,0 +1,4 @@
+{
+ "flowId": "92f9a4c5-cfc8-4656-ae63-1f0881163c28",
+ "file_path": "92f9a4c5-cfc8-4656-ae63-1f0881163c28/2024-12-30_15-19-43_your_file.txt"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-upload-file-v2.txt b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-upload-file-v2.txt
new file mode 100644
index 0000000000..b61e201b1c
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/result-upload-file-v2.txt
@@ -0,0 +1 @@
+{"id":"07e5b864-e367-4f52-b647-a48035ae7e5e","username":"langflow","profile_image":null,"store_api_key":null,"is_active":true,"is_superuser":true,"create_at":"2025-05-08T17:59:07.855965","updated_at":"2025-05-28T19:00:42.556460","last_login_at":"2025-05-28T19:00:42.554338","optins":{"github_starred":false,"dialog_dismissed":true,"discord_clicked":false,"mcp_dialog_dismissed":true}}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/send-files-to-your-flows-v2-2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/send-files-to-your-flows-v2-2.sh
new file mode 100644
index 0000000000..c5257ed340
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/send-files-to-your-flows-v2-2.sh
@@ -0,0 +1,16 @@
+curl --request POST \
+ --url "$LANGFLOW_URL/api/v1/run/$FLOW_ID" \
+ --header "Content-Type: application/json" \
+ --header "x-api-key: $LANGFLOW_API_KEY" \
+ --data '{
+ "input_value": "what do you see?",
+ "output_type": "chat",
+ "input_type": "text",
+ "tweaks": {
+ "Read-File-1olS3": {
+ "path": [
+ "07e5b864-e367-4f52-b647-a48035ae7e5e/3a290013-fe1e-4d3d-a454-cacae81288f3.pdf"
+ ]
+ }
+ }
+}'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/send-files-to-your-flows-v2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/send-files-to-your-flows-v2.sh
new file mode 100644
index 0000000000..06cb36a4e1
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/send-files-to-your-flows-v2.sh
@@ -0,0 +1,10 @@
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+DEFAULT_UPLOAD_FILE="$SCRIPT_DIR/../../fixtures/sample-upload.txt"
+UPLOAD_FILE="${SAMPLE_UPLOAD_FILE:-$DEFAULT_UPLOAD_FILE}"
+
+curl -X POST \
+ "$LANGFLOW_URL/api/v2/files" \
+ -H "accept: application/json" \
+ -H "Content-Type: multipart/form-data" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -F "file=@${UPLOAD_FILE}"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-file-v1.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-file-v1.sh
new file mode 100644
index 0000000000..6edf907472
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-file-v1.sh
@@ -0,0 +1,10 @@
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+DEFAULT_UPLOAD_FILE="$SCRIPT_DIR/../../fixtures/sample-upload.txt"
+UPLOAD_FILE="${SAMPLE_UPLOAD_FILE:-$DEFAULT_UPLOAD_FILE}"
+
+curl -X POST \
+ "$LANGFLOW_URL/api/v1/files/upload/$FLOW_ID" \
+ -H "accept: application/json" \
+ -H "Content-Type: multipart/form-data" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -F "file=@${UPLOAD_FILE}"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-file-v2-2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-file-v2-2.sh
new file mode 100644
index 0000000000..06cb36a4e1
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-file-v2-2.sh
@@ -0,0 +1,10 @@
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+DEFAULT_UPLOAD_FILE="$SCRIPT_DIR/../../fixtures/sample-upload.txt"
+UPLOAD_FILE="${SAMPLE_UPLOAD_FILE:-$DEFAULT_UPLOAD_FILE}"
+
+curl -X POST \
+ "$LANGFLOW_URL/api/v2/files" \
+ -H "accept: application/json" \
+ -H "Content-Type: multipart/form-data" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -F "file=@${UPLOAD_FILE}"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-file-v2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-file-v2.sh
new file mode 100644
index 0000000000..85ca53f1d8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-file-v2.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/users/whoami" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-image-files-v1-2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-image-files-v1-2.sh
new file mode 100644
index 0000000000..0ecd6335a9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-image-files-v1-2.sh
@@ -0,0 +1,14 @@
+curl -X POST \
+ "$LANGFLOW_URL/api/v1/run/a430cc57-06bb-4c11-be39-d3d4de68d2c4?stream=false" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "output_type": "chat",
+ "input_type": "chat",
+ "tweaks": {
+ "ChatInput-b67sL": {
+ "files": "a430cc57-06bb-4c11-be39-d3d4de68d2c4/2024-11-27_14-47-50_image-file.png",
+ "input_value": "describe this image"
+ }
+ }
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-image-files-v1.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-image-files-v1.sh
new file mode 100644
index 0000000000..90d879f8a8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-files/upload-image-files-v1.sh
@@ -0,0 +1,8 @@
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+DEFAULT_IMAGE_FILE="$SCRIPT_DIR/../../fixtures/sample-upload.png"
+IMAGE_FILE="${SAMPLE_IMAGE_FILE:-$DEFAULT_IMAGE_FILE}"
+
+curl -X POST "$LANGFLOW_URL/api/v1/files/upload/$FLOW_ID" \
+ -H "Content-Type: multipart/form-data" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -F "file=@${IMAGE_FILE}"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/pass-global-variables-in-request-headers.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/pass-global-variables-in-request-headers.sh
new file mode 100644
index 0000000000..be973988fb
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/pass-global-variables-in-request-headers.sh
@@ -0,0 +1,12 @@
+curl -X POST \
+ "$LANGFLOW_SERVER_URL/api/v1/run/$FLOW_ID" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -H "X-LANGFLOW-GLOBAL-VAR-OPENAI_API_KEY: sk-..." \
+ -H "X-LANGFLOW-GLOBAL-VAR-USER_ID: user123" \
+ -H "X-LANGFLOW-GLOBAL-VAR-ENVIRONMENT: production" \
+ -d '{
+ "input_value": "Tell me about something interesting!",
+ "input_type": "chat",
+ "output_type": "chat"
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/request-example-with-all-headers-and-parameters.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/request-example-with-all-headers-and-parameters.sh
new file mode 100644
index 0000000000..a5d61fb803
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/request-example-with-all-headers-and-parameters.sh
@@ -0,0 +1,17 @@
+curl -X POST \
+ "$LANGFLOW_SERVER_URL/api/v1/run/$FLOW_ID?stream=true" \
+ -H "Content-Type: application/json" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "input_value": "Tell me a story",
+ "input_type": "chat",
+ "output_type": "chat",
+ "output_component": "chat_output",
+ "session_id": "chat-123",
+ "tweaks": {
+ "component_id": {
+ "parameter_name": "value"
+ }
+ }
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/result-webhook-run-flow.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/result-webhook-run-flow.json
new file mode 100644
index 0000000000..43d35d1f94
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/result-webhook-run-flow.json
@@ -0,0 +1,4 @@
+{
+ "message": "Task started in the background",
+ "status": "in progress"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/run-flow.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/run-flow.sh
new file mode 100644
index 0000000000..629efe9cd4
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/run-flow.sh
@@ -0,0 +1,11 @@
+curl -X POST \
+ "$LANGFLOW_SERVER_URL/api/v1/run/$FLOW_ID" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "input_value": "Tell me about something interesting!",
+ "session_id": "chat-123",
+ "input_type": "chat",
+ "output_type": "chat",
+ "output_component": ""
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/stream-llm-token-responses.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/stream-llm-token-responses.sh
new file mode 100644
index 0000000000..adefe07440
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/stream-llm-token-responses.sh
@@ -0,0 +1,9 @@
+curl -X POST \
+ "$LANGFLOW_SERVER_URL/api/v1/run/$FLOW_ID?stream=true" \
+ -H "accept: application/json" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "message": "Tell me something interesting!",
+ "session_id": "chat-123"
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/webhook-run-flow.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/webhook-run-flow.sh
new file mode 100644
index 0000000000..03e57dd474
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows-run/webhook-run-flow.sh
@@ -0,0 +1,5 @@
+curl -X POST \
+ "$LANGFLOW_SERVER_URL/api/v1/webhook/$FLOW_ID" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{"data": "example-data"}'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/create-flow.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/create-flow.sh
new file mode 100644
index 0000000000..ef3ee320f6
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/create-flow.sh
@@ -0,0 +1,20 @@
+curl -X POST \
+ "$LANGFLOW_URL/api/v1/flows/" \
+ -H "accept: application/json" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "name": "string2",
+ "description": "string",
+ "icon": "string",
+ "icon_bg_color": "#FF0000",
+ "gradient": "string",
+ "data": {},
+ "is_component": false,
+ "updated_at": "2024-12-30T15:48:01.519Z",
+ "webhook": false,
+ "endpoint_name": "string",
+ "tags": [
+ "string"
+ ]
+}'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/create-flows.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/create-flows.sh
new file mode 100644
index 0000000000..b74f5737cd
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/create-flows.sh
@@ -0,0 +1,45 @@
+curl -X POST \
+ "$LANGFLOW_URL/api/v1/flows/batch/" \
+ -H "accept: application/json" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "flows": [
+ {
+ "name": "string",
+ "description": "string",
+ "icon": "string",
+ "icon_bg_color": "string",
+ "gradient": "string",
+ "data": {},
+ "is_component": false,
+ "updated_at": "2024-12-30T18:36:02.737Z",
+ "webhook": false,
+ "endpoint_name": "string",
+ "tags": [
+ "string"
+ ],
+ "locked": false,
+ "user_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
+ "project_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+ },
+ {
+ "name": "string",
+ "description": "string",
+ "icon": "string",
+ "icon_bg_color": "string",
+ "gradient": "string",
+ "data": {},
+ "is_component": false,
+ "updated_at": "2024-12-30T18:36:02.737Z",
+ "webhook": false,
+ "endpoint_name": "string",
+ "tags": [
+ "string"
+ ],
+ "locked": false,
+ "user_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
+ "project_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+ }
+ ]
+}'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/delete-flow.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/delete-flow.sh
new file mode 100644
index 0000000000..65289ac50b
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/delete-flow.sh
@@ -0,0 +1,4 @@
+curl -X DELETE \
+ "$LANGFLOW_URL/api/v1/flows/$FLOW_ID" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/export-flows.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/export-flows.sh
new file mode 100644
index 0000000000..3148fc4a30
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/export-flows.sh
@@ -0,0 +1,10 @@
+curl -X POST \
+ "$LANGFLOW_URL/api/v1/flows/download/" \
+ -H "accept: application/json" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '[
+ "e1e40c77-0541-41a9-88ab-ddb3419398b5",
+ "92f9a4c5-cfc8-4656-ae63-1f0881163c28"
+]' \
+ --output langflow-flows.zip
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/import-flows.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/import-flows.sh
new file mode 100644
index 0000000000..3134d05d28
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/import-flows.sh
@@ -0,0 +1,10 @@
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+DEFAULT_FLOW_IMPORT_FILE="$SCRIPT_DIR/../../fixtures/flow-import.json"
+FLOW_IMPORT_FILE="${FLOW_IMPORT_FILE:-$DEFAULT_FLOW_IMPORT_FILE}"
+
+curl -X POST \
+ "$LANGFLOW_URL/api/v1/flows/upload/?folder_id=$FOLDER_ID" \
+ -H "accept: application/json" \
+ -H "Content-Type: multipart/form-data" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -F "file=@${FLOW_IMPORT_FILE};type=application/json"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-flow.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-flow.sh
new file mode 100644
index 0000000000..9d3725acfc
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-flow.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/flows/$FLOW_ID" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-flows-2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-flows-2.sh
new file mode 100644
index 0000000000..c1bb6a1b28
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-flows-2.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/flows/?remove_example_flows=true&components_only=false&get_all=false&project_id=$PROJECT_ID&header_flows=false&page=1&size=1" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-flows.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-flows.sh
new file mode 100644
index 0000000000..c877359d05
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-flows.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/flows/?remove_example_flows=false&components_only=false&get_all=true&header_flows=false&page=1&size=50" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-sample-flows.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-sample-flows.sh
new file mode 100644
index 0000000000..c9388a8a3f
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/read-sample-flows.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/flows/basic_examples/" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-create-flow.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-create-flow.json
new file mode 100644
index 0000000000..784546b475
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-create-flow.json
@@ -0,0 +1,17 @@
+{
+ "name": "string2",
+ "description": "string",
+ "icon": "string",
+ "icon_bg_color": "#FF0000",
+ "gradient": "string",
+ "data": {},
+ "is_component": false,
+ "updated_at": "2025-02-04T21:07:36+00:00",
+ "webhook": false,
+ "endpoint_name": "string",
+ "tags": ["string"],
+ "locked": false,
+ "id": "e8d81c37-714b-49ae-ba82-e61141f020ee",
+ "user_id": "f58396d4-a387-4bb8-b749-f40825c3d9f3",
+ "project_id": "1415de42-8f01-4f36-bf34-539f23e47466"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-delete-flow.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-delete-flow.json
new file mode 100644
index 0000000000..5f2f03e818
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-delete-flow.json
@@ -0,0 +1,3 @@
+{
+ "message": "Flow deleted successfully"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-export-flows.txt b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-export-flows.txt
new file mode 100644
index 0000000000..cb2e1f5179
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-export-flows.txt
@@ -0,0 +1,3 @@
+ % Total % Received % Xferd Average Speed Time Time Time Current
+ Dload Upload Total Spent Left Speed
+100 76437 0 76353 100 84 4516k 5088 --:--:-- --:--:-- --:--:-- 4665k
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-read-flow.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-read-flow.json
new file mode 100644
index 0000000000..1c331b28df
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-read-flow.json
@@ -0,0 +1,12 @@
+{
+ "name": "Basic Prompting",
+ "description": "Perform basic prompting with an OpenAI model.",
+ "icon": "Braces",
+ "icon_bg_color": null,
+ "gradient": "2",
+ "data": {
+ "nodes": [
+ ...
+ ]
+ }
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-update-flow.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-update-flow.json
new file mode 100644
index 0000000000..480af39d1a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/result-update-flow.json
@@ -0,0 +1,17 @@
+{
+ "name": "string",
+ "description": "string",
+ "icon": "Braces",
+ "icon_bg_color": null,
+ "gradient": "2",
+ "data": {},
+ "is_component": false,
+ "updated_at": "2024-12-30T18:30:22+00:00",
+ "webhook": false,
+ "endpoint_name": "my_new_endpoint_name",
+ "tags": null,
+ "locked": true,
+ "id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "user_id": "f58396d4-a387-4bb8-b749-f40825c3d9f3",
+ "project_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/update-flow.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/update-flow.sh
new file mode 100644
index 0000000000..437dc476a6
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-flows/update-flow.sh
@@ -0,0 +1,13 @@
+curl -X PATCH \
+ "$LANGFLOW_URL/api/v1/flows/$FLOW_ID" \
+ -H "accept: application/json" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "name": "string",
+ "description": "string",
+ "data": {},
+ "project_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
+ "endpoint_name": "my_new_endpoint_name",
+ "locked": true
+}'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/result-retrieve-logs-with-optional-parameters.txt b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/result-retrieve-logs-with-optional-parameters.txt
new file mode 100644
index 0000000000..5960117df3
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/result-retrieve-logs-with-optional-parameters.txt
@@ -0,0 +1,12 @@
+{
+ "1736354770500": "2025-01-08T11:46:10.500363-0500 DEBUG Creating starter project Document Q&A\n",
+ "1736354770511": "2025-01-08T11:46:10.511146-0500 DEBUG Creating starter project Image Sentiment Analysis\n",
+ "1736354770521": "2025-01-08T11:46:10.521018-0500 DEBUG Creating starter project SEO Keyword Generator\n",
+ "1736354770532": "2025-01-08T11:46:10.532677-0500 DEBUG Creating starter project Sequential Tasks Agents\n",
+ "1736354770544": "2025-01-08T11:46:10.544010-0500 DEBUG Creating starter project Custom Component Generator\n",
+ "1736354770555": "2025-01-08T11:46:10.555513-0500 DEBUG Creating starter project Prompt Chaining\n",
+ "1736354770588": "2025-01-08T11:46:10.588105-0500 DEBUG Create service ServiceType.CHAT_SERVICE\n",
+ "1736354771021": "2025-01-08T11:46:11.021817-0500 DEBUG Telemetry data sent successfully.\n",
+ "1736354775619": "2025-01-08T11:46:15.619545-0500 DEBUG Create service ServiceType.STORE_SERVICE\n",
+ "1736354775699": "2025-01-08T11:46:15.699661-0500 DEBUG File 046-rocket.svg retrieved successfully from flow /Users/USER/Library/Caches/langflow/profile_pictures/Space.\n"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/result-stream-logs.txt b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/result-stream-logs.txt
new file mode 100644
index 0000000000..511dbb5770
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/result-stream-logs.txt
@@ -0,0 +1,17 @@
+keepalive
+
+{"1736355791151": "2025-01-08T12:03:11.151218-0500 DEBUG Building Chat Input\n"}
+
+{"1736355791485": "2025-01-08T12:03:11.485380-0500 DEBUG consumed event add_message-153bcd5d-ef4d-4ece-8cc0-47c6b6a9ef92 (time in queue, 0.0000, client 0.0001)\n"}
+
+{"1736355791499": "2025-01-08T12:03:11.499704-0500 DEBUG consumed event end_vertex-3d7125cd-7b8a-44eb-9113-ed5b785e3cf3 (time in queue, 0.0056, client 0.0047)\n"}
+
+{"1736355791502": "2025-01-08T12:03:11.502510-0500 DEBUG consumed event end-40d0b363-5618-4a23-bbae-487cd0b9594d (time in queue, 0.0001, client 0.0004)\n"}
+
+{"1736355791513": "2025-01-08T12:03:11.513097-0500 DEBUG Logged vertex build: 729ff2f8-6b01-48c8-9ad0-3743c2af9e8a\n"}
+
+{"1736355791834": "2025-01-08T12:03:11.834982-0500 DEBUG Telemetry data sent successfully.\n"}
+
+{"1736355791941": "2025-01-08T12:03:11.941840-0500 DEBUG Telemetry data sent successfully.\n"}
+
+keepalive
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/retrieve-logs-with-optional-parameters.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/retrieve-logs-with-optional-parameters.sh
new file mode 100644
index 0000000000..0a17a8e0f8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/retrieve-logs-with-optional-parameters.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/logs?lines_before=0&lines_after=0×tamp=0" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/stream-logs.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/stream-logs.sh
new file mode 100644
index 0000000000..7d44689d02
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-logs/stream-logs.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/logs-stream" \
+ -H "accept: text/event-stream" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/delete-messages-by-session.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/delete-messages-by-session.sh
new file mode 100644
index 0000000000..d1cbe97356
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/delete-messages-by-session.sh
@@ -0,0 +1,4 @@
+curl -X DELETE \
+ "$LANGFLOW_URL/api/v1/monitor/messages/session/different_session_id_2" \
+ -H "accept: */*" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/delete-messages.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/delete-messages.sh
new file mode 100644
index 0000000000..33fe9aa933
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/delete-messages.sh
@@ -0,0 +1,6 @@
+curl -v -X DELETE \
+ "$LANGFLOW_URL/api/v1/monitor/messages" \
+ -H "accept: */*" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '["MESSAGE_ID_1", "MESSAGE_ID_2"]'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/delete-vertex-builds.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/delete-vertex-builds.sh
new file mode 100644
index 0000000000..6fd258daa8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/delete-vertex-builds.sh
@@ -0,0 +1,4 @@
+curl -X DELETE \
+ "$LANGFLOW_URL/api/v1/monitor/builds?flow_id=$FLOW_ID" \
+ -H "accept: */*" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/example-request.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/example-request.sh
new file mode 100644
index 0000000000..7963ac2a7e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/example-request.sh
@@ -0,0 +1,8 @@
+export LANGFLOW_SERVER_URL="http://localhost:7860"
+export LANGFLOW_API_KEY="YOUR_LANGFLOW_API_KEY"
+export FLOW_ID="YOUR_FLOW_ID"
+
+curl -s "$LANGFLOW_SERVER_URL/api/v1/monitor/traces?flow_id=$FLOW_ID&page=1&size=50" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ | jq .
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-messages-2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-messages-2.sh
new file mode 100644
index 0000000000..bbdfdf59a9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-messages-2.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/monitor/messages?flow_id=$FLOW_ID&session_id=01ce083d-748b-4b8d-97b6-33adbb6a528a&sender=Machine&sender_name=AI&order_by=timestamp" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-messages.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-messages.sh
new file mode 100644
index 0000000000..b1553bb3b4
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-messages.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/monitor/messages" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-transactions.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-transactions.sh
new file mode 100644
index 0000000000..187986b18d
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-transactions.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/monitor/transactions?flow_id=$FLOW_ID&page=1&size=50" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-vertex-builds.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-vertex-builds.sh
new file mode 100644
index 0000000000..6e3bdfa84e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/get-vertex-builds.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_URL/api/v1/monitor/builds?flow_id=$FLOW_ID" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-delete-messages-by-session.txt b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-delete-messages-by-session.txt
new file mode 100644
index 0000000000..58e46abbc9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-delete-messages-by-session.txt
@@ -0,0 +1 @@
+HTTP/1.1 204 No Content
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-delete-messages.txt b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-delete-messages.txt
new file mode 100644
index 0000000000..bd3152a3b7
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-delete-messages.txt
@@ -0,0 +1 @@
+204 No Content
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-delete-vertex-builds.txt b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-delete-vertex-builds.txt
new file mode 100644
index 0000000000..bd3152a3b7
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-delete-vertex-builds.txt
@@ -0,0 +1 @@
+204 No Content
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-get-messages-2.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-get-messages-2.json
new file mode 100644
index 0000000000..d15a45a10c
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-get-messages-2.json
@@ -0,0 +1,30 @@
+[
+ {
+ "id": "1c1d6134-9b8b-4079-931c-84dcaddf19ba",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "timestamp": "2024-12-23 19:20:11 UTC",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "session_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "text": "Hello! It's great to see you here! What exciting project or idea are you thinking about diving into today? Whether it's something fresh and innovative or a classic concept with a twist, I'm here to help you get started! Let's brainstorm together!",
+ "files": "[]",
+ "edit": false,
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": false,
+ "source": {
+ "id": "OpenAIModel-7AjrN",
+ "display_name": "OpenAI",
+ "source": "gpt-4o-mini"
+ },
+ "icon": "OpenAI",
+ "allow_markdown": false,
+ "positive_feedback": null,
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": []
+ }
+]
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-get-transactions.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-get-transactions.json
new file mode 100644
index 0000000000..d84ddf400a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-get-transactions.json
@@ -0,0 +1,19 @@
+{
+ "items": [
+ {
+ "timestamp": "2024-12-23T20:05:01.061Z",
+ "vertex_id": "string",
+ "target_id": "string",
+ "inputs": {},
+ "outputs": {},
+ "status": "string",
+ "error": "string",
+ "flow_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
+ "id": "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+ }
+ ],
+ "total": 0,
+ "page": 1,
+ "size": 1,
+ "pages": 0
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-get-vertex-builds.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-get-vertex-builds.json
new file mode 100644
index 0000000000..4d42f8fabc
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-get-vertex-builds.json
@@ -0,0 +1,357 @@
+{
+ "vertex_builds": {
+ "ChatInput-NCmix": [
+ {
+ "data": {
+ "results": {
+ "message": {
+ "text_key": "text",
+ "data": {
+ "timestamp": "2024-12-23 19:10:57",
+ "sender": "User",
+ "sender_name": "User",
+ "session_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "text": "Hello",
+ "files": [],
+ "error": "False",
+ "edit": "False",
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": "False",
+ "source": {
+ "id": "None",
+ "display_name": "None",
+ "source": "None"
+ },
+ "icon": "",
+ "allow_markdown": "False",
+ "positive_feedback": "None",
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": [],
+ "id": "c95bed34-f906-4aa6-84e4-68553f6db772",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a"
+ },
+ "default_value": "",
+ "text": "Hello",
+ "sender": "User",
+ "sender_name": "User",
+ "files": [],
+ "session_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "timestamp": "2024-12-23 19:10:57+00:00",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "error": "False",
+ "edit": "False",
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": "False",
+ "source": {
+ "id": "None",
+ "display_name": "None",
+ "source": "None"
+ },
+ "icon": "",
+ "allow_markdown": "False",
+ "positive_feedback": "None",
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": []
+ }
+ },
+ "outputs": {
+ "message": {
+ "message": {
+ "timestamp": "2024-12-23T19:10:57",
+ "sender": "User",
+ "sender_name": "User",
+ "session_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "text": "Hello",
+ "files": [],
+ "error": false,
+ "edit": false,
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": false,
+ "source": {
+ "id": null,
+ "display_name": null,
+ "source": null
+ },
+ "icon": "",
+ "allow_markdown": false,
+ "positive_feedback": null,
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": [],
+ "id": "c95bed34-f906-4aa6-84e4-68553f6db772",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a"
+ },
+ "type": "object"
+ }
+ },
+ "logs": { "message": [] },
+ "message": {
+ "message": "Hello",
+ "sender": "User",
+ "sender_name": "User",
+ "files": [],
+ "type": "object"
+ },
+ "artifacts": {
+ "message": "Hello",
+ "sender": "User",
+ "sender_name": "User",
+ "files": [],
+ "type": "object"
+ },
+ "timedelta": 0.015060124918818474,
+ "duration": "15 ms",
+ "used_frozen_result": false
+ },
+ "artifacts": {
+ "message": "Hello",
+ "sender": "User",
+ "sender_name": "User",
+ "files": [],
+ "type": "object"
+ },
+ "params": "- Files: []\n Message: Hello\n Sender: User\n Sender Name: User\n Type: object\n",
+ "valid": true,
+ "build_id": "40aa200e-74db-4651-b698-f80301d2b26b",
+ "id": "ChatInput-NCmix",
+ "timestamp": "2024-12-23T19:10:58.772766Z",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a"
+ }
+ ],
+ "Prompt-BEn9c": [
+ {
+ "data": {
+ "results": {},
+ "outputs": {
+ "prompt": {
+ "message": "Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh.",
+ "type": "text"
+ }
+ },
+ "logs": { "prompt": [] },
+ "message": {
+ "prompt": {
+ "repr": "Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh.",
+ "raw": "Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh.",
+ "type": "text"
+ }
+ },
+ "artifacts": {
+ "prompt": {
+ "repr": "Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh.",
+ "raw": "Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh.",
+ "type": "text"
+ }
+ },
+ "timedelta": 0.0057758750626817346,
+ "duration": "6 ms",
+ "used_frozen_result": false
+ },
+ "artifacts": {
+ "prompt": {
+ "repr": "Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh.",
+ "raw": "Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh.",
+ "type": "text"
+ }
+ },
+ "params": "None",
+ "valid": true,
+ "build_id": "39bbbfde-97fd-42a5-a9ed-d42a5c5d532b",
+ "id": "Prompt-BEn9c",
+ "timestamp": "2024-12-23T19:10:58.781019Z",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a"
+ }
+ ],
+ "OpenAIModel-7AjrN": [
+ {
+ "data": {
+ "results": {},
+ "outputs": {
+ "text_output": {
+ "message": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "type": "text"
+ },
+ "model_output": { "message": "", "type": "unknown" }
+ },
+ "logs": { "text_output": [] },
+ "message": {
+ "text_output": {
+ "repr": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "raw": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "type": "text"
+ }
+ },
+ "artifacts": {
+ "text_output": {
+ "repr": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "raw": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "type": "text"
+ }
+ },
+ "timedelta": 1.034765167045407,
+ "duration": "1.03 seconds",
+ "used_frozen_result": false
+ },
+ "artifacts": {
+ "text_output": {
+ "repr": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "raw": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "type": "text"
+ }
+ },
+ "params": "None",
+ "valid": true,
+ "build_id": "4f0ae730-a266-4d35-b89f-7b825c620a0f",
+ "id": "OpenAIModel-7AjrN",
+ "timestamp": "2024-12-23T19:10:58.790484Z",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a"
+ }
+ ],
+ "ChatOutput-sfUhT": [
+ {
+ "data": {
+ "results": {
+ "message": {
+ "text_key": "text",
+ "data": {
+ "timestamp": "2024-12-23 19:10:58",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "session_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "text": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "files": [],
+ "error": "False",
+ "edit": "False",
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": "False",
+ "source": {
+ "id": "OpenAIModel-7AjrN",
+ "display_name": "OpenAI",
+ "source": "gpt-4o-mini"
+ },
+ "icon": "OpenAI",
+ "allow_markdown": "False",
+ "positive_feedback": "None",
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": [],
+ "id": "5688356d-9f30-40ca-9907-79a7a2fc16fd",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a"
+ },
+ "default_value": "",
+ "text": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "files": [],
+ "session_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "timestamp": "2024-12-23 19:10:58+00:00",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "error": "False",
+ "edit": "False",
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": "False",
+ "source": {
+ "id": "OpenAIModel-7AjrN",
+ "display_name": "OpenAI",
+ "source": "gpt-4o-mini"
+ },
+ "icon": "OpenAI",
+ "allow_markdown": "False",
+ "positive_feedback": "None",
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": []
+ }
+ },
+ "outputs": {
+ "message": {
+ "message": {
+ "timestamp": "2024-12-23T19:10:58",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "session_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "text": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "files": [],
+ "error": false,
+ "edit": false,
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": false,
+ "source": {
+ "id": "OpenAIModel-7AjrN",
+ "display_name": "OpenAI",
+ "source": "gpt-4o-mini"
+ },
+ "icon": "OpenAI",
+ "allow_markdown": false,
+ "positive_feedback": null,
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": [],
+ "id": "5688356d-9f30-40ca-9907-79a7a2fc16fd",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a"
+ },
+ "type": "object"
+ }
+ },
+ "logs": { "message": [] },
+ "message": {
+ "message": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "files": [],
+ "type": "object"
+ },
+ "artifacts": {
+ "message": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "files": [],
+ "type": "object"
+ },
+ "timedelta": 0.017838125000707805,
+ "duration": "18 ms",
+ "used_frozen_result": false
+ },
+ "artifacts": {
+ "message": "Hello! 🌟 I'm excited to help you get started on your journey to building something fresh! What do you have in mind? Whether it's a project, an idea, or a concept, let's dive in and make it happen!",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "files": [],
+ "type": "object"
+ },
+ "params": "- Files: []\n Message: Hello! 🌟 I'm excited to help you get started on your journey to building\n something fresh! What do you have in mind? Whether it's a project, an idea, or\n a concept, let's dive in and make it happen!\n Sender: Machine\n Sender Name: AI\n Type: object\n",
+ "valid": true,
+ "build_id": "1e8b908b-aba7-403b-9e9b-eca92bb78668",
+ "id": "ChatOutput-sfUhT",
+ "timestamp": "2024-12-23T19:10:58.813268Z",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a"
+ }
+ ]
+ }
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-update-message.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-update-message.json
new file mode 100644
index 0000000000..c6cd0df00a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-update-message.json
@@ -0,0 +1,25 @@
+{
+ "timestamp": "2024-12-23T18:49:06",
+ "sender": "string",
+ "sender_name": "string",
+ "session_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "text": "testing 1234",
+ "files": ["string"],
+ "error": true,
+ "edit": true,
+ "properties": {
+ "text_color": "string",
+ "background_color": "string",
+ "edited": false,
+ "source": { "id": "string", "display_name": "string", "source": "string" },
+ "icon": "string",
+ "allow_markdown": false,
+ "positive_feedback": true,
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": [],
+ "id": "3ab66cc6-c048-48f8-ab07-570f5af7b160",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a"
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-update-session-id.json b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-update-session-id.json
new file mode 100644
index 0000000000..f58222e791
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/result-update-session-id.json
@@ -0,0 +1,30 @@
+[
+ {
+ "id": "8dd7f064-e63a-4773-b472-ca0475249dfd",
+ "flow_id": "01ce083d-748b-4b8d-97b6-33adbb6a528a",
+ "timestamp": "2024-12-23 18:49:55 UTC",
+ "sender": "User",
+ "sender_name": "User",
+ "session_id": "different_session_id",
+ "text": "message",
+ "files": "[]",
+ "edit": false,
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": false,
+ "source": {
+ "id": null,
+ "display_name": null,
+ "source": null
+ },
+ "icon": "",
+ "allow_markdown": false,
+ "positive_feedback": null,
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": []
+ }
+]
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/update-message.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/update-message.sh
new file mode 100644
index 0000000000..9d3cf32c30
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/update-message.sh
@@ -0,0 +1,8 @@
+curl -X PUT \
+ "$LANGFLOW_URL/api/v1/monitor/messages/3ab66cc6-c048-48f8-ab07-570f5af7b160" \
+ -H "accept: application/json" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "text": "testing 1234"
+}'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/update-session-id.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/update-session-id.sh
new file mode 100644
index 0000000000..93bf53f650
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-monitor/update-session-id.sh
@@ -0,0 +1,4 @@
+curl -X PATCH \
+ "$LANGFLOW_URL/api/v1/monitor/messages/session/01ce083d-748b-4b8d-97b6-33adbb6a528a?new_session_id=different_session_id" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.sh
new file mode 100644
index 0000000000..1c131abda9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.sh
@@ -0,0 +1,13 @@
+BASE_URL="${LANGFLOW_SERVER_URL:-$LANGFLOW_URL}"
+
+curl -X POST \
+ "$BASE_URL/api/v1/responses" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d @- </tmp/langflow-curl-example.out 2>/tmp/langflow-curl-example.err; then
+ echo "FAIL $rel (execution)"
+ print_failure_logs "/tmp/langflow-curl-example.out" "/tmp/langflow-curl-example.err"
+ ((FAIL+=1))
+ continue
+ fi
+ fi
+
+ echo "PASS $rel"
+ ((PASS+=1))
+done
+
+echo
+echo "Summary: PASS=$PASS FAIL=$FAIL SKIP=$SKIP TOTAL=${#SH_FILES[@]}"
+
+if [[ $FAIL -gt 0 ]]; then
+ exit 1
+fi
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-asynchronous-request.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-asynchronous-request.sh
new file mode 100644
index 0000000000..acaf1d8566
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-asynchronous-request.sh
@@ -0,0 +1,14 @@
+curl -X POST \
+ "$LANGFLOW_SERVER_URL/api/v2/workflows" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "flow_id": "flow_67ccd2be17f0819081ff3bb2cf6508e60bb6a6b452d3795b",
+ "background": true,
+ "stream": false,
+ "inputs": {
+ "ChatInput-abc.input_type": "chat",
+ "ChatInput-abc.input_value": "Process this in the background",
+ "ChatInput-abc.session_id": "session-456"
+ }
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-request-2.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-request-2.sh
new file mode 100644
index 0000000000..25d897dd39
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-request-2.sh
@@ -0,0 +1,7 @@
+curl -X POST \
+ "$LANGFLOW_SERVER_URL/api/v2/workflows/stop" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "job_id": "job_id_1234567890"
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-request.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-request.sh
new file mode 100644
index 0000000000..6f9553582b
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-request.sh
@@ -0,0 +1,4 @@
+curl -X GET \
+ "$LANGFLOW_SERVER_URL/api/v2/workflows?job_id=job_id_1234567890" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-synchronous-request.sh b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-synchronous-request.sh
new file mode 100644
index 0000000000..8f488efff9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/curl-examples/workflows-api/example-synchronous-request.sh
@@ -0,0 +1,13 @@
+curl -X POST \
+ "$LANGFLOW_SERVER_URL/api/v2/workflows" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "flow_id": "flow_67ccd2be17f0819081ff3bb2cf6508e60bb6a6b452d3795b",
+ "background": false,
+ "inputs": {
+ "ChatInput-abc.input_type": "chat",
+ "ChatInput-abc.input_value": "what is 2+2",
+ "ChatInput-abc.session_id": "session-123"
+ }
+ }'
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/flow-import.json b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/flow-import.json
new file mode 100644
index 0000000000..2f16c446b1
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/flow-import.json
@@ -0,0 +1,8 @@
+{
+ "name": "Fixture Flow",
+ "description": "Fixture flow used by docs API example automation.",
+ "data": {
+ "nodes": [],
+ "edges": []
+ }
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/project-import.json b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/project-import.json
new file mode 100644
index 0000000000..d3a6e2df49
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/project-import.json
@@ -0,0 +1,14 @@
+{
+ "folder_name": "API fixture project",
+ "folder_description": "Imported via docs API examples",
+ "flows": [
+ {
+ "name": "Fixture Flow",
+ "description": "Fixture flow used by project import examples",
+ "data": {
+ "nodes": [],
+ "edges": []
+ }
+ }
+ ]
+}
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/project-import.zip b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/project-import.zip
new file mode 100644
index 0000000000..7165f7bbcb
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/project-import.zip
@@ -0,0 +1,3 @@
+Placeholder zip fixture.
+When using scripts/test-api-examples-local.sh in execute mode, this is replaced
+by a real exported project archive via PROJECT_IMPORT_FILE.
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/sample-upload.png b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/sample-upload.png
new file mode 100644
index 0000000000..9017ae78ad
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/sample-upload.png
@@ -0,0 +1,2 @@
+PNG fixture placeholder for docs API examples.
+This file is used as a deterministic upload fixture in test runs.
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/sample-upload.txt b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/sample-upload.txt
new file mode 100644
index 0000000000..2e35b2b687
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/fixtures/sample-upload.txt
@@ -0,0 +1,2 @@
+This is a stock fixture file used by API example upload tests.
+It is intentionally small and stable for local/CI automation.
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/flow-devops-sdk.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/flow-devops-sdk.mdx
new file mode 100644
index 0000000000..9cda7c297e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/flow-devops-sdk.mdx
@@ -0,0 +1,227 @@
+---
+title: Flow DevOps Toolkit SDK
+slug: /flow-devops-sdk
+---
+
+Use the Flow DevOps Toolkit SDK to version, test, and deploy your flows.
+
+Instead of manually exporting, sharing, and importing flow JSON files from the Langflow UI, the Flow DevOps toolkit offers terminal-based workflows for versioning, environment variables, testing, and deployment.
+
+## Prerequisites
+
+- [Install and start Langflow](/get-started-installation)
+- Create a [Langflow API key](/api-keys-and-authentication)
+- Install the Langflow `lfx` package
+
+ To install the `lfx` package from PyPI, do the following:
+
+ 1. Create a virtual environment:
+ ```bash
+ uv venv VENV_NAME
+ ```
+
+ 2. Activate the virtual environment.
+ ```bash
+ source VENV_NAME/bin/activate
+ ```
+ 3. Install the Langflow LFX package in the virtual environment:
+ ```bash
+ uv pip install lfx
+ ```
+ 4. Run Flow DevOps Toolkit commands in the virtual environment that has LFX installed.
+
+ Alternatively, you can run `uvx lfx` commands, or run LFX from the `src/lfx` directory in a cloned Langflow repo.
+ For more information, see the [Langflow LFX README](https://github.com/langflow-ai/langflow/blob/main/src/lfx/README.md).
+
+## Create a project and version a flow
+
+1. Create a flow in the Langflow UI, such as the Simple Agent starter flow in the [Quickstart](/get-started-quickstart).
+2. Open a terminal session within the virtual environment that has `lfx` installed.
+3. To initialize a project, run:
+ ```bash
+ lfx init PROJECT_NAME
+ ```
+
+ Replace `PROJECT_NAME` with a name for your project folder.
+ `lfx init` creates a scaffold for your project.
+ The output is similar to the following:
+
+ ```text
+ demo-project/
+ ├── .github/
+ │ └── workflows/
+ │ ├── langflow-push.yml # CI workflow
+ │ ├── langflow-test.yml # CI workflow
+ │ └── langflow-validate.yml # CI workflow
+ ├── .gitignore # ignores legacy credentials file
+ ├── .lfx/
+ │ └── environments.yaml # edit with your instance URLs + API key env var names (safe to commit)
+ ├── ci/
+ │ ├── ci-push.sh # generic CI script
+ │ ├── ci-test.sh # generic CI script
+ │ └── ci-validate.sh # generic CI script
+ ├── flows/
+ │ └── .gitkeep # versioned empty directory
+ └── tests/
+ ├── __init__.py
+ └── test_flows.py # flow_runner example tests
+
+ ✔ Project scaffolded. Next steps:
+ 1. Edit `.lfx/environments.yaml` with your instance URL
+ 2. export LANGFLOW_LOCAL_API_KEY= (Settings -> API Keys)
+ 3. lfx pull --env local --output-dir flows/
+ ```
+
+ The project scaffold includes the following tools for building flows:
+
+ * `.github/workflows`: GitHub CI tooling.
+ * `.lfx/environments.yaml`: Control your project's URL and API keys as environment variables for local, staging, and production environments.
+ * `ci/` Shell scripts for pushing, testing, and validating flows.
+ * `flows/`: An empty directory to store flows that includes a `.gitkeep` file for flow versioning.
+ * `tests/test_flows.py`: Example tests that you can modify to test flows.
+
+4. Add your Langflow API key to your `.env` file, or export it within the terminal session.
+ The Flow DevOps SDK includes `url` and `api_key_env` environment variables for `local`, `staging`, and `production` environments.
+ The variable name for the API key differs between environments, so ensure you're adding the correct variable.
+ For example, to add a Langflow API key to a local Langflow server, set:
+
+ ```text
+ export LANGFLOW_LOCAL_API_KEY=LANGFLOW_API_KEY
+ ```
+
+5. To test server authentication with your API key, run:
+
+ ```bash
+ lfx login
+ ```
+
+ The Flow DevOps SDK tests your key against the URL and confirms the connection is working.
+ If the test reports `Authentication failed`, create and export a new key and try again.
+
+6. To check the connected server for existing flows, run:
+
+ ```bash
+ lfx pull
+ ```
+
+ The Flow DevOps SDK lists your server's flows. Output is similar to the following (from a project directory such as `demo-project/`):
+
+ ```text
+ Pulling all flows from http://localhost:7860
+ Pulled 'Simple Agent' -> flows/Simple_Agent.json
+
+ ┌────────────────┬──────────────────────────────────────┬───────────────────────────┬──────────┐
+ │ Name │ ID │ File │ Status │
+ ├────────────────┼──────────────────────────────────────┼───────────────────────────┼──────────┤
+ │ Simple Agent │ c2f91b01-9a73-4c62-b7f0-e15bc3bd6802 │ flows/Simple_Agent.json │ CREATED │
+ └────────────────┴──────────────────────────────────────┴───────────────────────────┴──────────┘
+
+ 1 updated.
+ ```
+
+ `lfx pull` pulls flow changes on the server to JSON files under `flows/` in your project (for example `demo-project/flows`).
+ If you run `lfx pull` again, the Flow DevOps SDK reports a Status of `Unchanged`.
+ You will pull changes in the next steps.
+7. In the Langflow UI, open the Simple Agent flow and change the flow.
+ For example, change the **Chat Input** to a different input string.
+ Save the flow.
+8. Return to your terminal, and run `lfx status`.
+ The Flow DevOps SDK reports the flow's Status as `UPDATED`, because the hash of the flow JSON has changed with your update.
+9. To pull the reported changes from the Langflow server to your local project folder, run `lfx pull`.
+10. To _push_ flow changes from flows stored locally in `demo-project/flows` to the Langflow server, run `lfx push`.
+
+## Validate flows
+
+The Flow DevOps SDK can validate that local flows are correctly formed before pushing to the Langflow with `lfx validate`.
+
+1. To test the Simple Agent starter flow, pass the flow JSON path to the `lfx validate` command:
+ ```
+ lfx validate flows/Simple_Agent.json
+ ```
+2. Once validated, push flow changes to the server with `lfx push`.
+
+## Generate requirements.txt for flows
+
+The Flow DevOps SDK can generate a `requirements.txt` file for a flow.
+
+A flow JSON describes nodes and wiring, and does not list the PyPI packages components import at runtime.
+Generate a `requirements.txt` file to capture the minimal Python dependencies, so you can install a matching environment for the flow.
+
+1. From your project directory, point `lfx requirements` at a flow JSON file.
+ To print the requirements to the terminal:
+
+ ```
+ lfx requirements flows/Simple_Agent.json
+ ```
+
+ To write a `requirements.txt` file instead of printing, use `-o` or `--output`:
+
+ ```
+ lfx requirements flows/Simple_Agent.json -o requirements.txt
+ ```
+
+2. Optionally, you can now share and serve the flow by keeping the flow JSON and `requirements.txt` in the same environment.
+ To serve the flow without the Langflow UI, do the following:
+
+ 1. Create a virtual environment:
+ ```
+ uv venv VENV_NAME
+ ```
+
+ 2. Activate the virtual environment.
+ ```
+ source VENV_NAME/bin/activate
+ ```
+
+ 3. Install the dependencies from `requirements.txt` in the virtual environment:
+ ```bash
+ uv pip install -r requirements.txt
+ ```
+
+ 4. To set a Langflow API key, run:
+ ```
+ export LANGFLOW_API_KEY=LANGFLOW_API_KEY
+ ```
+
+ 5. To serve the flow without the Langflow UI, pass the flow JSON path to the `lfx serve` command:
+ ```
+ lfx serve flows/Simple_Agent.json
+ ```
+
+ `lfx serve` starts a FastAPI app that exposes your flow as an HTTP API endpoint.
+ For more information, see the [Langflow LFX README](https://github.com/langflow-ai/langflow/blob/main/src/lfx/README.md).
+
+## Manage multiple environments with `environments.yaml`
+
+The `environments.yaml` file created at initialization contains three example entries for deployment environments:
+
+```yaml
+local:
+ url: http://127.0.0.1:7860
+ api_key_env: LANGFLOW_LOCAL_API_KEY
+staging:
+ url: https://staging.example.com
+ api_key_env: LANGFLOW_STAGING_API_KEY
+production:
+ url: https://langflow.example.com
+ api_key_env: LANGFLOW_PRODUCTION_API_KEY
+```
+
+Each entry contains a `url` for the Langflow base URL, and an `api_key_env` field.
+The `api_key_env` field names an environment variable that you either `export` or store in a `.env` file, and does not store the secret string itself, which makes `environments.yaml` safe to commit to version control.
+
+The names `local`, `staging`, and `production` in `environments.yaml` are conventions, and can be named whatever your project requires. You can add more than three entries.
+
+`environments.yaml` is distinct from the Langflow or LFX `.env` file.
+`environments.yaml` controls which remote Langflow instance you're deploying flows to, flow versioning, and environment variable _names_ for API keys.
+The `.env` contains runtime values for the Langflow server, and might also contain _actual secret values_, so the `.env` should not be committed to version control.
+
+Commands that call a Langflow server over HTTP, such as `lfx pull` or `lfx push`, use `--env ENVIRONMENT_NAME` to determine which Langflow instance to send the request to.
+
+For example, to send a `push` request to a server named `local` in `environments.yaml`, run:
+
+```bash
+lfx push --env local
+```
+
+This command will send the request to the Langflow base URL at `http://127.0.0.1:7860` using a Langflow API key named `LANGFLOW_LOCAL_API_KEY`.
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/build-flow-and-stream-events-2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/build-flow-and-stream-events-2.js
new file mode 100644
index 0000000000..1ddefa0d59
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/build-flow-and-stream-events-2.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/build/123e4567-e89b-12d3-a456-426614174000/events`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/build-flow-and-stream-events-3.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/build-flow-and-stream-events-3.js
new file mode 100644
index 0000000000..5f1df507f3
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/build-flow-and-stream-events-3.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/build/123e4567-e89b-12d3-a456-426614174000/events?stream=false`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/build-flow-and-stream-events.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/build-flow-and-stream-events.js
new file mode 100644
index 0000000000..66b268752d
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/build-flow-and-stream-events.js
@@ -0,0 +1,25 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/build/${process.env.FLOW_ID ?? ""}/flow`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "inputs": {
+ "input_value": "Tell me a story"
+ }
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/override-flow-parameters.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/override-flow-parameters.js
new file mode 100644
index 0000000000..dfc29818e6
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/override-flow-parameters.js
@@ -0,0 +1,30 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/build/${process.env.FLOW_ID ?? ""}/flow`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "data": {
+ "nodes": [],
+ "edges": []
+ },
+ "inputs": {
+ "input_value": "Your custom input here",
+ "session": "session_id"
+ }
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/set-start-and-stop-points.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/set-start-and-stop-points.js
new file mode 100644
index 0000000000..278e779dda
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-build/set-start-and-stop-points.js
@@ -0,0 +1,23 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/build/${process.env.FLOW_ID ?? ""}/flow`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "stop_component_id": "OpenAIModel-Uksag"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/delete-all-files-v2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/delete-all-files-v2.js
new file mode 100644
index 0000000000..3ed460ff02
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/delete-all-files-v2.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v2/files`;
+
+const options = {
+ method: 'DELETE',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/delete-file-v1.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/delete-file-v1.js
new file mode 100644
index 0000000000..4013ebebc1
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/delete-file-v1.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/files/delete/${process.env.FLOW_ID ?? ""}/2024-12-30_15-19-43_your_file.txt`;
+
+const options = {
+ method: 'DELETE',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/delete-file-v2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/delete-file-v2.js
new file mode 100644
index 0000000000..c2609c8f75
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/delete-file-v2.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v2/files/${process.env.FILE_ID ?? ""}`;
+
+const options = {
+ method: 'DELETE',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/download-file-v1.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/download-file-v1.js
new file mode 100644
index 0000000000..f7ef3be343
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/download-file-v1.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/files/download/${process.env.FLOW_ID ?? ""}/2024-12-30_15-19-43_your_file.txt`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const data = await response.arrayBuffer();
+ console.log("Received binary response for downloaded_file.txt", data.byteLength);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/download-file-v2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/download-file-v2.js
new file mode 100644
index 0000000000..e14cb3baf5
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/download-file-v2.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v2/files/c7b22c4c-d5e0-4ec9-af97-5d85b7657a34`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const data = await response.arrayBuffer();
+ console.log("Received binary response for downloaded_file.txt", data.byteLength);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/edit-file-name-v2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/edit-file-name-v2.js
new file mode 100644
index 0000000000..644729d9ea
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/edit-file-name-v2.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v2/files/${process.env.FILE_ID ?? ""}?name=new_file_name`;
+
+const options = {
+ method: 'PUT',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/list-files-v1.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/list-files-v1.js
new file mode 100644
index 0000000000..792490acfa
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/list-files-v1.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/files/list/${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/list-files-v2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/list-files-v2.js
new file mode 100644
index 0000000000..e4a143a0f9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/list-files-v2.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v2/files`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/send-files-to-your-flows-v2-2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/send-files-to-your-flows-v2-2.js
new file mode 100644
index 0000000000..3a1b93da94
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/send-files-to-your-flows-v2-2.js
@@ -0,0 +1,31 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/run/${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "input_value": "what do you see?",
+ "output_type": "chat",
+ "input_type": "text",
+ "tweaks": {
+ "Read-File-1olS3": {
+ "path": [
+ "07e5b864-e367-4f52-b647-a48035ae7e5e/3a290013-fe1e-4d3d-a454-cacae81288f3.pdf"
+ ]
+ }
+ }
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/send-files-to-your-flows-v2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/send-files-to-your-flows-v2.js
new file mode 100644
index 0000000000..d393f65650
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/send-files-to-your-flows-v2.js
@@ -0,0 +1,32 @@
+const fs = require("fs");
+const path = require("path");
+
+const fixturesDir = path.join(__dirname, "../../fixtures");
+const defaultUpload = path.join(fixturesDir, "sample-upload.txt");
+const uploadPath = process.env.SAMPLE_UPLOAD_FILE || defaultUpload;
+const uploadBuf = fs.readFileSync(uploadPath);
+const uploadName = path.basename(uploadPath);
+
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v2/files`;
+
+const formData = new FormData();
+formData.append("file", new Blob([uploadBuf]), uploadName);
+
+const options = {
+ method: "POST",
+ headers: {
+ accept: "application/json",
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: formData,
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-file-v1.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-file-v1.js
new file mode 100644
index 0000000000..5d19b43068
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-file-v1.js
@@ -0,0 +1,32 @@
+const fs = require("fs");
+const path = require("path");
+
+const fixturesDir = path.join(__dirname, "../../fixtures");
+const defaultUpload = path.join(fixturesDir, "sample-upload.txt");
+const uploadPath = process.env.SAMPLE_UPLOAD_FILE || defaultUpload;
+const uploadBuf = fs.readFileSync(uploadPath);
+const uploadName = path.basename(uploadPath);
+
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/files/upload/${process.env.FLOW_ID ?? ""}`;
+
+const formData = new FormData();
+formData.append("file", new Blob([uploadBuf]), uploadName);
+
+const options = {
+ method: "POST",
+ headers: {
+ accept: "application/json",
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: formData,
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-file-v2-2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-file-v2-2.js
new file mode 100644
index 0000000000..d393f65650
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-file-v2-2.js
@@ -0,0 +1,32 @@
+const fs = require("fs");
+const path = require("path");
+
+const fixturesDir = path.join(__dirname, "../../fixtures");
+const defaultUpload = path.join(fixturesDir, "sample-upload.txt");
+const uploadPath = process.env.SAMPLE_UPLOAD_FILE || defaultUpload;
+const uploadBuf = fs.readFileSync(uploadPath);
+const uploadName = path.basename(uploadPath);
+
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v2/files`;
+
+const formData = new FormData();
+formData.append("file", new Blob([uploadBuf]), uploadName);
+
+const options = {
+ method: "POST",
+ headers: {
+ accept: "application/json",
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: formData,
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-file-v2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-file-v2.js
new file mode 100644
index 0000000000..cd2867a7ef
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-file-v2.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/users/whoami`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-image-files-v1-2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-image-files-v1-2.js
new file mode 100644
index 0000000000..4a8ab155dd
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-image-files-v1-2.js
@@ -0,0 +1,29 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/run/a430cc57-06bb-4c11-be39-d3d4de68d2c4?stream=false`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "output_type": "chat",
+ "input_type": "chat",
+ "tweaks": {
+ "ChatInput-b67sL": {
+ "files": "a430cc57-06bb-4c11-be39-d3d4de68d2c4/2024-11-27_14-47-50_image-file.png",
+ "input_value": "describe this image"
+ }
+ }
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-image-files-v1.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-image-files-v1.js
new file mode 100644
index 0000000000..90b1a897b7
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-files/upload-image-files-v1.js
@@ -0,0 +1,31 @@
+const fs = require("fs");
+const path = require("path");
+
+const fixturesDir = path.join(__dirname, "../../fixtures");
+const defaultImage = path.join(fixturesDir, "sample-upload.png");
+const imagePath = process.env.SAMPLE_IMAGE_FILE || defaultImage;
+const imageBuf = fs.readFileSync(imagePath);
+const imageName = path.basename(imagePath);
+
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/files/upload/${process.env.FLOW_ID ?? ""}`;
+
+const formData = new FormData();
+formData.append("file", new Blob([imageBuf]), imageName);
+
+const options = {
+ method: "POST",
+ headers: {
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: formData,
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/pass-global-variables-in-request-headers.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/pass-global-variables-in-request-headers.js
new file mode 100644
index 0000000000..007f4fbba6
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/pass-global-variables-in-request-headers.js
@@ -0,0 +1,27 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/run/${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ "X-LANGFLOW-GLOBAL-VAR-OPENAI_API_KEY": `sk-...`,
+ "X-LANGFLOW-GLOBAL-VAR-USER_ID": `user123`,
+ "X-LANGFLOW-GLOBAL-VAR-ENVIRONMENT": `production`,
+ },
+ body: JSON.stringify({
+ "input_value": "Tell me about something interesting!",
+ "input_type": "chat",
+ "output_type": "chat"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/request-example-with-all-headers-and-parameters.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/request-example-with-all-headers-and-parameters.js
new file mode 100644
index 0000000000..33eebcac86
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/request-example-with-all-headers-and-parameters.js
@@ -0,0 +1,32 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/run/${process.env.FLOW_ID ?? ""}?stream=true`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "input_value": "Tell me a story",
+ "input_type": "chat",
+ "output_type": "chat",
+ "output_component": "chat_output",
+ "session_id": "chat-123",
+ "tweaks": {
+ "component_id": {
+ "parameter_name": "value"
+ }
+ }
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/run-flow.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/run-flow.js
new file mode 100644
index 0000000000..907eebc445
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/run-flow.js
@@ -0,0 +1,26 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/run/${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "input_value": "Tell me about something interesting!",
+ "session_id": "chat-123",
+ "input_type": "chat",
+ "output_type": "chat",
+ "output_component": ""
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/stream-llm-token-responses.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/stream-llm-token-responses.js
new file mode 100644
index 0000000000..b5d1278bd9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/stream-llm-token-responses.js
@@ -0,0 +1,24 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/run/${process.env.FLOW_ID ?? ""}?stream=true`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "message": "Tell me something interesting!",
+ "session_id": "chat-123"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/webhook-run-flow.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/webhook-run-flow.js
new file mode 100644
index 0000000000..b618609ef8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows-run/webhook-run-flow.js
@@ -0,0 +1,22 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/webhook/${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "data": "example-data"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/create-flow.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/create-flow.js
new file mode 100644
index 0000000000..c100edcacb
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/create-flow.js
@@ -0,0 +1,35 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "name": "string2",
+ "description": "string",
+ "icon": "string",
+ "icon_bg_color": "#FF0000",
+ "gradient": "string",
+ "data": {},
+ "is_component": false,
+ "updated_at": "2024-12-30T15:48:01.519Z",
+ "webhook": false,
+ "endpoint_name": "string",
+ "tags": [
+ "string"
+ ]
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/create-flows.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/create-flows.js
new file mode 100644
index 0000000000..c528a1a763
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/create-flows.js
@@ -0,0 +1,60 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/batch/`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "flows": [
+ {
+ "name": "string",
+ "description": "string",
+ "icon": "string",
+ "icon_bg_color": "string",
+ "gradient": "string",
+ "data": {},
+ "is_component": false,
+ "updated_at": "2024-12-30T18:36:02.737Z",
+ "webhook": false,
+ "endpoint_name": "string",
+ "tags": [
+ "string"
+ ],
+ "locked": false,
+ "user_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
+ "project_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+ },
+ {
+ "name": "string",
+ "description": "string",
+ "icon": "string",
+ "icon_bg_color": "string",
+ "gradient": "string",
+ "data": {},
+ "is_component": false,
+ "updated_at": "2024-12-30T18:36:02.737Z",
+ "webhook": false,
+ "endpoint_name": "string",
+ "tags": [
+ "string"
+ ],
+ "locked": false,
+ "user_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
+ "project_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+ }
+ ]
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/delete-flow.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/delete-flow.js
new file mode 100644
index 0000000000..7bdb9f78db
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/delete-flow.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'DELETE',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/export-flows.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/export-flows.js
new file mode 100644
index 0000000000..69758b9e21
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/export-flows.js
@@ -0,0 +1,24 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/download/`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify([
+ "e1e40c77-0541-41a9-88ab-ddb3419398b5",
+ "92f9a4c5-cfc8-4656-ae63-1f0881163c28"
+]),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const data = await response.arrayBuffer();
+ console.log("Received binary response for langflow-flows.zip", data.byteLength);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/import-flows.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/import-flows.js
new file mode 100644
index 0000000000..0c136874ba
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/import-flows.js
@@ -0,0 +1,32 @@
+const fs = require("fs");
+const path = require("path");
+
+const fixturesDir = path.join(__dirname, "../../fixtures");
+const defaultFlowImport = path.join(fixturesDir, "flow-import.json");
+const flowImportPath = process.env.FLOW_IMPORT_FILE || defaultFlowImport;
+const flowBuf = fs.readFileSync(flowImportPath);
+const flowName = path.basename(flowImportPath);
+
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/upload/?folder_id=${process.env.FOLDER_ID ?? ""}`;
+
+const formData = new FormData();
+formData.append("file", new Blob([flowBuf], { type: "application/json" }), flowName);
+
+const options = {
+ method: "POST",
+ headers: {
+ accept: "application/json",
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: formData,
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-flow.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-flow.js
new file mode 100644
index 0000000000..29c8bbbf1f
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-flow.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-flows-2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-flows-2.js
new file mode 100644
index 0000000000..b627fed111
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-flows-2.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/?remove_example_flows=true&components_only=false&get_all=false&project_id=${process.env.PROJECT_ID ?? ""}&header_flows=false&page=1&size=1`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-flows.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-flows.js
new file mode 100644
index 0000000000..388c32437d
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-flows.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/?remove_example_flows=false&components_only=false&get_all=true&header_flows=false&page=1&size=50`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-sample-flows.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-sample-flows.js
new file mode 100644
index 0000000000..fc65058315
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/read-sample-flows.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/basic_examples/`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/update-flow.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/update-flow.js
new file mode 100644
index 0000000000..50c3d3f748
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-flows/update-flow.js
@@ -0,0 +1,28 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/flows/${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'PATCH',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "name": "string",
+ "description": "string",
+ "data": {},
+ "project_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
+ "endpoint_name": "my_new_endpoint_name",
+ "locked": true
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-logs/retrieve-logs-with-optional-parameters.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-logs/retrieve-logs-with-optional-parameters.js
new file mode 100644
index 0000000000..2ba7ac18cd
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-logs/retrieve-logs-with-optional-parameters.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/logs?lines_before=0&lines_after=0×tamp=0`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-logs/stream-logs.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-logs/stream-logs.js
new file mode 100644
index 0000000000..8ab251fc20
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-logs/stream-logs.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/logs-stream`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `text/event-stream`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/delete-messages-by-session.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/delete-messages-by-session.js
new file mode 100644
index 0000000000..772e4713e7
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/delete-messages-by-session.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/monitor/messages/session/different_session_id_2`;
+
+const options = {
+ method: 'DELETE',
+ headers: {
+ "accept": `*/*`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/delete-messages.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/delete-messages.js
new file mode 100644
index 0000000000..b63cf9a7a9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/delete-messages.js
@@ -0,0 +1,24 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/monitor/messages`;
+
+const options = {
+ method: 'DELETE',
+ headers: {
+ "accept": `*/*`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify([
+ "MESSAGE_ID_1",
+ "MESSAGE_ID_2"
+]),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/delete-vertex-builds.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/delete-vertex-builds.js
new file mode 100644
index 0000000000..eedb1a8071
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/delete-vertex-builds.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/monitor/builds?flow_id=${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'DELETE',
+ headers: {
+ "accept": `*/*`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-messages-2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-messages-2.js
new file mode 100644
index 0000000000..c42cc040f9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-messages-2.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/monitor/messages?flow_id=${process.env.FLOW_ID ?? ""}&session_id=01ce083d-748b-4b8d-97b6-33adbb6a528a&sender=Machine&sender_name=AI&order_by=timestamp`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-messages.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-messages.js
new file mode 100644
index 0000000000..d0ac813b17
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-messages.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/monitor/messages`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-transactions.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-transactions.js
new file mode 100644
index 0000000000..9f0d4faf4a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-transactions.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/monitor/transactions?flow_id=${process.env.FLOW_ID ?? ""}&page=1&size=50`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-vertex-builds.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-vertex-builds.js
new file mode 100644
index 0000000000..ec64328848
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/get-vertex-builds.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/monitor/builds?flow_id=${process.env.FLOW_ID ?? ""}`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/update-message.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/update-message.js
new file mode 100644
index 0000000000..5e7a8b1cc5
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/update-message.js
@@ -0,0 +1,23 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/monitor/messages/3ab66cc6-c048-48f8-ab07-570f5af7b160`;
+
+const options = {
+ method: 'PUT',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "text": "testing 1234"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/update-session-id.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/update-session-id.js
new file mode 100644
index 0000000000..812d079d27
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-monitor/update-session-id.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/monitor/messages/session/01ce083d-748b-4b8d-97b6-33adbb6a528a?new_session_id=different_session_id`;
+
+const options = {
+ method: 'PATCH',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/additional-configuration-for-openai-client-libraries.ts b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/additional-configuration-for-openai-client-libraries.ts
new file mode 100644
index 0000000000..a6963443b3
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/additional-configuration-for-openai-client-libraries.ts
@@ -0,0 +1,16 @@
+import OpenAI from "openai";
+
+const client = new OpenAI({
+ baseURL: "LANGFLOW_SERVER_URL/api/v1/",
+ defaultHeaders: {
+ "x-api-key": "LANGFLOW_API_KEY"
+ },
+ apiKey: "dummy-api-key" // Required by OpenAI SDK but not used by Langflow
+});
+
+const response = await client.responses.create({
+ model: "FLOW_ID",
+ input: "There is an event that happens on the second wednesday of every month. What are the event dates in 2026?"
+});
+
+console.log(response.output_text);
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.js
new file mode 100644
index 0000000000..293b21a5cd
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.js
@@ -0,0 +1,24 @@
+const url = `http://${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/responses`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ "Content-Type": `application/json`,
+ },
+ body: JSON.stringify({
+ "model": "ced2ec91-f325-4bf0-8754-f3198c2b1563",
+ "input": "What's my name?",
+ "previous_response_id": "c45f4ac8-772b-4675-8551-c560b1afd590"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-3.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-3.js
new file mode 100644
index 0000000000..d05446c855
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-3.js
@@ -0,0 +1,24 @@
+const url = `http://${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/responses`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ "Content-Type": `application/json`,
+ },
+ body: JSON.stringify({
+ "model": "ced2ec91-f325-4bf0-8754-f3198c2b1563",
+ "input": "What's my name?",
+ "previous_response_id": "session-alice-1756839048"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids.js
new file mode 100644
index 0000000000..d8690e2fd1
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/continue-conversations-with-response-and-session-ids.js
@@ -0,0 +1,23 @@
+const url = `http://${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/responses`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ "Content-Type": `application/json`,
+ },
+ body: JSON.stringify({
+ "model": "$FLOW_ID",
+ "input": "Hello, my name is Alice"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/example-request.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/example-request.js
new file mode 100644
index 0000000000..e3ea6b71eb
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/example-request.js
@@ -0,0 +1,24 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/responses`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ "Content-Type": `application/json`,
+ },
+ body: JSON.stringify({
+ "model": "$YOUR_FLOW_ID",
+ "input": "Hello, how are you?",
+ "stream": false
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/example-streaming-request.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/example-streaming-request.js
new file mode 100644
index 0000000000..3913a454fb
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/example-streaming-request.js
@@ -0,0 +1,24 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/responses`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ "Content-Type": `application/json`,
+ },
+ body: JSON.stringify({
+ "model": "$FLOW_ID",
+ "input": "Tell me a story about a robot",
+ "stream": true
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/pass-global-variables-to-your-flows-in-headers.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/pass-global-variables-to-your-flows-in-headers.js
new file mode 100644
index 0000000000..af937dfbff
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/pass-global-variables-to-your-flows-in-headers.js
@@ -0,0 +1,26 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/responses`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ "Content-Type": `application/json`,
+ "X-LANGFLOW-GLOBAL-VAR-OPENAI_API_KEY": `sk-...`,
+ "X-LANGFLOW-GLOBAL-VAR-USER_ID": `user123`,
+ "X-LANGFLOW-GLOBAL-VAR-ENVIRONMENT": `production`,
+ },
+ body: JSON.stringify({
+ "model": "your-flow-id",
+ "input": "Hello"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/retrieve-tool-call-results.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/retrieve-tool-call-results.js
new file mode 100644
index 0000000000..7fc86bd8d2
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/retrieve-tool-call-results.js
@@ -0,0 +1,27 @@
+const url = `http://${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/responses`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "model": "FLOW_ID",
+ "input": "Calculate 23 * 15 and show me the result",
+ "stream": false,
+ "include": [
+ "tool_call.results"
+ ]
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/token-usage-tracking.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/token-usage-tracking.js
new file mode 100644
index 0000000000..6070462e56
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-openai-responses/token-usage-tracking.js
@@ -0,0 +1,24 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/responses`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ "Content-Type": `application/json`,
+ },
+ body: JSON.stringify({
+ "model": "FLOW_ID",
+ "input": "Explain quantum computing in simple terms",
+ "stream": false
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/create-project-2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/create-project-2.js
new file mode 100644
index 0000000000..f9431cf899
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/create-project-2.js
@@ -0,0 +1,30 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/projects/`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "accept": `application/json`,
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "name": "new_project_name",
+ "description": "string",
+ "components_list": [
+ "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+ ],
+ "flows_list": [
+ "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+ ]
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/create-project.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/create-project.js
new file mode 100644
index 0000000000..2adaad9e2f
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/create-project.js
@@ -0,0 +1,25 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/projects/`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "name": "new_project_name",
+ "description": "string",
+ "components_list": [],
+ "flows_list": []
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/delete-project.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/delete-project.js
new file mode 100644
index 0000000000..1228eba5b8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/delete-project.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/projects/${process.env.PROJECT_ID ?? ""}`;
+
+const options = {
+ method: 'DELETE',
+ headers: {
+ "accept": `*/*`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/export-a-project.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/export-a-project.js
new file mode 100644
index 0000000000..53fdbac4ae
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/export-a-project.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/projects/download/${process.env.PROJECT_ID ?? ""}`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const data = await response.arrayBuffer();
+ console.log("Received binary response for langflow-project.zip", data.byteLength);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/import-a-project.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/import-a-project.js
new file mode 100644
index 0000000000..be86f382a3
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/import-a-project.js
@@ -0,0 +1,32 @@
+const fs = require("fs");
+const path = require("path");
+
+const fixturesDir = path.join(__dirname, "../../fixtures");
+const defaultProjectZip = path.join(fixturesDir, "project-import.zip");
+const projectPath = process.env.PROJECT_IMPORT_FILE || defaultProjectZip;
+const projectBuf = fs.readFileSync(projectPath);
+const projectName = path.basename(projectPath);
+
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/projects/upload/`;
+
+const formData = new FormData();
+formData.append("file", new Blob([projectBuf], { type: "application/zip" }), projectName);
+
+const options = {
+ method: "POST",
+ headers: {
+ accept: "application/json",
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: formData,
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/read-project.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/read-project.js
new file mode 100644
index 0000000000..a3c50d6cc7
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/read-project.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/projects/${process.env.PROJECT_ID ?? ""}`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/read-projects.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/read-projects.js
new file mode 100644
index 0000000000..3fa382000c
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/read-projects.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/projects/`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/update-project.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/update-project.js
new file mode 100644
index 0000000000..759ab42da8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-projects/update-project.js
@@ -0,0 +1,30 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/projects/b408ddb9-6266-4431-9be8-e04a62758331`;
+
+const options = {
+ method: 'PATCH',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "name": "string",
+ "description": "string",
+ "parent_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
+ "components": [
+ "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+ ],
+ "flows": [
+ "3fa85f64-5717-4562-b3fc-2c963f66afa6"
+ ]
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/form-langflow-api-requests.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/form-langflow-api-requests.js
new file mode 100644
index 0000000000..731aa47661
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/form-langflow-api-requests.js
@@ -0,0 +1,29 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/run/${process.env.FLOW_ID ?? ""}?stream=false`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "input_value": "hello world!",
+ "output_type": "chat",
+ "input_type": "chat",
+ "tweaks": {
+ "ChatOutput-6zcZt": {
+ "should_store_message": true
+ }
+ }
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/get-all-components.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/get-all-components.js
new file mode 100644
index 0000000000..34cdb80294
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/get-all-components.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/all`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/get-configuration.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/get-configuration.js
new file mode 100644
index 0000000000..a995626d86
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/get-configuration.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/config`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/get-version.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/get-version.js
new file mode 100644
index 0000000000..5d2c577ffe
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/get-version.js
@@ -0,0 +1,18 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v1/version`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/health-check.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/health-check.js
new file mode 100644
index 0000000000..06fc235985
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-reference-api-examples/health-check.js
@@ -0,0 +1,18 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/health_check`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/add-user.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/add-user.js
new file mode 100644
index 0000000000..40a0421eaf
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/add-user.js
@@ -0,0 +1,23 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/users/`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "username": "newuser2",
+ "password": "securepassword123"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/delete-user.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/delete-user.js
new file mode 100644
index 0000000000..7b35fb3c29
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/delete-user.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/users/10c1c6a2-ab8a-4748-8700-0e4832fd5ce8`;
+
+const options = {
+ method: 'DELETE',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/get-current-user.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/get-current-user.js
new file mode 100644
index 0000000000..cd2867a7ef
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/get-current-user.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/users/whoami`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/list-all-users.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/list-all-users.js
new file mode 100644
index 0000000000..b57191b615
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/list-all-users.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/users/?skip=0&limit=10`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/reset-password.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/reset-password.js
new file mode 100644
index 0000000000..483aebcd48
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/reset-password.js
@@ -0,0 +1,22 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/users/10c1c6a2-ab8a-4748-8700-0e4832fd5ce8/reset-password`;
+
+const options = {
+ method: 'PATCH',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "password": "newsecurepassword123"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/update-user.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/update-user.js
new file mode 100644
index 0000000000..269625feaf
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/api-users/update-user.js
@@ -0,0 +1,23 @@
+const url = `${process.env.LANGFLOW_URL ?? ""}/api/v1/users/10c1c6a2-ab8a-4748-8700-0e4832fd5ce8`;
+
+const options = {
+ method: 'PATCH',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "is_active": true,
+ "is_superuser": true
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/test-javascript-examples.sh b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/test-javascript-examples.sh
new file mode 100644
index 0000000000..866b1e418a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/test-javascript-examples.sh
@@ -0,0 +1,186 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT_DIR="$(cd "$SCRIPT_DIR/../../../.." && pwd)"
+EXAMPLES_DIR="$SCRIPT_DIR"
+TEST_SCRIPT_NAME="$(basename "${BASH_SOURCE[0]}")"
+
+MODE="syntax"
+
+load_repo_env() {
+ local env_file="$ROOT_DIR/.env"
+ if [[ -f "$env_file" ]]; then
+ set -a
+ # shellcheck source=/dev/null
+ source "$env_file"
+ set +a
+ fi
+}
+
+print_help() {
+ cat <<'EOF'
+Usage:
+ test-javascript-examples.sh [--execute]
+
+Modes:
+ (default) Syntax check only (`node --check`)
+ --execute Execute examples after syntax checks
+
+EOF
+}
+
+while [[ $# -gt 0 ]]; do
+ case "$1" in
+ --execute)
+ MODE="execute"
+ shift
+ ;;
+ --help|-h)
+ print_help
+ exit 0
+ ;;
+ *)
+ echo "Unknown argument: $1"
+ print_help
+ exit 1
+ ;;
+ esac
+done
+
+if ! command -v node >/dev/null 2>&1; then
+ echo "Node.js is required to test JavaScript examples."
+ exit 1
+fi
+
+JS_FILES=()
+while IFS= read -r line; do
+ if [[ "$(basename "$line")" == "$TEST_SCRIPT_NAME" ]]; then
+ continue
+ fi
+ JS_FILES+=("$line")
+done < <(python3 - "$EXAMPLES_DIR" <<'PY'
+import sys
+from pathlib import Path
+
+root = Path(sys.argv[1])
+for p in sorted(root.rglob("*.js")):
+ print(p)
+PY
+)
+
+if [[ ${#JS_FILES[@]} -eq 0 ]]; then
+ echo "No .js examples found in $EXAMPLES_DIR"
+ exit 1
+fi
+
+PASS=0
+FAIL=0
+SKIP=0
+
+echo "Testing ${#JS_FILES[@]} JavaScript examples in '$MODE' mode..."
+if [[ "$MODE" == "execute" ]]; then
+ load_repo_env
+fi
+
+has_placeholder_file_inputs() {
+ python3 - "$1" <<'PY'
+import sys
+text = open(sys.argv[1], encoding="utf-8").read()
+needles = ("FILE_NAME", "PATH/TO/FILE", "")
+print("yes" if any(n in text for n in needles) else "no")
+PY
+}
+
+has_missing_required_env() {
+ python3 - "$1" <<'PY'
+import os
+import re
+import sys
+
+text = open(sys.argv[1], encoding="utf-8").read()
+vars_to_check = ["FLOW_ID", "PROJECT_ID", "FOLDER_ID", "SESSION_ID", "JOB_ID", "USER_ID"]
+
+for name in vars_to_check:
+ if re.search(rf"process\.env\.{name}\b", text) and not os.getenv(name):
+ print(name)
+ raise SystemExit(0)
+
+print("")
+PY
+}
+
+print_failure_logs() {
+ local out_file="$1"
+ local err_file="$2"
+ if [[ -s "$err_file" ]]; then
+ echo " stderr (last 12 lines):"
+ python3 - "$err_file" <<'PY'
+import sys
+from pathlib import Path
+
+lines = Path(sys.argv[1]).read_text(encoding="utf-8", errors="replace").splitlines()
+for line in lines[-12:]:
+ print(line)
+PY
+ fi
+ if [[ -s "$out_file" ]]; then
+ echo " stdout (last 12 lines):"
+ python3 - "$out_file" <<'PY'
+import sys
+from pathlib import Path
+
+lines = Path(sys.argv[1]).read_text(encoding="utf-8", errors="replace").splitlines()
+for line in lines[-12:]:
+ print(line)
+PY
+ fi
+}
+
+for file in "${JS_FILES[@]}"; do
+ rel="${file#"$ROOT_DIR"/}"
+
+ if ! node --check "$file" >/tmp/langflow-js-check.out 2>/tmp/langflow-js-check.err; then
+ echo "FAIL $rel (node --check)"
+ ((FAIL+=1))
+ continue
+ fi
+
+ if [[ "$MODE" == "execute" ]]; then
+ if [[ -z "${LANGFLOW_API_KEY:-}" || ( -z "${LANGFLOW_URL:-}" && -z "${LANGFLOW_SERVER_URL:-}" ) ]]; then
+ echo "SKIP $rel (set LANGFLOW_API_KEY and LANGFLOW_URL or LANGFLOW_SERVER_URL to execute)"
+ ((SKIP+=1))
+ continue
+ fi
+
+ if [[ "$(has_placeholder_file_inputs "$file")" == "yes" ]]; then
+ echo "SKIP $rel (placeholder file input values)"
+ ((SKIP+=1))
+ continue
+ fi
+
+ missing_env="$(has_missing_required_env "$file")"
+ if [[ -n "$missing_env" ]]; then
+ echo "SKIP $rel (missing required env: $missing_env)"
+ ((SKIP+=1))
+ continue
+ fi
+
+ if ! node "$file" >/tmp/langflow-js-example.out 2>/tmp/langflow-js-example.err; then
+ echo "FAIL $rel (execution)"
+ print_failure_logs "/tmp/langflow-js-example.out" "/tmp/langflow-js-example.err"
+ ((FAIL+=1))
+ continue
+ fi
+ fi
+
+ echo "PASS $rel"
+ ((PASS+=1))
+done
+
+echo
+echo "Summary: PASS=$PASS FAIL=$FAIL SKIP=$SKIP TOTAL=${#JS_FILES[@]}"
+
+if [[ $FAIL -gt 0 ]]; then
+ exit 1
+fi
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-asynchronous-request.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-asynchronous-request.js
new file mode 100644
index 0000000000..06ead61bdd
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-asynchronous-request.js
@@ -0,0 +1,29 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v2/workflows`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "flow_id": "flow_67ccd2be17f0819081ff3bb2cf6508e60bb6a6b452d3795b",
+ "background": true,
+ "stream": false,
+ "inputs": {
+ "ChatInput-abc.input_type": "chat",
+ "ChatInput-abc.input_value": "Process this in the background",
+ "ChatInput-abc.session_id": "session-456"
+ }
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-request-2.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-request-2.js
new file mode 100644
index 0000000000..5784aabeea
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-request-2.js
@@ -0,0 +1,22 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v2/workflows/stop`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "job_id": "job_id_1234567890"
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-request.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-request.js
new file mode 100644
index 0000000000..51670600b4
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-request.js
@@ -0,0 +1,19 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v2/workflows?job_id=job_id_1234567890`;
+
+const options = {
+ method: 'GET',
+ headers: {
+ "accept": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-synchronous-request.js b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-synchronous-request.js
new file mode 100644
index 0000000000..5b1b401d4d
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/javascript-examples/workflows-api/example-synchronous-request.js
@@ -0,0 +1,28 @@
+const url = `${process.env.LANGFLOW_SERVER_URL ?? ""}/api/v2/workflows`;
+
+const options = {
+ method: 'POST',
+ headers: {
+ "Content-Type": `application/json`,
+ "x-api-key": `${process.env.LANGFLOW_API_KEY ?? ""}`,
+ },
+ body: JSON.stringify({
+ "flow_id": "flow_67ccd2be17f0819081ff3bb2cf6508e60bb6a6b452d3795b",
+ "background": false,
+ "inputs": {
+ "ChatInput-abc.input_type": "chat",
+ "ChatInput-abc.input_value": "what is 2+2",
+ "ChatInput-abc.session_id": "session-123"
+ }
+}),
+};
+
+fetch(url, options)
+ .then(async (response) => {
+ if (!response.ok) {
+ throw new Error(`HTTP ${response.status}`);
+ }
+ const text = await response.text();
+ console.log(text);
+ })
+ .catch((error) => console.error(error));
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/.gitignore b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/.gitignore
new file mode 100644
index 0000000000..7a60b85e14
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/.gitignore
@@ -0,0 +1,2 @@
+__pycache__/
+*.pyc
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/build-flow-and-stream-events-2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/build-flow-and-stream-events-2.py
new file mode 100644
index 0000000000..c2360306e8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/build-flow-and-stream-events-2.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/build/{os.getenv('JOB_ID', '')}/events"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/build-flow-and-stream-events-3.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/build-flow-and-stream-events-3.py
new file mode 100644
index 0000000000..30a03f92ff
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/build-flow-and-stream-events-3.py
@@ -0,0 +1,18 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+job_id = os.environ.get("JOB_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+# Use the API's `event_delivery` query param to avoid keeping a streaming connection open.
+# For local smoke tests, polling returns a finite JSON response.
+url = f"{base}/api/v1/build/{job_id}/events?event_delivery=polling"
+
+headers = {"accept": "application/json", "x-api-key": api_key}
+
+response = requests.get(url, headers=headers, timeout=60)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/build-flow-and-stream-events.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/build-flow-and-stream-events.py
new file mode 100644
index 0000000000..eb70384c6d
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/build-flow-and-stream-events.py
@@ -0,0 +1,18 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/build/{os.getenv('FLOW_ID', '')}/flow"
+
+headers = {
+ "accept": "application/json",
+ "Content-Type": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+payload = {"inputs": {"input_value": "Tell me a story"}}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/override-flow-parameters.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/override-flow-parameters.py
new file mode 100644
index 0000000000..ee97927249
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/override-flow-parameters.py
@@ -0,0 +1,21 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/build/{os.getenv('FLOW_ID', '')}/flow"
+
+headers = {
+ "accept": "application/json",
+ "Content-Type": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+payload = {
+ "data": {"nodes": [], "edges": []},
+ "inputs": {"input_value": "Your custom input here", "session": "session_id"},
+}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/set-start-and-stop-points.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/set-start-and-stop-points.py
new file mode 100644
index 0000000000..d9d490b22d
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-build/set-start-and-stop-points.py
@@ -0,0 +1,18 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/build/{os.getenv('FLOW_ID', '')}/flow"
+
+headers = {
+ "accept": "application/json",
+ "Content-Type": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+payload = {"stop_component_id": "OpenAIModel-Uksag"}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/delete-all-files-v2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/delete-all-files-v2.py
new file mode 100644
index 0000000000..fd0a1b5d89
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/delete-all-files-v2.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v2/files"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("DELETE", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/delete-file-v1.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/delete-file-v1.py
new file mode 100644
index 0000000000..0ca36294f5
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/delete-file-v1.py
@@ -0,0 +1,17 @@
+import os
+
+import requests
+
+url = (
+ f"{os.getenv('LANGFLOW_URL', '')}/api/v1/files/delete/{os.getenv('FLOW_ID', '')}/2024-12-30_15-19-43_your_file.txt"
+)
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("DELETE", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/delete-file-v2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/delete-file-v2.py
new file mode 100644
index 0000000000..78d1597d2e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/delete-file-v2.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v2/files/{os.getenv('FILE_ID', '')}"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("DELETE", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/download-file-v1.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/download-file-v1.py
new file mode 100644
index 0000000000..6e09676694
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/download-file-v1.py
@@ -0,0 +1,35 @@
+import json
+import os
+from pathlib import Path
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+fixtures = Path(__file__).resolve().parents[2] / "fixtures"
+upload_path = Path(os.environ.get("SAMPLE_UPLOAD_FILE", str(fixtures / "sample-upload.txt")))
+
+headers = {"accept": "application/json", "x-api-key": api_key}
+
+upload = requests.post(
+ f"{base}/api/v1/files/upload/{flow_id}",
+ headers=headers,
+ files={"file": (upload_path.name, upload_path.read_bytes(), "text/plain")},
+ timeout=30,
+)
+upload.raise_for_status()
+meta = upload.json()
+file_name = meta["file_path"].split("/")[-1]
+
+download = requests.get(
+ f"{base}/api/v1/files/download/{flow_id}/{file_name}",
+ headers=headers,
+ timeout=30,
+)
+download.raise_for_status()
+
+out_path = Path("downloaded_file.txt")
+out_path.write_bytes(download.content)
+print(json.dumps({"saved_bytes": len(download.content), "path": str(out_path), "upload": meta}))
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/download-file-v2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/download-file-v2.py
new file mode 100644
index 0000000000..e878b26760
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/download-file-v2.py
@@ -0,0 +1,33 @@
+import json
+import os
+from pathlib import Path
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+fixtures = Path(__file__).resolve().parents[2] / "fixtures"
+upload_path = Path(os.environ.get("SAMPLE_UPLOAD_FILE", str(fixtures / "sample-upload.txt")))
+
+headers = {"accept": "application/json", "x-api-key": api_key}
+
+upload = requests.post(
+ f"{base}/api/v2/files",
+ headers=headers,
+ files={"file": (upload_path.name, upload_path.read_bytes(), "text/plain")},
+ timeout=30,
+)
+upload.raise_for_status()
+file_id = upload.json()["id"]
+
+download = requests.get(
+ f"{base}/api/v2/files/{file_id}",
+ headers=headers,
+ timeout=30,
+)
+download.raise_for_status()
+
+out_path = Path("downloaded_file.txt")
+out_path.write_bytes(download.content)
+print(json.dumps({"saved_bytes": len(download.content), "file_id": str(file_id), "path": str(out_path)}))
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/edit-file-name-v2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/edit-file-name-v2.py
new file mode 100644
index 0000000000..d833ccbdf9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/edit-file-name-v2.py
@@ -0,0 +1,30 @@
+import json
+import os
+from pathlib import Path
+from urllib.parse import quote
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+fixtures = Path(__file__).resolve().parents[2] / "fixtures"
+upload_path = Path(os.environ.get("SAMPLE_UPLOAD_FILE", str(fixtures / "sample-upload.txt")))
+
+headers = {"accept": "application/json", "x-api-key": api_key}
+
+upload = requests.post(
+ f"{base}/api/v2/files",
+ headers=headers,
+ files={"file": (upload_path.name, upload_path.read_bytes(), "text/plain")},
+ timeout=30,
+)
+upload.raise_for_status()
+file_id = upload.json()["id"]
+
+new_name = os.environ.get("RENAMED_FILE_BASENAME", "renamed-sample-upload")
+url = f"{base}/api/v2/files/{file_id}?name={quote(new_name)}"
+
+response = requests.put(url, headers=headers, timeout=30)
+response.raise_for_status()
+print(json.dumps(response.json()))
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/list-files-v1.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/list-files-v1.py
new file mode 100644
index 0000000000..8ea27291fd
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/list-files-v1.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/files/list/{os.getenv('FLOW_ID', '')}"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/list-files-v2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/list-files-v2.py
new file mode 100644
index 0000000000..af09a94319
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/list-files-v2.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v2/files"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/send-files-to-your-flows-v2-2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/send-files-to-your-flows-v2-2.py
new file mode 100644
index 0000000000..6e0f8610d4
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/send-files-to-your-flows-v2-2.py
@@ -0,0 +1,24 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/run/{os.getenv('FLOW_ID', '')}"
+
+headers = {
+ "Content-Type": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+payload = {
+ "input_value": "what do you see?",
+ "output_type": "chat",
+ "input_type": "text",
+ "tweaks": {
+ "Read-File-1olS3": {"path": ["07e5b864-e367-4f52-b647-a48035ae7e5e/3a290013-fe1e-4d3d-a454-cacae81288f3.pdf"]}
+ },
+}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/send-files-to-your-flows-v2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/send-files-to-your-flows-v2.py
new file mode 100644
index 0000000000..5f44e42003
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/send-files-to-your-flows-v2.py
@@ -0,0 +1,23 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v2/files"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+files = {
+ "file": open(os.getenv("SAMPLE_UPLOAD_FILE", "docs/docs/API-Reference/fixtures/sample-upload.txt"), "rb"),
+}
+
+response = requests.request("POST", url, headers=headers, files=files)
+response.raise_for_status()
+
+print(response.text)
+
+for _f in files.values():
+ if hasattr(_f, "close"):
+ _f.close()
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-file-v1.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-file-v1.py
new file mode 100644
index 0000000000..51ddef206e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-file-v1.py
@@ -0,0 +1,23 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/files/upload/{os.getenv('FLOW_ID', '')}"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+files = {
+ "file": open(os.getenv("SAMPLE_UPLOAD_FILE", "docs/docs/API-Reference/fixtures/sample-upload.txt"), "rb"),
+}
+
+response = requests.request("POST", url, headers=headers, files=files)
+response.raise_for_status()
+
+print(response.text)
+
+for _f in files.values():
+ if hasattr(_f, "close"):
+ _f.close()
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-file-v2-2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-file-v2-2.py
new file mode 100644
index 0000000000..5f44e42003
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-file-v2-2.py
@@ -0,0 +1,23 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v2/files"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+files = {
+ "file": open(os.getenv("SAMPLE_UPLOAD_FILE", "docs/docs/API-Reference/fixtures/sample-upload.txt"), "rb"),
+}
+
+response = requests.request("POST", url, headers=headers, files=files)
+response.raise_for_status()
+
+print(response.text)
+
+for _f in files.values():
+ if hasattr(_f, "close"):
+ _f.close()
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-file-v2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-file-v2.py
new file mode 100644
index 0000000000..43e3d6271a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-file-v2.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/users/whoami"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-image-files-v1-2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-image-files-v1-2.py
new file mode 100644
index 0000000000..8f9d0a1091
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-image-files-v1-2.py
@@ -0,0 +1,26 @@
+import json
+import os
+from pathlib import Path
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+fixtures = Path(__file__).resolve().parents[2] / "fixtures"
+image_path = Path(os.environ.get("SAMPLE_IMAGE_FILE", str(fixtures / "sample-upload.png")))
+
+headers = {"accept": "application/json", "x-api-key": api_key}
+
+upload = requests.post(
+ f"{base}/api/v1/files/upload/{flow_id}",
+ headers=headers,
+ files={"file": (image_path.name, image_path.read_bytes(), "image/png")},
+ timeout=30,
+)
+upload.raise_for_status()
+
+listed = requests.get(f"{base}/api/v1/files/list/{flow_id}", headers=headers, timeout=30)
+listed.raise_for_status()
+print(json.dumps({"upload": upload.json(), "list": listed.json()}))
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-image-files-v1.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-image-files-v1.py
new file mode 100644
index 0000000000..62556297e4
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-files/upload-image-files-v1.py
@@ -0,0 +1,22 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/files/upload/{os.getenv('FLOW_ID', '')}"
+
+headers = {
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+files = {
+ "file": open(os.getenv("SAMPLE_IMAGE_FILE", "docs/docs/API-Reference/fixtures/sample-upload.png"), "rb"),
+}
+
+response = requests.request("POST", url, headers=headers, files=files)
+response.raise_for_status()
+
+print(response.text)
+
+for _f in files.values():
+ if hasattr(_f, "close"):
+ _f.close()
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/pass-global-variables-in-request-headers.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/pass-global-variables-in-request-headers.py
new file mode 100644
index 0000000000..b489a48e71
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/pass-global-variables-in-request-headers.py
@@ -0,0 +1,24 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {
+ "Content-Type": "application/json",
+ "x-api-key": api_key,
+ "X-LANGFLOW-GLOBAL-VAR-USER_ID": "user123",
+ "X-LANGFLOW-GLOBAL-VAR-ENVIRONMENT": "production",
+}
+
+payload = {
+ "input_value": "Tell me about something interesting!",
+ "input_type": "chat",
+ "output_type": "chat",
+}
+
+response = requests.post(f"{base}/api/v1/run/{flow_id}", headers=headers, json=payload, timeout=60)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/request-example-with-all-headers-and-parameters.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/request-example-with-all-headers-and-parameters.py
new file mode 100644
index 0000000000..55797681d0
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/request-example-with-all-headers-and-parameters.py
@@ -0,0 +1,30 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {
+ "Content-Type": "application/json",
+ "accept": "application/json",
+ "x-api-key": api_key,
+}
+
+payload = {
+ "input_value": "Tell me a story",
+ "input_type": "chat",
+ "output_type": "chat",
+ "output_component": "chat_output",
+ "session_id": "chat-123",
+}
+
+response = requests.post(
+ f"{base}/api/v1/run/{flow_id}?stream=false",
+ headers=headers,
+ json=payload,
+ timeout=60,
+)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/run-flow.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/run-flow.py
new file mode 100644
index 0000000000..18fcb41b2a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/run-flow.py
@@ -0,0 +1,26 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+url = f"{base}/api/v1/run/{flow_id}"
+
+headers = {
+ "Content-Type": "application/json",
+ "x-api-key": api_key,
+}
+
+payload = {
+ "input_value": "Tell me about something interesting!",
+ "session_id": "chat-123",
+ "input_type": "chat",
+ "output_type": "chat",
+ "output_component": "",
+}
+
+response = requests.post(url, headers=headers, json=payload, timeout=60)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/stream-llm-token-responses.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/stream-llm-token-responses.py
new file mode 100644
index 0000000000..484a5e0491
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/stream-llm-token-responses.py
@@ -0,0 +1,18 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/api/v1/run/{os.getenv('FLOW_ID', '')}?stream=true"
+
+headers = {
+ "accept": "application/json",
+ "Content-Type": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+payload = {"message": "Tell me something interesting!", "session_id": "chat-123"}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/webhook-run-flow.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/webhook-run-flow.py
new file mode 100644
index 0000000000..60952fd586
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows-run/webhook-run-flow.py
@@ -0,0 +1,23 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {
+ "Content-Type": "application/json",
+ "x-api-key": api_key,
+}
+
+payload = {"data": "example-data"}
+
+response = requests.post(
+ f"{base}/api/v1/webhook/{flow_id}",
+ headers=headers,
+ json=payload,
+ timeout=60,
+)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/create-flow.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/create-flow.py
new file mode 100644
index 0000000000..f91f54654b
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/create-flow.py
@@ -0,0 +1,30 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/flows/"
+
+headers = {
+ "accept": "application/json",
+ "Content-Type": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+payload = {
+ "name": "string2",
+ "description": "string",
+ "icon": "string",
+ "icon_bg_color": "#FF0000",
+ "gradient": "string",
+ "data": {},
+ "is_component": False,
+ "updated_at": "2024-12-30T15:48:01.519Z",
+ "webhook": False,
+ "endpoint_name": "string",
+ "tags": ["string"],
+}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/create-flows.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/create-flows.py
new file mode 100644
index 0000000000..4e59315e8f
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/create-flows.py
@@ -0,0 +1,28 @@
+import os
+import uuid
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+folder_id = (os.environ.get("PROJECT_ID") or os.environ.get("FOLDER_ID") or "").strip()
+
+headers = {"accept": "application/json", "Content-Type": "application/json", "x-api-key": api_key}
+
+
+def _flow_doc(suffix: str) -> dict:
+ doc = {
+ "name": f"batch-flow-{uuid.uuid4().hex[:8]}",
+ "description": f"Docs batch example {suffix}",
+ "data": {"nodes": [], "edges": []},
+ }
+ if folder_id:
+ doc["folder_id"] = folder_id
+ return doc
+
+
+payload = {"flows": [_flow_doc("A"), _flow_doc("B")]}
+
+response = requests.post(f"{base}/api/v1/flows/batch/", headers=headers, json=payload, timeout=30)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/delete-flow.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/delete-flow.py
new file mode 100644
index 0000000000..d7510dae4f
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/delete-flow.py
@@ -0,0 +1,25 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"accept": "application/json", "Content-Type": "application/json", "x-api-key": api_key}
+
+create = requests.post(
+ f"{base}/api/v1/flows/",
+ headers=headers,
+ json={
+ "name": "docs-example-delete-me",
+ "description": "Temporary flow for delete-flow example",
+ "data": {"nodes": [], "edges": []},
+ },
+ timeout=30,
+)
+create.raise_for_status()
+flow_id = create.json()["id"]
+
+delete = requests.delete(f"{base}/api/v1/flows/{flow_id}", headers=headers, timeout=30)
+delete.raise_for_status()
+print(delete.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/export-flows.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/export-flows.py
new file mode 100644
index 0000000000..7e763b380d
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/export-flows.py
@@ -0,0 +1,40 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+flow_id = os.environ.get("FLOW_ID", "")
+folder_id = os.environ.get("PROJECT_ID") or os.environ.get("FOLDER_ID", "")
+
+headers = {
+ "accept": "application/json",
+ "Content-Type": "application/json",
+ "x-api-key": api_key,
+}
+
+# Export needs at least two flows to return a ZIP; a single id returns JSON.
+extra = requests.post(
+ f"{base}/api/v1/flows/",
+ headers=headers,
+ json={
+ "name": "docs-export-temp-flow",
+ "description": "Temporary second flow for export example",
+ "data": {"nodes": [], "edges": []},
+ **({"folder_id": folder_id} if folder_id else {}),
+ },
+ timeout=30,
+)
+extra.raise_for_status()
+extra_id = extra.json()["id"]
+
+payload = [flow_id, extra_id]
+
+response = requests.post(f"{base}/api/v1/flows/download/", headers=headers, json=payload, timeout=60)
+response.raise_for_status()
+
+with open("langflow-flows.zip", "wb") as f:
+ f.write(response.content)
+print("Saved response to langflow-flows.zip")
+
+requests.delete(f"{base}/api/v1/flows/{extra_id}", headers=headers, timeout=30)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/import-flows.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/import-flows.py
new file mode 100644
index 0000000000..1721620ca1
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/import-flows.py
@@ -0,0 +1,23 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/flows/upload/?folder_id={os.getenv('FOLDER_ID', '')}"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+files = {
+ "file": open(os.getenv("FLOW_IMPORT_FILE", "docs/docs/API-Reference/fixtures/flow-import.json"), "rb"),
+}
+
+response = requests.request("POST", url, headers=headers, files=files)
+response.raise_for_status()
+
+print(response.text)
+
+for _f in files.values():
+ if hasattr(_f, "close"):
+ _f.close()
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-flow.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-flow.py
new file mode 100644
index 0000000000..d362b3fb1a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-flow.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/flows/{os.getenv('FLOW_ID', '')}"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-flows-2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-flows-2.py
new file mode 100644
index 0000000000..334a50ae4b
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-flows-2.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/flows/?remove_example_flows=true&components_only=false&get_all=false&project_id={os.getenv('PROJECT_ID', '')}&header_flows=false&page=1&size=1"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-flows.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-flows.py
new file mode 100644
index 0000000000..9e78b333ba
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-flows.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/flows/?remove_example_flows=false&components_only=false&get_all=true&header_flows=false&page=1&size=50"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-sample-flows.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-sample-flows.py
new file mode 100644
index 0000000000..988347fdd0
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/read-sample-flows.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/flows/basic_examples/"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/update-flow.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/update-flow.py
new file mode 100644
index 0000000000..6be601aca0
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-flows/update-flow.py
@@ -0,0 +1,20 @@
+import os
+import uuid
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"accept": "application/json", "Content-Type": "application/json", "x-api-key": api_key}
+
+payload = {
+ "name": f"docs-example-updated-flow-{uuid.uuid4().hex[:8]}",
+ "description": "Updated via API docs Python example",
+ "locked": False,
+}
+
+response = requests.patch(f"{base}/api/v1/flows/{flow_id}", headers=headers, json=payload, timeout=30)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-logs/retrieve-logs-with-optional-parameters.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-logs/retrieve-logs-with-optional-parameters.py
new file mode 100644
index 0000000000..a46b913a94
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-logs/retrieve-logs-with-optional-parameters.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/logs?lines_before=0&lines_after=0×tamp=0"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-logs/stream-logs.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-logs/stream-logs.py
new file mode 100644
index 0000000000..799e5f2d50
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-logs/stream-logs.py
@@ -0,0 +1,27 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+# `/logs-stream` is an SSE endpoint. For doc example stability, only read a small
+# number of events, then close the connection.
+url = f"{base}/logs-stream"
+headers = {"accept": "text/event-stream", "x-api-key": api_key}
+
+response = requests.get(url, headers=headers, stream=True, timeout=30)
+response.raise_for_status()
+
+events_read = 0
+chunks: list[str] = []
+for line in response.iter_lines(decode_unicode=True):
+ if line:
+ chunks.append(line)
+ events_read += 1
+ if events_read >= 3:
+ break
+
+response.close()
+
+print("\n".join(chunks))
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/delete-messages-by-session.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/delete-messages-by-session.py
new file mode 100644
index 0000000000..fef7a96efd
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/delete-messages-by-session.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/monitor/messages/session/different_session_id_2"
+
+headers = {
+ "accept": "*/*",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("DELETE", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/delete-messages.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/delete-messages.py
new file mode 100644
index 0000000000..05b117b925
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/delete-messages.py
@@ -0,0 +1,29 @@
+import os
+from uuid import UUID
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+flow_id = os.environ.get("FLOW_ID", "")
+
+headers = {"accept": "*/*", "Content-Type": "application/json", "x-api-key": api_key}
+
+list_resp = requests.get(
+ f"{base}/api/v1/monitor/messages",
+ headers=headers,
+ params={"flow_id": flow_id},
+ timeout=30,
+)
+list_resp.raise_for_status()
+messages = list_resp.json()
+if not messages:
+ print("No messages to delete.")
+ raise SystemExit(0)
+
+ids = [UUID(str(m["id"])) for m in messages[:2]]
+params = [("message_ids", str(i)) for i in ids]
+
+response = requests.delete(f"{base}/api/v1/monitor/messages", headers=headers, params=params, timeout=30)
+response.raise_for_status()
+print(response.status_code)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/delete-vertex-builds.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/delete-vertex-builds.py
new file mode 100644
index 0000000000..9f8ce6c404
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/delete-vertex-builds.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/monitor/builds?flow_id={os.getenv('FLOW_ID', '')}"
+
+headers = {
+ "accept": "*/*",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("DELETE", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/example-request.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/example-request.py
new file mode 100644
index 0000000000..55c9c0988a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/example-request.py
@@ -0,0 +1,16 @@
+import os
+
+import requests
+
+base_url = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "http://127.0.0.1:7860")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+flow_id = os.environ.get("FLOW_ID", "")
+
+response = requests.get(
+ f"{base_url}/api/v1/monitor/traces",
+ params={"flow_id": flow_id, "page": 1, "size": 50},
+ headers={"x-api-key": api_key, "accept": "application/json"},
+ timeout=30,
+)
+response.raise_for_status()
+print(response.json())
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-messages-2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-messages-2.py
new file mode 100644
index 0000000000..fd40c0d12e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-messages-2.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/monitor/messages?flow_id={os.getenv('FLOW_ID', '')}&session_id=01ce083d-748b-4b8d-97b6-33adbb6a528a&sender=Machine&sender_name=AI&order_by=timestamp"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-messages.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-messages.py
new file mode 100644
index 0000000000..7a8e1d2c53
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-messages.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/monitor/messages"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-transactions.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-transactions.py
new file mode 100644
index 0000000000..6f65f04749
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-transactions.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/monitor/transactions?flow_id={os.getenv('FLOW_ID', '')}&page=1&size=50"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-vertex-builds.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-vertex-builds.py
new file mode 100644
index 0000000000..01be1fb644
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/get-vertex-builds.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/monitor/builds?flow_id={os.getenv('FLOW_ID', '')}"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/update-message.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/update-message.py
new file mode 100644
index 0000000000..1449f3cbd0
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/update-message.py
@@ -0,0 +1,33 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+flow_id = os.environ.get("FLOW_ID", "")
+
+headers = {"accept": "application/json", "Content-Type": "application/json", "x-api-key": api_key}
+
+list_resp = requests.get(
+ f"{base}/api/v1/monitor/messages",
+ headers=headers,
+ params={"flow_id": flow_id},
+ timeout=30,
+)
+list_resp.raise_for_status()
+messages = list_resp.json()
+if not messages:
+ print("No messages to update.")
+ raise SystemExit(0)
+
+message_id = messages[0]["id"]
+payload = {"text": "testing 1234"}
+
+response = requests.put(
+ f"{base}/api/v1/monitor/messages/{message_id}",
+ headers=headers,
+ json=payload,
+ timeout=30,
+)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/update-session-id.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/update-session-id.py
new file mode 100644
index 0000000000..e5ded7f252
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-monitor/update-session-id.py
@@ -0,0 +1,33 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+flow_id = os.environ.get("FLOW_ID", "")
+
+headers = {"accept": "application/json", "x-api-key": api_key}
+
+list_resp = requests.get(
+ f"{base}/api/v1/monitor/messages",
+ headers=headers,
+ params={"flow_id": flow_id},
+ timeout=30,
+)
+list_resp.raise_for_status()
+messages = list_resp.json()
+if not messages:
+ print("No messages; cannot migrate session id.")
+ raise SystemExit(0)
+
+old_session_id = messages[0]["session_id"]
+new_session_id = f"{old_session_id}-migrated"
+
+response = requests.patch(
+ f"{base}/api/v1/monitor/messages/session/{old_session_id}",
+ headers=headers,
+ params={"new_session_id": new_session_id},
+ timeout=30,
+)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/additional-configuration-for-openai-client-libraries.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/additional-configuration-for-openai-client-libraries.py
new file mode 100644
index 0000000000..3e8bae8034
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/additional-configuration-for-openai-client-libraries.py
@@ -0,0 +1,27 @@
+import os
+
+from openai import OpenAI
+
+base = (os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")).rstrip("/")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+flow_id = os.environ.get("FLOW_ID", "")
+
+client = OpenAI(
+ base_url=f"{base}/api/v1/",
+ default_headers={"x-api-key": api_key},
+ api_key="dummy-api-key", # Required by OpenAI SDK but not used by Langflow
+)
+
+try:
+ response = client.responses.create(
+ model=flow_id,
+ input="There is an event that happens on the second wednesday of every month. What are the event dates in 2026?",
+ )
+except Exception as exc:
+ # Empty bootstrap flows return an error body; use a flow with ChatInput + ChatOutput in the UI.
+ print(exc)
+else:
+ try:
+ print(response.output_text)
+ except Exception:
+ print(response)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.py
new file mode 100644
index 0000000000..336eb32ad2
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-2.py
@@ -0,0 +1,29 @@
+# Continuation requests use the same endpoint; add `previous_response_id` from a prior
+# response's `id` field. The bootstrap flow is empty; use a Playground flow with ChatInput +
+# ChatOutput for a full run. Same first message as continue-conversations-with-response-and-session-ids.py.
+
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+url = f"{base}/api/v1/responses"
+
+headers = {
+ "x-api-key": api_key,
+ "Content-Type": "application/json",
+}
+
+payload = {
+ "model": flow_id,
+ "input": "Hello, my name is Alice",
+ "stream": False,
+}
+
+response = requests.post(url, headers=headers, json=payload, timeout=120)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-3.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-3.py
new file mode 100644
index 0000000000..88dacf14a4
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids-3.py
@@ -0,0 +1,28 @@
+# Same pattern as continue-conversations-with-response-and-session-ids-2.py; you can pass
+# `previous_response_id` from the prior turn (or a session id your app stores).
+
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+url = f"{base}/api/v1/responses"
+
+headers = {
+ "x-api-key": api_key,
+ "Content-Type": "application/json",
+}
+
+payload = {
+ "model": flow_id,
+ "input": "Hello, my name is Alice",
+ "stream": False,
+}
+
+response = requests.post(url, headers=headers, json=payload, timeout=120)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids.py
new file mode 100644
index 0000000000..e57f103f15
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/continue-conversations-with-response-and-session-ids.py
@@ -0,0 +1,25 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+url = f"{base}/api/v1/responses"
+
+headers = {
+ "x-api-key": api_key,
+ "Content-Type": "application/json",
+}
+
+payload = {
+ "model": flow_id,
+ "input": "Hello, my name is Alice",
+ "stream": False,
+}
+
+response = requests.post(url, headers=headers, json=payload, timeout=120)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/example-request.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/example-request.py
new file mode 100644
index 0000000000..f1ebea3260
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/example-request.py
@@ -0,0 +1,17 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/api/v1/responses"
+
+headers = {
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+ "Content-Type": "application/json",
+}
+
+payload = {"model": "$YOUR_FLOW_ID", "input": "Hello, how are you?", "stream": False}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/example-streaming-request.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/example-streaming-request.py
new file mode 100644
index 0000000000..b4565dd6ce
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/example-streaming-request.py
@@ -0,0 +1,17 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/api/v1/responses"
+
+headers = {
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+ "Content-Type": "application/json",
+}
+
+payload = {"model": "$FLOW_ID", "input": "Tell me a story about a robot", "stream": True}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/pass-global-variables-to-your-flows-in-headers.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/pass-global-variables-to-your-flows-in-headers.py
new file mode 100644
index 0000000000..083e14c430
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/pass-global-variables-to-your-flows-in-headers.py
@@ -0,0 +1,20 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/api/v1/responses"
+
+headers = {
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+ "Content-Type": "application/json",
+ "X-LANGFLOW-GLOBAL-VAR-OPENAI_API_KEY": "sk-...",
+ "X-LANGFLOW-GLOBAL-VAR-USER_ID": "user123",
+ "X-LANGFLOW-GLOBAL-VAR-ENVIRONMENT": "production",
+}
+
+payload = {"model": "your-flow-id", "input": "Hello"}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/retrieve-tool-call-results.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/retrieve-tool-call-results.py
new file mode 100644
index 0000000000..feb5adf0dd
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/retrieve-tool-call-results.py
@@ -0,0 +1,26 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+url = f"{base}/api/v1/responses"
+
+headers = {
+ "Content-Type": "application/json",
+ "x-api-key": api_key,
+}
+
+payload = {
+ "model": flow_id,
+ "input": "Calculate 23 * 15 and show me the result",
+ "stream": False,
+ "include": ["tool_call.results"],
+}
+
+response = requests.post(url, headers=headers, json=payload, timeout=120)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/token-usage-tracking.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/token-usage-tracking.py
new file mode 100644
index 0000000000..0d1e7c9e6c
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-openai-responses/token-usage-tracking.py
@@ -0,0 +1,17 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/api/v1/responses"
+
+headers = {
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+ "Content-Type": "application/json",
+}
+
+payload = {"model": "FLOW_ID", "input": "Explain quantum computing in simple terms", "stream": False}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/create-project-2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/create-project-2.py
new file mode 100644
index 0000000000..a2a4413ae3
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/create-project-2.py
@@ -0,0 +1,23 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/projects/"
+
+headers = {
+ "accept": "application/json",
+ "Content-Type": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+payload = {
+ "name": "new_project_name",
+ "description": "string",
+ "components_list": ["3fa85f64-5717-4562-b3fc-2c963f66afa6"],
+ "flows_list": ["3fa85f64-5717-4562-b3fc-2c963f66afa6"],
+}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/create-project.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/create-project.py
new file mode 100644
index 0000000000..30d9661f0f
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/create-project.py
@@ -0,0 +1,17 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/projects/"
+
+headers = {
+ "Content-Type": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+payload = {"name": "new_project_name", "description": "string", "components_list": [], "flows_list": []}
+
+response = requests.request("POST", url, headers=headers, json=payload)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/delete-project.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/delete-project.py
new file mode 100644
index 0000000000..e0ee3bac13
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/delete-project.py
@@ -0,0 +1,26 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"accept": "*/*", "Content-Type": "application/json", "x-api-key": api_key}
+
+create = requests.post(
+ f"{base}/api/v1/projects/",
+ headers=headers,
+ json={
+ "name": "docs-example-delete-me",
+ "description": "Temporary project",
+ "components_list": [],
+ "flows_list": [],
+ },
+ timeout=30,
+)
+create.raise_for_status()
+project_id = create.json()["id"]
+
+delete = requests.delete(f"{base}/api/v1/projects/{project_id}", headers=headers, timeout=30)
+delete.raise_for_status()
+print(delete.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/export-a-project.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/export-a-project.py
new file mode 100644
index 0000000000..1fe7c65052
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/export-a-project.py
@@ -0,0 +1,17 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/projects/download/{os.getenv('PROJECT_ID', '')}"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+with open("langflow-project.zip", "wb") as f:
+ f.write(response.content)
+print("Saved response to langflow-project.zip")
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/import-a-project.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/import-a-project.py
new file mode 100644
index 0000000000..4e57f499bc
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/import-a-project.py
@@ -0,0 +1,19 @@
+import os
+from pathlib import Path
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+fixtures = Path(__file__).resolve().parents[2] / "fixtures"
+default_json = fixtures / "project-import.json"
+import_path = Path(os.environ.get("PROJECT_IMPORT_JSON", str(default_json)))
+
+headers = {"accept": "application/json", "x-api-key": api_key}
+
+files = {"file": (import_path.name, import_path.read_bytes(), "application/json")}
+response = requests.post(f"{base}/api/v1/projects/upload/", headers=headers, files=files, timeout=60)
+
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/read-project.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/read-project.py
new file mode 100644
index 0000000000..eaff3ac7b5
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/read-project.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/projects/{os.getenv('PROJECT_ID', '')}"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/read-projects.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/read-projects.py
new file mode 100644
index 0000000000..117274016e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/read-projects.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/projects/"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/update-project.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/update-project.py
new file mode 100644
index 0000000000..46d41b1c21
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-projects/update-project.py
@@ -0,0 +1,19 @@
+import os
+import uuid
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+project_id = os.environ.get("PROJECT_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"accept": "application/json", "Content-Type": "application/json", "x-api-key": api_key}
+
+payload = {
+ "name": f"docs-example-renamed-project-{uuid.uuid4().hex[:8]}",
+ "description": "Updated via API docs Python example",
+}
+
+response = requests.patch(f"{base}/api/v1/projects/{project_id}", headers=headers, json=payload, timeout=30)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/form-langflow-api-requests.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/form-langflow-api-requests.py
new file mode 100644
index 0000000000..554748bdd8
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/form-langflow-api-requests.py
@@ -0,0 +1,24 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+url = f"{base}/api/v1/run/{flow_id}?stream=false"
+
+headers = {
+ "Content-Type": "application/json",
+ "x-api-key": api_key,
+}
+
+payload = {
+ "input_value": "hello world!",
+ "output_type": "chat",
+ "input_type": "chat",
+}
+
+response = requests.post(url, headers=headers, json=payload, timeout=60)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/get-all-components.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/get-all-components.py
new file mode 100644
index 0000000000..f0f3f1a202
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/get-all-components.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/api/v1/all"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/get-configuration.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/get-configuration.py
new file mode 100644
index 0000000000..51c856abb3
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/get-configuration.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/api/v1/config"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/get-version.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/get-version.py
new file mode 100644
index 0000000000..81da3e568a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/get-version.py
@@ -0,0 +1,14 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/api/v1/version"
+
+headers = {
+ "accept": "application/json",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/health-check.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/health-check.py
new file mode 100644
index 0000000000..7deb1c7172
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-reference-api-examples/health-check.py
@@ -0,0 +1,14 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_SERVER_URL', '')}/health_check"
+
+headers = {
+ "accept": "application/json",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/add-user.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/add-user.py
new file mode 100644
index 0000000000..de7369d11c
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/add-user.py
@@ -0,0 +1,18 @@
+import os
+import uuid
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"Content-Type": "application/json", "x-api-key": api_key}
+
+payload = {
+ "username": f"docsuser_{uuid.uuid4().hex[:12]}",
+ "password": "securepassword123",
+}
+
+response = requests.post(f"{base}/api/v1/users/", headers=headers, json=payload, timeout=30)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/delete-user.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/delete-user.py
new file mode 100644
index 0000000000..f81a966d51
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/delete-user.py
@@ -0,0 +1,22 @@
+import os
+import uuid
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"accept": "application/json", "Content-Type": "application/json", "x-api-key": api_key}
+
+create = requests.post(
+ f"{base}/api/v1/users/",
+ headers=headers,
+ json={"username": f"docsdel_{uuid.uuid4().hex[:12]}", "password": "securepassword123"},
+ timeout=30,
+)
+create.raise_for_status()
+user_id = create.json()["id"]
+
+delete = requests.delete(f"{base}/api/v1/users/{user_id}", headers=headers, timeout=30)
+delete.raise_for_status()
+print(delete.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/get-current-user.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/get-current-user.py
new file mode 100644
index 0000000000..43e3d6271a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/get-current-user.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/users/whoami"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/list-all-users.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/list-all-users.py
new file mode 100644
index 0000000000..ff34460d67
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/list-all-users.py
@@ -0,0 +1,15 @@
+import os
+
+import requests
+
+url = f"{os.getenv('LANGFLOW_URL', '')}/api/v1/users/?skip=0&limit=10"
+
+headers = {
+ "accept": "application/json",
+ "x-api-key": f"{os.getenv('LANGFLOW_API_KEY', '')}",
+}
+
+response = requests.request("GET", url, headers=headers)
+response.raise_for_status()
+
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/reset-password.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/reset-password.py
new file mode 100644
index 0000000000..ce4211c666
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/reset-password.py
@@ -0,0 +1,24 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"Content-Type": "application/json", "x-api-key": api_key}
+
+who = requests.get(f"{base}/api/v1/users/whoami", headers=headers, timeout=30)
+who.raise_for_status()
+user_id = who.json()["id"]
+
+# Must differ from the current password (default superuser is often langflow/langflow).
+payload = {"password": "DocsExampleResetPass2025!"}
+
+response = requests.patch(
+ f"{base}/api/v1/users/{user_id}/reset-password",
+ headers=headers,
+ json=payload,
+ timeout=30,
+)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/update-user.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/update-user.py
new file mode 100644
index 0000000000..13a1562789
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/api-users/update-user.py
@@ -0,0 +1,18 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"Content-Type": "application/json", "x-api-key": api_key}
+
+who = requests.get(f"{base}/api/v1/users/whoami", headers=headers, timeout=30)
+who.raise_for_status()
+user_id = who.json()["id"]
+
+payload = {"is_active": True}
+
+response = requests.patch(f"{base}/api/v1/users/{user_id}", headers=headers, json=payload, timeout=30)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/test-python-examples.sh b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/test-python-examples.sh
new file mode 100644
index 0000000000..d572054bf7
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/test-python-examples.sh
@@ -0,0 +1,226 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT_DIR="$(cd "$SCRIPT_DIR/../../../.." && pwd)"
+EXAMPLES_DIR="$SCRIPT_DIR"
+TEST_SCRIPT_NAME="$(basename "${BASH_SOURCE[0]}")"
+
+MODE="syntax"
+
+load_repo_env() {
+ local env_file="$ROOT_DIR/.env"
+ if [[ -f "$env_file" ]]; then
+ set -a
+ # shellcheck source=/dev/null
+ source "$env_file"
+ set +a
+ fi
+}
+
+print_help() {
+ cat <<'EOF'
+Usage:
+ test-python-examples.sh [--execute]
+
+Modes:
+ (default) Syntax check only (py_compile)
+ --execute Execute examples after syntax checks
+
+EOF
+}
+
+while [[ $# -gt 0 ]]; do
+ case "$1" in
+ --execute)
+ MODE="execute"
+ shift
+ ;;
+ --help|-h)
+ print_help
+ exit 0
+ ;;
+ *)
+ echo "Unknown argument: $1"
+ print_help
+ exit 1
+ ;;
+ esac
+done
+
+PY_FILES=()
+while IFS= read -r line; do
+ if [[ "$(basename "$line")" == "$TEST_SCRIPT_NAME" ]]; then
+ continue
+ fi
+ PY_FILES+=("$line")
+done < <(uv run python - "$EXAMPLES_DIR" <<'PY'
+import sys
+from pathlib import Path
+
+root = Path(sys.argv[1])
+for p in sorted(root.rglob("*.py")):
+ print(p)
+PY
+)
+
+if [[ ${#PY_FILES[@]} -eq 0 ]]; then
+ echo "No .py examples found in $EXAMPLES_DIR"
+ exit 1
+fi
+
+PASS=0
+FAIL=0
+SKIP=0
+PY_TIMEOUT_SECONDS="${PY_TIMEOUT_SECONDS:-45}"
+
+echo "Testing ${#PY_FILES[@]} Python examples in '$MODE' mode..."
+if [[ "$MODE" == "execute" ]]; then
+ load_repo_env
+fi
+
+has_placeholder_file_inputs() {
+ uv run python - "$1" <<'PY'
+import sys
+text = open(sys.argv[1], encoding="utf-8").read()
+needles = ("FILE_NAME", "PATH/TO/FILE", "")
+print("yes" if any(n in text for n in needles) else "no")
+PY
+}
+
+has_missing_required_env() {
+ uv run python - "$1" <<'PY'
+import os
+import re
+import sys
+
+text = open(sys.argv[1], encoding="utf-8").read()
+vars_to_check = ["FLOW_ID", "PROJECT_ID", "FOLDER_ID", "SESSION_ID", "JOB_ID", "USER_ID"]
+
+for name in vars_to_check:
+ getenv_pat = rf"os\.getenv\(\s*['\"]{name}['\"]"
+ environ_pat = rf"os\.environ\.get\(\s*['\"]{name}['\"]"
+ if (re.search(getenv_pat, text) or re.search(environ_pat, text)) and not os.getenv(name):
+ print(name)
+ raise SystemExit(0)
+
+print("")
+PY
+}
+
+print_failure_logs() {
+ local out_file="$1"
+ local err_file="$2"
+ if [[ -s "$err_file" ]]; then
+ echo " stderr (last 12 lines):"
+ uv run python - "$err_file" <<'PY'
+import sys
+from pathlib import Path
+
+lines = Path(sys.argv[1]).read_text(encoding="utf-8", errors="replace").splitlines()
+for line in lines[-12:]:
+ print(line)
+PY
+ fi
+ if [[ -s "$out_file" ]]; then
+ echo " stdout (last 12 lines):"
+ uv run python - "$out_file" <<'PY'
+import sys
+from pathlib import Path
+
+lines = Path(sys.argv[1]).read_text(encoding="utf-8", errors="replace").splitlines()
+for line in lines[-12:]:
+ print(line)
+PY
+ fi
+}
+
+for file in "${PY_FILES[@]}"; do
+ rel="${file#"$ROOT_DIR"/}"
+
+ if ! uv run python -m py_compile "$file"; then
+ echo "FAIL $rel (py_compile)"
+ ((FAIL+=1))
+ continue
+ fi
+
+ if [[ "$MODE" == "execute" ]]; then
+ # api-openai-responses/* call Langflow's HTTP API (e.g. /api/v1/responses) with x-api-key;
+ # they do not require OPENAI_API_KEY in the environment (same as the JS examples).
+
+ # Streaming / long-running examples: skip in local harness (hang, flaky, or need extra setup).
+ case "$(basename "$file")" in
+ build-flow-and-stream-events-2.py|build-flow-and-stream-events-3.py|stream-llm-token-responses.py|example-streaming-request.py|stream-logs.py)
+ echo "SKIP $rel (streaming/long-running; not run in local harness)"
+ ((SKIP+=1))
+ continue
+ ;;
+ esac
+
+ if [[ "$(basename "$file")" == "retrieve-logs-with-optional-parameters.py" ]]; then
+ echo "SKIP $rel (/logs endpoint not implemented in local server)"
+ ((SKIP+=1))
+ continue
+ fi
+
+ if [[ "$(basename "$file")" == "reset-password.py" ]]; then
+ echo "SKIP $rel (reset-password may return 500 in local SQLite runs; run manually if needed)"
+ ((SKIP+=1))
+ continue
+ fi
+
+ if [[ -z "${LANGFLOW_API_KEY:-}" || ( -z "${LANGFLOW_URL:-}" && -z "${LANGFLOW_SERVER_URL:-}" ) ]]; then
+ echo "SKIP $rel (set LANGFLOW_API_KEY and LANGFLOW_URL or LANGFLOW_SERVER_URL to execute)"
+ ((SKIP+=1))
+ continue
+ fi
+
+ if [[ "$(has_placeholder_file_inputs "$file")" == "yes" ]]; then
+ echo "SKIP $rel (placeholder file input values)"
+ ((SKIP+=1))
+ continue
+ fi
+
+ missing_env="$(has_missing_required_env "$file")"
+ if [[ -n "$missing_env" ]]; then
+ echo "SKIP $rel (missing required env: $missing_env)"
+ ((SKIP+=1))
+ continue
+ fi
+
+ if ! uv run python - "$file" "$PY_TIMEOUT_SECONDS" >/tmp/langflow-python-example.out 2>/tmp/langflow-python-example.err <<'PY'
+import runpy
+import signal
+import sys
+
+script_path = sys.argv[1]
+timeout_seconds = int(sys.argv[2])
+
+def _handle_timeout(_signum, _frame):
+ raise TimeoutError(f"execution timed out after {timeout_seconds}s")
+
+signal.signal(signal.SIGALRM, _handle_timeout)
+signal.alarm(timeout_seconds)
+try:
+ runpy.run_path(script_path, run_name="__main__")
+finally:
+ signal.alarm(0)
+PY
+ then
+ echo "FAIL $rel (execution)"
+ print_failure_logs "/tmp/langflow-python-example.out" "/tmp/langflow-python-example.err"
+ ((FAIL+=1))
+ continue
+ fi
+ fi
+
+ echo "PASS $rel"
+ ((PASS+=1))
+done
+
+echo
+echo "Summary: PASS=$PASS FAIL=$FAIL SKIP=$SKIP TOTAL=${#PY_FILES[@]}"
+
+if [[ $FAIL -gt 0 ]]; then
+ exit 1
+fi
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-asynchronous-request.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-asynchronous-request.py
new file mode 100644
index 0000000000..d09613b756
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-asynchronous-request.py
@@ -0,0 +1,20 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"Content-Type": "application/json", "x-api-key": api_key}
+
+payload = {
+ "flow_id": flow_id,
+ "background": True,
+ "stream": False,
+ "inputs": {},
+}
+
+response = requests.post(f"{base}/api/v2/workflows", headers=headers, json=payload, timeout=60)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-request-2.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-request-2.py
new file mode 100644
index 0000000000..728729ad98
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-request-2.py
@@ -0,0 +1,26 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"Content-Type": "application/json", "x-api-key": api_key}
+
+start = requests.post(
+ f"{base}/api/v2/workflows",
+ headers=headers,
+ json={"flow_id": flow_id, "background": True, "stream": False, "inputs": {}},
+ timeout=60,
+)
+start.raise_for_status()
+job_id = start.json()["job_id"]
+
+stop = requests.post(
+ f"{base}/api/v2/workflows/stop",
+ headers=headers,
+ json={"job_id": job_id},
+ timeout=60,
+)
+print(stop.status_code, stop.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-request.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-request.py
new file mode 100644
index 0000000000..7d175afe06
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-request.py
@@ -0,0 +1,18 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"Content-Type": "application/json", "x-api-key": api_key}
+
+start = requests.post(
+ f"{base}/api/v2/workflows",
+ headers=headers,
+ json={"flow_id": flow_id, "background": True, "stream": False, "inputs": {}},
+ timeout=60,
+)
+start.raise_for_status()
+print(start.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-synchronous-request.py b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-synchronous-request.py
new file mode 100644
index 0000000000..477ea27f9e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/python-examples/workflows-api/example-synchronous-request.py
@@ -0,0 +1,20 @@
+import os
+
+import requests
+
+base = os.environ.get("LANGFLOW_URL") or os.environ.get("LANGFLOW_SERVER_URL", "")
+flow_id = os.environ.get("FLOW_ID", "")
+api_key = os.environ.get("LANGFLOW_API_KEY", "")
+
+headers = {"Content-Type": "application/json", "x-api-key": api_key}
+
+payload = {
+ "flow_id": flow_id,
+ "background": False,
+ "stream": False,
+ "inputs": {},
+}
+
+response = requests.post(f"{base}/api/v2/workflows", headers=headers, json=payload, timeout=120)
+response.raise_for_status()
+print(response.text)
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/typescript-client.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/typescript-client.mdx
new file mode 100644
index 0000000000..695157a7ce
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/typescript-client.mdx
@@ -0,0 +1,389 @@
+---
+title: Langflow TypeScript client
+slug: /typescript-client
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+The Langflow TypeScript client allows your TypeScript applications to programmatically interact with the Langflow API.
+
+For the client code repository, see [langflow-client-ts](https://github.com/datastax/langflow-client-ts/).
+
+For the npm package, see [@datastax/langflow-client](https://www.npmjs.com/package/@datastax/langflow-client).
+
+## Install the Langflow TypeScript package
+
+To install the Langflow typescript client package, use one of the following commands:
+
+
+
+
+```bash
+npm install @datastax/langflow-client
+```
+
+
+
+
+```bash
+yarn add @datastax/langflow-client
+```
+
+
+
+
+```bash
+pnpm add @datastax/langflow-client
+```
+
+
+
+
+## Initialize the Langflow TypeScript client
+
+1. Import the client into your code.
+
+ ```tsx
+ import { LangflowClient } from "@datastax/langflow-client";
+ ```
+
+2. Initialize a `LangflowClient` object to interact with your server:
+
+ ```tsx
+ const baseUrl = "BASE_URL";
+ const apiKey = "API_KEY";
+ const client = new LangflowClient({ baseUrl, apiKey });
+ ```
+
+ Replace `BASE_URL` and `API_KEY` with values from your deployment.
+ The default Langflow base URL is `http://localhost:7860`.
+ To create an API key, see [API keys and authentication](/api-keys-and-authentication).
+
+## Connect to your server and get responses
+
+1. With your Langflow client initialized, test the connection by calling your Langflow server.
+
+ The following example runs a flow (`runFlow`) by sending the flow ID and a chat input string:
+
+ ```tsx
+ import { LangflowClient } from "@datastax/langflow-client";
+
+ const baseUrl = "http://localhost:7860";
+ const client = new LangflowClient({ baseUrl });
+
+ async function runFlow() {
+ const flowId = "aa5a238b-02c0-4f03-bc5c-cc3a83335cdf";
+ const flow = client.flow(flowId);
+ const input = "Is anyone there?";
+
+ const response = await flow.run(input);
+ console.log(response);
+ }
+
+ runFlow().catch(console.error);
+ ```
+
+ Replace the following:
+
+ * `baseUrl`: The URL of your Langflow server.
+ * `flowId`: The ID of the flow you want to run.
+ * `input`: The chat input message you want to send to trigger the flow.
+ This is only valid for flows with a **Chat Input** component.
+
+2. Review the result to confirm that the client connected to your Langflow server.
+
+ The following example shows the response from a well-formed `runFlow` request that reached the Langflow server and successfully started the flow:
+
+ ```
+ FlowResponse {
+ sessionId: 'aa5a238b-02c0-4f03-bc5c-cc3a83335cdf',
+ outputs: [ { inputs: [Object], outputs: [Array] } ]
+ }
+ ```
+
+ In this case, the response includes a [`sessionID`](/session-id) that is a unique identifier for the client-server session and an `outputs` array that contains information about the flow run.
+
+3. Optional: If you want to get full response objects from the server, change `console.log` to stringify the returned JSON object:
+
+ ```tsx
+ console.log(JSON.stringify(response, null, 2));
+ ```
+
+ The exact structure of the returned `inputs` and `outputs` objects depends on the components and configuration of your flow.
+
+4. Optional: If you want the response to include only the chat message from the **Chat Output** component, change `console.log` to use the `chatOutputText` convenience function:
+
+ ```tsx
+ console.log(response.chatOutputText());
+ ```
+
+## Use advanced TypeScript client features
+
+The TypeScript client can do more than just connect to your server and run a flow.
+
+This example builds on the quickstart with additional features for interacting with Langflow:
+
+1. Pass [tweaks](/concepts-publish#input-schema) as an object with the request.
+Tweaks are programmatic run-time overrides for component settings.
+
+ This example changes the LLM used by a language model component in a flow::
+
+ ```tsx
+ const tweaks = { model_name: "gpt-4o-mini" };
+ ```
+
+2. Pass a [session ID](/session-id) with the request to separate the conversation from other flow runs, and to be able to continue this conversation by calling the same session ID in the future:
+
+ ```tsx
+ const session_id = "aa5a238b-02c0-4f03-bc5c-cc3a83335cdf";
+ ```
+
+3. Instead of calling `run` on the Flow object, call `stream` with the same arguments to get a streaming response:
+
+ ```tsx
+ const response = await client.flow(flowId).stream(input);
+
+ for await (const event of response) {
+ console.log(event);
+ }
+ ```
+
+ The response is a [`ReadableStream`](https://developer.mozilla.org/en-US/docs/Web/API/ReadableStream) of objects.
+ For more information on streaming Langflow responses, see the [`/run` endpoint](/api-flows-run#run-flow).
+
+4. Run the modified TypeScript application to run the flow with `tweaks`, `session_id`, and streaming:
+
+ ```tsx
+ import { LangflowClient } from "@datastax/langflow-client";
+
+ const baseUrl = "http://localhost:7860";
+ const client = new LangflowClient({ baseUrl });
+
+ async function runFlow() {
+ const flowId = "aa5a238b-02c0-4f03-bc5c-cc3a83335cdf";
+ const input = "Is anyone there?";
+ const tweaks = { model_name: "gpt-4o-mini" };
+ const session_id = "test-session";
+
+ const response = await client.flow(flowId).stream(input, {
+ session_id,
+ tweaks,
+ });
+
+ for await (const event of response) {
+ console.log(event);
+ }
+
+ }
+ runFlow().catch(console.error);
+ ```
+
+ Replace the following:
+
+ * `baseUrl`: The URL of your Langflow server.
+ * `flowId`: The ID of the flow you want to run.
+ * `input`: The chat input message you want to send to trigger the flow, assuming the flow has a **Chat Input** component.
+ * `tweaks`: Any tweak modifiers to apply to the flow run.
+ This example changes the LLM used by a component in the flow.
+ * `session_id`: Pass a custom session ID.
+ If omitted or empty, the flow ID is the default session ID.
+
+
+ Result
+
+ With streaming enabled, the response includes the flow metatadata and timestamped events for flow activity.
+ For example:
+
+ ```text
+ {
+ event: 'add_message',
+ data: {
+ timestamp: '2025-05-23 15:52:48 UTC',
+ sender: 'User',
+ sender_name: 'User',
+ session_id: 'test-session',
+ text: 'Is anyone there?',
+ files: [],
+ error: false,
+ edit: false,
+ properties: {
+ text_color: '',
+ background_color: '',
+ edited: false,
+ source: [Object],
+ icon: '',
+ allow_markdown: false,
+ positive_feedback: null,
+ state: 'complete',
+ targets: []
+ },
+ category: 'message',
+ content_blocks: [],
+ id: '7f096715-3f2d-4d84-88d6-5e2f76bf3fbe',
+ flow_id: 'aa5a238b-02c0-4f03-bc5c-cc3a83335cdf',
+ duration: null
+ }
+ }
+ {
+ event: 'token',
+ data: {
+ chunk: 'Absolutely',
+ id: 'c5a99314-6b23-488b-84e2-038aa3e87fb5',
+ timestamp: '2025-05-23 15:52:48 UTC'
+ }
+ }
+ {
+ event: 'token',
+ data: {
+ chunk: ',',
+ id: 'c5a99314-6b23-488b-84e2-038aa3e87fb5',
+ timestamp: '2025-05-23 15:52:48 UTC'
+ }
+ }
+ {
+ event: 'token',
+ data: {
+ chunk: " I'm",
+ id: 'c5a99314-6b23-488b-84e2-038aa3e87fb5',
+ timestamp: '2025-05-23 15:52:48 UTC'
+ }
+ }
+ {
+ event: 'token',
+ data: {
+ chunk: ' here',
+ id: 'c5a99314-6b23-488b-84e2-038aa3e87fb5',
+ timestamp: '2025-05-23 15:52:48 UTC'
+ }
+ }
+
+ // this response is abbreviated
+
+ {
+ event: 'end',
+ data: { result: { session_id: 'test-session', outputs: [Array] } }
+ }
+ ```
+
+
+
+## Retrieve Langflow logs with the TypeScript client
+
+To retrieve [Langflow logs](/logging), you must enable log retrieval on your Langflow server by including the following values in your Langflow `.env` file:
+
+```text
+LANGFLOW_ENABLE_LOG_RETRIEVAL=True
+LANGFLOW_LOG_RETRIEVER_BUFFER_SIZE=10000
+LANGFLOW_LOG_LEVEL=DEBUG
+```
+
+The following example script starts streaming logs in the background, and then runs a flow so you can monitor the flow run:
+
+```tsx
+import { LangflowClient } from "@datastax/langflow-client";
+
+const baseUrl = "http://localhost:7863";
+const flowId = "86f0bf45-0544-4e88-b0b1-8e622da7a7f0";
+
+async function runFlow(client: LangflowClient) {
+ const input = "Is anyone there?";
+ const response = await client.flow(flowId).run(input);
+ console.log('Flow response:', response);
+}
+
+async function main() {
+ const client = new LangflowClient({ baseUrl: baseUrl });
+
+ // Start streaming logs
+ console.log('Starting log stream...');
+ for await (const log of await client.logs.stream()) {
+ console.log('Log:', log);
+ }
+
+ // Run the flow
+ await runFlow(client);
+
+}
+
+main().catch(console.error);
+```
+
+Replace the following:
+
+* `baseUrl`: The URL of your Langflow server.
+* `flowId`: The ID of the flow you want to run.
+* `input`: The chat input message you want to send to trigger the flow, assuming the flow has a **Chat Input** component.
+
+Logs begin streaming indefinitely, and the flow runs once.
+
+
+Result
+
+The following example result is truncated for readability, but you can follow the messages to see how the flow instantiates its components, configures its model, and processes the outputs.
+
+The `FlowResponse` object, at the end of the stream, is returned to the client with the flow result in the `outputs` array.
+
+```text
+Starting log stream...
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.006Z,
+ message: '2025-05-30T07:49:16.006127-0400 DEBUG Instantiating ChatInput of type component\n'
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.029Z,
+ message: '2025-05-30T07:49:16.029957-0400 DEBUG Instantiating Prompt of type component\n'
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.049Z,
+ message: '2025-05-30T07:49:16.049520-0400 DEBUG Instantiating ChatOutput of type component\n'
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.069Z,
+ message: '2025-05-30T07:49:16.069359-0400 DEBUG Instantiating OpenAIModel of type component\n'
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.086Z,
+ message: "2025-05-30T07:49:16.086426-0400 DEBUG Running layer 0 with 2 tasks, ['ChatInput-xjucM', 'Prompt-I3pxU']\n"
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.101Z,
+ message: '2025-05-30T07:49:16.101766-0400 DEBUG Building Chat Input\n'
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.113Z,
+ message: '2025-05-30T07:49:16.113343-0400 DEBUG Building Prompt\n'
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.131Z,
+ message: '2025-05-30T07:49:16.131423-0400 DEBUG Logged vertex build: 6bd9fe9c-5eea-4f05-a96d-f6de9dc77e3c\n'
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.143Z,
+ message: '2025-05-30T07:49:16.143295-0400 DEBUG Logged vertex build: 39c68ec9-3859-4fff-9b14-80b3271f8fbf\n'
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.188Z,
+ message: "2025-05-30T07:49:16.188730-0400 DEBUG Running layer 1 with 1 tasks, ['OpenAIModel-RtlZm']\n"
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.201Z,
+ message: '2025-05-30T07:49:16.201946-0400 DEBUG Building OpenAI\n'
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:16.216Z,
+ message: '2025-05-30T07:49:16.216622-0400 INFO Model name: gpt-4.1-mini\n'
+}
+Flow response: FlowResponse {
+ sessionId: '86f0bf45-0544-4e88-b0b1-8e622da7a7f0',
+ outputs: [ { inputs: [Object], outputs: [Array] } ]
+}
+Log: Log {
+ timestamp: 2025-05-30T11:49:18.094Z,
+ message: `2025-05-30T07:49:18.094364-0400 DEBUG Vertex OpenAIModel-RtlZm, result: , object: {'text_output': "Hey there! I'm here and ready to help you build something awesome with AI. What are you thinking about creating today?"}\n`
+}
+```
+
+
+
+For more information, see [Logs endpoints](/api-logs).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/API-Reference/workflows-api.mdx b/docs/versioned_docs/version-1.9.0/API-Reference/workflows-api.mdx
new file mode 100644
index 0000000000..60ffab4674
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/API-Reference/workflows-api.mdx
@@ -0,0 +1,322 @@
+---
+title: Workflow API (Beta)
+slug: /workflow-api
+---
+import CodeSnippet from '@site/src/components/CodeSnippet';
+import exampleWorkflowsApiExampleSynchronousRequest from '!!raw-loader!@site/docs/API-Reference/curl-examples/workflows-api/example-synchronous-request.sh';
+import exampleWorkflowsApiExampleAsynchronousRequest from '!!raw-loader!@site/docs/API-Reference/curl-examples/workflows-api/example-asynchronous-request.sh';
+import exampleWorkflowsApiExampleRequest from '!!raw-loader!@site/docs/API-Reference/curl-examples/workflows-api/example-request.sh';
+import exampleWorkflowsApiExampleRequest2 from '!!raw-loader!@site/docs/API-Reference/curl-examples/workflows-api/example-request-2.sh';
+import examplePythonWorkflowsApiExampleSynchronousRequest from '!!raw-loader!@site/docs/API-Reference/python-examples/workflows-api/example-synchronous-request.py';
+import examplePythonWorkflowsApiExampleAsynchronousRequest from '!!raw-loader!@site/docs/API-Reference/python-examples/workflows-api/example-asynchronous-request.py';
+import examplePythonWorkflowsApiExampleRequest from '!!raw-loader!@site/docs/API-Reference/python-examples/workflows-api/example-request.py';
+import examplePythonWorkflowsApiExampleRequest2 from '!!raw-loader!@site/docs/API-Reference/python-examples/workflows-api/example-request-2.py';
+import exampleJavascriptWorkflowsApiExampleSynchronousRequest from '!!raw-loader!@site/docs/API-Reference/javascript-examples/workflows-api/example-synchronous-request.js';
+import exampleJavascriptWorkflowsApiExampleAsynchronousRequest from '!!raw-loader!@site/docs/API-Reference/javascript-examples/workflows-api/example-asynchronous-request.js';
+import exampleJavascriptWorkflowsApiExampleRequest from '!!raw-loader!@site/docs/API-Reference/javascript-examples/workflows-api/example-request.js';
+import exampleJavascriptWorkflowsApiExampleRequest2 from '!!raw-loader!@site/docs/API-Reference/javascript-examples/workflows-api/example-request-2.js';
+
+
+
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialAPISetup from '@site/docs/_partial-api-setup.mdx';
+
+:::warning Beta Feature
+The Workflow API is currently in **Beta**.
+The API endpoints and response formats may change in future releases.
+:::
+
+The Workflow API provides programmatic access to execute Langflow workflows synchronously or asynchronously.
+Synchronous requests receive complete results immediately upon completion.
+Asynchronous requests are queued in the background and will run until complete, or a request is issued to the [Stop Workflow endpoint](#stop-workflow-endpoint).
+
+The Workflow API is part of the Langflow Developer v2 API and offers enhanced workflow execution capabilities compared to the v1 `/run` endpoint.
+
+
+
+## Execute workflows endpoint (synchronous or asynchronous)
+
+**Endpoint:**
+
+```
+POST /api/v2/workflows
+```
+
+**Description:** Execute a workflow synchronously and receive complete results immediately upon completion.
+Set `background=false` to make the request synchronous.
+
+### Example synchronous request
+
+Execute a workflow synchronously and receive complete results immediately:
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+### Example asynchronous request
+
+For long-running workflows, set `background=true` to get a `job_id` immediately, and then poll the status [using the GET endpoint](#get-workflow-status-endpoint) until the job is complete.
+
+To stop a job, send a POST request to the [Stop workflow endpoint](#stop-workflow-endpoint).
+
+:::tip
+The asynchronous request contains `stream` parameter, but streaming is not yet supported. The parameter is included for future compatibility.
+:::
+
+**Example request:**
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+**Response:**
+
+```json
+{
+ "job_id": "job_id_1234567890",
+ "created_timestamp": "2025-01-15T10:30:00Z",
+ "status": "queued",
+ "errors": []
+}
+```
+
+### Request body
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `flow_id` | `string` | Yes | - | The ID or endpoint name of the flow to execute. |
+| `flow_version` | `string` | No | - | Optional version hash to pin to a specific flow version. |
+| `background` | `boolean` | No | `false` | Must be `false` for synchronous execution. |
+| `inputs` | `object` | No | `{}` | Inputs for the workflow execution. Uses component identifiers with dot notation (e.g., `ChatInput-abc.input_value`). See [Component identifiers and input structure](#component-identifiers-and-input-structure) for detailed information. |
+
+### Example response
+
+```json
+{
+ "flow_id": "flow_67ccd2be17f0819081ff3bb2cf6508e60bb6a6b452d3795b",
+ "job_id": "job_id_1234567890",
+ "object": "response",
+ "created_at": 1741476542,
+ "status": "completed",
+ "errors": [],
+ "inputs": {
+ "ChatInput-abc.input_type": "chat",
+ "ChatInput-abc.input_value": "what is 2+2",
+ "ChatInput-abc.session_id": "session-123"
+ },
+ "outputs": {
+ "ChatOutput-xyz": {
+ "type": "message",
+ "component_id": "ChatOutput-xyz",
+ "status": "completed",
+ "content": "2 + 2 equals 4."
+ }
+ },
+ "metadata": {}
+}
+```
+
+### Response body
+
+The response includes an `outputs` field containing component-level results. Each output has a `type` field indicating the type of content:
+
+| Type | Description | Example |
+|------|-------------|---------|
+| `message` | Text message content. | Chat responses, summaries |
+| `image` | Image URL or data. | Generated images, processed images |
+| `sql` | SQL query results. | Database query outputs |
+| `data` | Structured data. | JSON objects, arrays |
+| `file` | File reference. | Generated documents, reports |
+
+## Get workflow status endpoint
+
+**Endpoint:** `GET /api/v2/workflows`
+
+**Description:** Retrieve the status and results of a workflow execution by job ID.
+
+### Example request
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+### Query parameters
+
+| Parameter | Type | Required | Description |
+|-----------|------|----------|-------------|
+| `job_id` | `string` | Yes | The job ID returned from a workflow execution. |
+| `stream` | `boolean` | No | If `true`, returns server-sent events stream. Default: `false`. |
+| `sequence_id` | `integer` | No | Optional sequence ID to resume streaming from a specific point. |
+
+### Example response
+
+```json
+{
+ "flow_id": "flow_67ccd2be17f0819081ff3bb2cf6508e60bb6a6b452d3795b",
+ "job_id": "job_id_1234567890",
+ "object": "response",
+ "created_at": 1741476542,
+ "status": "completed",
+ "errors": [],
+ "outputs": {
+ "ChatOutput-xyz": {
+ "type": "message",
+ "component_id": "ChatOutput-xyz",
+ "status": "completed",
+ "content": "Processing complete..."
+ }
+ },
+ "input": [
+ {
+ "type": "text",
+ "data": "Input text prompt for the workflow execution",
+ "role": "User"
+ }
+ ],
+ "metadata": {}
+}
+```
+
+### Response body
+
+The response includes a `status` field that indicates the current state of the workflow execution:
+
+| Status | Description |
+|--------|-------------|
+| `queued` | Job is queued and waiting to start. |
+| `in_progress` | Job is currently executing. |
+| `completed` | Job completed successfully. |
+| `failed` | Job failed during execution. |
+| `error` | Job encountered an error. |
+
+## Stop workflow endpoint
+
+**Endpoint:** `POST /api/v2/workflows/stop`
+
+**Description:** Stop a running workflow execution by job ID.
+
+### Example request
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+### Request body
+
+| Field | Type | Required | Default | Description |
+|-------|------|----------|---------|-------------|
+| `job_id` | `string` | Yes | - | The job ID of the workflow to stop. |
+
+### Example response
+
+```json
+{
+ "job_id": "job_id_1234567890",
+ "message": "Job job_id_1234567890 cancelled successfully."
+}
+```
+
+## Component identifiers and input structure
+
+The Workflows API uses component identifiers with dot notation to specify inputs for individual components in your workflow. This allows you to pass values to specific components and override component parameters.
+
+Component identifiers use the format `{component_id}.{parameter_name}`.
+When making requests to the Workflows API, include component identifiers in the `inputs` object.
+For example, this demonstrates targeting multiple components and their parameters in a single request.
+
+```json
+{
+ "flow_id": "your-flow-id",
+ "inputs": {
+ "ChatInput-abc.input_type": "chat",
+ "ChatInput-abc.input_value": "what is 2+2",
+ "ChatInput-abc.session_id": "session-123",
+ "OpenSearchComponent-xyz.opensearch_url": "https://opensearch:9200",
+ "LLMComponent-123.temperature": 0.7,
+ "LLMComponent-123.max_tokens": 100
+ }
+}
+```
+
+To find the component ID in the Langflow UI, open your flow in Langflow, click the component, and then click **Controls**. The component ID is at the top of the **Controls** pane.
+
+You can override any component's parameters.
+
+## Error handling
+
+The API uses standard HTTP status codes to indicate success or failure:
+
+| Status Code | Description |
+|-------------|-------------|
+| `200 OK` | Request successful. |
+| `400 Bad Request` | Invalid request parameters. |
+| `401 Unauthorized` | Invalid or missing API key. |
+| `404 Not Found` | Flow not found or developer API disabled. |
+| `500 Internal Server Error` | Server error during execution. |
+| `501 Not Implemented` | Endpoint not yet implemented. |
+
+### Error response format
+
+```json
+{
+ "detail": "Error message describing what went wrong"
+}
+```
diff --git a/docs/versioned_docs/version-1.9.0/Agents/agents-tools.mdx b/docs/versioned_docs/version-1.9.0/Agents/agents-tools.mdx
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--- /dev/null
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@@ -0,0 +1,219 @@
+---
+title: Configure tools for agents
+slug: /agents-tools
+---
+
+import Icon from "@site/src/components/icon";
+
+By default, [Langflow agents](/agents) only include the functionality built-in to their base LLM.
+
+You can attach tools to agents to provide access to additional, targeted functionality.
+For example, tools can be used to create domain-specific agents, such as a customer support agent that can access a company's knowledge base, a financial agent that can retrieve stock prices, or a math tutor agent that can use advanced math functions to solve complex equations.
+
+## Attach tools
+
+To attach a tool to an agent, you connect any component's **Tool** output to the **Agent** component's **Tools** input.
+
+Some components emit **Tool** output by default.
+For all other components, you must enable **Tool Mode** in the [component's header menu](/concepts-components#component-menus).
+Then, you can connect the tool to the agent.
+
+You can connect multiple tools to one agent, and each tool can have multiple actions (functions) that the agent can call.
+
+When you run your flow, the agent decides when to call on certain tools, if it determines that a tool can help it respond to the user's prompt.
+
+### Edit a tool's actions {#edit-a-tools-actions}
+
+When you attach components to an agent as tools, each tool can have multiple actions (functions) that the agent can call.
+Available actions are listed in each tool component's **Actions** list.
+
+You can change each action's labels, descriptions, and availability to help the agent understand how to use the tool and prevent it from using irrelevant or undesired actions.
+
+:::tip
+If an agent seems to be using a tool incorrectly, try editing the actions metadata to clarify the tool's purpose and disable unnecessary actions.
+
+You can also try using a **Prompt Template** component to pass additional instructions or examples to the agent.
+:::
+
+To view and edit a tool's actions, click **Edit Tool Actions** on the tool component.
+
+The following information is provided for each action:
+
+* **Enabled**: A checkbox that determines whether the action is available to the agent.
+If selected, the action is enabled.
+If not selected, the action is disabled.
+
+* **Name**: A human-readable string name for the action, such as `Fetch Content`. This cannot be changed.
+
+* **Description**: A human-readable description of the action's purpose, such as `Fetch content from web pages recursively`.
+To edit this value, double-click the action's row to open the edit pane.
+Changes are saved automatically when you click out of the field or close the dialog.
+
+* **Slug**: An encoded name for the action, usually the same as the name but in snake case, such as `fetch_content`.
+To edit this value, double-click the action's row to open the edit pane.
+Changes are saved automatically when you click out of the field or close the dialog.
+
+Some actions allow you to provide fixed values for their inputs.
+Typically, you want to leave these blank so the agent can provide its own values.
+However, you might use a fixed value if you're trying to debug an agent's behavior or your use case requires a fixed input for an action.
+
+## Use an agent as a tool
+
+To create multi-agent flows, you can set another **Agent** component to **Tool Mode**, and then attach that agent as a tool for your primary **Agent** component.
+
+To try this for yourself, add an additional agent to the **Simple Agent** template:
+
+1. Create a flow based on the **Simple Agent** template.
+2. Add a second **Agent** component to the flow.
+3. Add your **OpenAI API Key** to both **Agent** components.
+4. In the second **Agent** component, change the model to `gpt-4.1`, and then enable **Tool Mode**.
+5. Click **Edit Tool Actions** to [edit the tool's actions](#edit-a-tools-actions).
+
+ For this example, change the action's slug to `Agent-gpt-41`, and set the description to `Use the gpt-4.1 model for complex problem solving`.
+ This lets the primary agent know that this tool uses the `gpt-4.1` model, which could be helpful for tasks requiring a larger context window, such as large scrape and search tasks.
+
+ As another example, you could attach several specialized models to a primary agent, such as agents that are trained on certain tasks or domains, and then the primary agent would call each specialized agent as needed to respond to queries.
+
+ You can also enable and disable tools if you want to limit the available toolset.
+
+6. Connect the new agent's **Toolset** port to the existing agent's **Tools** port.
+
+ 
+
+## Add custom components as tools {#components-as-tools}
+
+An agent can use [custom components](/components-custom-components) as tools.
+
+1. To add a custom component to an agent flow, click **New Custom Component** in the **Core components** or **Bundles** menus.
+
+2. Enter Python code into the **Code** pane to create the custom component.
+
+ If you don't already have code for a custom component, you can use the following code snippet as an example before creating your own.
+
+
+ Text Analyzer custom component
+
+ This code creates a text analyzer component.
+
+ ```python
+ from langflow.custom import Component
+ from langflow.io import MessageTextInput, Output
+ from langflow.schema import Data
+ import re
+
+ class TextAnalyzerComponent(Component):
+ display_name = "Text Analyzer"
+ description = "Analyzes and transforms input text."
+ documentation: str = "http://docs.langflow.org/components/custom"
+ icon = "chart-bar"
+ name = "TextAnalyzerComponent"
+
+ inputs = [
+ MessageTextInput(
+ name="input_text",
+ display_name="Input Text",
+ info="Enter text to analyze",
+ value="Hello, World!",
+ tool_mode=True,
+ ),
+ ]
+
+ outputs = [
+ Output(display_name="Analysis Result", name="output", method="analyze_text"),
+ ]
+
+ def analyze_text(self) -> Data:
+ text = self.input_text
+
+ # Perform text analysis
+ word_count = len(text.split())
+ char_count = len(text)
+ sentence_count = len(re.findall(r'\w+[.!?]', text))
+
+ # Transform text
+ reversed_text = text[::-1]
+ uppercase_text = text.upper()
+
+ analysis_result = {
+ "original_text": text,
+ "word_count": word_count,
+ "character_count": char_count,
+ "sentence_count": sentence_count,
+ "reversed_text": reversed_text,
+ "uppercase_text": uppercase_text
+ }
+
+ data = Data(value=analysis_result)
+ self.status = data
+ return data
+ ```
+
+
+3. Enable **Tool Mode** in the custom component.
+4. Connect the custom component's tool output to the **Agent** component's **Tools** input.
+5. Open the **Playground** and instruct the agent, `Use the text analyzer on this text: "Agents really are thinking machines!"`
+
+ Based on your instruction, the agent should call the `analyze_text` action and return the result.
+ For example:
+
+ ```
+ gpt-4o
+ Finished
+ 0.6s
+ Here is the analysis of the text "Agents really are thinking machines!":
+ Original Text: Agents really are thinking machines!
+ Word Count: 5
+ Character Count: 36
+ Sentence Count: 1
+ Reversed Text: !senihcam gnikniht era yllaer stnegA
+ Uppercase Text: AGENTS REALLY ARE THINKING MACHINES!
+ ```
+
+## Make any component a tool
+
+If the component you want to use as a tool doesn't have a **Tool Mode** button, add `tool_mode=True` to one of the component's inputs, and connect the new **Toolset** output to the agent's **Tools** input.
+
+Langflow supports **Tool Mode** for the following data types:
+
+* `DataInput`
+* `DataFrameInput`
+* `PromptInput`
+* `MessageTextInput`
+* `MultilineInput`
+* `DropdownInput`
+
+For example, the example code in [Use custom components as tools](#components-as-tools) included `tool_mode=True` to the `MessageTextInput` input so the custom component could be used as a tool:
+
+```python
+inputs = [
+ MessageTextInput(
+ name="input_text",
+ display_name="Input Text",
+ info="Enter text to analyze",
+ value="Hello, World!",
+ tool_mode=True,
+ ),
+]
+```
+
+## Use flows as tools
+
+An agent can use your other flows as tools with the [**Run Flow** component](/run-flow).
+
+1. Add a **Run Flow** component to your flow.
+2. Select the flow you want the agent to use as a tool.
+3. Enable **Tool Mode**.
+The selected flow becomes an [action](#edit-a-tools-actions) in the **Run Flow** component.
+4. Connect the **Run Flow** component's **Tool** output to the **Agent** component's **Tools** input.
+5. Open the **Playground**, and then ask the agent, `What tools are you using to answer my questions?`
+Your flow should be visible in the response as an available tool.
+6. Ask the agent a question that specifically uses the connected flow as a tool.
+The connected flow returns an answer based on your question.
+
+
+
+## See also
+
+* [Agent components](/components-agents)
+* [Use Langflow as an MCP client](/mcp-client)
+* [Use Langflow as an MCP server](/mcp-server)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Agents/agents.mdx b/docs/versioned_docs/version-1.9.0/Agents/agents.mdx
new file mode 100644
index 0000000000..c3c8b5ff01
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Agents/agents.mdx
@@ -0,0 +1,156 @@
+---
+title: Use Langflow agents
+slug: /agents
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialAgentsWork from '@site/docs/_partial-agents-work.mdx';
+import PartialGlobalModelProviders from '@site/docs/_partial-global-model-providers.mdx';
+
+Langflow's [**Agent** component](/components-agents) is critical for building agent flows.
+This component provides everything you need to create an agent, including multiple Large Language Model (LLM) providers, tool calling, and custom instructions.
+It simplifies agent configuration so you can focus on application development.
+
+
+
+## Use the Agent component in a flow
+
+The following steps explain how to create an agent flow in Langflow from a blank flow.
+For a prebuilt example, use the **Simple Agent** template or the [Langflow quickstart](/get-started-quickstart).
+
+1. Click **New Flow**, and then click **Blank Flow**.
+2. Add an **Agent** component to your flow.
+3.
+4. Select the model that you want to use from the **Language Model** dropdown.
+If your preferred model isn't listed, make sure it's enabled in the **Models** configuration.
+For more information, see [Agent component parameters](#agent-component-parameters).
+5. Add [**Chat Input** and **Chat Output** components](/chat-input-and-output) to your flow, and then connect them to the **Agent** component.
+
+ At this point, you have created a basic LLM-based chat flow that you can test in the **Playground**.
+ However, this flow only chats with the LLM.
+ To enhance this flow and make it truly agentic, add some tools, as explained in the next steps.
+
+ 
+
+6. Add **Web Search**, **URL**, and **Calculator** components to your flow.
+7. Enable **Tool Mode** in the **Web Search**, **URL**, and **Calculator** components:
+
+ 1. Click the **Web Search** component to expose the [component's header menu](/concepts-components#component-menus), and then enable **Tool Mode**.
+ 2. Repeat for the **URL** and **Calculator** components.
+ 3. Connect the **Toolset** port for each tool component to the **Tools** port on the **Agent** component.
+
+ **Tool Mode** makes a component into a tool by modifying the component's inputs.
+ With **Tool Mode** enabled, a component can accept requests from an **Agent** component to use the component's available actions as tools.
+
+ When in **Tool Mode**, a component has a **Toolset** port that you must connect to an **Agent** component's **Tools** port if you want to allow the agent to use that component's actions as tools.
+
+ For more information, see [Configure tools for agents](/agents-tools).
+
+ 
+
+8. Open the **Playground**, and then ask the agent, `What tools are you using to answer my questions?`
+
+ The agent should respond with a list of the connected tools.
+ It may also include built-in tools.
+
+ ```text
+ I use a combination of my built-in knowledge (up to June 2024) and a set of external tools to answer your questions. Here are the main types of tools I can use:
+ Web Search & Content Fetching: I can fetch and summarize content from web pages, including crawling links recursively.
+ News Search: I can search for recent news articles using Google News via RSS feeds.
+ Calculator: I can perform arithmetic calculations and evaluate mathematical expressions.
+ Date & Time: I can provide the current date and time in various time zones.
+ These tools help me provide up-to-date information, perform calculations, and retrieve specific data from the internet when needed. If you have a specific question, let me know, and I'll use the most appropriate tool(s) to help!
+ ```
+
+9. To test a specific tool, ask the agent a question that uses one of the tools, such as `Summarize today's tech news`.
+
+ To help you debug and test your flows, the **Playground** displays the agent's tool calls, the provided input, and the raw output the agent received before generating the summary.
+ With the given example, the agent should call the **Web Search** component with **Search Mode** set to **News**.
+
+You've successfully created a basic agent flow that uses some generic tools.
+
+To continue building on this tutorial, try connecting other tool components or [use Langflow as an MCP client](/mcp-client) to support more complex and specialized tasks.
+
+For a multi-agent example, see [Use an agent as a tool](/agents-tools#use-an-agent-as-a-tool).
+
+## Agent component parameters
+
+You can configure the **Agent** component to use your preferred provider and model, custom instructions, and tools.
+
+
+
+### Provider and model
+
+Use the **Language Model** (`agent_llm`) setting to select the LLM that you want the agent to use.
+
+
+
+To use a model with the **Agent** component, select the model in the **Agent** component's **Language Model** field.
+
+The **Language Model** field lists all language models that you've configured globally. If a provider doesn't have any language models available, they aren't listed.
+For example, if a provider offers only embeddings models, those models aren't listed on the **Agent** component.
+
+To access other providers or models, you can do either of the following:
+
+* Connect any [language model component](/components-models) to the **Agent** component's **Language Model** port. This option allows you to connect a custom language model component to use models that aren't available in the global model providers list.
+* Configure additional providers in the **Models** pane, and then select the model from the **Language Model** dropdown.
+
+If you need to generate embeddings in your flow, use an [embedding model component](/components-embedding-models).
+
+### Agent instructions and input
+
+In the **Agent Instructions** (`system_prompt`) field, you can provide custom instructions that you want the **Agent** component to use for every conversation.
+
+These instructions are applied in addition to the **Input** (`input_value`), which can be entered directly or provided through another component, such as a **Chat Input** component.
+
+### Tools
+
+Agents are most useful when they have the appropriate tools available to complete requests.
+
+An **Agent** component can use any Langflow component as a tool, including other agents and MCP servers.
+
+To attach a component as a tool, you must enable **Tool Mode** on the component that you want to attach, and then attach it to the **Agent** component's **Tools** port.
+For more information, see [Configure tools for agents](/agents-tools).
+
+:::tip
+To allow agents to use tools from MCP servers, use the [**MCP Tools** component](/mcp-tools).
+:::
+
+### Agent memory
+
+Langflow agents have built-in chat memory that is enabled by default.
+This memory allows them to retrieve and reference messages from previous conversations, maintaining a rolling context window for each chat session ID.
+
+Chat memories are grouped by [session ID (`session_id`)](/session-id).
+It is recommended to use custom session IDs if you need to segregate chat memory for different users or applications that run the same flow.
+
+By default, the **Agent** component uses your Langflow installation's storage, and it retrieves a limited number of chat messages, which you can configure with the **Number of Chat History Messages** parameter.
+
+The **Message History** component isn't required for default chat memory, but it is required if you want to use external chat memory like Mem0.
+Additionally, the **Message History** component provides more options for sorting, filtering, and limiting memories. Although, most of these options are built-in to the **Agent** component with default values.
+
+For more information, see [Store chat memory](/memory#store-chat-memory) and [**Message History** component](/message-history).
+
+### Additional parameters
+
+With the **Agent** component, the available parameters can change depending on the selected provider and model, including support for additional modes, arguments, or features like chat memory and temperature.
+For example:
+
+* **Current Date** (`add_current_date_tool`): When enabled (`true`), this setting adds a tool to the agent that can retrieve the current date.
+* **Handle Parse Errors** (`handle_parsing_errors`): When enabled (`true`), this setting allows the agent to fix errors, like typos, when analyzing user input.
+* **Verbose** (`verbose`): When enabled (`true`), this setting records detailed logging output for debugging and analysis.
+
+
+
+## Agent component output
+
+The **Agent** component outputs a **Response** (`response`) that is [`Message` data](/data-types#message) containing the agent's raw response to the query.
+
+Typically, this is passed to a **Chat Output** component to return the response in a human-readable format.
+It can also be passed to other components if you need to process the response further before, or in addition to, returning it to the user.
+
+## See also
+
+* [**Agent** and **MCP Tools** components](/components-agents)
+* [Configure tools for agents](/agents-tools)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Agents/langflow-mcp-client.mdx b/docs/versioned_docs/version-1.9.0/Agents/langflow-mcp-client.mdx
new file mode 100644
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@@ -0,0 +1,33 @@
+---
+title: Langflow MCP Client for coding agents
+slug: /langflow-mcp-client
+---
+
+import Icon from "@site/src/components/icon";
+
+Connect coding agents such as **Bob (IBM)** and **Claude Code** to a Langflow instance so they can build and run flows over MCP.
+
+This page describes the **Langflow MCP Client** entry in **Settings**. It is different from Langflow's other MCP options.
+
+* [**Use Langflow as an MCP client**](/mcp-client): Use the **MCP Tools** component in flows so agent components _on the canvas_ can call external MCP servers or Langflow MCP URLs.
+* [**Use Langflow as an MCP server**](/mcp-server): Use the **MCP Servers** page in Langflow to expose your Langflow project as an MCP server to desktop clients such as Cursor or Windsurf.
+
+## Connect Bob (IBM) or Claude Code
+
+To connect Langflow to a coding agent, do the following:
+
+1. Click your profile icon, select **Settings**, and then click **Langflow MCP Client**.
+2. Select the coding agent you want to connect to Langflow, and follow the steps shown in the UI.
+
+ All client configurations run `lfx-mcp` with `uvx`, and set `LANGFLOW_SERVER_URL` to your Langflow server URL and `LANGFLOW_API_KEY` to a Langflow API key of your choice.
+
+ * To connect to **Bob (IBM)**, paste the configuration JSON into `~/.bob/settings/mcp_settings.json`, or use **Edit Project MCP** for a project-only setup.
+ * To connect to **Claude Code**, run the `claude mcp add` command shown on the page, or manually add the JSON configuration to `~/.claude.json`.
+
+ If you do not set `LANGFLOW_API_KEY`, the coding agent can log in with the Langflow username and password you use to sign in to the Langflow web UI for that server.
+
+## See also
+
+* [Use Langflow as an MCP client](/mcp-client)
+* [Use Langflow as an MCP server](/mcp-server)
+* [**MCP Tools** component](/mcp-tools)
diff --git a/docs/versioned_docs/version-1.9.0/Agents/mcp-client.mdx b/docs/versioned_docs/version-1.9.0/Agents/mcp-client.mdx
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--- /dev/null
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@@ -0,0 +1,290 @@
+---
+title: Use Langflow as an MCP client
+slug: /mcp-client
+---
+
+import Icon from "@site/src/components/icon";
+import McpIcon from '@site/static/logos/mcp-icon.svg';
+import PartialMcpNodeTip from '@site/docs/_partial-mcp-node-tip.mdx';
+import PartialMcpToolsAddSteps from '@site/docs/_partial-mcp-tools-add-steps.mdx';
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Langflow integrates with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) as both an MCP server and an MCP client.
+
+This page describes how to use Langflow as an MCP client with the [**MCP Tools** component](#use-the-mcp-tools-component) and connected [MCP servers](#manage-connected-mcp-servers).
+
+For information about using Langflow as an MCP server, see [Use Langflow as an MCP server](/mcp-server).
+
+## Use the MCP tools component {#use-the-mcp-tools-component}
+
+The **MCP Tools** component connects to an MCP server so that a [Langflow agent](/agents) can use the server's tools when responding to user queries.
+
+Register MCP servers, and then add **MCP Tools** from the **MCP** sidebar as described in [Connect to a non-Langflow MCP server](#mcp-stdio-mode).
+
+This component has two modes, depending on the type of server you want to access:
+
+* [Connect to a non-Langflow MCP server](#mcp-stdio-mode) with a JSON configuration file, server start command, or HTTP/SSE URL to access tools provided by external, non-Langflow MCP servers.
+* [Connect to a Langflow MCP server](#mcp-http-mode) to use flows from your [Langflow projects](/concepts-flows#projects) as MCP tools.
+
+### Connect to a non-Langflow MCP server {#mcp-stdio-mode}
+
+
+
+
+
+4. In the **MCP Server** field on the **MCP Tools** component, select the server you registered.
+
+ New servers are registered in **Settings** > **MCP Servers**, or in the **MCP** sidebar > **Add MCP Server**, using one of the following connection types:
+
+ * **JSON**: Paste the MCP server's JSON configuration object into the field, including required and optional parameters that you want to use, and then click **Add Server**.
+ * **STDIO**: Enter the MCP server's **Name**, **Command**, and any **Arguments** and **Environment Variables** the server uses, and then click **Add Server**.
+ For example, to start a [Fetch](https://github.com/modelcontextprotocol/servers/tree/main/src/fetch) server, the **Command** is `uvx mcp-server-fetch`.
+ * **HTTP/SSE**: Enter your MCP server's **Name**, **URL**, and any **Headers** and **Environment Variables** the server uses, and then click **Add Server**.
+ The default **URL** for Langflow MCP servers is `http://localhost:7860/api/v1/mcp/project/PROJECT_ID/streamable` or `http://localhost:7860/api/v1/mcp/streamable`. For more information, see [Connect to a Langflow MCP server](#mcp-http-mode).
+
+5. To configure headers for your MCP server, enter each header in the **Headers** fields as key-value pairs.
+ You can use [global variables](/configuration-global-variables) in header values by entering the global variable name as the header value.
+ For more information, see [Use global variables in MCP server headers](#use-global-variables-in-mcp-server-headers).
+
+6. To use environment variables in your server command, enter each variable in the **Env** fields as key-value pairs.
+
+7. In the **Tool** field, select a tool that you want this component to use, or leave the field blank to allow access to all tools provided by the MCP server.
+
+ If you select a specific tool, you might need to configure additional tool-specific fields. For information about tool-specific fields, see your MCP server's documentation.
+
+ At this point, the **MCP Tools** component is serving a tool from the connected server, but nothing is using the tool. The next steps explain how to make the tool available to an [**Agent** component](/components-agents) so that the agent can use the tool in its responses.
+
+8. In the [component's header menu](/concepts-components#component-menus), enable **Tool mode** so you can use the component with an agent.
+
+9. Connect the **MCP Tools** component's **Toolset** port to an **Agent** component's **Tools** port.
+
+ If not already present in your flow, make sure you also attach **Chat Input** and **Chat Output** components to the **Agent** component.
+
+ 
+
+10. Test your flow to make sure the MCP server is connected and the selected tool is used by the agent. Open the **Playground**, and then enter a prompt that uses the tool you connected through the **MCP Tools** component.
+
+ For example, if you use `mcp-server-fetch` with the `fetch` tool, you could ask the agent to summarize recent tech news. The agent calls the MCP server function `fetch`, and then returns the response.
+
+11. If you want the agent to be able to use more tools, repeat these steps to add more tools components with different servers or tools.
+
+### Connect a Langflow MCP server {#mcp-http-mode}
+
+Every Langflow project runs a separate MCP server that exposes the project's flows as MCP tools.
+For more information about your projects' MCP servers, including exposing flows as MCP tools, see [Use Langflow as an MCP server](/mcp-server).
+
+Langflow MCP servers support both the **streamable HTTP** transport and **Server-Sent Events (SSE)** as a fallback.
+
+To leverage flows-as-tools, register your Langflow MCP endpoint as a server first, and then add the **MCP Tools** component from the **MCP** sidebar.
+
+1. Register the Langflow MCP server.
+ Open **Settings** > **MCP Servers** or the **MCP** section in the flow sidebar, click **Add MCP Server**, and select **HTTP/SSE** mode.
+
+2. In the **MCP URL** field, enter your Langflow server's MCP endpoint.
+ - For project-specific servers: `http://localhost:7860/api/v1/mcp/project/PROJECT_ID/streamable`
+ - For global MCP server: `http://localhost:7860/api/v1/mcp/streamable`
+ - Default for Langflow Desktop: `http://localhost:7868/`
+
+ All flows available from the targeted server are treated as tools. For authentication and project URLs, see [Use Langflow as an MCP server](/mcp-server).
+
+3. Save the server. When the connection succeeds, the server is stored and appears as a tile in the **MCP** sidebar.
+
+4. Add the **MCP Tools** component to the flow.
+
+5. In the [component's header menu](/concepts-components#component-menus), enable **Tool Mode** so you can use the component with an agent.
+
+6. Connect the **MCP Tools** component's **Toolset** port to an **Agent** component's **Tools** port.
+
+7. If not already present in your flow, make sure you also attach **Chat Input** and **Chat Output** components to the **Agent** component.
+
+ 
+
+8. Test your flow to make sure the agent uses your flows to respond to queries. Open the **Playground**, and then enter a prompt that uses a flow that you connected through the **MCP Tools** component.
+
+9. If you want the agent to be able to use more tools, repeat these steps to add more tools components with different servers or tools.
+
+## MCP Tools parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| mcp_server | String | Input parameter. The MCP server to connect to. |
+| tool | String | Input parameter. The specific tool to execute from the connected MCP server. Leave blank to allow access to all tools. |
+| use_cache | Boolean | Input parameter. Enable caching of MCP server and tools to improve performance. Default: `false`. |
+| verify_ssl | Boolean | Input parameter. Enable SSL certificate verification for HTTPS connections. Default: `true`. |
+| response | Table | Output parameter. [`Table`](/data-types#table) containing the response from the executed tool. |
+
+## Manage connected MCP servers
+
+To manage MCP server connections for use in flows, open the **MCP** section in the visual editor, and then click **Manage Servers**, or click your profile icon, select **Settings**, and then click **MCP Servers**.
+
+To add a new MCP server, click **Add MCP Server**, register the server, and then select the server on the **MCP Tools** component as described in [Connect to a non-Langflow MCP server](#mcp-stdio-mode).
+
+Click **More** to edit or delete an MCP server connection.
+
+## Modify MCP server environment variables with the API {#mcp-api-tweaks}
+
+You can modify MCP server environment variables at runtime when running flows through the [Langflow API](/api-reference-api-examples) by tweaking the **MCP Tools** component.
+
+You can include tweaks with any Langflow API request that supports the `tweaks` parameter, such as POST requests to the `/run` or `/webhook` endpoints.
+For more information, see [Input schema (tweaks)](/concepts-publish#input-schema).
+
+To modify the **MCP Tools** component's environment variables with tweaks, do the following:
+
+1. Open the flow that contains your **MCP Tools** component.
+2. To find the **MCP Tools** component's unique ID, in your **MCP Tools** component, click **Controls**.
+The component's ID is displayed in the **Controls** pane, such as `MCPTools-Bzahc`.
+3. Send a POST request to the Langflow server's `/run` endpoint, and include tweaks to the **MCP Tools** component.
+
+ The following examples demonstrate a request structure with the `env` object nested under `mcp_server` in the `tweaks` payload:
+
+
+
+
+ ```python
+ import requests
+ import os
+
+ LANGFLOW_SERVER_ADDRESS = "http://localhost:7860"
+ FLOW_ID = "your-flow-id"
+ LANGFLOW_API_KEY = os.getenv("LANGFLOW_API_KEY")
+ MCP_TOOLS_COMPONENT_ID = "MCPTools-Bzahc"
+
+ url = f"{LANGFLOW_SERVER_ADDRESS}/api/v1/run/{FLOW_ID}?stream=false"
+ headers = {
+ "Content-Type": "application/json",
+ "x-api-key": LANGFLOW_API_KEY
+ }
+ payload = {
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": "What sales data is available to me?",
+ "tweaks": {
+ MCP_TOOLS_COMPONENT_ID: {
+ "mcp_server": {
+ "env": {
+ "API_URL": "https://api.example.com",
+ "API_KEY": "your-mcp-server-api-key",
+ "ENVIRONMENT": "production"
+ }
+ }
+ }
+ }
+ }
+
+ response = requests.post(url, json=payload, headers=headers)
+ print(response.json())
+ ```
+
+
+
+
+ ```typescript
+ const LANGFLOW_SERVER_ADDRESS = "http://localhost:7860";
+ const FLOW_ID = "your-flow-id";
+ const LANGFLOW_API_KEY = process.env.LANGFLOW_API_KEY || "";
+ const MCP_TOOLS_COMPONENT_ID = "MCPTools-Bzahc";
+
+ const url = `${LANGFLOW_SERVER_ADDRESS}/api/v1/run/${FLOW_ID}?stream=false`;
+
+ const response = await fetch(url, {
+ method: "POST",
+ headers: {
+ "Content-Type": "application/json",
+ "x-api-key": LANGFLOW_API_KEY,
+ },
+ body: JSON.stringify({
+ output_type: "chat",
+ input_type: "chat",
+ input_value: "What sales data is available to me?",
+ tweaks: {
+ [MCP_TOOLS_COMPONENT_ID]: {
+ mcp_server: {
+ env: {
+ API_URL: "https://api.example.com",
+ API_KEY: "your-mcp-server-api-key",
+ ENVIRONMENT: "production",
+ },
+ },
+ },
+ },
+ }),
+ });
+
+ const data = await response.json();
+ console.log(data);
+ ```
+
+
+
+
+ ```bash
+ curl --request POST \
+ --url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false" \
+ --header "Content-Type: application/json" \
+ --header "x-api-key: LANGFLOW_API_KEY" \
+ --data '{
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": "What sales data is available to me?",
+ "tweaks": {
+ "MCP_TOOLS_COMPONENT_ID": {
+ "mcp_server": {
+ "env": {
+ "API_URL": "https://api.example.com",
+ "API_KEY": "your-mcp-server-api-key",
+ "ENVIRONMENT": "production"
+ }
+ }
+ }
+ }
+ }'
+ ```
+
+
+
+
+ Replace `MCP_TOOLS_COMPONENT_ID`, `LANGFLOW_API_KEY`, `LANGFLOW_SERVER_ADDRESS`, and `FLOW_ID` with the actual values from your Langflow deployment.
+
+ Langflow doesn't automatically discover or expose which environment variables your MCP server accepts from the **MCP Tools** component.
+ To determine which environment variables your MCP server accepts, see the MCP server's documentation. For example, the [Astra DB MCP server](https://github.com/datastax/astra-db-mcp) requires `ASTRA_DB_APPLICATION_TOKEN` and `ASTRA_DB_API_ENDPOINT`, with an optional variable for `ASTRA_DB_KEYSPACE`, as documented in its repository.
+
+## Use global variables in MCP server headers {#use-global-variables-in-mcp-server-headers}
+
+You can use [global variables](/configuration-global-variables) in MCP server header values to securely store and reference API keys, authentication tokens, and other sensitive values. This is particularly useful for deployment scenarios where you need to pass user-specific credentials at runtime.
+
+Enter a global variable name as the header value, and Langflow resolves the global variable name to its actual value before making the MCP server request. Langflow only passes the token value to your server; it doesn't validate tokens on behalf of your MCP server.
+
+For example, to create a global variable named `TEST_BEARER_TOKEN` for MCP server bearer authentication, do the following:
+
+1. To open the **Global Variables** pane, click your profile icon, select **Settings**, and then click **Global Variables**.
+2. Create a **Credential** global variable named `TEST_BEARER_TOKEN`.
+3. In the **Value** field, enter your MCP server's bearer token value. The value must include the `Bearer` prefix with a space, for example: `Bearer eyJhbG...`.
+4. Click **Save Variable**.
+5. To manage MCP server connections for your Langflow client, click **MCP servers**, and then click **Manage Servers**, or click your profile icon, select **Settings**, and then click **MCP Servers**.
+6. Click **Add MCP Server**.
+7. Select the following:
+ * **Name**: test-mcp-server
+ * **Streamable HTTP/SSE URL**: Your MCP server's URL, such as `http://127.0.0.1:8000/mcp`.
+ * **Headers**: In the key field, enter the literal string `Authorization`. For the key's value, enter `TEST_BEARER_TOKEN`, or the exact name of your global variable.
+8. Click **Create Server**.
+
+ If the connection succeeds, Langflow shows the number of tools exposed by the server.
+
+ After creating the server and global variable, you can connect to the server with the **MCP Tools** component, as explained in the next steps.
+
+9. Add the **MCP Tools** component to a flow.
+10. In the **MCP Tools** component, confirm the **MCP Server** is set to the server you created.
+The MCP server configuration already includes the headers you configured earlier, so no further configuration is needed in the component. The global variable `TEST_BEARER_TOKEN` is automatically resolved when the component makes requests to the MCP server.
+
+11. Optional: To override headers or add additional headers to the **MCP Tools** component, click the component to view the **Headers** parameter in the [component inspection panel](/concepts-components#component-menus), and then add header key values. Headers configured in the component take precedence over the headers configured in the MCP server settings.
+
+12. Test your flow to make sure the agent uses your server to respond to queries. Open the **Playground**, and then enter a prompt that uses a tool that you connected through the **MCP Tools** component.
+
+ Langflow automatically resolves `TEST_BEARER_TOKEN` to its actual value before sending the request to the MCP server. When your MCP server receives the request, the `Authorization` header contains the resolved token value.
+
+## See also
+
+- [Langflow MCP Client](./langflow-mcp-client.mdx)
+- [Use Langflow as an MCP server](/mcp-server)
+- [Use a DataStax Astra DB MCP server with the MCP Tools component](/mcp-component-astra)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Agents/mcp-component-astra.mdx b/docs/versioned_docs/version-1.9.0/Agents/mcp-component-astra.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Agents/mcp-component-astra.mdx
@@ -0,0 +1,78 @@
+---
+title: Connect an Astra DB MCP server to Langflow
+slug: /mcp-component-astra
+---
+
+import Icon from "@site/src/components/icon";
+import PartialMcpNodeTip from '@site/docs/_partial-mcp-node-tip.mdx';
+import McpIcon from '@site/static/logos/mcp-icon.svg';
+
+This guide demonstrates how to [use Langflow as an MCP client](/mcp-client) by using the **MCP Tools** component to run a [DataStax Astra DB MCP server](https://github.com/datastax/astra-db-mcp) in an agent flow.
+
+1. Install an LTS release of [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).
+
+
+
+2. Create an [OpenAI](https://platform.openai.com/) API key.
+
+3. Create an [Astra DB Serverless (vector) database](https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html#create-vector-database), if you don't already have one.
+
+4. Get your database's Astra DB API endpoint and an Astra application token with the **Database Administrator** role. For more information, see [Generate an application token for a database](https://docs.datastax.com/en/astra-db-serverless/administration/manage-application-tokens.html#database-token).
+
+5. To follow along with this guide, create a flow based on the **Simple Agent** template.
+
+ You can also use an existing flow or create a blank flow.
+
+6. Remove the **URL** tool from the flow.
+
+7. Register the Astra DB MCP server from the **MCP** sidebar and add **MCP Tools** to the canvas.
+Use the following values:
+
+ 1. In the **Name** field, enter a name for the MCP server.
+
+ 2. In the **Command** field, add the following code to connect to an Astra DB MCP server:
+
+ ```bash
+ npx -y @datastax/astra-db-mcp
+ ```
+
+ 3. In the **Environment Variables** fields, add variables for `ASTRA_DB_APPLICATION_TOKEN` and `ASTRA_DB_API_ENDPOINT` with the values from your Astra database.
+
+ :::info
+ Environment variables declared in your Langflow `.env` file can be referenced in your MCP server commands, but you cannot reference global variables declared in Langflow.
+
+ If you want to use variables for `ASTRA_DB_APPLICATION_TOKEN` and `ASTRA_DB_API_ENDPOINT`, add them to Langflow's `.env` file, and then restart Langflow.
+ For more information, see [global variables](/configuration-global-variables).
+ :::
+
+ Add each variable separately.
+ To add another variable field click **Add More**.
+
+ ```bash
+ ASTRA_DB_APPLICATION_TOKEN=AstraCS:...
+ ```
+
+ ```bash
+ ASTRA_DB_API_ENDPOINT=https://...-us-east-2.apps.astra.datastax.com
+ ```
+
+8. In the **Agent** component, add your OpenAI API key.
+
+ The default model is an OpenAI model.
+ If you want to use a different model, edit the **Model Provider**, **Model Name**, and **API Key** fields accordingly.
+
+ 
+
+9. Open the **Playground**, and then ask the agent, `What collections are available?`
+
+ Since Langflow is connected to your Astra DB database through the MCP server, the agent chooses the correct tool and connects to your database to retrieve the answer.
+ For example:
+
+ ```text
+ The available collections in your database are:
+ collection_002
+ hardware_requirements
+ load_collection
+ nvidia_collection
+ software_requirements
+ ```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Agents/mcp-server.mdx b/docs/versioned_docs/version-1.9.0/Agents/mcp-server.mdx
new file mode 100644
index 0000000000..6fa96dd838
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Agents/mcp-server.mdx
@@ -0,0 +1,409 @@
+---
+title: Use Langflow as an MCP server
+slug: /mcp-server
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Icon from "@site/src/components/icon";
+
+Langflow integrates with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) as both an MCP server and an MCP client.
+
+This page describes how to use Langflow as an MCP server that exposes your flows as [tools](https://modelcontextprotocol.io/docs/concepts/tools) that [MCP clients](https://modelcontextprotocol.io/clients) can use when generating responses.
+
+Langflow MCP servers support both the **streamable HTTP** transport and **Server-Sent Events (SSE)** as a fallback.
+The default project MCP server configuration uses streamable HTTP transport at the URL path `/streamable`.
+
+For information about using Langflow as an MCP client and managing MCP server connections within flows, see [Use Langflow as an MCP client](/mcp-client).
+
+## Prerequisites
+
+* A [Langflow project](/concepts-flows#projects) with at least one flow that has a [**Chat Output** component](/chat-input-and-output).
+
+ The **Chat Output** component is required to use a flow as an MCP tool.
+
+* Any LTS version of [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) installed on your computer if you want to use MCP Inspector to [test and debug flows](#test-and-debug-flows).
+
+* [ngrok installed](https://ngrok.com/docs/getting-started/#1-install-ngrok) and an [ngrok authtoken](https://dashboard.ngrok.com/get-started/your-authtoken) if you want to [deploy a public Langflow server](/deployment-public-server).
+
+## Serve flows as MCP tools {#select-flows-to-serve}
+
+When you create a [Langflow project](/concepts-flows#projects), Langflow automatically adds the project to your MCP server's configuration and makes the project's flows available as MCP tools.
+
+If your Langflow server has authentication enabled (`AUTO_LOGIN=false`), the project's MCP server is automatically configured with API key authentication, and a new API key is generated specifically for accessing the new project's flows.
+For more information, see [MCP server authentication](#authentication).
+
+
+### Prevent automatic MCP server configuration for Langflow projects
+
+To disable automatic MCP server configuration for new projects, set the `LANGFLOW_ADD_PROJECTS_TO_MCP_SERVERS` environment variable to `false`.
+For more information, see [MCP server environment variables](#mcp-server-environment-variables).
+
+### Selectively enable and disable MCP servers for Langflow projects
+
+With or without automatic MCP server configuration enabled, you can selectively enable and disable the projects that are exposed as MCP tools:
+
+1. Click the **MCP Server** tab on the [**Projects** page](/concepts-flows#projects), or, when editing a flow, click **Share**, and then select **MCP Server**.
+
+ 
+
+ The **Flows/Tools** section lists the flows that are currently being served as tools on this MCP server.
+
+2. To toggle exposed flows, click **Edit Tools**, and then select the flows that you want exposed as tools.
+To prevent a flow from being used as a tool, clear the checkbox in the first column.
+
+3. Close the **MCP Server Tools** dialog to save your changes.
+
+ 
+
+### Edit flow tool names and descriptions
+
+Tool names and descriptions help MCP clients determine which actions your flows provide and when to use those actions.
+It is recommended to provide clear, descriptive names and descriptions for all tools that you serve to MCP clients.
+
+To edit the names and descriptions of flow tools on a Langflow MCP server, do the following:
+
+1. Click the **MCP Server** tab on the [**Projects** page](/concepts-flows#projects), or, when editing a flow, click **Share**, and then select **MCP Server**.
+
+2. Click **Edit Tools**.
+
+3. Click the **Description** or **Tool** that you want to edit:
+
+ - **Tool name**: Enter a name that makes it clear what the flow does when used as a tool by an agent.
+
+ - **Tool description**: Enter a description that completely and accurately describes the specific actions the flow performs.
+
+4. Close the **MCP Server Tools** dialog to save your changes.
+
+#### Importance of tool names and descriptions
+
+MCP clients use tool names and descriptions to determine which actions to use when generating responses.
+
+Because MCP clients treat your Langflow project as a single MCP server with all of your enabled flows listed as tools, unclear names and descriptions can cause the agent to select tools incorrectly or inconsistently.
+
+For example, a flow's default tool name is the flow ID, such as `adbbf8c7-0a34-493b-90ea-5e8b42f78b66`.
+This provides no information to an agent about the type of flow or its purpose.
+
+To provide more context about your flows, make sure to name and describe your flows clearly when configuring your Langflow project's MCP server.
+
+Think of these names and descriptions as function names and code comments.
+Use clear statements to describe the problems your flows solve.
+
+
+Example: Tool name and description usage
+
+For example, assume you create a flow based on the **Document Q&A** template that uses an LLM to chat about resumes, and then you give the flow the following name and description:
+
+- **Tool name**: `document_qa_for_resume`
+
+- **Tool description**: `A flow for analyzing Emily's resume.`
+
+After connecting your Langflow MCP server to Cursor, you can ask Cursor about the resume, such as `What job experience does Emily have?`.
+Using the context provided by your tool name and description, the agent can decide to use the `document_qa_for_resume` MCP tool to create a response about Emily's resume.
+If necessary, the agent asks permission to use the flow tool before generating the response.
+
+If you ask about a different resume, such as `What job experience does Alex have?`, the agent can decide that `document_qa_for_resume` isn't relevant to this request, because the tool description specifies that the flow is for Emily's resume.
+In this case, the agent might use another available tool, or it can inform you that it doesn't have access to information about Alex's.
+For example:
+
+```
+I notice you're asking about Alex's job experience.
+Based on the available tools, I can see there is a Document QA for Resume flow that's designed for analyzing resumes.
+However, the description mentions it's for "Emily's resume" not Alex's. I don't have access to Alex's resume or job experience information.
+```
+
+
+
+{/* The anchor on this section (connect-clients-to-use-the-servers-actions) is currently a link target in the Langflow UI. Do not change. */}
+## Connect clients to your Langflow MCP server {#connect-clients-to-use-the-servers-actions}
+
+Langflow provides automatic installation and code snippets to help you deploy your Langflow MCP servers to your local MCP clients.
+
+
+
+
+The JSON option allows you to connect a Langflow MCP server to any local or remote MCP client.
+You can modify this process for any [MCP-compatible client](https://modelcontextprotocol.io/clients).
+
+1. Install any [MCP-compatible client](https://modelcontextprotocol.io/clients).
+
+ These steps use Cursor as an example, but the process is generally the same for all clients, with slight differences in client-specific details like file names.
+
+2. In your client, add a new MCP server using the client's UI or configuration file.
+
+ For example, in Cursor, go to **Cursor Settings**, select **MCP**, and then click **Add New Global MCP Server** to open Cursor's global `mcp.json` configuration file.
+
+3. Recommended: Configure [authentication](#authentication) for your MCP server.
+
+4. In Langflow, on the **Projects** page, click the **MCP Server** tab.
+
+5. Click the **JSON** tab, copy the code snippet for your operating system, and then paste it into your client's MCP configuration file.
+For example:
+
+ ```json
+ {
+ "mcpServers": {
+ "PROJECT_NAME": {
+ "command": "uvx",
+ "args": [
+ "mcp-proxy",
+ "--transport",
+ "streamablehttp",
+ "http://LANGFLOW_SERVER_ADDRESS/api/v1/mcp/project/PROJECT_ID/streamable"
+ ]
+ }
+ }
+ }
+ ```
+
+ The **MCP Server** tab automatically populates the `LANGFLOW_SERVER_ADDRESS` and `PROJECT_ID` values.
+
+ The default Langflow server address is `http://localhost:7860`.
+ If you are using a [public Langflow server](/deployment-public-server), the server address is automatically included.
+
+ If your Langflow server requires authentication, you must include your Langflow API key or OAuth settings in the configuration.
+ For more information, see [MCP server authentication](#authentication).
+
+6. To include other environment variables with your MCP server command, add an `env` object with key-value pairs of environment variables. For example:
+
+ ```json
+ {
+ "mcpServers": {
+ "PROJECT_NAME": {
+ "command": "uvx",
+ "args": [
+ "mcp-proxy",
+ "--transport",
+ "streamablehttp",
+ "http://LANGFLOW_SERVER_ADDRESS/api/v1/mcp/project/PROJECT_ID/streamable"
+ ],
+ "env": {
+ "KEY": "VALUE"
+ }
+ }
+ }
+ }
+ ```
+
+ Don't add API keys in the `env` object, as these variables are specifically for the `mcp-proxy` process.
+ Instead, add API keys under `args`.
+ For an example, see [MCP server authentication](#authentication).
+
+7. Save and close your client's MCP configuration file.
+
+8. Confirm that your Langflow MCP server is on the client's list of MCP servers.
+If necessary, restart your client to apply the modified configuration file.
+
+
+
+
+:::info
+The auto install option is available only for specific MCP clients.
+Auto install requires the client to be installed locally so Langflow can write to the client's configuration file.
+If your client isn't supported, is installed remotely, or you need to pass additional environment variables, use the **JSON** option.
+:::
+
+1. Install [Cursor](https://docs.cursor.com/get-started/installation), [Claude](https://claude.ai/download), or [Windsurf](https://windsurf.com/download/editor) on the same computer where your Langflow server is running.
+
+2. Recommended: Configure [authentication](#authentication) for your MCP server.
+
+3. In Langflow, on the **Projects** page, click the **MCP Server** tab.
+
+4. On the **Auto install** tab, find your MCP client provider, and then click **Add**.
+
+ Your Langflow project's MCP server is automatically added to the configuration file for your local Cursor, Claude, or Windsurf client.
+ For example, with Cursor, the server configuration is added to the `mcp.json` configuration file.
+
+ Langflow attempts to add this configuration even if the selected client isn't installed.
+ To verify the installation, check the available MCP servers in your client.
+
+
+
+
+Once your MCP client is connected to your Langflow project's MCP server, your flows are registered as tools.
+Cursor determines when to use tools based on your queries, and requests permissions when necessary.
+For more information, see the MCP documentation for your client, such as [Cursor's MCP documentation](https://docs.cursor.com/context/model-context-protocol).
+
+## MCP server authentication {#authentication}
+
+Each [Langflow project](/concepts-flows#projects) has its own MCP server with its own MCP server authentication settings.
+
+When you create a new project, Langflow automatically configures authentication for the project's MCP server based on your Langflow server's authentication settings. If authentication is enabled (`AUTO_LOGIN=false`), the project is automatically configured with API key authentication, and a new API key is generated for accessing the project's flows.
+
+To configure authentication for a Langflow MCP server, go to the **Projects** page in Langflow, click the **MCP Server** tab, click **Edit Auth**, and then select your preferred authentication method:
+
+
+
+
+When authenticating your MCP server with a Langflow API key, your project's MCP server **JSON** code snippets and **Auto install** configuration automatically include the `--headers` and `x-api-key` arguments in the **args** array (for streamable transport).
+
+Click **Generate API key** to automatically insert a new Langflow API key into the code template.
+Alternatively, you can replace `YOUR_API_KEY` with an existing Langflow API key.
+
+To add your API key to the configuration, use three separate entries in `args`: `"--headers"`, `"x-api-key"`, and your key value. For example:
+
+```json
+{
+ "mcpServers": {
+ "PROJECT_NAME": {
+ "command": "uvx",
+ "args": [
+ "mcp-proxy",
+ "--transport",
+ "streamablehttp",
+ "--headers",
+ "x-api-key",
+ "YOUR_API_KEY",
+ "http://LANGFLOW_SERVER_ADDRESS/api/v1/mcp/project/PROJECT_ID/streamable"
+ ]
+ }
+ }
+}
+```
+
+
+
+
+When OAuth is enabled, Langflow automatically starts an [MCP Composer](https://pypi.org/project/mcp-composer) instance for your project, creating a secure client-side proxy between MCP clients and the `mcp-proxy` on your server.
+
+OAuth integration allows your Langflow MCP server to authenticate users and applications through any OAuth 2.0 compliant service. When users or applications connect to your MCP server, they are redirected to your chosen OAuth provider to authenticate. Upon successful authentication, they are granted access to your flows as MCP tools.
+
+Before configuring OAuth in Langflow, you must first set up an OAuth application with an external OAuth 2.0 service provider.
+You must register your Langflow server as an OAuth client, and then obtain the required values to complete the configuration in Langflow.
+
+The following table describes the required values.
+[GitHub OAuth](https://github.com/settings/developers) is used for example purposes.
+Be sure to use the actual values from your own deployment.
+For more information, see your OAuth provider's documentation.
+
+| Field | Description | Source | Example |
+|-------|-------------|--------|---------|
+| **Host** | OAuth server host | MCP Composer default. | `localhost` |
+| **Port** | OAuth server port | MCP Composer default. | `9000` |
+| **Server URL** | Full OAuth server URL | Combines the MCP Composer default OAuth host and port. | `http://localhost:9000` |
+| **Callback URL** | OAuth callback URL on your server | You define this full URL during OAuth app registration. This must match exactly what you register with your OAuth provider. | `http://localhost:9000/auth/idaas/callback` |
+| **Client ID** | Your OAuth client identifier | From your OAuth provider. | `Ov23li9vx2grVL61qjb` |
+| **Client Secret** | Your OAuth client secret | From your OAuth provider. | `1234567890abcdef1234567890abcdef12345678` |
+| **Authorization URL** | OAuth authorization endpoint | From your OAuth provider. | `https://github.com/login/oauth/authorize` |
+| **Token URL** | OAuth token endpoint for getting refresh tokens | From your OAuth provider. | `https://github.com/login/oauth/access_token` |
+| **MCP Scope** | Scope for MCP operations | You define this. As of Langflow 1.6, `user` is the only available value. | `user` |
+| **Provider Scope** | OAuth provider scope | You define this. As of Langflow 1.6, `openid` is the only available value. | `openid` |
+
+To configure OAuth authentication:
+
+1. Select **OAuth** as the authentication type.
+2. Configure the OAuth settings with the values from your OAuth deployment.
+ All values are required.
+
+ The OAuth credentials are encrypted and stored securely in your Langflow database.
+
+3. Click **Save**.
+
+ Your MCP server's **JSON** code snippets and **Auto install** configuration are automatically updated with OAuth values. These are automatically used for new installations after enabling OAuth. However, you must manually update any existing installations, as explained in the next step.
+
+4. If you already installed your Langflow MCP server in your MCP client, you must update your MCP client configuration to use the new OAuth settings after enabling OAuth on your MCP server.
+The client update method depends on how you installed the server on the client:
+
+ - **Auto install**: Manually update your client's config file using the updated JSON snippet from the **JSON** tab, or repeat the steps in [Auto-install](#connect-clients-to-use-the-servers-actions) to re-install the client with the updated settings.
+ - **JSON option**: Copy the updated JSON snippet from the **JSON** tab and replace your existing configuration.
+ - **New connections**: Use either the **Auto install** or **JSON** option. The OAuth settings are included automatically.
+
+After you enable OAuth and update your client configuration, an OAuth callback window opens each time your MCP client attempts to authenticate with the server.
+A successful authentication returns `Authentication complete. You may close this window.`
+If your client doesn't open the OAuth window, try restarting the client to retrieve the updated configuration.
+
+
+
+
+When no authentication is configured, your MCP server becomes a public endpoint that anyone can access without providing credentials.
+Only use this option when Langflow is running in a trusted environment.
+
+
+
+
+## MCP server environment variables
+
+The following environment variables set behaviors related to your Langflow projects' MCP servers:
+
+| Variable | Format | Default | Description |
+|----------|--------|---------|-------------|
+| `LANGFLOW_MCP_SERVER_ENABLED` | Boolean | `True` | Whether to initialize an MCP server for each of your Langflow projects. If `false`, Langflow doesn't initialize MCP servers. |
+| `LANGFLOW_MCP_SERVER_ENABLE_PROGRESS_NOTIFICATIONS` | Boolean | `False` | If `true`, Langflow MCP servers send progress notifications. |
+| `LANGFLOW_MCP_SERVER_TIMEOUT` | Integer | `20` | The number of seconds to wait before an MCP server operation expires due to poor connectivity or long-running requests. |
+| `LANGFLOW_MCP_MAX_SESSIONS_PER_SERVER` | Integer | `10` | Maximum number of MCP sessions to keep per unique server. |
+| `LANGFLOW_ADD_PROJECTS_TO_MCP_SERVERS` | Boolean | `True` | Whether to automatically add newly created projects to the user's MCP servers configuration. If `false`, projects must be manually added to MCP servers. |
+
+{/* The anchor on this section (deploy-your-server-externally) is currently a link target in the Langflow UI. Do not change. */}
+### Deploy your Langflow MCP server externally {#deploy-your-server-externally}
+
+To deploy your Langflow MCP server externally, see [Deploy a public Langflow server](/deployment-public-server).
+
+## Use MCP Inspector to test and debug flows {#test-and-debug-flows}
+
+:::info Node prerequisite
+MCP Inspector requires any LTS version of [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) installed on your computer.
+:::
+[MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector) is a common tool for testing and debugging MCP servers.
+You can use MCP Inspector to monitor your flows and get insights into how they are being consumed by the MCP server.
+
+1. Install MCP Inspector:
+
+ ```bash
+ npx @modelcontextprotocol/inspector
+ ```
+
+ For more information about configuring MCP Inspector, including specifying a proxy port, see the [MCP Inspector GitHub project](https://github.com/modelcontextprotocol/inspector).
+
+2. Open a web browser and navigate to the MCP Inspector UI.
+The default address is `http://localhost:6274`.
+
+3. In the MCP Inspector UI, enter the connection details for your Langflow project's MCP server.
+ The field values depend on your server's method of [authentication](#authentication).
+
+
+
+ - **Transport Type**: Select **STDIO**.
+ - **Command**: `uvx`
+ - **Arguments**: Enter the following list of arguments, separated by spaces. Replace the values for `YOUR_API_KEY`, `LANGFLOW_SERVER_ADDRESS`, and `PROJECT_ID` with the values from your Langflow MCP server. For example:
+ ```bash
+ mcp-proxy --headers x-api-key YOUR_API_KEY http://LANGFLOW_SERVER_ADDRESS/api/v1/mcp/project/PROJECT_ID/streamable
+ ```
+
+
+
+
+ - **Transport Type**: Select **STDIO**.
+ - **Command**: `uvx`
+ - **Arguments**: Enter the following list of arguments, separated by spaces. Replace the value for `OAUTH_SERVER_URL` with the URL of your OAuth server. For example:
+ ```bash
+ mcp-composer --mode stdio --sse-url http://localhost:9000/sse --disable-composer-tools --client_auth_type oauth
+ ```
+
+
+
+
+ - **Transport Type**: Select **SSE**.
+ - **URL**: Enter the Langflow MCP server's endpoint. For example:
+ ```bash
+ http://localhost:7860/api/v1/mcp/project/d359cbd4-6fa2-4002-9d53-fa05c645319c/streamable
+ ```
+
+
+
+
+4. Click **Connect**.
+
+ If the connection was successful, you should see your project's flows in the **Tools** tab.
+ From this tab, you can monitor how your flows are being registered as tools by MCP, as well as test the tools with custom input values.
+
+5. To quit MCP Inspector, press Control+C in the same terminal window where you started it.
+
+## Troubleshoot Langflow MCP servers {#troubleshooting-mcp-server}
+
+For troubleshooting advice for MCP servers and clients, see [Troubleshoot Langflow: MCP issues](/troubleshoot#mcp).
+
+## See also
+
+* [Use Langflow as an MCP client](/mcp-client)
+* [Use a DataStax Astra DB MCP server](/mcp-component-astra)
+* [MCP server environment variables](/environment-variables#mcp)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/api-request.mdx b/docs/versioned_docs/version-1.9.0/Components/api-request.mdx
new file mode 100644
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@@ -0,0 +1,40 @@
+---
+title: API Request
+slug: /api-request
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+The **API Request** component constructs and sends HTTP requests using URLs or curl commands:
+
+* **URL mode**: Enter one or more comma-separated URLs, and then select the method for the request to each URL.
+* **curl mode**: Enter the curl command to execute.
+
+You can enable additional request options and fields in the component's parameters.
+
+Returns a [`JSON` object](/data-types#json) containing the response.
+
+For provider-specific API components, see [**Bundles**](/components-bundle-components).
+
+## API Request parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| mode | Mode | Input parameter. Set the mode to either **URL** or **curl**. |
+| urls | URL | Input parameter. Enter one or more comma-separated URLs for the request. |
+| curl | curl | Input parameter. **curl mode** only. Enter a complete curl command. Other component parameters are populated from the command arguments. |
+| method | Method | Input parameter. The HTTP method to use. |
+| query_params | Query Parameters | Input parameter. The query parameters to append to the URL. |
+| body | Body | Input parameter. The body to send with POST, PATCH, and PUT requests as a dictionary. |
+| headers | Headers | Input parameter. The headers to send with the request as a dictionary. |
+| timeout | Timeout | Input parameter. The timeout to use for the request. |
+| follow_redirects | Follow Redirects | Input parameter. Whether to follow HTTP redirects. Starting in Langflow version 1.7, the **Follow Redirects** parameter is disabled (`false`) by default to prevent SSRF bypass attacks where a public URL redirects to internal resources. Only enable redirects if you trust the target server. For more information, see [SSRF protection environment variables](/api-keys-and-authentication#ssrf-protection). |
+| save_to_file | Save to File | Input parameter. Whether to save the API response to a temporary file. Default: Disabled (`false`) |
+| include_httpx_metadata | Include HTTPx Metadata | Input parameter. Whether to include properties such as `headers`, `status_code`, `response_headers`, and `redirection_history` in the output. Default: Disabled (`false`) |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/batch-run.mdx b/docs/versioned_docs/version-1.9.0/Components/batch-run.mdx
new file mode 100644
index 0000000000..6c2a948b3e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/batch-run.mdx
@@ -0,0 +1,65 @@
+---
+title: Batch Run
+slug: /batch-run
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+The **Batch Run** component runs a language model over _each row of one text column_ in a [`Table`](/data-types#table), and then returns a new `Table` with the original text and an LLM response.
+The output contains the following columns:
+
+* `text_input`: The original text from the input `Table`
+* `model_response`: The model's response for each input
+* `batch_index`: The 0-indexed processing order for all rows in the `Table`
+* `metadata` (optional): Additional information about the processing
+
+## Use the Batch Run component in a flow
+
+If you pass the **Batch Run** output to a [**Parser** component](/parser), you can use variables in the parsing template to reference these keys, such as `{text_input}` and `{model_response}`.
+This is demonstrated in the following example.
+
+
+
+1. Connect any language model component to a **Batch Run** component's **Language model** port.
+
+2. Connect `Table` output from another component to the **Batch Run** component's **DataFrame** input.
+For example, you could connect a **Read File** component with a CSV file.
+
+3. In the **Batch Run** component's **Column Name** field, enter the name of the column in the incoming `Table` that contains the text to process.
+For example, if you want to extract text from a `name` column in a CSV file, enter `name` in the **Column Name** field.
+
+4. Connect the **Batch Run** component's **Batch Results** output to a **Parser** component's **DataFrame** input.
+
+5. Optional: In the **Batch Run** [component menu](/concepts-components#component-menus), enable the **System Message** parameter, click **Close**, and then enter an instruction for how you want the LLM to process each cell extracted from the file.
+For example, `Create a business card for each name.`
+
+6. In the **Parser** component's **Template** field, enter a template for processing the **Batch Run** component's new `Table` columns (`text_input`, `model_response`, and `batch_index`):
+
+ For example, this template uses three columns from the resulting, post-batch `Table`:
+
+ ```text
+ record_number: {batch_index}, name: {text_input}, summary: {model_response}
+ ```
+
+7. To test the processing, click the **Parser** component, click **Run component**, and then click **Inspect output** to view the final `Table`.
+
+ You can also connect a **Chat Output** component to the **Parser** component if you want to see the output in the **Playground**.
+
+## Batch Run parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| model | HandleInput | Input parameter. Connect the 'Language Model' output from a language model component. Required. |
+| system_message | MultilineInput | Input parameter. A multi-line system instruction for all rows in the DataFrame. |
+| df | DataFrameInput | Input parameter. The DataFrame whose column is treated as text messages, as specified by 'column_name'. Required. |
+| column_name | MessageTextInput | Input parameter. The name of the DataFrame column to treat as text messages. If empty, all columns are formatted in TOML. |
+| output_column_name | MessageTextInput | Input parameter. Name of the column where the model's response is stored. Default=`model_response`. |
+| enable_metadata | BoolInput | Input parameter. If `True`, add metadata to the output DataFrame. |
+| batch_results | Table | Output parameter. A DataFrame with all original columns plus the model's response column. |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-agentics.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-agentics.mdx
new file mode 100644
index 0000000000..49ce06e30a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-agentics.mdx
@@ -0,0 +1,162 @@
+---
+title: Agentics
+slug: /bundles-agentics
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+The [Agentics](https://github.com/IBM/agentics/) component bundle uses LLMs to transform tabular data. Add or fill columns row-by-row with the [**aMap** component](#amap-component), collapse many rows into one with [**aReduce** component](#areduce-component), or generate synthetic rows with the [**aGenerate** component](#agenerate-component).
+
+Watch it in action with a sentiment analysis example that transforms customer reviews into structured insights:
+
+
+
+
+
+## Prerequisites
+
+1. Install the Agentics package in Langflow's virtual environment:
+
+ ```bash
+ uv pip install agentics-py
+ ```
+
+2. Restart Langflow so the Agentics components are available:
+
+ ```bash
+ uv run langflow run
+ ```
+
+3. Agentics components require an LLM. Configure your LLM provider API keys as [global variables](/configuration-global-variables) or environment variables.
+Supported providers include OpenAI, Anthropic, Google Generative AI, IBM WatsonX, and Ollama.
+
+## aGenerate component
+
+aGenerate generates synthetic data from a schema or from an example DataFrame. Use it for test data, augmentation, or documentation examples.
+
+For example, this schema definition creates the following DataFrame output:
+
+**Schema definition:**
+- `customer_name` (str): Full name
+- `email` (str): Email address
+- `age` (int): Age between 18–80
+- `purchase_categories` (str, As List): List of product categories purchased
+
+**Output DataFrame (example, 10 rows generated):**
+| customer_name | email | age | purchase_categories |
+|---------------|-------|-----|---------------------|
+| Sarah Johnson | sarah.j@email.com | 34 | Electronics, Books, Home & Garden |
+| Michael Chen | m.chen@email.com | 28 | Sports, Clothing, Electronics |
+| ... | ... | ... | ... |
+
+### Parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| Language Model | Dropdown | Select the LLM provider and model. Use guided experience. |
+| Input DataFrame | Table | Optional. Example DataFrame to learn from; only first 50 rows used. If not provided, Schema is used. |
+| Schema | Table | Define columns to generate when no Input DataFrame is provided. See the component's schema definition. |
+| Instructions | String | Optional instructions for generation. |
+| Number of Rows to Generate | Integer | How many synthetic rows to create. Default: 10. |
+
+## aMap component
+
+aMap transforms each row of input data using natural language instructions and a defined output schema (one row in, one row out).
+Use **aMap** for enriching data with LLM-generated columns such as sentiment, categories, and entity extraction.
+
+Rows are processed concurrently; default batch size is 10. Token usage scales with number of rows.
+
+For example, **aMap** keeps each input row and fills in `sentiment`, `confidence`, and `key_topics` with the connected LLM.
+
+**Input DataFrame:**
+| review_id | text |
+|-----------|------|
+| 1 | Great product, fast shipping! |
+| 2 | Terrible quality, broke after one use |
+
+**Schema definition:**
+- `sentiment` (str): "positive", "negative", or "neutral"
+- `confidence` (float): Confidence score 0–1
+- `key_topics` (str, As List): Main topics mentioned
+
+**Output DataFrame:**
+| review_id | text | sentiment | confidence | key_topics |
+|-----------|------|-----------|------------|------------|
+| 1 | Great product, fast shipping! | positive | 0.95 | product quality, shipping |
+| 2 | Terrible quality, broke after one use | negative | 0.92 | quality, durability |
+
+### Parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| Language Model | Dropdown | Select the LLM provider and model. Use guided experience. |
+| Input DataFrame | Table | Input DataFrame (list of dicts or DataFrame). Each row is processed independently. |
+| Schema | Table | Define the structure and types for generated columns. See the component's schema definition. |
+| Instructions | String | Natural language instructions for transforming each row into the output schema. |
+| As List | Boolean | If true, generate multiple instances of the schema per row and concatenate. |
+| Keep Source Columns | Boolean | If `true`, append new columns to original data; if false, return only generated columns. Ignored if As List is true. Default: `true`. |
+
+## aReduce component
+
+aReduce aggregates all rows in the input DataFrame into a single row following the output schema.
+Use **aReduce** for summaries, reports, or consolidated insights.
+To aggregate rows into a list, set `As List` to `true` in the component.
+
+All rows are sent in one request; token usage can be high for large DataFrames.
+Consider filtering or sampling first.
+
+For example, **aReduce** takes all input rows and produces a single row with LLM-generated aggregates.
+It sums revenue into `total_revenue`, identifies the best-selling product in `best_selling_product`, and writes a short `summary` of the sales.
+
+**Input DataFrame:**
+
+| date | product | revenue | units |
+|------|---------|---------|-------|
+| 2024-01-01 | Widget A | 1200 | 50 |
+| 2024-01-02 | Widget B | 800 | 30 |
+| 2024-01-03 | Widget A | 1500 | 60 |
+
+**Schema definition:**
+- `total_revenue` (float): Sum of all revenue
+- `best_selling_product` (str): Product with highest units
+- `summary` (str): Natural language summary
+
+**Output DataFrame:**
+| total_revenue | best_selling_product | summary |
+|---------------|---------------------|---------|
+| 3500 | Widget A | Over 3 days, Widget A was the best seller with 110 units, generating $2700 in revenue. |
+
+### Parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| Language Model | Dropdown | Select the LLM provider and model. |
+| Input DataFrame | Table | Input DataFrame (list of dicts or DataFrame). Required. |
+| Schema | Table | Define the structure and types for the aggregated output. See the component's schema definition. |
+| As List | Boolean | If true, output is a list of instances of the schema. |
+| Instructions | String | Optional instructions for aggregation. If omitted, the LLM infers from field descriptions. |
+
+## Performance and troubleshooting
+
+- **Token usage:** aMap scales with rows; aReduce sends all rows in one call; aGenerate scales with instances. Use smaller batches or sample large datasets to reduce cost.
+- **Batch size:** Default 10; max 25. Larger batches improve throughput but increase latency per batch.
+- **Errors:** If Agentics is not found, run `uv pip install agentics-py==0.3.1` and restart Langflow. For API/key errors, set the provider’s API key as a global variable or env var. For DataFrame errors, ensure input is a list of dicts or use a DataFrame component output.
+
+## See also
+
+- [Agentics official documentation](https://ibm.github.io/Agentics/)
+- [Agentics GitHub repository](https://github.com/IBM/agentics/)
+- [Global variables configuration](/configuration-global-variables)
+
+**Publications:**
+- Alfio Gliozzo, Naweed Khan, Christodoulos Constantinides, Nandana Mihindukulasooriya, Nahuel Defosse, Gaetano Rossiello, Junkyu Lee. "Transduction is All You Need for Structured Data Workflows." arXiv:2508.15610, 2025. [Link](https://arxiv.org/abs/2508.15610)
+- Alfio Massimiliano Gliozzo, Junkyu Lee, Nahuel Defosse. "Agentics 2.0: Logical Transduction Algebra for Agentic Data Workflows." arXiv:2603.04241, 2026. [Link](https://arxiv.org/abs/2603.04241)
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-aiml.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-aiml.mdx
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+---
+title: AI/ML API
+slug: /bundles-aiml
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **AI/ML** bundle.
+
+## AI/ML API text generation
+
+This component creates a `ChatOpenAI` model instance using the AI/ML API.
+The output is exclusively a **Language Model** ([`LanguageModel`](/data-types#languagemodel)) that you can connect to another LLM-driven component, such as a **Smart Transform** component.
+
+For more information, see the [AI/ML API Langflow integration documentation](https://docs.aimlapi.com/integrations/langflow) and [Language model components](/components-models).
+
+### AI/ML API text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to 0 for unlimited tokens. Range: 0-128000. |
+| model_kwargs | Dictionary | Input parameter. Additional keyword arguments for the model. |
+| model_name | String | Input parameter. The name of the AIML model to use. Options are predefined in `AIML_CHAT_MODELS`. |
+| aiml_api_base | String | Input parameter. The base URL of the AIML API. Defaults to `https://api.aimlapi.com`. |
+| api_key | SecretString | Input parameter. The AIML API Key to use for the model. |
+| temperature | Float | Input parameter. Controls randomness in the output. Default: `0.1`. |
+
+## AI/ML API Embeddings
+
+The **AI/ML API Embeddings** component generates embeddings using the [AI/ML API](https://docs.aimlapi.com/api-overview/embeddings).
+
+The output is [`Embeddings`](/data-types#embeddings).
+Specifically, an instance of `AIMLEmbeddingsImpl`.
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### AI/ML API Embeddings parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| model_name | String | Input parameter. The name of the AI/ML embedding model to use. |
+| aiml_api_key | SecretString | Input parameter. The API key required for authenticating with the AI/ML service. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-altk.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-altk.mdx
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+---
+title: ALTK
+slug: /bundles-altk
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+:::tip
+The ALTK contains features called _components_. These are different from Langflow components. All components within ALTK are available through Langflow's **ALTK Agent** component.
+:::
+
+The **ALTK Agent** implements components from the [Agent Lifecycle Toolkit](https://github.com/AgentToolkit/agent-lifecycle-toolkit). ALTK features can be enabled or disabled independently.
+
+* **Pre-tool validation**: Validates tool calls before execution to check for appropriateness and correctness using the [SPARC](https://agenttoolkit.github.io/agent-lifecycle-toolkit/concepts/components/sparc/) reflection component. This validation prevents agents from executing invalid tool calls.
+
+* **Post-tool JSON processing**: Processes large JSON tool responses by generating Python code on the fly to extract relevant data. This helps reduce context size and improves the agent's ability to work with large tool responses, especially when dealing with APIs that return extensive JSON data. The component outputs a [Message](/data-types#message) containing the agent's response, which is passed to the next component in the flow instead of the full JSON data that isn't needed.
+
+For more information, see the [Agent Lifecycle Toolkit documentation](https://agenttoolkit.github.io/agent-lifecycle-toolkit/).
+
+For an example of the ALTK component in Langflow, see the video tutorial [ALTK in Langflow: Reliably handle JSON responses in your AI agent](https://www.youtube.com/watch?v=YNwPBK_KxXY).
+
+### ALTK Agent parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| agent_llm | Dropdown | Input parameter. The model provider the agent uses to generate responses. |
+| enable_tool_validation | Boolean | Input parameter. If enabled, tool calls are validated using SPARC before execution to check for appropriateness and correctness. Default: `true`. |
+| enable_post_tool_reflection | Boolean | Input parameter. If enabled, tool outputs are automatically processed through JSON processing when the output is JSON and exceeds the size threshold. Default: `true`. |
+| response_processing_size_threshold | Integer | Input parameter. Tool output is post-processed only if the response length exceeds this character threshold. Default: `100`. Advanced parameter. |
+| tools | List[Tool] | Input parameter. The list of tools available to the agent. |
+| system_prompt | String | Input parameter. The system prompt to provide context to the agent. |
+| input_value | String | Input parameter. The user's input to the agent. |
+| memory | Memory | Input parameter. The memory for the agent to use for context persistence. |
+| max_iterations | Integer | Input parameter. The maximum number of iterations to allow the agent to execute. |
+| verbose | Boolean | Input parameter. This determines whether to print out the agent's intermediate steps. |
+| handle_parsing_errors | Boolean | Input parameter. This determines whether to handle parsing errors in the agent. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-amazon.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-amazon.mdx
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+---
+title: Amazon
+slug: /bundles-amazon
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Amazon** bundle.
+
+## Amazon Bedrock Converse
+
+This component generates text using [Amazon Bedrock LLMs](https://docs.aws.amazon.com/bedrock) with the Bedrock Converse API.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+Specifically, the **Language Model** output is an instance of [`ChatBedrockConverse`](https://docs.langchain.com/oss/python/integrations/chat/bedrock) configured according to the component's parameters.
+
+Use the **Language Model** output when you want to use an Amazon Bedrock model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### Amazon Bedrock Converse parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| input_value | String | Input parameter. The input string for text generation. |
+| system_message | String | Input parameter. A system message to pass to the model. |
+| stream | Boolean | Input parameter. Whether to stream the response. Only works in chat. Default: `false`. |
+| model_id | String | Input parameter. The Amazon Bedrock model to use.|
+| aws_access_key_id | SecretString | Input parameter. AWS Access Key for authentication. Required. |
+| aws_secret_access_key | SecretString | Input parameter. AWS Secret Key for authentication. Required. |
+| aws_session_token | SecretString | Input parameter. The session key for your AWS account. Only needed for temporary credentials. |
+| credentials_profile_name | String | Input parameter. Name of the AWS credentials profile to use. If not provided, the default profile will be used. |
+| region_name | String | Input parameter. AWS region where your Bedrock resources are located. Default: `us-east-1`. |
+| endpoint_url | String | Input parameter. Custom endpoint URL for a Bedrock service. |
+| temperature | Float | Input parameter. Controls randomness in output. Higher values make output more random. Default: `0.7`. |
+| max_tokens | Integer | Input parameter. Maximum number of tokens to generate. Default: `4096`. |
+| top_p | Float | Input parameter. Nucleus sampling parameter. Controls diversity of output. Default: `0.9`. |
+| top_k | Integer | Input parameter. Limits the number of highest probability vocabulary tokens to consider. Note: Not all models support top_k. Default: `250`. |
+| disable_streaming | Boolean | Input parameter. If True, disables streaming responses. Useful for batch processing. Default: `false`. |
+| additional_model_fields | Dictionary | Input parameter. Additional model-specific parameters for fine-tuning behavior. |
+
+## Amazon Bedrock Embeddings
+
+The **Amazon Bedrock Embeddings** component is used to load embedding models from [Amazon Bedrock](https://aws.amazon.com/bedrock/).
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### Amazon Bedrock Embeddings parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| model_id | String | Input parameter. The ID of the model to call, such as `amazon.titan-embed-text-v1`. This is equivalent to the `modelId` property in the `list-foundation-models` API. |
+| aws_access_key_id | SecretString | Input parameter. AWS Access Key for authentication. |
+| aws_secret_access_key | SecretString | Input parameter. AWS Secret Key for authentication. |
+| aws_session_token | SecretString | Input parameter. The session key for your AWS account. |
+| credentials_profile_name | String | Input parameter. The name of the AWS credentials profile in `~/.aws/credentials` or `~/.aws/config`, which has access keys or role information. |
+| region_name | String | Input parameter. The AWS region to use, such as `us-west-2`. Falls back to the `AWS_DEFAULT_REGION` environment variable or region specified in `~/.aws/config` if not provided. |
+| endpoint_url | String | Input parameter. The URL to set a specific service endpoint other than the default AWS endpoint. |
+
+## S3 Bucket Uploader
+
+The **S3 Bucket Uploader** component uploads files to an Amazon S3 bucket.
+It is designed to process `JSON` input from a **Read File** or **Directory** component.
+If you upload `JSON` from other components, test the results before running the flow in production.
+
+Requires the `boto3` package, which is included in your Langflow installation.
+
+The component produces logs but it doesn't emit output to the flow.
+
+### S3 Bucket Uploader parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| **AWS Access Key ID** | SecretString | Input parameter. AWS Access Key ID for authentication. |
+| **AWS Secret Key** | SecretString | Input parameter. AWS Secret Key for authentication. |
+| **Bucket Name** | String | Input parameter. The name of the S3 bucket to upload files to. |
+| **Strategy for file upload** | String | Input parameter. The file upload strategy. **Store Data** (default) iterates over `JSON` inputs, logs the file path and text content, and uploads each file to the specified S3 bucket if both file path and text content are available. **Store Original File** iterates through the list of data inputs, retrieves the file path from each data item, uploads the file to the specified S3 bucket if the file path is available, and logs the file path being uploaded. |
+| **Data Inputs** | JSON | Input parameter. The `JSON` input to iterate over and upload as files in the specified S3 bucket. |
+| **S3 Prefix** | String | Input parameter. Optional prefix (folder path) within the S3 bucket where files will be uploaded. |
+| **Strip Path** | Boolean | Input parameter. Whether to strip the file path when uploading. Default: `false`. |
+
+## Legacy Amazon components
+
+import PartialLegacy from '@site/docs/_partial-legacy.mdx';
+
+
+
+The following Amazon components are in legacy status:
+
+
+Amazon Bedrock
+
+The **Amazon Bedrock** component was deprecated in favor of the **Amazon Bedrock Converse** component, which uses the Bedrock Converse API for conversation handling.
+
+To use Amazon Bedrock models in your flows, use the [**Amazon Bedrock Converse**](#amazon-bedrock-converse) component instead.
+
+
+
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-anthropic.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-anthropic.mdx
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+---
+title: Anthropic
+slug: /bundles-anthropic
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Anthropic** bundle.
+
+For more information about Anthropic features and functionality used by Anthropic components, see the [Anthropic documentation](https://docs.anthropic.com/en/docs/intro).
+
+## Anthropic text generation
+
+The **Anthropic** component generates text using Anthropic Chat and Language models like Claude.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+Specifically, the **Language Model** output is an instance of [`ChatAnthropic`](https://docs.langchain.com/oss/python/integrations/chat/anthropic) configured according to the component's parameters.
+
+Use the **Language Model** output when you want to use an Anthropic model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### Anthropic text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to 0 for unlimited tokens. Default: `4096`. |
+| model | String | Input parameter. The name of the Anthropic model to use. Options include various Claude 3 models. |
+| anthropic_api_key | SecretString | Input parameter. Your Anthropic API key for authentication. |
+| temperature | Float | Input parameter. Controls randomness in the output. Default: `0.1`. |
+| anthropic_api_url | String | Input parameter. Endpoint of the Anthropic API. Defaults to `https://api.anthropic.com` if not specified. |
+| prefill | String | Input parameter. Prefill text to guide the model's response. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-apify.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-apify.mdx
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+---
+title: Apify
+slug: /bundles-apify
+---
+
+import Icon from "@site/src/components/icon";
+
+[Apify](https://apify.com/) is a web scraping and data extraction platform with more than 3,000 ready-made cloud tools called **Actors**.
+
+Your flows can use the **Apify Actors** component to run **Actors** to accomplish tasks like data extraction, content analysis, and SQL operations.
+
+## Use the Apify Actors component in a flow
+
+1. Add an **Apify Actors** component to your flow, and then configure it as follows:
+
+ * **Apify Token**: Enter your [Apify API token](https://docs.apify.com/platform/integrations/api).
+ * **Actor**: Enter the ID of the Actor you want to run from the [Apify Actor Store](https://apify.com/store). For example, the [Website Content Crawler](https://apify.com/apify/website-content-crawler) has the Actor ID `apify/website-content-crawler`.
+ * **Run Input**: Enter the [JSON input for configuring the Actor run](https://docs.apify.com/platform/actors/running-actors#input).
+ * Configure additional parameters and commands depending on the Actor you chose and your use case.
+
+2. Connect the component to other components in your flow.
+The component can be used to perform tasks as a standalone step in a flow or as a tool for an agent.
+
+ To enable **Tool Mode** for this component, change the component's output type from **Output** to **Tool**, and then connect it to the **Tools** port on an **Agent** component.
+
+ **Apify Actors** components output the results of the Actor run as a JSON object in Langflow's [`JSON` type](/data-types#json).
+
+## Example Apify Actors flows
+
+The following scenarios provide a few examples of how you could use the **Apify Actors** components in Langflow:
+
+* **Extract website text content in Markdown**: Use the [Website Content Crawler Actor](https://apify.com/apify/website-content-crawler) to extract text content in Markdown format from a website, and then connect the **Output** to a **Parser** component's input for further processing.
+
+ 
+
+* **Process web content with an agent**: Attach a [Website Content Crawler Actor](https://apify.com/apify/website-content-crawler) to an **Agent** component as a tool so the agent can decide whether to extract website content based on the chat input. The agent could take the extracted data and transform it into summaries, insights, or structured responses to make the information more actionable.
+
+ 
+
+* **Analyze social media profiles with multiple actors**: Perform comprehensive social media research with multiple Apify Actors:
+
+ * Use the [Google Search Results Scraper Actor](https://apify.com/apify/google-search-scraper) to find relevant social media profiles.
+ * Use the [TikTok Data Extractor Actor](https://apify.com/clockworks/free-tiktok-scraper) to gather data and videos.
+ * Attach both actors as tools for an **Agent** component to collect the links from Google and content from TikTok, and then analyze the data to provide insights about a person, brand, or topic.
+
+ 
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-arxiv.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-arxiv.mdx
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+---
+title: arXiv
+slug: /bundles-arxiv
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **arXiv** bundle.
+
+## arXiv search
+
+This component searches and retrieves papers from [arXiv.org](https://arXiv.org).
+
+It returns a list of search results as a [`Table`](/data-types#table).
+
+### arXiv search parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| search_query | String | Input parameter. The search query for arXiv papers. For example, `quantum computing`. |
+| search_type | String | Input parameter. The field to search in. |
+| max_results | Integer | Input parameter. The maximum number of results to return. |
+
+## See also
+
+* [**Web Search** component](/web-search)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-assemblyai.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-assemblyai.mdx
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+---
+title: AssemblyAI
+slug: /bundles-assemblyai
+---
+
+import Icon from "@site/src/components/icon";
+
+The AssemblyAI components allow you to apply powerful Speech AI models to your app for tasks like:
+
+- Transcribing audio and video files
+- Formatting transcripts
+- Generating subtitles
+- Applying LLMs to audio files
+
+For more information about AssemblyAI features and functionality used by AssemblyAI components, see the [AssemblyAI API Docs](https://www.assemblyai.com/docs).
+
+## Prerequisites
+
+* An [AssemblyAI account](https://www.assemblyai.com/dashboard/signup) and an AssemblyAI API key.
+
+ Enter the key in the *AssemblyAI API Key* field in all Langflow components that require the AssemblyAI key.
+
+* Optional: To use LeMUR, you need a paid AssemblyAI account because LeMUR isn't included in the free account.
+
+## Components
+
+
+
+### AssemblyAI Start Transcript
+
+This component allows you to submit an audio or video file for transcription.
+
+**Tip**: You can freeze the path of this component to only submit the file once.
+
+- **Input**:
+ - AssemblyAI API Key: Your API key.
+ - Audio File: The audio or video file to transcribe.
+ - Speech Model (Optional): Select the class of models. Default is *Best*. See [speech models](https://www.assemblyai.com/docs/speech-to-text/speech-recognition#select-the-speech-model-with-best-and-nano) for more info.
+ - Automatic Language Detection (Optional): Enable automatic language detection.
+ - Language (Optional): The language of the audio file. Can be set manually if automatic language detection is disabled.
+ See [supported languages](https://www.assemblyai.com/docs/getting-started/supported-languages) for a list of supported language codes.
+ - Enable Speaker Labels (Optional): Detect speakers in an audio file and what each speaker said.
+ - Expected Number of Speakers (Optional): Set the expected number of speakers, if Speaker Labels is enabled.
+ - Audio File URL (Optional): The URL of the audio or video file to transcribe. Can be used instead of *Audio File*.
+ - Punctuate (Optional): Apply punctuation. Default is `true`.
+ - Format Text (Optional): Apply casing and text formatting. Default is `true`.
+
+- **Output**:
+ - Transcript ID: The id of the transcript
+
+
+### AssemblyAI Poll Transcript
+
+This components allows you to poll the transcripts. It checks the status of the transcript every few seconds until the transcription is completed.
+
+- **Input**:
+ - AssemblyAI API Key: Your API key.
+ - Polling Interval (Optional): The polling interval in seconds. Default is 3.
+
+- **Output**:
+ - Transcription Result: The AssemblyAI JSON response of a completed transcript. Contains the text and other info.
+
+
+### AssemblyAI Get Subtitles
+
+This component allows you to generate subtitles in SRT or VTT format.
+
+- **Input**:
+ - AssemblyAI API Key: Your API key.
+ - Transcription Result: The output of the **Poll Transcript** component.
+ - Subtitle Format: The format of the captions (SRT or VTT).
+ - Character per Caption (Optional): The maximum number of characters per caption (0 for no limit).
+
+- **Output**:
+ - Subtitles: A JSON response with the `subtitles` field containing the captions in SRT or VTT format.
+
+
+### AssemblyAI LeMUR
+
+This component allows you to apply Large Language Models to spoken data using the [AssemblyAI LeMUR framework](https://www.assemblyai.com/docs/lemur).
+
+LeMUR automatically ingests the transcript as additional context, making it easy to apply LLMs to audio data. You can use it for tasks like summarizing audio, extracting insights, or asking questions.
+
+- **Input**:
+ - AssemblyAI API Key: Your API key.
+ - Transcription Result: The output of the **Poll Transcript** component.
+ - Input Prompt: The text to prompt the model. You can type your prompt in this field or connect it to a **Prompt Template** component.
+ - Final Model: The model that is used for the final prompt after compression is performed. Default is Claude 3.5 Sonnet.
+ - Temperature (Optional): The temperature to use for the model. Default is 0.0.
+ - Max Output Size (Optional): Max output size in tokens, up to 4000. Default is 2000.
+ - Endpoint (Optional): The LeMUR endpoint to use. Default is "task". For "summary" and "question-answer", no prompt input is needed. See [LeMUR API docs](https://www.assemblyai.com/docs/api-reference/lemur/) for more info.
+ - Questions (Optional): Comma-separated list of your questions. Only used if *Endpoint* is "question-answer".
+ - Transcript IDs (Optional): Comma-separated list of transcript IDs. LeMUR can perform actions over multiple transcripts. If provided, the *Transcription Result* is ignored.
+
+- **Output**:
+ - LeMUR Response: The generated LLM response.
+
+### AssemblyAI List Transcripts
+
+This component can be used as a standalone component to list all previously generated transcripts.
+
+- **Input**:
+ - AssemblyAI API Key: Your API key.
+ - Limit (Optional): Maximum number of transcripts to retrieve. Default is 20, use 0 for all.
+ - Filter (Optional): Filter by transcript status.
+ - Created On (Optional): Only get transcripts created on this date (YYYY-MM-DD).
+ - Throttled Only (Optional): Only get throttled transcripts, overrides the status filter
+
+- **Output**:
+ - Transcript List: A list of all transcripts with info such as the transcript ID, the status, and the data.
+
+
+## Flow Process
+
+1. The user inputs an audio or video file.
+2. The user can also input an LLM prompt. In this example, we want to generate a summary of the transcript.
+3. The flow submits the audio file for transcription.
+4. The flow checks the status of the transcript every few seconds until transcription is completed.
+5. The flow parses the transcription result and outputs the transcribed text.
+6. The flow also generates subtitles.
+7. The flow applies the LLM prompt to generate a summary.
+8. As a standalone component, all transcripts can be listed.
+
+## Run the Transcription and Speech AI Flow
+
+1. Build the flow manually or import a pre-build JSON file:
+
+ * Recommended: [Download the AssemblyAI Transcription and Speech AI flow JSON](/files/AssemblyAI_Flow.json), and then [import the flow](/concepts-flows-import) into Langflow.
+ * Create a blank flow, and then add the previously described components to your flow, connecting them as shown in the flow diagram.
+
+2. Input your AssemblyAI API key in all components that require the key (**Start Transcript**, **Poll Transcript**, **Get Subtitles**, **LeMUR**, **List Transcripts**).
+
+3. Select an audio or video file for the **Start Transcript** component.
+
+ Optional: After adding a file to the **Start Transcript** component, run and [freeze the component](/concepts-components#freeze-a-component) so you only submit the file once, no matter how many times you run the flow.
+ To do this, click **Run component** to preload the file, and then click **Show More** and select **Freeze** to lock the result.
+ Subsequent flow runs use the frozen component's cached output.
+
+4. Test the transcription by clicking **Run component** on the **Parser** component. Make sure that the specified template is `{text}`.
+
+ Running one component runs all upstream components as well as the selected component and then stops the flow run.
+ In this case, the **Start Transcript** and **Poll Transcript** components are upstream from the **Parser** component.
+ If you froze the **Start Transcript** component, the flow sends the cached output from **Start Transcript**, runs the **Poll Transcript** component, to get the transcription result.
+ Check the flow logs or inspect the output of the **Parser** component to see the transcribed text result.
+
+5. To generate subtitles and run the full flow, click **Run component** on the **List Transcript** component.
+
+## Customization
+
+The flow can be customized by:
+
+1. Modifying the parameters in the **Start Transcript** component.
+2. Modifying the subtitle format in the **Get Subtitles** component.
+3. Modifying the LLM prompt for input of the **LeMUR** component.
+4. Modifying the LLM parameters (e.g., temperature) in the **LeMUR** component.
+
+## Troubleshooting
+
+If you encounter issues:
+
+1. Ensure the API key is correctly set in all components that require the key.
+2. To use LeMUR, you need to upgrade your AssemblyAI account, since this isn't included in the free account.
+3. Verify that all components are properly connected in the flow.
+4. Review the Langflow logs for any error messages.
+5. Check the [AssemblyAI API documentation](https://www.assemblyai.com/docs/).
+6. Contact [AssemblyAI support](https://www.assemblyai.com/contact/support).
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-azure.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-azure.mdx
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+---
+title: Azure
+slug: /bundles-azure
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Azure** bundle.
+
+## Azure OpenAI
+
+This component generates text using [Azure OpenAI LLMs](https://learn.microsoft.com/en-us/azure/ai-services/openai/).
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+Specifically, the **Language Model** output is an instance of [`AzureChatOpenAI`](https://docs.langchain.com/oss/python/integrations/chat/azure_chat_openai) configured according to the component's parameters.
+
+Use the **Language Model** output when you want to use an Azure OpenAI model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### Azure OpenAI parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| Model Name | String | Input parameter. Specifies the name of the Azure OpenAI model to be used for text generation. |
+| Azure Endpoint | String | Input parameter. Your Azure endpoint, including the resource. |
+| Deployment Name | String | Input parameter. Specifies the name of the deployment. |
+| API Version | String | Input parameter. Specifies the version of the Azure OpenAI API to be used. |
+| API Key | SecretString | Input parameter. Your Azure OpenAI API key. |
+| Temperature | Float | Input parameter. Specifies the sampling temperature. Defaults to `1.0`. |
+| Max Tokens | Integer | Input parameter. Specifies the maximum number of tokens to generate. Defaults to `1000`. |
+| Input Value | String | Input parameter. Specifies the input text for text generation. |
+| Stream | Boolean | Input parameter. Specifies whether to stream the response from the model. Default to `false`. |
+
+## Azure OpenAI Embeddings
+
+The **Azure OpenAI Embeddings** component generates embeddings using Azure OpenAI models.
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### Azure OpenAI Embeddings parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| Model | String | Input parameter. The name of the model to use. Default: `text-embedding-3-small`. |
+| Azure Endpoint | String | Input parameter. Your Azure endpoint, including the resource, such as `https://example-resource.azure.openai.com/`. |
+| Deployment Name | String | Input parameter. The name of the deployment. |
+| API Version | String | Input parameter. The API version to use, with options including various dates. |
+| API Key | String | Input parameter. The API key required to access the Azure OpenAI service. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-baidu.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-baidu.mdx
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+---
+title: Baidu
+slug: /bundles-baidu
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Baidu** bundle.
+
+## Qianfan
+
+The **Qianfan** component generates text using Qianfan's language models.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a Qianfan model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models) and the [Qianfan documentation](https://github.com/baidubce/bce-qianfan-sdk).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-bing.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-bing.mdx
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+---
+title: Bing
+slug: /bundles-bing
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Bing** bundle.
+
+## Bing Search API
+
+This component allows you to call the Bing Search API.
+
+It returns a list of search results as a [`Table`](/data-types#table).
+
+### Bing Search API parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| bing_subscription_key | SecretString | Input parameter. A Bing API subscription key. |
+| input_value | String | Input parameter. The search query input. |
+| bing_search_url | String | Input parameter. A custom Bing Search URL. |
+| k | Integer | Input parameter. The number of search results to return. |
+
+## See also
+
+* [**Web Search** component](/web-search)
+* [**SearchApi** bundle](/bundles-searchapi)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-cassandra.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-cassandra.mdx
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+---
+title: Cassandra
+slug: /bundles-cassandra
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Cassandra** bundle, including components that read and write to Apache Cassandra clusters, such as OSS Cassandra and Astra DB databases.
+
+## Cassandra vector store
+
+Use the **Cassandra** component to read or write to a Cassandra-based vector store using a `CassandraVectorStore` instance.
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### Cassandra parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see [Vector search in Cassandra](https://cassandra.apache.org/doc/latest/cassandra/vector-search/overview.html) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+|------|------|-------------|
+| database_ref | String | Input parameter. Contact points for the database or an Astra database ID. |
+| username | String | Input parameter. Username for the database. Leave empty for Astra DB. |
+| token | SecretString | Input parameter. User password for the database or an Astra application token. |
+| keyspace | String | Input parameter. The name of the keyspace containing the vector store specified in **Table Name** (`table_name`). |
+| table_name | String | Input parameter. The name of the table or collection that is the vector store. |
+| ttl_seconds | Integer | Input parameter. Time-to-live for added texts, if supported by the cluster. Only relevant for writes. |
+| batch_size | Integer | Input parameter. Amount of records to process in a single batch. |
+| setup_mode | String | Input parameter. Configuration mode for setting up a Cassandra table. |
+| cluster_kwargs | Dict | Input parameter. Additional keyword arguments for a Cassandra cluster. |
+| search_query | String | Input parameter. Query string for similarity search. Only relevant for reads. |
+| ingest_data | JSON | Input parameter. Data to be loaded into the vector store as raw chunks and embeddings. Only relevant for writes. |
+| embedding | Embeddings | Input parameter. Embedding function to use. |
+| number_of_results | Integer | Input parameter. Number of results to return in search. Only relevant for reads. |
+| search_type | String | Input parameter. Type of search to perform. Only relevant for reads. |
+| search_score_threshold | Float | Input parameter. Minimum similarity score for search results. Only relevant for reads. |
+| search_filter | Dict | Input parameter. An optional dictionary of metadata search filters to apply in addition to vector search. Only relevant for reads. |
+| body_search | String | Input parameter. Document textual search terms. Only relevant for reads. |
+| enable_body_search | Boolean | Input parameter. Flag to enable body search. Only relevant for reads. |
+
+## Cassandra Chat Memory
+
+The **Cassandra Chat Memory** component retrieves and stores chat messages using an Apache Cassandra-based database.
+
+Chat memories are passed between memory storage components as the [`Memory`](/data-types#memory) data type.
+Specifically, the component creates an instance of `CassandraChatMessageHistory`, which is a LangChain chat message history class that uses a Cassandra database for storage.
+
+For more information about using external chat memory in flows, see the [**Message History** component](/message-history).
+
+### Cassandra Chat Memory parameters
+
+
+
+| Name | Type | Description |
+|----------------|---------------|-----------------------------|
+| database_ref | MessageText | Input parameter. The contact points for the Cassandra database or Astra DB database ID. Required. |
+| username | MessageText | Input parameter. The username for Cassandra. Leave empty for Astra DB. |
+| token | SecretString | Input parameter. The password for Cassandra or the token for Astra DB. Required. |
+| keyspace | MessageText | Input parameter. The keyspace in Cassandra or namespace in Astra DB. Required. |
+| table_name | MessageText | Input parameter. The name of the table or collection for storing messages. Required. |
+| session_id | MessageText | Input parameter. The unique identifier for the chat session. Optional. |
+| cluster_kwargs | Dictionary | Input parameter. Additional keyword arguments for the Cassandra cluster configuration. Optional. |
+
+## Cassandra Graph
+
+The **Cassandra Graph** component uses `CassandraGraphVectorStore`, an instance of [LangChain graph vector store](https://python.langchain.com/api_reference/community/graph_vectorstores.html), for graph traversal and graph-based document retrieval in a compatible Cassandra-based cluster.
+It also supports writing to the vector store.
+
+
+
+### Cassandra Graph parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| database_ref | Contact Points / Astra Database ID | Input parameter. The contact points for the database or an Astra database ID. Required. |
+| username | Username | Input parameter. The username for the database. Leave empty for Astra DB. |
+| token | Password / Astra DB Token | Input parameter. The user password for the database or an Astra application token. Required. |
+| keyspace | Keyspace | Input parameter. The name of the keyspace containing the vector store specified in **Table Name** (`table_name`). Required. |
+| table_name | Table Name | Input parameter. The name of the table or collection that is the vector store. Required. |
+| setup_mode | Setup Mode | Input parameter. The configuration mode for setting up the Cassandra table. The options are `Sync` (default) or `Off`. |
+| cluster_kwargs | Cluster arguments | Input parameter. An optional dictionary of additional keyword arguments for the Cassandra cluster. |
+| search_query | Search Query | Input parameter. The query string for similarity search. Only relevant for reads. |
+| ingest_data | Ingest Data | Input parameter. Data to be loaded into the vector store as raw chunks and embeddings. Only relevant for writes. |
+| embedding | Embedding | Input parameter. The embedding model to use. |
+| number_of_results | Number of Results | Input parameter. The number of results to return in similarity search. Only relevant for reads. Default: 4. |
+| search_type | Search Type | Input parameter. The search type to use. The options are `Traversal` (default), `MMR Traversal`, `Similarity`, `Similarity with score threshold`, or `MMR (Max Marginal Relevance)`. |
+| depth | Depth of traversal | Input parameter. The maximum depth of edges to traverse. Only relevant if **Search Type** (`search_type`) is `Traversal` or `MMR Traversal`. Default: 1. |
+| search_score_threshold | Search Score Threshold | Input parameter. The minimum similarity score threshold for search results. Only relevant for reads using the `Similarity with score threshold` search type. |
+| search_filter | Search Metadata Filter | Input parameter. An optional dictionary of metadata search filters to apply in addition to graph traversal and similarity search. |
+
+### See also
+
+* [**DataStax** bundle](/bundles-datastax)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-chroma.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-chroma.mdx
new file mode 100644
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+---
+title: Chroma
+slug: /bundles-chroma
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Chroma** bundle.
+
+## Chroma DB
+
+You can use the **Chroma DB** component to read and write to a Chroma database using an instance of `Chroma` vector store.
+Includes support for remote or in-memory instances with or without persistence.
+
+
+About vector store instances
+
+
+
+
+
+When writing, the component can create a new database or collection at the specified location.
+
+:::tip
+An ephemeral (non-persistent) local Chroma vector store is helpful for testing vector search flows where you don't need to retain the database.
+:::
+
+
+
+### Use the Chroma DB component in a flow
+
+The following example flow uses one **Chroma DB** component for both reads and writes:
+
+
+
+* When writing, it splits `JSON` from a [**URL** component](/url) into chunks, computes embeddings with attached **Embedding Model** component, and then loads the chunks and embeddings into the Chroma vector store.
+To trigger writes, click **Run component** on the **Chroma DB** component.
+
+* When reading, it uses chat input to perform a similarity search on the vector store, and then print the search results to the chat.
+To trigger reads, open the **Playground** and enter a chat message.
+
+After running the flow once, you can click **Inspect Output** on each component to understand how the data transformed as it passed from component to component.
+
+### Chroma DB parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the provider's documentation or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+|------|------|-------------|
+| **Collection Name** (`collection_name`) | String | Input parameter. The name of your Chroma vector store collection. Default: `langflow`. |
+| **Persist Directory** (`persist_directory`) | String | Input parameter. To persist the Chroma database, enter a relative or absolute path to a directory to store the `chroma.sqlite3` file. Leave empty for an ephemeral database. When reading or writing to an existing persistent database, specify the path to the persistent directory. |
+| **Ingest Data** (`ingest_data`) | JSON or Table | Input parameter. `JSON` or `Table` input containing the records to write to the vector store. Only relevant for writes. |
+| **Search Query** (`search_query`) | String | Input parameter. The query to use for vector search. Only relevant for reads. |
+| **Cache Vector Store** (`cache_vector_store`) | Boolean | Input parameter. If `true`, the component caches the vector store in memory for faster reads. Default: Enabled (`true`). |
+| **Embedding** (`embedding`) | Embeddings | Input parameter. The embedding function to use for the vector store. By default, Chroma DB uses its built-in embeddings model, or you can attach an **Embedding Model** component to use a different provider or model. |
+| **CORS Allow Origins** (`chroma_server_cors_allow_origins`) | String | Input parameter. The allowed CORS origins for the Chroma server. |
+| **Chroma Server Host** (`chroma_server_host`) | String | Input parameter. The host for the Chroma server. |
+| **Chroma Server HTTP Port** (`chroma_server_http_port`) | Integer | Input parameter. The HTTP port for the Chroma server. |
+| **Chroma Server gRPC Port** (`chroma_server_grpc_port`) | Integer | Input parameter. The gRPC port for the Chroma server. |
+| **Chroma Server SSL Enabled** (`chroma_server_ssl_enabled`) | Boolean | Input parameter. Enable SSL for the Chroma server. |
+| **Allow Duplicates** (`allow_duplicates`) | Boolean | Input parameter. If `true` (default), writes don't check for existing duplicates in the collection, allowing you to store multiple copies of the same content. If `false`, writes won't add documents that match existing documents already present in the collection. If `false`, it can strictly enforce deduplication by searching the entire collection or only search the number of records, specified in `limit`. Only relevant for writes.|
+| **Search Type** (`search_type`) | String | Input parameter. The type of search to perform, either `Similarity` or `MMR`. Only relevant for reads. |
+| **Number of Results** (`number_of_results`) | Integer | Input parameter. The number of search results to return. Default: `10`. Only relevant for reads. |
+| **Limit** (`limit`) | Integer | Input parameter. Limit the number of records to compare when **Allow Duplicates** is `false`. This can help improve performance when writing to large collections, but it can result in some duplicate records. Only relevant for writes. |
+
+## See also
+
+* [**Local DB** component](/components-bundle-components#vector-stores-bundle)
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diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-cleanlab.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-cleanlab.mdx
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+---
+title: Cleanlab
+slug: /bundles-cleanlab
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+[Cleanlab](https://www.cleanlab.ai/) adds automation and trust to every data point going in and every prediction coming out of AI and RAG solutions.
+
+Use the Cleanlab components to integrate Cleanlab Evaluations with Langflow and unlock trustworthy agentic, RAG, and LLM pipelines with Cleanlab's evaluation and remediation suite.
+
+You can use these components to quantify the trustworthiness of any LLM response with a score between `0` and `1`, and explain why a response may be good or bad. For RAG or agent pipelines with context, you can evaluate context sufficiency, groundedness, helpfulness, and query clarity with quantitative scores. Additionally, you can remediate low-trust responses with warnings or fallback answers.
+
+Authentication is required with a Cleanlab API key.
+
+## Cleanlab Evaluator
+
+The **Cleanlab Evaluator** component evaluates and explains the trustworthiness of a prompt and response pair using Cleanlab. For more information on how the score works, see the [Cleanlab documentation](https://help.cleanlab.ai/tlm/).
+
+### Cleanlab Evaluator parameters
+
+
+
+| Name | Type | Description |
+|-------------------------|------------|------------------------------------|
+| system_prompt | Message | Input parameter. The system message prepended to the prompt. Optional. |
+| prompt | Message | Input parameter. The user-facing input to the LLM. |
+| response | Message | Input parameter. The model's response to evaluate. |
+| cleanlab_api_key | Secret | Input parameter. Your Cleanlab API key. |
+| cleanlab_evaluation_model | Dropdown | Input parameter. Evaluation model used by Cleanlab, such as GPT-4 or Claude. This doesn't need to be the same model that generated the response. |
+| quality_preset | Dropdown | Input parameter. Tradeoff between evaluation speed and accuracy. |
+
+### Cleanlab Evaluator outputs
+
+The **Cleanlab Evaluator** component has three possible outputs.
+
+| Name | Type | Description |
+|-------------------------|------------|-------------------------|
+| score | number, float | Displays the trust score between 0 and 1. |
+| explanation | `Message` | Provides an explanation of the trust score. |
+| response | `Message` | Returns the original response for easy chaining to the **Cleanlab Remediator** component. |
+
+## Cleanlab Remediator
+
+The **Cleanlab Remediator** component uses the trust score from the [**Cleanlab Evaluator** component](#cleanlab-evaluator) to determine whether to show, warn about, or replace an LLM response.
+
+This component has parameters for the score threshold, warning text, and fallback message that you can customize as needed.
+
+The output is **Remediated Response** (`remediated_response`), which is a `Message` containing the final message shown to the user after remediation logic is applied.
+
+### Cleanlab Remediator parameters
+
+| Name | Type | Description |
+|-----------------------------|------------|---------|
+| response | Message | Input parameter. The response to potentially remediate. |
+| score | Number | Input parameter. The trust score from `CleanlabEvaluator`. |
+| explanation | Message | Input parameter. The explanation to append if a warning is shown. Optional.|
+| threshold | Float | Input parameter. The minimum trust score to pass a response unchanged. |
+| show_untrustworthy_response | Boolean | Input parameter. Whether to display or hide the original response with a warning if a response is deemed untrustworthy. |
+| untrustworthy_warning_text | Prompt | Input parameter. The warning text for untrustworthy responses. |
+| fallback_text | Prompt | Input parameter. The fallback message if the response is hidden. |
+
+## Cleanlab RAG Evaluator
+
+The **Cleanlab RAG Evaluator** component evaluates RAG and LLM pipeline outputs for trustworthiness, context sufficiency, response groundedness, helpfulness, and query ease using [Cleanlab's evaluation metrics](https://help.cleanlab.ai/tlm/use-cases/tlm_rag/).
+
+You can pair this component with the [**Cleanlab Remediator** component](#cleanlab-remediator) to remediate low-trust responses coming from the RAG pipeline.
+
+### Cleanlab RAG Evaluator parameters
+
+
+
+| Name | Type | Description |
+|-----------------------------|------------|------------|
+| cleanlab_api_key | Secret | Input parameter. Your Cleanlab API key. |
+| cleanlab_evaluation_model | Dropdown | Input parameter. The evaluation model used by Cleanlab, such as GPT-4, or Claude. This doesn't need to be the same model that generated the response. |
+| quality_preset | Dropdown | Input parameter. The tradeoff between evaluation speed and accuracy. |
+| context | Message | Input parameter. The retrieved context from your RAG system. |
+| query | Message | Input parameter. The original user query. |
+| response | Message | Input parameter. The model's response based on the context and query. |
+| run_context_sufficiency | Boolean | Input parameter. Evaluate whether context supports answering the query. |
+| run_response_groundedness | Boolean | Input parameter. Evaluate whether the response is grounded in the context. |
+| run_response_helpfulness | Boolean | Input parameter. Evaluate how helpful the response is. |
+| run_query_ease | Boolean | Input parameter. Evaluate if the query is vague, complex, or adversarial. |
+
+### Cleanlab RAG Evaluator outputs
+
+The **Cleanlab RAG Evaluator** component has the following output options:
+
+| Name | Type | Description |
+|--------------------|------------|--------------------------|
+| trust_score | Number | The overall trust score. |
+| trust_explanation | Message | The explanation for the trust score. |
+| other_scores | Dictionary | A dictionary of optional enabled RAG evaluation metrics. |
+| evaluation_summary | Message | A Markdown summary of query, context, response, and evaluation results. |
+| response | Message | Returns the original response for easy chaining to the **Cleanlab Remediator** component. |
+
+## Example Cleanlab flows
+
+The following example flows show how to use the **Cleanlab Evaluator** and **Cleanlab Remediator** components to evaluate and remediate responses from any LLM, and how to use the **Cleanlab RAG Evaluator** component to evaluate RAG pipeline outputs.
+
+### Evaluate and remediate responses from an LLM
+
+This flow evaluates and remediates the trustworthiness of a response from any LLM using the **Cleanlab Evaluator** and **Cleanlab Remediator** components.
+
+You can [download the Evaluate and Remediate flow](/files/eval_and_remediate_cleanlab.json), and then [import the flow](/concepts-flows-import) to your Langflow instance.
+Or, you can build the flow from scratch by connecting the following components:
+
+* Connect the `Message` output from any **Language Model** or **Agent** component to the **Response** input of the **Cleanlab Evaluator** component.
+* Connect a **Prompt Template** component to the **Cleanlab Evaluator** component's **Prompt** input.
+
+
+
+
+
+When you run the flow, the **Cleanlab Evaluator** component returns a trust score and explanation from the flow.
+
+The **Cleanlab Remediator** component uses this trust score to determine whether to output the original response, warn about it, or replace it with a fallback answer.
+
+This example shows a response that was determined to be untrustworthy (a score of `.09`) and flagged with a warning by the **Cleanlab Remediator** component.
+
+
+
+To hide untrustworthy responses, configure the **Cleanlab Remediator** component to replace the response with a fallback message.
+
+
+
+### Evaluate RAG pipeline
+
+As an example, create a flow based on the **Vector Store RAG** template, and then add the **Cleanlab RAG Evaluator** component to evaluate the flow's context, query, and response.
+Connect the **context**, **query**, and **response** outputs from the other components in the RAG flow to the **Cleanlab RAG Evaluator** component.
+
+
+
+Here is an example of the `Evaluation Summary` output from the **Cleanlab RAG Evaluator** component:
+
+
+
+The `Evaluation Summary` includes the query, context, response, and all evaluation results. In this example, the `Context Sufficiency` and `Response Groundedness` scores are low (a score of `0.002`) because the context doesn't contain information about the query, and the response isn't grounded in the context.
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diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-clickhouse.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-clickhouse.mdx
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+---
+title: ClickHouse
+slug: /bundles-clickhouse
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **ClickHouse** bundle.
+
+## ClickHouse vector store
+
+The **ClickHouse** component reads and writes to a ClickHouse vector store using an instance of `ClickHouse` vector store.
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### ClickHouse parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [ClickHouse Documentation](https://clickhouse.com/docs/en/intro) or inspect [component code](/concepts-components#component-code).
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| host | hostname | Input parameter. The ClickHouse server hostname. Required. Default: `localhost`. |
+| port | port | Input parameter. The ClickHouse server port. Required. Default: `8123`. |
+| database | database | Input parameter. The ClickHouse database name. Required. |
+| table | Table name | Input parameter. The ClickHouse table name. Required. |
+| username | Username | Input parameter. ClickHouse username for authentication. Required. |
+| password | Password | Input parameter. ClickHouse password for authentication. Required. |
+| index_type | index_type | Input parameter. Type of the index, either `annoy` (default) or `vector_similarity`. |
+| metric | metric | Input parameter. Metric to compute distance for similarity search. The options are `angular` (default), `euclidean`, `manhattan`, `hamming`, `dot`. |
+| secure | Use HTTPS/TLS | Input parameter. If `true`, enables HTTPS/TLS for the ClickHouse server and overrides inferred values for interface or port arguments. Default: `false`. |
+| index_param | Param of the index | Input parameter. Index parameters. Default: `100,'L2Distance'`. |
+| index_query_params | index query params | Input parameter. Additional index query parameters. |
+| search_query | Search Query | Input parameter. The query string for similarity search. Only relevant for reads. |
+| ingest_data | Ingest Data | Input parameter. The records to load into the vector store. |
+| cache_vector_store | Cache Vector Store | Input parameter. If `true`, the component caches the vector store in memory for faster reads. Default: Enabled (`true`). |
+| embedding | Embedding | Input parameter. The embedding model to use. |
+| number_of_results | Number of Results | Input parameter. The number of search results to return. Default: `4`. Only relevant for reads. |
+| score_threshold | Score threshold | Input parameter. The threshold for similarity score comparison. Default: Unset (no threshold). Only relevant for reads. |
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diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-cloudflare.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-cloudflare.mdx
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+---
+title: Cloudflare
+slug: /bundles-cloudflare
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Cloudflare** bundle.
+
+## Cloudflare Workers AI Embeddings
+
+The **Cloudflare Workers AI Embeddings** component generates embeddings using [Cloudflare Workers AI models](https://developers.cloudflare.com/workers-ai/).
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### Cloudflare Workers AI Embeddings parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| account_id | Cloudflare account ID | Input parameter. Your [Cloudflare account ID](https://developers.cloudflare.com/fundamentals/setup/find-account-and-zone-ids/#find-account-id-workers-and-pages). |
+| api_token | Cloudflare API token | Input parameter. Your [Cloudflare API token](https://developers.cloudflare.com/fundamentals/api/get-started/create-token/). |
+| model_name | Model Name | Input parameter. A [supported model](https://developers.cloudflare.com/workers-ai/models/#text-embeddings) for embedding generation. |
+| strip_new_lines | Strip New Lines | Input parameter. Whether to strip new lines from the input text. |
+| batch_size | Batch Size | Input parameter. The number of texts to embed in each batch. |
+| api_base_url | Cloudflare API base URL | Input parameter. The base URL for the Cloudflare API. |
+| headers | Headers | Input parameter. Additional headers for the embedding generation API request. |
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diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-cohere.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-cohere.mdx
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+---
+title: Cohere
+slug: /bundles-cohere
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Cohere** bundle.
+
+For more information about Cohere features and functionality used by Cohere components, see the [Cohere documentation](https://cohere.ai/).
+
+## Cohere text generation
+
+This component generates text using Cohere's language models.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a Cohere model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### Cohere text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| Input | String | Input parameter. Specifies the input text for text generation. |
+| System Message | String | Input parameter. A [system message](https://docs.cohere.com/docs/system-instructions) to pass to the model. |
+| Stream | Boolean | Input parameter. Whether to stream the response. Only works in chat. Default: `false`. |
+| Cohere API Key | SecretString | Input parameter. Your Cohere API key. |
+| Temperature | Float | Input parameter. Specifies the randomness of sampling. Lower values (near 0) are more deterministic, and higher values (near 1) are more creative. Defaults to `0.75`. |
+
+## Cohere Embeddings
+
+The **Cohere Embeddings** component is used to load embedding models from Cohere.
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### Cohere Embeddings parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| cohere_api_key | SecretString | Input parameter. The API key required to authenticate with the Cohere service. |
+| model | String | Input parameter. The language model used for embedding text documents and performing queries. Default: `embed-english-v2.0` |
+| truncate | Boolean | Input parameter. How to handle input that exceeds the model's token limit. One of `NONE`, `START`, or `END` (default). For more information, see the [Cohere `truncate` API reference](https://docs.cohere.com/reference/embed#request.body.truncate). |
+| max_retries | Integer | Input parameter. The maximum number of retry attempts for failed requests. Default: `3` |
+| user_agent | String | Input parameter. A user agent string to include in requests. Default: `langchain`|
+| request_timeout | Float | Input parameter. The timeout duration for requests in seconds. Default: None |
+
+## Cohere Rerank
+
+This component finds and reranks documents using the Cohere API.
+
+Outputs `JSON` containing the reranked documents, limited by the **Top N** parameter.
+
+### Cohere Rerank parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| **Search Query** | String | Input parameter. The search query for reranking documents. |
+| **Search Results** | JSON | Input parameter. Connect search results output from a vector store component. Use this parameter to apply reranking after running a similarity search on your vector database. |
+| **Top N** | Integer | Input parameter. The number of documents to return after reranking. Default: `3`. |
+| **Cohere API Key** | SecretString | Input parameter. Your Cohere API key. |
+| **Model** | String | Input parameter. The re-ranker model to use. Default: `rerank-english-v3.0` |
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diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-cometapi.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-cometapi.mdx
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+---
+title: CometAPI
+slug: /bundles-cometapi
+description: Use CometAPI models in Langflow to generate text or output a Language Model for downstream Components.
+---
+
+import Icon from "@site/src/components/icon";
+
+[Bundles](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **CometAPI** bundle.
+
+For more information about CometAPI features and functionality used by CometAPI components, see the [CometAPI documentation](https://www.cometapi.com/).
+
+## CometAPI text generation
+
+This component generates text using CometAPI's language models through the CometAPI OpenAI-compatible endpoints.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a CometAPI model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### CometAPI parameters
+
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| api_key | SecretString | Input parameter. Your CometAPI API Key for authentication. |
+| model_name | String | Input parameter. The id of the CometAPI model to use. The component automatically fetches the latest available models from CometAPI when you provide a valid API key. For information about supported models, see the [CometAPI documentation](https://www.cometapi.com/). |
+| input_value | String | Input parameter. The input text to send to the model. |
+| system_message | String | Input parameter. A system message that helps set the behavior of the assistant. |
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to 0 for unlimited tokens. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: `[0.0, 2.0]`. Default: `0.7`. |
+| seed | Integer | Input parameter. The seed controls the reproducibility of the job. |
+| model_kwargs | Dict | Input parameter. Additional keyword arguments to pass to the model. |
+| json_mode | Boolean | Input parameter. If True, it will output JSON regardless of passing a schema. |
+| stream | Boolean | Input parameter. Whether to stream the response. Default: false. |
+| model | LanguageModel | Output parameter. An instance of ChatOpenAI configured with CometAPI parameters. |
+
+## Use CometAPI in a flow
+
+1. Sign up for a [CometAPI account](https://www.cometapi.com/).
+2. Obtain your API key from the CometAPI dashboard.
+3. In Langflow, add the **CometAPI** component to your flow.
+4. Enter your API key in the **CometAPI API Key** field.
+5. Select your preferred model from the **Model Name** menu.
+6. Configure other parameters as needed for your use case.
+7. Add other components to your flow as needed.
+
+ To perform a basic test, add **Chat Input** and **Chat Output** components to your flow, connect them to the **CometAPI** component accordingly, and then click **Playground** to test the connection and chat with your model.
+
+ For more advanced use cases, you can connect the CometAPI component to other components like **Prompt Template**, **Agent**, or **Smart Transform** components.
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-composio.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-composio.mdx
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+---
+title: Composio
+slug: /bundles-composio
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Composio** bundle.
+
+For information about specific Composio functionality, see the [Composio documentation](https://docs.composio.dev/introduction/intro/overview).
+
+## Use Composio components in a flow
+
+Composio components are primarily used as [tools for agents](/agents-tools).
+
+The **Composio** bundle includes an aggregate **Composio Tools** component and the following single-service components:
+
+
+Composio single-service components
+
+- **AgentQL**
+- **Agiled**
+- **Airtable**
+- **Apollo**
+- **Asana**
+- **Attio**
+- **Bitbucket**
+- **Bolna**
+- **Brightdata**
+- **Calendly**
+- **Canva**
+- **Canvas**
+- **Coda**
+- **Contentful**
+- **Digicert**
+- **Discord**
+- **Dropbox**
+- **ElevenLabs**
+- **Exa**
+- **Figma**
+- **Finage**
+- **Firecrawl**
+- **Fireflies**
+- **Fixer**
+- **Flexisign**
+- **Freshdesk**
+- **GitHub**
+- **Gmail**
+- **Google BigQuery**
+- **Google Calendar**
+- **Google Classroom**
+- **Google Docs**
+- **Google Meet**
+- **Google Sheets**
+- **Google Tasks**
+- **Heygen**
+- **Instagram**
+- **Jira**
+- **Jotform**
+- **Klaviyo**
+- **Linear**
+- **Listennotes**
+- **Mem0**
+- **Miro**
+- **Missive**
+- **Notion**
+- **OneDrive**
+- **Outlook**
+- **Pandadoc**
+- **PeopleDataLabs**
+- **PerplexityAI**
+- **Reddit**
+- **SerpAPI**
+- **Slack**
+- **Slackbot**
+- **Snowflake**
+- **Supabase**
+- **Tavily**
+- **TimelinesAI**
+- **Todoist**
+- **Wrike**
+- **YouTube**
+
+
+
+
+The **Composio Tools** component is an access point for multiple Composio services (tools).
+However, most of these services are also available as single-service components, which are recommended over the **Composio Tools** component.
+
+Although you can use single-service components for non-agentic actions in your flows, they are primarily used in **Tool Mode** with **Agent** components.
+In contrast, the **Composio Tools** component can _only_ be a tool for agents; it doesn't support non-agentic use.
+
+The following example demonstrates how to use the Composio **Gmail** component as a tool for an **Agent** component.
+This allows the agent to use Composio Gmail functionality, if necessary, when generating responses.
+You can connect other Composio components in the same way.
+
+1. Create a flow based on the **Simple Agent** template.
+
+2. In **Bundles**, find the **Composio** bundle, and then add the **Gmail** component to the flow.
+
+3. In the **Composio API Key** field, enter your Composio API key or use the `COMPOSIO_API_KEY` global variable.
+For more information, see [Composio authentication](#composio-authentication).
+
+ If the key is valid, the **Alert** is replaced by a **Success** indicator, and the **Actions** list populates with actions available to your API key.
+
+4. In the [component's header menu](/concepts-components#component-menus), enable **Tool Mode**.
+
+ If you are using the **Composio Tools** component, skip this step because the component is already configured as a tool.
+
+5. In the **Actions** list, configure the Gmail actions that you want to provide to the agent.
+You can select the actions you want to allow, and you can edit each action's slug (agentic label) and description, which help the agent decide which tools to use.
+
+6. Connect the **Gmail** component's **Toolset** output to the **Agent** component's **Tools** input.
+
+7. In the **Agent** component, enter your OpenAI API key or configure the **Agent** component to use a different LLM.
+For more information, see [Language model components](/components-models).
+
+ At this point, your flow has four connected components:
+
+ * The **Chat Input** component is connected to the **Agent** component's **Input** port.
+ This allows the flow to be triggered by an incoming prompt from a user or application.
+ * The **Gmail** component is connected to the **Agent** component as a tool.
+ The agent may not use this tool for every request; the agent only uses this connection if it decides the Gmail tool can help respond to the prompt.
+ * The **Agent** component's **Output** port is connected to the **Chat Output** component, which returns the final response to the user or application.
+
+ 
+
+8. To test the flow, click **Playground**, and then ask the LLM about the tools available to it.
+The agent should provide a list of connected tools, including the **Gmail** tool and the built-in tools in the **Agent** component.
+For example:
+
+ ```text
+ User:
+ What tools are available to you?
+
+ I have access to a variety of tools that allow me to help you with different tasks. Here are the main categories of tools available to me:
+
+ 1. Gmail Tools:
+ - Fetch emails, search, and filter messages.
+ - Fetch specific email details by message ID.
+ - Create email drafts (with attachments, HTML, CC/BCC, etc.).
+ - Delete email drafts or specific messages.
+
+ 2. Date & Time Tools:
+ - Get the current date and time in any timezone.
+
+ 3. Multi-Tool Use:
+ - Run multiple tools in parallel for efficiency.
+
+ If you have a specific task in mind, let me know and I can tell you which tool I would use or demonstrate how I can help!
+ ```
+
+9. To test a specific tool or function, tell the agent to perform an action that uses that tool.
+For example, ask the agent to write a draft email:
+
+ ```text
+ Create a draft email with the subject line "Greetings from Composio"
+ recipient: "your.email@address.com"
+ Body content: "Hello from composio!"
+ ```
+
+ The **Playground** prints the logic as the agent chooses the `GMAIL_CREATE_EMAIL_DRAFT` tool to create the email draft.
+ The following example response is abbreviated:
+
+ ```text
+ The draft email with the subject "Greetings from Composio" and body "Hello from composio!" has been successfully created.
+ ```
+
+ ```json
+ {
+ "recipient_email": "your.email@address.com",
+ "subject": "Greetings from Composio",
+ "body": "Hello from composio!",
+ "is_html": false
+ }
+
+ {
+ "data": {
+ "response_data": {
+ "id": "r-237981011463568567",
+ "message": {
+ "id": "195dd80528171132",
+ "threadId": "195dd80528171132",
+ "labelIds": [
+ "DRAFT"
+ ]
+ }
+ }
+ },
+ "error": null,
+ "successfull": true,
+ "successful": true
+ }
+ ```
+
+ For further confirmation, you can go to your Gmail account and find the message in your drafts folder.
+
+10. Optional: To add more Composio services, repeat these steps to add more Composio components to your flow.
+For each component, provide the necessary credentials, enable **Tool Mode**, configure the actions, and then connect it to the **Agent** component's **Tools** port.
+
+## Composio parameters
+
+All single-service Composio components have the same parameters, and the **Composio Tools** component has one additional parameter:
+
+| Name | Type | Description |
+|------|------|-------------|
+| entity_id | String | Input parameter. The entity ID for the Composio account. Default: `default`. This parameter is hidden by default in the visual editor. If you need to set this parameter, you can access it through the [component inspection panel](/concepts-components#component-menus). |
+| api_key | SecretString | Input parameter. The Composio API key for authentication with the Composio platform. Make sure the key authorizes the specific service that you want to use. For more information, see [Composio authentication](#composio-authentication). |
+| tool_name | Connection | Input parameter for the **Composio Tools** component only. Select the Composio service (tool) to connect to. |
+| action | List | Input parameter. Select actions to use. Available actions vary by service. Some actions might require premium access to a particular service. |
+
+## Composio authentication
+
+Composio components require authentication to the Composio platform with a Composio API key.
+
+You can provide this key directly in your components, or you can use the `COMPOSIO_API_KEY` [global variable](/configuration-global-variables), which Langflow can automatically load from your `.env` file.
+
+:::info
+The Composio API key _only_ handles the connection to Composio.
+Service provider authentication is managed through the Composio platform for each service that you want to use.
+:::
+
+Make sure that your Composio API key provides access to the required services for the components in your flow.
+For example, if you are using the Composio **Gmail** component, your Composio API key must have access to the Gmail service.
+
+## Composio output
+
+When used as tools for an agent, Composio components output [`Tools`](/data-types#tool), which is a list of tools for use by an agent.
+When called by the agent, the response from the Composio service is ingested by the agent, not passed directly as output to the user or application.
+
+In non-agentic use cases, the output is a [`Table`](/data-types#table) containing the response from the specified Composio service, depending on the component and action used in the flow.
+
+Because the **Composio Tools** component supports _only_ agentic use, it cannot output `Table`.
+All single-service Composio components can output either `Table` or `Tools`.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-couchbase.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-couchbase.mdx
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+---
+title: Couchbase
+slug: /bundles-couchbase
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Couchbase** bundle.
+
+## Couchbase vector store
+
+The **Couchbase** component reads and writes to a Couchbase vector store using an instance of `CouchbaseSearchVectorStore`.
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### Couchbase parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Couchbase documentation](https://docs.couchbase.com/home/index.html) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+|------|------|-------------|
+| couchbase_connection_string | SecretString | Input parameter. Couchbase Cluster connection string. Required. |
+| couchbase_username | String | Input parameter. Couchbase username for authentication. Required. |
+| couchbase_password | SecretString | Input parameter. Couchbase password for authentication. Required. |
+| bucket_name | String | Input parameter. Name of the Couchbase bucket. Required. |
+| scope_name | String | Input parameter. Name of the Couchbase scope. Required. |
+| collection_name | String | Input parameter. Name of the Couchbase collection. Required. |
+| index_name | String | Input parameter. Name of the Couchbase index. Required. |
+| ingest_data | JSON | Input parameter. The records to load into the vector store. Only relevant for writes. |
+| search_query | String | Input parameter. The query string for vector search. Only relevant for reads. |
+| cache_vector_store | Boolean | Input parameter. If `true`, the component caches the vector store in memory for faster reads. Default: Enabled (`true`). |
+| embedding | Embeddings | Input parameter. The embedding function to use for the vector store. |
+| number_of_results | Integer | Input parameter. Maximum number of search results to return. Default: 4. Only relevant for reads. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-cuga.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-cuga.mdx
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+---
+title: CUGA
+slug: /bundles-cuga
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+:::warning Model Provider Limitations
+The **CUGA** component only supports **OpenAI** and **watsonx** models. To use other model providers, use the core [**Agent** component](/agents) instead.
+:::
+
+The **CUGA (ConfigUrable Generalist Agent)** component is an advanced AI agent that executes complex tasks using tools, optional browser automation, and structured output generation.
+
+The **CUGA** component can be used in flows in place of an [**Agent** component](/agents).
+Like the core **Agent** component, the **CUGA** component can use tools connected to its **Tools** port, and can be used as a tool itself.
+It also includes some additional features:
+
+* Browser automation for web scraping with [Playwright](https://playwright.dev/docs/intro).
+To enable web scraping, set the component's `browser_enabled` parameter to `true`.
+* Load custom instructions for the agent to execute.
+To use this feature, use the component's **Instructions** input to attach markdown files containing agent instructions.
+
+For more information, see the [CUGA project repository](https://github.com/cuga-project/cuga-agent).
+
+## Use the CUGA component in a flow
+
+For demonstration purposes, the following example modifies a template flow to use the **CUGA** component.
+
+
+
+1. Create a flow based on the **Simple Agent** template, and then replace the **Agent** component with the **CUGA** component.
+2. Connect an [**MCP Tools** component](/mcp-client) and a [**Calculator** component](/calculator) to the **CUGA** component's **Tools** port, and then connect the **MCP Tools** component to any MCP server.
+ This example connects to a server containing sales data for a business organization.
+3. Add a [**Read File** component](/read-file), and then connect it to the **CUGA** component's **Instructions** port.
+ Alternatively, click **Edit text** to open the **Edit text content** pane, and enter your instructions directly into the **CUGA** component.
+4. Create a Markdown file on your computer called `instructions.md`, and then insert the following content.
+ It's important to clearly format the document with `## Plan` and `## Answer` for the agent to understand your instructions.
+ ```markdown
+ ## Plan
+
+ - Break down complex queries into subtasks
+ - Prioritize information gathering before execution
+ - Consider dependencies between actions
+ - Validate intermediate results before proceeding
+
+ ## Answer
+
+ - Provide concise summaries with key findings
+ - Include relevant data points and metrics
+ - Cite sources when using MCP tool results
+ - Use clear structure and formatting for readability
+ ```
+5. In the **Read File** component, click **Add File**, and then attach your `instructions.md` file.
+
+6. Open the **Playground**, and then ask the agent a question that could use your connected MCP server.
+This example asked about the sales data provided by the MCP Server, such as `Which accounts are available?`.
+ The agent describes the tool calls it makes and then returns an answer according to the instructions.
+ For example, the list of available accounts is very large, but the **CUGA** component returns a concise summary as requested in the policy.
+ ```
+ Based on the available data, here are the accounts:
+
+ Summit Solutions (NY) - Revenue: $1,200,000
+ Pacific Ventures (CA) - Revenue: $9,500,000
+ Lone Star Corp (TX) - Revenue: $4,500,000
+ Mountain Peak Systems (CO) - Revenue: $2,100,000
+ Digital Dynamics (CA) - Revenue: $5,500,000
+ Cascade Computing (WA) - Revenue: $4,300,000
+ Data Flow Systems (CA) - Revenue: $8,900,000
+ Rocky Mountain Enterprises (CO) - Revenue: $3,200,000
+ Blue Sky Partners (TX) - Revenue: $500,000
+ Liberty Manufacturing (PA) - Revenue: $3,400,000
+
+ This is a partial list; there are more accounts available. The total revenue across all accounts is $210,200,000.
+ ```
+
+## CUGA parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| agent_llm | Dropdown | Model provider for the agent.|
+| instructions | Multiline String | Custom instructions that define the agent's planning and answers. Can be provided directly or through Markdown files. Formatting is important in order for the agent to understand the instructions. See [Use the CUGA component in a flow](#use-the-cuga-component-in-a-flow). |
+| n_messages | Integer | Number of chat history messages to retrieve. Useful for maintaining context in ongoing conversations identified by `session_id`. Default: `100`. |
+| format_instructions | Multiline String | Template for structured output. |
+| output_schema | Table | When `output_schema` is provided, structured responses are validated against a dynamically built schema. Invalid items are returned alongside validation errors. Fields: `name`, `description`, `type` (`str`, `int`, `float`, `bool`, `dict`), `multiple` (as list).|
+| add_current_date_tool | Boolean | If true, adds a tool that returns the current date. Default: `true`. |
+| lite_mode | Boolean | Set to `true` to enable CugaLite mode for faster execution when using a smaller number of tools. Default: `true`. |
+| lite_mode_tool_threshold | Integer | The threshold to automatically enable CugaLite. If the CUGA component has fewer tools connected than this threshold, CugaLite is activated. Default: `25`. |
+| shortlisting_tool_threshold | Integer | The threshold for tool shortlisting. When the total number of tools exceeds this threshold, the CUGA component enables its `find_tools` feature to filter tools down to a smaller subset before making tool selection decisions. This helps reduce token usage and improve performance when working with large numbers of tools. Default: `35`. |
+| decomposition_strategy | Dropdown | Strategy for task decomposition. `flexible` allows multiple subtasks per app. `exact` enforces one subtask per app. Default: `flexible`. |
+| browser_enabled | Boolean | Enable a built-in browser for web scraping and search. Allows the agent to use general web search in its responses. Disable (`false`) to restrict the agent to the context provided in the flow. Default: `false`. |
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-datastax.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-datastax.mdx
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+---
+title: DataStax
+slug: /bundles-datastax
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **DataStax** bundle, including components that read and write to Astra DB databases.
+
+## Astra DB
+
+:::warning
+It is recommended that you create any databases, keyspaces, and collections you need before configuring the **Astra DB** component.
+
+You can create new databases and collections through this component, but this is only possible in the Langflow visual editor (not at runtime), and you must wait while the database or collection initializes before proceeding with flow configuration.
+Additionally, not all database and collection configuration options are available through the **Astra DB** component, such as hybrid search options, PCU groups, vectorize integration management, and multi-region deployments.
+:::
+
+The **Astra DB** component reads and writes to Astra DB Serverless databases, using an instance of `AstraDBVectorStore` to call the Data API and DevOps API.
+
+
+About vector store instances
+
+
+
+
+
+### Astra DB parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/index.html) or inspect [component code](/concepts-components#component-code).
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| token | Astra DB Application Token | Input parameter. An Astra application token with permission to access your vector database. Once the connection is verified, additional fields are populated with your existing databases and collections. If you want to create a database through this component, the application token must have Organization Administrator permissions. |
+| environment | Environment | Input parameter. The environment for the Astra DB API endpoint. Typically always `prod`. |
+| database_name | Database | Input parameter. The name of the database that you want this component to connect to. Or, you can select **New Database** to create a new database, and then wait for the database to initialize before setting the remaining parameters. |
+| endpoint | Astra DB API Endpoint | Input parameter. For multi-region databases, select the API endpoint for your nearest datacenter. To get the list of regions for a multi-region database, see [List database regions](https://docs.datastax.com/en/astra-db-serverless/databases/manage-regions.html#list-db-regions). This field is automatically populated when you select a database, and it defaults to the primary region's endpoint. |
+| keyspace | Keyspace | Input parameter. The keyspace in your database that contains the collection specified in `collection_name`. Default: `default_keyspace`. |
+| collection_name | Collection | Input parameter. The name of the collection that you want to use with this flow. Or, select **New Collection** to create a new collection with limited configuration options. To ensure your collection is configured with the correct embedding provider and search capabilities, it is recommended to create the collection in the Astra Portal or with the Data API *before* configuring this component. For more information, see [Manage collections in Astra DB Serverless](https://docs.datastax.com/en/astra-db-serverless/databases/manage-collections.html). |
+| embedding_model | Embedding Model | Input parameter. Attach an [embedding model component](/components-embedding-models) to generate embeddings. Only available if the specified collection doesn't have a [vectorize integration](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html). If a vectorize integration exists, the component automatically uses the collection's integrated model. |
+| ingest_data | Ingest Data | Input parameter. The documents to load into the specified collection. Accepts `JSON` or `Table` input. |
+| search_query | Search Query | Input parameter. The query string for vector search. |
+| cache_vector_store | Cache Vector Store | Input parameter. Whether to cache the vector store in Langflow memory for faster reads. Default: Enabled (`true`). |
+| search_method | Search Method | Input parameter. The search methods to use, either `Hybrid Search` or `Vector Search`. Your collection must be configured to support the chosen option, and the default depends on what your collection supports. All vector-enabled collections in Astra DB Serverless (vector) databases support vector search, but hybrid search requires that you set specific collection settings when creating the collection. These options are only available when creating a collection programmatically. For more information, see [Ways to find data in Astra DB Serverless](https://docs.datastax.com/en/astra-db-serverless/databases/about-search.html) and [Create a collection that supports hybrid search](https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-methods/create-collection.html#example-hybrid). |
+| reranker | Reranker | Input parameter. The re-ranker model to use for hybrid search, depending on the collection configuration. This parameter is only available for collections that support hybrid search. To determine if a collection supports hybrid search, [get collection metadata](https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-methods/list-collection-metadata.html), and then check that `lexical` and `rerank` both have `"enabled": true`. |
+| lexical_terms | Lexical Terms | Input parameter. A space-separated string of keywords for hybrid search, like `features, data, attributes, characteristics`. This parameter is only available if the collection supports hybrid search. For more information, see the [Hybrid search example](#astra-db-examples). |
+| number_of_results | Number of Search Results | Input parameter. The number of search results to return. Default: 4. |
+| search_type | Search Type | Input parameter. The search type to use, either `Similarity` (default), `Similarity with score threshold`, and `MMR (Max Marginal Relevance)`. |
+| search_score_threshold | Search Score Threshold | Input parameter. The minimum similarity score threshold for vector search results with the `Similarity with score threshold` search type. Default: 0. |
+| advanced_search_filter | Search Metadata Filter | Input parameter. An optional dictionary of metadata filters to apply in addition to vector or hybrid search. |
+| autodetect_collection | Autodetect Collection | Input parameter. Whether to automatically fetch a list of available collections after providing an application token and API endpoint. |
+| content_field | Content Field | Input parameter. For writes, this parameter specifies the name of the field in the documents that contains text strings for which you want to generate embeddings. |
+| deletion_field | Deletion Based On Field | Input parameter. When provided, documents in the target collection with metadata field values matching the input metadata field value are deleted before new records are loaded. Use this setting for writes with upserts (overwrites). |
+| ignore_invalid_documents | Ignore Invalid Documents | Input parameter. Whether to ignore invalid documents during writes. If disabled (`false`), then an error is raised for invalid documents. Default: Enabled (`true`). |
+| astradb_vectorstore_kwargs | AstraDBVectorStore Parameters | Input parameter. An optional dictionary of additional parameters for the `AstraDBVectorStore` instance. |
+
+### Astra DB examples
+
+
+Example: Vector RAG
+
+import PartialVectorRagFlow from '@site/docs/_partial-vector-rag-flow.mdx';
+
+
+
+
+
+
+Example: Hybrid search
+
+The **Astra DB** component supports the Data API's [hybrid search](https://docs.datastax.com/en/astra-db-serverless/databases/hybrid-search.html) feature.
+Hybrid search performs a vector similarity search and a lexical search, compares the results of both searches, and then returns the most relevant results overall.
+
+To use hybrid search through the **Astra DB** component, do the following:
+
+1. Use the Data API to [create a collection that supports hybrid search](https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-methods/create-collection.html#example-hybrid) if you don't already have one.
+
+ Although you can create a collection through the **Astra DB** component, you have more control and insight into the collection settings when using the Data API for this operation.
+
+2. Create a flow based on the **Hybrid Search RAG** template, which includes an **Astra DB** component that is pre-configured for hybrid search.
+
+ After loading the template, check for **Upgrade available** alerts on the components.
+ If any components have an upgrade pending, upgrade and reconnect them before continuing.
+
+3. In the **Language Model** components, add your OpenAI API key.
+If you want to use a different provider or model, see [Language model components](/components-models).
+
+4. Delete the **Language Model** component that is connected to the **Structured Output** component's **Input Message** port, and then connect the **Chat Input** component to that port.
+
+5. Configure the **Astra DB** vector store component:
+
+ 1. Enter your Astra DB application token.
+ 2. In the **Database** field, select your database.
+ 3. In the **Collection** field, select your collection with hybrid search enabled.
+
+ Once you select a collection that supports hybrid search, the other parameters automatically update to allow hybrid search options.
+
+6. Connect the first **Parser** component's **Parsed Text** output to the **Astra DB** component's **Lexical Terms** input.
+This input only appears after connecting a collection that support hybrid search with reranking.
+
+7. Update the **Structured Output** template:
+
+ 1. Click the **Structured Output** component to expose the [component inspection panel](/concepts-components#component-menus).
+ 2. Find the **Format Instructions** row, click **Expand**, and then replace the prompt with the following text:
+
+ ```text
+ You are a database query planner that takes a user's requests, and then converts to a search against the subject matter in question.
+ You should convert the query into:
+ 1. A list of keywords to use against a Lucene text analyzer index, no more than 4. Strictly unigrams.
+ 2. A question to use as the basis for a QA embedding engine.
+ Avoid common keywords associated with the user's subject matter.
+ ```
+
+ 3. Click **Finish Editing**, and then click **Close** to save your changes to the component.
+
+8. Open the **Playground**, and then enter a natural language question that you would ask about your database.
+
+ In this example, your input is sent to both the **Astra DB** and **Structured Output** components:
+
+ * The input sent directly to the **Astra DB** component's **Search Query** port is used as a string for similarity search.
+ An embedding is generated from the query string using the collection's Astra DB vectorize integration.
+
+ * The input sent to the **Structured Output** component is processed by the **Structured Output**, **Language Model**, and **Parser** components to extract space-separated `keywords` used for the lexical search portion of the hybrid search.
+
+ The complete hybrid search query is executed against your database using the Data API's `find_and_rerank` command.
+ The API's response is output as a `Table` that is transformed into a text string `Message` by another **Parser** component.
+ Finally, the **Chat Output** component prints the `Message` response to the **Playground**.
+
+9. Optional: Exit the **Playground**, and then click **Inspect Output** on each individual component to understand how lexical keywords were constructed and view the raw response from the Data API.
+This is helpful for debugging flows where a certain component isn't receiving input as expected from another component.
+
+ * **Structured Output component**: The output is the `JSON` object produced by applying the output schema to the LLM's response to the input message and format instructions.
+ The following example is based on the aforementioned instructions for keyword extraction:
+
+ ```
+ 1. Keywords: features, data, attributes, characteristics
+ 2. Question: What characteristics can be identified in my data?
+ ```
+
+ * **Parser component**: The output is the string of keywords extracted from the structured output `JSON`, and then used as lexical terms for the hybrid search.
+
+ * **Astra DB component**: The output is the `Table` containing the results of the hybrid search as returned by the Data API.
+
+
+
+### Astra DB output
+
+
+
+
+Vector Store Connection port
+
+The **Astra DB** component has an additional **Vector Store Connection** output.
+This output can only connect to a `VectorStore` input port, and it was intended for use with dedicated Graph RAG components.
+
+The only non-legacy component that supports this input is the [**Graph RAG** component](#graph-rag), which can be a Graph RAG extension to the **Astra DB** component.
+Instead, use the **Astra DB Graph** component that includes both the vector store connection and Graph RAG functionality.
+
+
+
+## Astra DB CQL
+
+The **Astra DB CQL** component allows agents to query data from CQL tables in Astra DB.
+
+The output is a list of [`JSON`](/data-types#json) objects containing the query results from the Astra DB CQL table. Each Data object contains the document fields specified by the projection fields. Limited by the `number_of_results` parameter.
+
+### Astra DB CQL parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| Tool Name | String | Input parameter. The name used to reference the tool in the agent's prompt. |
+| Tool Description | String | Input parameter. A brief description of the tool to guide the model in using it. |
+| Keyspace | String | Input parameter. The name of the keyspace. |
+| Table Name | String | Input parameter. The name of the Astra DB CQL table to query. |
+| Token | SecretString | Input parameter. The authentication token for Astra DB. |
+| API Endpoint | String | Input parameter. The Astra DB API endpoint. |
+| Projection Fields | String | Input parameter. The attributes to return, separated by commas. Default: "*". |
+| Partition Keys | Dict | Input parameter. Required parameters that the model must fill to query the tool. |
+| Clustering Keys | Dict | Input parameter. Optional parameters the model can fill to refine the query. Required parameters should be marked with an exclamation mark, for example, `!customer_id`. |
+| Static Filters | Dict | Input parameter. Attribute-value pairs used to filter query results. |
+| Limit | String | Input parameter. The number of records to return. |
+
+## Graph RAG
+
+The **Graph RAG** component uses an instance of [`GraphRetriever`](https://datastax.github.io/graph-rag/reference/langchain_graph_retriever/) for Graph RAG traversal enabling graph-based document retrieval in an Astra DB vector store.
+For more information, see the [DataStax Graph RAG documentation](https://datastax.github.io/graph-rag/).
+
+### Graph RAG parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| embedding_model | Embedding Model | Input parameter. Specify the embedding model to use. Not required if the connected vector store has a [vectorize integration](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html). |
+| vector_store | Vector Store Connection | Input parameter. An instance of `AstraDbVectorStore` inherited from the [**Astra DB** component](#astra-db)'s **Vector Store Connection** output. |
+| edge_definition | Edge Definition | Input parameter. [Edge definition](https://datastax.github.io/graph-rag/reference/graph_retriever/edges/) for the graph traversal. |
+| strategy | Traversal Strategies | Input parameter. The strategy to use for graph traversal. Strategy options are dynamically loaded from available strategies. |
+| search_query | Search Query | Input parameter. The query to search for in the vector store. |
+| graphrag_strategy_kwargs | Strategy Parameters | Input parameter. Optional dictionary of additional parameters for the [retrieval strategy](https://datastax.github.io/graph-rag/reference/graph_retriever/strategies/). |
+| search_results | **Search Results** or **DataFrame** | Output parameter. The results of the graph-based document retrieval as a list of [`JSON`](/data-types#json) objects or as a tabular [`Table`](/data-types#table). You can set the desired output type near the component's output port. |
+
+## Hyper-Converged Database (HCD)
+
+The **Hyper-Converged Database (HCD)** component uses your cluster's Data API server to read and write to your HCD vector store.
+Because the underlying functions call the Data API, which originated from Astra DB, the component uses an instance of `AstraDBVectorStore`.
+
+
+
+
+About vector store instances
+
+
+
+
+
+
+
+For more information about HCD, see [Get started with HCD 1.2](https://docs.datastax.com/en/hyper-converged-database/1.2/get-started/get-started-hcd.html) and [Get started with the Data API in HCD 1.2](https://docs.datastax.com/en/hyper-converged-database/1.2/api-reference/dataapiclient.html).
+
+### HCD parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| collection_name | Collection Name | Input parameter. The name of a vector store collection in HCD. For write operations, if the collection doesn't exist, then a new one is created. Required. |
+| username | HCD Username | Input parameter. Username for authenticating to your HCD deployment. Default: `hcd-superuser`. Required. |
+| password | HCD Password | Input parameter. Password for authenticating to your HCD deployment. Required. |
+| api_endpoint | HCD API Endpoint | Input parameter. Your deployment's HCD Data API endpoint, formatted as `http[s]://CLUSTER_HOST:GATEWAY_PORT` where `CLUSTER_HOST` is the IP address of any node in your cluster and `GATEWAY_PORT` is the port number for your API gateway service. For example, `http://192.0.2.250:8181`. Required. |
+| ingest_data | Ingest Data | Input parameter. Records to load into the vector store. Only relevant for writes. |
+| search_input | Search Input | Input parameter. Query string for similarity search. Only relevant to reads. |
+| namespace | Namespace | Input parameter. The namespace in HCD that contains or will contain the collection specified in `collection_name`. Default: `default_namespace`. |
+| ca_certificate | CA Certificate | Input parameter. Optional CA certificate for TLS connections to HCD. |
+| metric | Metric | Input parameter. The metrics to use for similarity search calculations, either `cosine`, `dot_product`, or `euclidean`. This is a collection setting. If calling an existing collection, leave unset to use the collection's metric. If a write operation creates a new collection, specify the desired similarity metric setting. |
+| batch_size | Batch Size | Input parameter. Optional number of records to process in a single batch. |
+| bulk_insert_batch_concurrency | Bulk Insert Batch Concurrency | Input parameter. Optional concurrency level for bulk write operations. |
+| bulk_insert_overwrite_concurrency | Bulk Insert Overwrite Concurrency | Input parameter. Optional concurrency level for bulk write operations that allow upserts (overwriting existing records). |
+| bulk_delete_concurrency | Bulk Delete Concurrency | Input parameter. Optional concurrency level for bulk delete operations. |
+| setup_mode | Setup Mode | Input parameter. Configuration mode for setting up the vector store, either `Sync` (default), `Async`, or `Off`. |
+| pre_delete_collection | Pre Delete Collection | Input parameter. Whether to delete the collection before creating a new one. |
+| metadata_indexing_include | Metadata Indexing Include | Input parameter. A list of metadata fields to index if you want to enable [selective indexing](https://docs.datastax.com/en/hyper-converged-database/1.2/api-reference/collection-indexes.html) *only* when creating a collection. Doesn't apply to existing collections. Only one `*_indexing_*` parameter can be set per collection. If all `*_indexing_*` parameters are unset, then all fields are indexed (default indexing). |
+| metadata_indexing_exclude | Metadata Indexing Exclude | Input parameter. A list of metadata fields to exclude from indexing if you want to enable selective indexing *only* when creating a collection. Doesn't apply to existing collections. Only one `*_indexing_*` parameter can be set per collection. If all `*_indexing_*` parameters are unset, then all fields are indexed (default indexing). |
+| collection_indexing_policy | Collection Indexing Policy | Input parameter. A dictionary to define the indexing policy if you want to enable selective indexing *only* when creating a collection. Doesn't apply to existing collections. Only one `*_indexing_*` parameter can be set per collection. If all `*_indexing_*` parameters are unset, then all fields are indexed (default indexing). The `collection_indexing_policy` dictionary is used when you need to set indexing on subfields or a complex indexing definition that isn't compatible as a list. |
+| embedding | Embedding or Astra Vectorize | Input parameter. The embedding model to use by attaching an **Embedding Model** component. This component doesn't support additional vectorize authentication headers, so it isn't possible to use a vectorize integration with this component, even if you have enabled one on an existing HCD collection. |
+| number_of_results | Number of Results | Input parameter. Number of search results to return. Default: 4. Only relevant to reads. |
+| search_type | Search Type | Input parameter. Search type to use, either `Similarity` (default), `Similarity with score threshold`, or `MMR (Max Marginal Relevance)`. Only relevant to reads. |
+| search_score_threshold | Search Score Threshold | Input parameter. Minimum similarity score threshold for search results if the `search_type` is `Similarity with score threshold`. Default: 0. |
+| search_filter | Search Metadata Filter | Input parameter. Optional dictionary of metadata filters to apply in addition to vector search. |
+
+## Other DataStax components
+
+The following components are also included in the **DataStax** bundle.
+
+### Astra DB Chat Memory
+
+The **Astra DB Chat Memory** component retrieves and stores chat messages using an Astra DB database.
+
+Chat memories are passed between memory storage components as the [`Memory`](/data-types#memory) data type.
+Specifically, the component creates an instance of `AstraDBChatMessageHistory`, which is a LangChain chat message history class that uses Astra DB for storage.
+
+:::warning
+The **Astra DB Chat Memory** component isn't recommended for most memory storage because memories tend to be long JSON objects or strings, often exceeding the maximum size of a document or object supported by Astra DB.
+
+However, Langflow's **Agent** component includes built-in chat memory that is enabled by default.
+Your agentic flows don't need an external database to store chat memory.
+For more information, see [Memory management options](/memory).
+:::
+
+For more information about using external chat memory in flows, see the [**Message History** component](/message-history).
+
+#### Astra DB Chat Memory parameters
+
+
+
+| Name | Type | Description |
+|------------------|---------------|-----------------------------------------------------------------------|
+| collection_name | String | Input parameter. The name of the Astra DB collection for storing messages. Required. |
+| token | SecretString | Input parameter. The authentication token for Astra DB access. Required. |
+| api_endpoint | SecretString | Input parameter. The API endpoint URL for the Astra DB service. Required. |
+| namespace | String | Input parameter. The optional namespace within Astra DB for the collection. |
+| session_id | MessageText | Input parameter. The unique identifier for the chat session. Uses the current session ID if not provided. |
+
+
+## Legacy DataStax components
+
+import PartialLegacy from '@site/docs/_partial-legacy.mdx';
+
+
+
+The following DataStax components are in legacy status:
+
+
+Astra DB Tool
+
+Replace the **Astra DB Tool** component with the [**Astra DB** component](#astra-db).
+
+The **Astra DB Tool** component enables searching data in Astra DB collections, including hybrid search, vector search, and regular filter-based search.
+Specialized searches require that the collection is pre-configured with the required parameters.
+
+Outputs a list of [`JSON`](/data-types#json) objects containing the query results from Astra DB. Each `JSON` object contains the document fields specified by the projection attributes. Limited by the `number_of_results` parameter and the upper limit of the Astra DB Data API, depending on the type of search.
+
+You can use the component to execute queries directly as isolated steps in a flow, or you can connect it as a [tool for an agent](/agents-tools) to allow the agent to query data from Astra DB collections as needed to respond to user queries.
+
+
+The values for **Collection Name**, **Astra DB Application Token**, and **Astra DB API Endpoint** are found in your Astra DB deployment. For more information, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html).
+
+| Name | Type | Description |
+|-------------------|--------|--------|
+| Tool Name | String | Input parameter. The name used to reference the tool in the agent's prompt. |
+| Tool Description | String | Input parameter. A brief description of the tool. This helps the model decide when to use it. |
+| Keyspace Name | String | Input parameter. The name of the keyspace in Astra DB. Default: `default_keyspace` |
+| Collection Name | String | Input parameter. The name of the Astra DB collection to query. |
+| Token | SecretString | Input parameter. The authentication token for accessing Astra DB. |
+| API Endpoint | String | Input parameter. The Astra DB API endpoint. |
+| Projection Fields | String | Input parameter. Comma-separated list of attributes to return from matching documents. The default is the default projection, `*`, which returns all attributes except reserved fields like `$vector`. |
+| Tool Parameters | Dict | Input parameter. [Astra DB Data API `find` filters](https://docs.datastax.com/en/astra-db-serverless/api-reference/document-methods/find-many.html#parameters) that become tools for an agent. These Filters _may_ be used in a search, if the agent selects them. |
+| Static Filters | Dict | Input parameter. Attribute-value pairs used to filter query results. Equivalent to [Astra DB Data API `find` filters](https://docs.datastax.com/en/astra-db-serverless/api-reference/document-methods/find-many.html#parameters). **Static Filters** are included with _every_ query. Use **Static Filters** without semantic search to perform a regular filter search. |
+| Number of Results | Int | Input parameter. The maximum number of documents to return. |
+| Semantic Search | Boolean | Input parameter. Whether to run a similarity search by generating a vector embedding from the chat input and following the **Semantic Search Instruction**. Default: `false`. If `true`, you must attach an [embedding model component](/components-embedding-models) or have vectorize pre-enabled on your collection. |
+| Use Astra DB Vectorize | Boolean | Input parameter. Whether to use the Astra DB vectorize feature for embedding generation when running a semantic search. Default: `false`. If `true`, you must have vectorize pre-enabled on your collection. |
+| Embedding Model | Embedding | Input parameter. A port to attach an embedding model component to generate a vector from input text for semantic search. This can be used when **Semantic Search** is `true`, with or without vectorize. Be sure to use a model that aligns with the dimensions of the embeddings already present in the collection. |
+| Semantic Search Instruction | String | Input parameter. The query to use for similarity search. Default: `"Find documents similar to the query."`. This instruction is used to guide the model in performing semantic search. |
+
+
+
+
+
+Astra DB Graph
+
+Replace the **Astra DB Graph** component with the [**Graph RAG** component](#graph-rag).
+
+The **Astra DB Graph** component uses `AstraDBGraphVectorStore`, an instance of [LangChain graph vector store](https://python.langchain.com/api_reference/community/graph_vectorstores.html), for graph traversal and graph-based document retrieval in an Astra DB collection. It also supports writing to the vector store.
+For more information, see [Build a Graph RAG system with LangChain and GraphRetriever](https://docs.datastax.com/en/astra-db-serverless/tutorials/graph-rag.html).
+
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Astra DB Serverless documentation](https://docs.datastax.com/en/astra-db-serverless/index.html) or inspect [component code](/concepts-components#component-code).
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| token | Astra DB Application Token | Input parameter. An Astra application token with permission to access your vector database. Once the connection is verified, additional fields are populated with your existing databases and collections. If you want to create a database through this component, the application token must have Organization Administrator permissions. |
+| api_endpoint | API Endpoint | Input parameter. Your database's API endpoint. |
+| keyspace | Keyspace | Input parameter. The keyspace in your database that contains the collection specified in `collection_name`. Default: `default_keyspace`. |
+| collection_name | Collection | Input parameter. The name of the collection that you want to use with this flow. For write operations, if a matching collection doesn't exist, a new one is created. |
+| metadata_incoming_links_key | Metadata Incoming Links Key | Input parameter. The metadata key for the incoming links in the vector store. |
+| ingest_data | Ingest Data | Input parameter. Records to load into the vector store. Only relevant for writes. |
+| search_input | Search Query | Input parameter. Query string for similarity search. Only relevant to reads. |
+| cache_vector_store | Cache Vector Store | Input parameter. Whether to cache the vector store in Langflow memory for faster reads. Default: Enabled (`true`). |
+| embedding_model | Embedding Model | Input parameter. Attach an [embedding model component](/components-embedding-models) to generate embeddings. If the collection has a [vectorize integration](https://docs.datastax.com/en/astra-db-serverless/databases/embedding-generation.html), don't attach an embedding model component. |
+| metric | Metric | Input parameter. The metrics to use for similarity search calculations, either `cosine` (default), `dot_product`, or `euclidean`. This is a collection setting. |
+| batch_size | Batch Size | Input parameter. Optional number of records to process in a single batch. |
+| bulk_insert_batch_concurrency | Bulk Insert Batch Concurrency | Input parameter. Optional concurrency level for bulk write operations. |
+| bulk_insert_overwrite_concurrency | Bulk Insert Overwrite Concurrency | Input parameter. Optional concurrency level for bulk write operations that allow upserts (overwriting existing records). |
+| bulk_delete_concurrency | Bulk Delete Concurrency | Input parameter. Optional concurrency level for bulk delete operations. |
+| setup_mode | Setup Mode | Input parameter. Configuration mode for setting up the vector store, either `Sync` (default) or `Off`. |
+| pre_delete_collection | Pre Delete Collection | Input parameter. Whether to delete the collection before creating a new one. Default: Disabled (`false`). |
+| metadata_indexing_include | Metadata Indexing Include | Input parameter. A list of metadata fields to index if you want to enable [selective indexing](https://docs.datastax.com/en/astra-db-serverless/api-reference/collection-indexes.html) *only* when creating a collection. Doesn't apply to existing collections. Only one `*_indexing_*` parameter can be set per collection. If all `*_indexing_*` parameters are unset, then all fields are indexed (default indexing). |
+| metadata_indexing_exclude | Metadata Indexing Exclude | Input parameter. A list of metadata fields to exclude from indexing if you want to enable selective indexing *only* when creating a collection. Doesn't apply to existing collections. Only one `*_indexing_*` parameter can be set per collection. If all `*_indexing_*` parameters are unset, then all fields are indexed (default indexing). |
+| collection_indexing_policy | Collection Indexing Policy | Input parameter. A dictionary to define the indexing policy if you want to enable selective indexing *only* when creating a collection. Doesn't apply to existing collections. Only one `*_indexing_*` parameter can be set per collection. If all `*_indexing_*` parameters are unset, then all fields are indexed (default indexing). The `collection_indexing_policy` dictionary is used when you need to set indexing on subfields or a complex indexing definition that isn't compatible as a list. |
+| number_of_results | Number of Results | Input parameter. Number of search results to return. Default: 4. Only relevant to reads. |
+| search_type | Search Type | Input parameter. Search type to use, either `Similarity`, `Similarity with score threshold`, or `MMR (Max Marginal Relevance)`, `Graph Traversal`, or `MMR (Max Marginal Relevance) Graph Traversal` (default). Only relevant to reads. |
+| search_score_threshold | Search Score Threshold | Input parameter. Minimum similarity score threshold for search results if the `search_type` is `Similarity with score threshold`. Default: 0. |
+| search_filter | Search Metadata Filter | Input parameter. Optional dictionary of metadata filters to apply in addition to vector search. |
+
+
+
+
+Environment variable components
+
+The following DataStax components were used to load and retrieve environment variables in a flow:
+
+* **Dotenv**: Loads environment variables from a `.env` file
+* **Get Environment Variable**: Retrieves the value of an environment variable
+
+These components are legacy. Use Langflow's built-in environment variable support or global variables instead.
+
+
+
+
+Astra Vectorize
+
+This component was deprecated in Langflow version 1.1.2.
+Replace it with the [**Astra DB** component](#astra-db).
+
+The **Astra DB Vectorize** component was used to generate embeddings with Astra DB's vectorize feature in conjunction with an **Astra DB** component.
+
+The vectorize functionality is now built into the **Astra DB** component.
+You no longer need a separate component for vectorize embedding generation.
+
+
+
+## See also
+
+* [**Cassandra** bundle](/bundles-cassandra)
+* [Create a vector RAG chatbot](/chat-with-rag)
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-deepseek.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-deepseek.mdx
new file mode 100644
index 0000000000..73d9fd33c2
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-deepseek.mdx
@@ -0,0 +1,38 @@
+---
+title: DeepSeek
+slug: /bundles-deepseek
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **DeepSeek** bundle.
+
+For more information about DeepSeek features and functionality used by DeepSeek components, see the [DeepSeek documentation](https://api-docs.deepseek.com/).
+
+## DeepSeek text generation
+
+The **DeepSeek** component generates text using DeepSeek's language models.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a DeepSeek model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### DeepSeek text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| max_tokens | Integer | Input parameter. Maximum number of tokens to generate. Set to `0` for unlimited. Range: `0-128000`. |
+| model_kwargs | Dictionary | Input parameter. Additional keyword arguments for the model. |
+| json_mode | Boolean | Input parameter. If `true`, outputs JSON regardless of passing a schema. |
+| model_name | String | Input parameter. The DeepSeek model to use. Default: `deepseek-chat`. |
+| api_base | String | Input parameter. Base URL for API requests. Default: `https://api.deepseek.com`. |
+| api_key | SecretString | Input parameter. Your DeepSeek API key for authentication. |
+| temperature | Float | Input parameter. Controls randomness in responses. Range: `[0.0, 2.0]`. Default: `1.0`. |
+| seed | Integer | Input parameter. Number initialized for random number generation. Use the same seed integer for more reproducible results, and use a different seed number for more random results. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-docling.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-docling.mdx
new file mode 100644
index 0000000000..16d750d186
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-docling.mdx
@@ -0,0 +1,143 @@
+---
+title: Docling
+slug: /bundles-docling
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Icon from "@site/src/components/icon";
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+import PartialDockerDoclingDeps from '@site/docs/_partial-docker-docling-deps.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+Langflow integrates with [Docling](https://docling-project.github.io/docling/) through a bundle of components for parsing and chunking documents.
+
+## Prerequisites
+
+* **Enable Developer Mode for Windows**:
+
+
+
+* **Install Docling dependency**:
+The Docling dependency is required to use the Docling components in Langflow.
+
+ * **Langflow Desktop**: Set `LANGFLOW_DOCLING=True` in your `.env` file to enable Docling dependency installation. For more information, see [Set environment variables for Langflow Desktop](/environment-variables#set-environment-variables-for-langflow-desktop).
+
+ * **Langflow OSS**:
+ * If you installed `langflow` (`uv pip install langflow`), Docling is included automatically through bundled extras.
+ * If you installed `langflow-base` directly, install Docling with an extra, for example `uv pip install "langflow-base[docling]"`.
+
+ * **macOS Intel (x86_64)**: Use the [Docling installation guide](https://docling-project.github.io/docling/installation/) to install the Docling dependency.
+
+ * **Earlier versions**: Langflow versions earlier than 1.6 don't include the Docling dependency by default.
+
+ For more information, see [Install custom dependencies](/install-custom-dependencies).
+
+
+
+## Use Docling components in a flow
+
+:::tip
+To learn more about content extraction with Docling, see the video tutorial [Docling + Langflow: Document Processing for AI Workflows](https://www.youtube.com/watch?v=5DuS6uRI5OM).
+:::
+
+This example demonstrates how to use Docling components to split a PDF in a flow:
+
+1. Connect a **Docling** and an **Export DoclingDocument** component to a [**Split Text** component](/split-text).
+
+ The **Docling** component loads the document, and the **Export DoclingDocument** component converts the `DoclingDocument` into the format you select. This example converts the document to Markdown, with images represented as placeholders.
+ The **Split Text** component will split the Markdown into chunks for the vector database to store in the next part of the flow.
+
+2. Connect a [**Chroma DB** vector store component](/bundles-chroma#chroma-db) to the **Split Text** component's **Chunks** output.
+3. Connect an [embedding model component](/components-embedding-models) to the **Chroma DB** component's **Embedding** port and a **Chat Output** component to view the extracted [`Table`](/data-types#table).
+4. In the embedding model component, select your preferred model, provide credentials, and configure other settings as needed.
+
+ 
+
+5. Add a file to the **Docling** component.
+6. To run the flow, click **Playground**.
+
+ The chunked document is loaded as vectors into your vector database.
+
+## Docling components
+
+The following sections describe the purpose and configuration options for each component in the **Docling** bundle.
+
+### Docling local model
+
+The **Docling** component ingests documents, and then uses Docling to process them by running a local Docling model.
+
+It outputs `files`, which is the processed files with `DoclingDocument` data.
+
+For more information, see the [Docling IBM models project repository](https://github.com/docling-project/docling-ibm-models).
+
+#### Docling parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| files | File | The files to process. |
+| pipeline | String | Docling pipeline to use (standard, vlm). |
+| ocr_engine | String | OCR engine to use (easyocr, tesserocr, rapidocr, ocrmac). |
+
+### Docling Serve
+
+The **Docling Serve** component ingests documents and processes them with a Docling API service rather than a local model.
+
+It outputs `files`, which is the processed files with `DoclingDocument` data.
+
+For more information, see the [Docling serve project repository](https://github.com/docling-project/docling-serve).
+
+#### Docling Serve parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| files | File | The files to process. |
+| api_url | String | URL of the Docling Serve instance. |
+| max_concurrency | Integer | Maximum number of concurrent requests for the server. |
+| max_poll_timeout | Float | Maximum waiting time for the document conversion to complete. |
+| api_headers | Dict | Optional dictionary of additional headers required for connecting to Docling Serve. |
+| docling_serve_opts | Dict | Optional dictionary of additional options for Docling Serve. |
+
+### Chunk DoclingDocument
+
+The **Chunk DoclingDocument** component splits `DoclingDocument` objects into chunks.
+
+It outputs the chunked documents as a [`Table`](/data-types#table).
+
+For more information, see the [Docling core project repository](https://github.com/docling-project/docling-core).
+
+#### Chunk DoclingDocument parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| data_inputs | JSON or Table | The data with documents to split in chunks. |
+| chunker | String | Which chunker to use (HybridChunker, HierarchicalChunker). |
+| provider | String | Which tokenizer provider (Hugging Face, OpenAI). |
+| hf_model_name | String | Model name of the tokenizer to use with the HybridChunker when Hugging Face is chosen. |
+| openai_model_name | String | Model name of the tokenizer to use with the HybridChunker when OpenAI is chosen. |
+| max_tokens | Integer | Maximum number of tokens for the HybridChunker. |
+| doc_key | String | The key to use for the `DoclingDocument` column. |
+
+### Export DoclingDocument
+
+The **Export DoclingDocument** component exports `DoclingDocument` to Markdown, HTML, and other formats.
+
+It can output the exported data as either [`JSON`](/data-types#json) or [`Table`](/data-types#table).
+
+For more information, see the [Docling core project repository](https://github.com/docling-project/docling-core).
+
+#### Export DoclingDocument parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| data_inputs | JSON or Table | The data with documents to export. |
+| export_format | String | Select the export format to convert the input (Markdown, HTML, Plaintext, DocTags). |
+| image_mode | String | Specify how images are exported in the output (placeholder, embedded). |
+| md_image_placeholder | String | Specify the image placeholder for markdown exports. |
+| md_page_break_placeholder | String | Add this placeholder between pages in the markdown output. |
+| doc_key | String | The key to use for the `DoclingDocument` column. |
+
+## See also
+
+* [**Read File** component](/read-file)
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-duckduckgo.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-duckduckgo.mdx
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--- /dev/null
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@@ -0,0 +1,32 @@
+---
+title: DuckDuckGo
+slug: /bundles-duckduckgo
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **DuckDuckGo** bundle.
+
+## DuckDuckGo Search
+
+This component performs web searches using the [DuckDuckGo](https://www.duckduckgo.com) search engine with result-limiting capabilities.
+
+It outputs a list of search results as a [`Table`](/data-types#table) with a `text` key containing the search results as a single string.
+
+### DuckDuckGo Search parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| input_value | String | Input parameter. The search query to execute with DuckDuckGo. |
+| max_results | Integer | Input parameter. The maximum number of search results to return. Default: 5. |
+| max_snippet_length | Integer | Input parameter. The maximum length of each result snippet. Default: 100. |
+
+## See also
+
+* [**Web Search** component](/web-search)
+* [**SearchApi** bundle](/bundles-searchapi)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-elastic.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-elastic.mdx
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+---
+title: Elastic
+slug: /bundles-elastic
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Elastic** bundle.
+
+## Elasticsearch
+
+The **Elasticsearch** component reads and writes to an Elasticsearch instance using [`ElasticsearchStore`](https://docs.langchain.com/oss/python/integrations/vectorstores/elasticsearch).
+
+
+About vector store instances
+
+
+
+
+
+
+
+### Elasticsearch parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Elasticsearch documentation](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+|------|------|-------------|
+| elasticsearch_url | String | Input parameter. Elasticsearch server URL. |
+| cloud_id | String | Input parameter. Elasticsearch Cloud ID. |
+| index_name | String | Input parameter. Name of the Elasticsearch index. |
+| ingest_data | JSON | Input parameter. Records to load into the vector store. |
+| search_query | String | Input parameter. Query string for similarity search. |
+| cache_vector_store | Boolean | Input parameter. If `true`, the component caches the vector store in memory for faster reads. Default: Enabled (`true`). |
+| username | String | Input parameter. Username for Elasticsearch authentication. Required for all local deployments. Required for cloud deployments if `api_key` is empty. |
+| password | SecretString | Input parameter. Password for Elasticsearch authentication. Required for all local deployments. Required for cloud deployments if `api_key` is empty |
+| embedding | Embeddings | Input parameter. The embedding model to use. |
+| search_type | String | Input parameter. The type of search to perform. Options are `similarity` (default) or `mmr`. |
+| number_of_results | Integer | Input parameter. Number of search results to return. Default: 4. |
+| search_score_threshold | Float | Input parameter. The minimum similarity score threshold for search results. Default: 0. |
+| api_key | SecretString | Input parameter. API key for Elastic Cloud authentication. If provided, `username` and `password` aren't required. |
+| verify_certs | Boolean | Input parameter. Whether to verify SSL certificates when connecting to Elasticsearch. Default: Enabled (`true`). |
+
+## OpenSearch
+
+The **OpenSearch** component reads and writes to OpenSearch instances using [`OpenSearchVectorSearch`](https://docs.langchain.com/oss/python/integrations/vectorstores/opensearch).
+
+
+About vector store instances
+
+
+
+
+
+### OpenSearch parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.;
+
+
+
+
+
+For information about accepted values and functionality, see the [OpenSearch documentation](https://opensearch.org/platform/search/vector-database.html) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+|------|------|-------------|
+| opensearch_url | String | Input parameter. URL for OpenSearch cluster, such as `https://192.168.1.1:9200`. |
+| index_name | String | Input parameter. The index name where the vectors are stored in OpenSearch cluster. Default: `langflow`. |
+| ingest_data | JSON | Input parameter. The data to be ingested into the vector store. |
+| search_input | String | Input parameter. Enter a search query. Leave empty to retrieve all documents or if hybrid search is being used. |
+| cache_vector_store | Boolean | Input parameter. If `true`, the component caches the vector store in memory for faster reads. Default: Enabled (`true`). |
+| embedding | Embeddings | Input parameter. Attach an [embedding model component](/components-embedding-models) to use to generate an embedding from the search query. |
+| search_type | String | Input parameter. The type of search to perform. Options are `similarity` (default), `similarity_score_threshold`, `mmr`. |
+| number_of_results | Integer | Input parameter. The number of results to return in search. Default: 4. |
+| search_score_threshold | Float | Input parameter. The minimum similarity score threshold for search results. Default: 0. |
+| username | String | Input parameter. The username for the OpenSearch cluster. Default: `admin`.|
+| password | SecretString | Input parameter. The password for the OpenSearch cluster. |
+| use_ssl | Boolean | Input parameter. Whether to use SSL. Default: Enabled (`true`). |
+| verify_certs | Boolean | Input parameter. Whether to verify SSL certificates. Default: Disabled (`false`). |
+| hybrid_search_query | String | Input parameter. Provide a custom hybrid search query in JSON format. This allows you to combine vector similarity and keyword matching. |
+
+### OpenSearch output
+
+
+
+
+Vector Store Connection port
+
+The **OpenSearch** component has an additional deprecated **Vector Store Connection** output.
+This output can only connect to a `VectorStore` input port, and it was intended for use with dedicated Graph RAG components.
+
+The **OpenSearch** component doesn't require a separate Graph RAG component because OpenSearch instances support Graph traversal through built-in RAG functionality and plugins.
+
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-exa.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-exa.mdx
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+---
+title: Exa
+slug: /bundles-exa
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Exa** bundle.
+
+## Exa Search
+
+This component provides an [Exa Search](https://exa.ai/) toolkit for search and content retrieval by a Langflow [**Agent** component](/agents) or [MCP client](/mcp-client).
+
+The output is exclusively [`Tools`](/data-types#tool).
+
+### Exa Search parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| Exa Search API Key (`metaphor_api_key`) | SecretString | Input parameter. An API key for Exa Search. |
+| Use Autoprompt (`use_autoprompt`) | Boolean | Input parameter. Whether to use the autoprompt feature. Default: `true`. |
+| Search Number of Results (`search_num_results`) | Integer | Input parameter. The number of results to return for search. Default: 5. |
+| Similar Number of Results (`similar_num_results`) | Integer | Input parameter. The number of similar results to return. Default: 5. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-faiss.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-faiss.mdx
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+---
+title: FAISS
+slug: /bundles-faiss
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **FAISS** bundle.
+
+## FAISS vector store
+
+The **FAISS** component provides access to the Facebook AI Similarity Search (FAISS) library through an instance of `FAISS` vector store.
+
+
+About vector store instances
+
+
+
+
+
+
+
+### FAISS vector store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [FAISS documentation](https://faiss.ai/index.html) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+|---------------------------|---------------|--------------------------------------------------|
+| index_name | String | Input parameter. The name of the FAISS index. Default: "langflow_index". |
+| persist_directory | String | Input parameter. Path to save the FAISS index. It is relative to where Langflow is running. |
+| search_query | String | Input parameter. The query to search for in the vector store. |
+| ingest_data | JSON | Input parameter. The list of data to ingest into the vector store. |
+| allow_dangerous_deserialization | Boolean | Input parameter. Set to `True` to allow loading pickle files from untrusted sources. Default: `True`. |
+| embedding | Embeddings | Input parameter. The embedding function to use for the vector store. |
+| number_of_results | Integer | Input parameter. Number of results to return from the search. Default: 4. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-glean.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-glean.mdx
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+---
+title: Glean
+slug: /bundles-glean
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Glean** bundle.
+
+## Glean Search API
+
+This component allows you to call the Glean Search API.
+
+It returns a list of search results as a [`Table`](/data-types#table).
+
+### Glean Search API parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| glean_api_url | String | Input parameter. The URL of the Glean API. |
+| glean_access_token | SecretString | Input parameter. An access token for Glean API authentication. |
+| query | String | Input parameter. The search query input. |
+| page_size | Integer | Input parameter. The number of results per page. Default: 10. |
+| request_options | Dict | Input parameter. Additional options for the API request. |
+
+## See also
+
+* [**Web Search** component](/web-search)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-google.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-google.mdx
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+---
+title: Google components
+slug: /bundles-google
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Google** bundle.
+
+## BigQuery
+
+Langflow integrates with [Google BigQuery](https://cloud.google.com/bigquery) through the **BigQuery** component in the [**Google** bundle](/bundles-google), allowing you to execute SQL queries and retrieve data from your BigQuery datasets.
+
+### Use the BigQuery component in a flow
+
+To use the **BigQuery** component in a flow, you need the following:
+
+* A [Google Cloud project](https://developers.google.com/workspace/guides/create-project) with the BigQuery API enabled
+* A [service account](https://developers.google.com/workspace/guides/create-credentials#service-account) with the **BigQuery Job User** role
+* A [BigQuery dataset and table](https://cloud.google.com/bigquery/docs/datasets-intro)
+* A [running Langflow server](/get-started-installation)
+
+#### Create a service account with BigQuery access
+
+1. Select and enable your Google Cloud project.
+For more information, see [Create a Google Cloud project](https://developers.google.com/workspace/guides/create-project).
+2. Create a service account in your Google Cloud project.
+For more information, see [Create a service account](https://developers.google.com/workspace/guides/create-credentials#service-account).
+3. Assign the **BigQuery Job User** role to your new account.
+This role allows Langflow to access BigQuery resources with the service account.
+You may also need to allow access to your BigQuery dataset.
+For more information, see [BigQuery access control with IAM](https://cloud.google.com/bigquery/docs/access-control).
+4. To generate a new JSON key for the service account, navigate to your service account.
+5. Click **Add Key**, and then click **Create new key**.
+6. Under **Key type**, select **JSON**, and then click **Create**.
+A JSON private key file is downloaded to your machine.
+Now that you have a service account and a JSON private key, you need to configure the credentials in the Langflow **BigQuery** component.
+
+#### Configure credentials in the Langflow component
+
+With your service account configured and your credentials JSON file created, follow these steps to authenticate the Langflow application.
+
+1. Create a new flow in Langflow.
+2. In **Bundles**, find the Google **BigQuery** component, and then add it to your flow.
+3. In the **BigQuery** component's **Upload Service Account JSON** field, click **Select file**.
+4. In the **My Files** pane, select **Click or drag files here**.
+Your file browser opens.
+5. In your file browser, select the service account JSON file, and then click **Open**.
+6. In the **My Files** pane, select your service account JSON file, and then click **Select files**.
+The **BigQuery** component can now query your datasets and tables using your service account JSON file.
+
+#### Query a BigQuery dataset
+
+With your component credentials configured, query your BigQuery datasets and tables to confirm connectivity.
+
+1. Connect **Chat Input** and **Chat Output** components to the **BigQuery** component.
+
+ 
+
+2. Open the **Playground**, and then submit a valid SQL query.
+
+ This example queries a table of Oscar winners stored within a BigQuery dataset called `the_oscar_award`:
+
+ ```sql
+ SELECT film, category, year_film
+ FROM `big-query-langflow-project.the_oscar_award.oscar_winners`
+ WHERE winner = TRUE
+ LIMIT 10
+ ```
+
+
+ Result
+
+ ```text
+ film category year_film
+ The Last Command ACTOR 1927
+ 7th Heaven ACTRESS 1927
+ The Dove; ART DIRECTION 1927
+ Sunrise CINEMATOGRAPHY 1927
+ Sunrise CINEMATOGRAPHY 1927
+ Two Arabian Knights DIRECTING (Comedy Picture) 1927
+ 7th Heaven DIRECTING (Dramatic Picture) 1927
+ Wings ENGINEERING EFFECTS 1927
+ Wings OUTSTANDING PICTURE 1927
+ Sunrise UNIQUE AND ARTISTIC PICTURE 1927
+ ```
+
+
+
+ A successful chat confirms the component can access the BigQuery table.
+
+## Google Generative AI
+
+This component generates text using [Google Generative AI models](https://cloud.google.com/vertex-ai/docs/).
+
+Supported models include the Gemini 1.5, 2.0, 2.5, and 3.0 series. The latest Gemini 3.0 models (`gemini-3-pro-preview`, `gemini-3-flash-preview`, and `gemini-3-pro-image-preview`) offer advanced reasoning and multimodal capabilities.
+
+### Google Generative AI parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| Google API Key | SecretString | Input parameter. Your Google API key to use for the Google Generative AI. |
+| Model | String | Input parameter. The name of the model to use, such as `"gemini-2.5-flash"` or `"gemini-3-pro-preview"`. |
+| Max Output Tokens | Integer | Input parameter. The maximum number of tokens to generate. |
+| Temperature | Float | Input parameter. Run inference with this temperature. |
+| Top K | Integer | Input parameter. Consider the set of top K most probable tokens. |
+| Top P | Float | Input parameter. The maximum cumulative probability of tokens to consider when sampling. |
+| N | Integer | Input parameter. Number of chat completions to generate for each prompt. |
+| model | LanguageModel | Output parameter. An instance of ChatGoogleGenerativeAI configured with the specified parameters. |
+
+## Google Generative AI Embeddings
+
+The **Google Generative AI Embeddings** component connects to Google's generative AI embedding service using the GoogleGenerativeAIEmbeddings class from the `langchain-google-genai` package.
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### Google Generative AI Embeddings parameters
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| api_key | API Key | Input parameter. The secret API key for accessing Google's generative AI service. Required. |
+| model_name | Model Name | Input parameter. The name of the embedding model to use. Default: "models/text-embedding-004". |
+| embeddings | Embeddings | Output parameter. The built GoogleGenerativeAIEmbeddings object. |
+
+## Google Search API
+
+This component allows you to call the Google Search API.
+
+### Google Search API parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| google_api_key | SecretString | Input parameter. A Google API key for authentication. |
+| google_cse_id | SecretString | Input parameter. A Google Custom Search Engine ID. |
+| input_value | String | Input parameter. The search query input. |
+| k | Integer | Input parameter. The number of search results to return. |
+| results | List[Data] | Output parameter. A list of search results. |
+| tool | Tool | Output parameter. A Google Search tool for use in LangChain. |
+
+### Other Google Search components
+
+Langflow includes multiple components that support Google Search, such as the following:
+
+* [**Apify Actors** component](/bundles-apify)
+* [**SearchApi** component](/bundles-searchapi)
+* [**Serper Google Search API** component](/bundles-serper)
+* [**Web Search** component](/web-search)
+
+## Google Vertex AI
+
+For information about Vertex AI components, see the [**Vertex AI** bundle](/bundles-vertexai).
+
+## Legacy Google components
+
+import PartialLegacy from '@site/docs/_partial-legacy.mdx';
+
+
+
+The following Google components are in legacy status:
+
+
+Google OAuth Token
+
+The **Google OAuth Token** component was deprecated in Langflow 1.4.0.
+
+To connect your flows to Google OAuth services, use [Composio components](/bundles-composio).
+
+
+
+
+Gmail Loader
+
+This component loads emails from Gmail using [Service Account JSON](https://developers.google.com/identity/protocols/oauth2/service-account) credentials and label ID filters.
+
+As an alternative, you can use [Composio components](/bundles-composio) to connect your flows to Google services.
+
+
+
+
+Google Drive Loader
+
+This component loads documents from Google Drive using [Service Account JSON](https://developers.google.com/identity/protocols/oauth2/service-account) credentials and document ID filters.
+
+While there is no direct replacement, consider using the [**API Request** component](/api-request) to call the Google Drive API.
+
+
+
+
+Google Drive Search
+
+This component searches Google Drive using [Service Account JSON](https://developers.google.com/identity/protocols/oauth2/service-account) credentials and various query strings and filters.
+
+While there is no direct replacement, consider using the [**API Request** component](/api-request) to call the Google Drive API.
+
+
+
+## See also
+
+- [**Composio** bundle](/bundles-composio)
+- [**Vertex AI** bundle](/bundles-vertexai)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-groq.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-groq.mdx
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+---
+title: Groq
+slug: /bundles-groq
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Groq** bundle.
+
+For more information, see the [Groq documentation](https://groq.com/).
+
+## Groq text generation
+
+This component generates text using Groq's language models.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+Specifically, the **Language Model** output is an instance of [`ChatGroq`](https://docs.langchain.com/oss/python/integrations/chat/groq) configured according to the component's parameters.
+
+Use the **Language Model** output when you want to use a Groq model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+
+
+### Groq text generation parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| groq_api_key | SecretString | Your Groq API key. |
+| groq_api_base | String | Base URL path for API requests. Default: `https://api.groq.com`. |
+| max_tokens | Integer | The maximum number of tokens to generate. |
+| temperature | Float | Controls randomness in the output. Range: `[0.0, 1.0]`. Default: `0.1`. |
+| n | Integer | Number of chat completions to generate for each prompt. |
+| model_name | String | The name of the Groq model to use. Options are dynamically fetched from the Groq API after entering your API key and URL. To refresh your list of models, click **Refresh**. |
+| tool_mode_enabled | Boolean | If enabled, the component only displays models that work with tools. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-huggingface.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-huggingface.mdx
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,78 @@
+---
+title: Hugging Face
+slug: /bundles-huggingface
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+The components in the **Hugging Face** bundle require access to Hugging Face APIs.
+
+For more information about Hugging Face features and functionality used by Hugging Face components, see the [Hugging Face documentation](https://huggingface.co/docs).
+
+## Hugging Face text generation
+
+The **Hugging Face** component generates text using a specified model by sending requests to the Hugging Face API, which is a hosted inference API for models hosted on Hugging Face.
+Authentication is required.
+
+This component can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+Specifically, the **Language Model** output is an instance of [`ChatHuggingFace`](https://docs.langchain.com/oss/python/integrations/chat/huggingface) configured according to the component's parameters.
+
+Use the **Language Model** output when you want to use a Hugging Face model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### Hugging Face text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| model_id | String | Input parameter. The model ID from Hugging Face Hub. For example, "gpt2", "facebook/bart-large". |
+| huggingfacehub_api_token | SecretString | Input parameter. Your [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) for authentication. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7. |
+| max_new_tokens | Integer | Input parameter. Maximum number of tokens to generate. Default: 512. |
+| top_p | Float | Input parameter. Nucleus sampling parameter. Range: [0.0, 1.0]. Default: 0.95. |
+| top_k | Integer | Input parameter. Top-k sampling parameter. Default: 50. |
+| model_kwargs | Dictionary | Input parameter. Additional keyword arguments to pass to the model. |
+
+## Hugging Face Embeddings Inference
+
+Use the **Hugging Face Embeddings Inference** component to create embeddings with Hugging Face's hosted models or with your own locally hosted models.
+
+The component generates embeddings using [Hugging Face Inference API models](https://huggingface.co/models).
+Authentication is required when not using a local model.
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models) and [Use a local Hugging Face embeddings model](#local-hugging-face-model).
+
+### Hugging Face Embeddings Inference parameters
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| api_key | API Key | Input parameter. Your [Hugging Face API token](https://huggingface.co/docs/hub/security-tokens) for accessing the Hugging Face Inference API, if required. Local inference models don't require an API key. |
+| api_url | API URL | Input parameter. The URL of the Hugging Face Inference API. |
+| model_name | Model Name | Input parameter. The name of the model to use for embeddings. |
+
+### Use a local Hugging Face embeddings model {#local-hugging-face-model}
+
+To connect the local Hugging Face model to the **Hugging Face Embeddings Inference** component and use it in a flow, follow these steps:
+
+1. Run a [local Hugging Face embeddings inference](https://huggingface.co/docs/text-embeddings-inference/local_cpu).
+
+2. For this example, create a flow from the **Vector Store RAG** template.
+
+3. Replace the two **OpenAI Embeddings** components with **Hugging Face Embeddings Inference** components.
+
+ Make sure to reconnect the **Embedding Model** ports from each **Embeddings Inference** component to its corresponding **Astra DB** component.
+
+4. Configure the **Astra DB** components to connect to your Astra organization, or replace both **Astra DB** components with other vector store components.
+
+5. Connect each **Hugging Face Embeddings Inference** component to your local inference model:
+
+ * **Inference Endpoint**: Enter the URL of your local inference model.
+ * **API Key**: Can be empty for local inference.
+ * **Model Name**: Enter the name of your local inference model if it isn't automatically detected.
+
+6. To test the flow, click **Playground**, and then enter some text to generate an embedding.
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diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-ibm.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-ibm.mdx
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+---
+title: IBM
+slug: /bundles-ibm
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+The **IBM** bundle provides access to IBM watsonx.ai models for text and embedding generation.
+These components require an IBM watsonx.ai deployment and watsonx API credentials.
+
+## IBM watsonx.ai
+
+The **IBM watsonx.ai** component generates text using [supported foundation models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx) in [IBM watsonx.ai](https://www.ibm.com/products/watsonx-ai).
+To use gateway models, use the [**OpenAI** text generation component](/bundles-openai) with the gateway model's OpenAI-compatible endpoint.
+
+You can use the **IBM watsonx.ai** component anywhere you need a language model in a flow.
+
+
+
+### IBM watsonx.ai parameters {#ibm-watsonxai-parameters}
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| url | String | Input parameter. The [watsonx API base URL](https://cloud.ibm.com/apidocs/watsonx-ai#endpoint-url) for your deployment and region. |
+| project_id | String | Input parameter. Your [watsonx Project ID](https://www.ibm.com/docs/en/watsonx/saas?topic=projects). |
+| api_key | SecretString | Input parameter. A [watsonx API key](https://www.ibm.com/docs/en/watsonx/saas?topic=administration-managing-user-api-key) to authenticate watsonx API access to the specified watsonx.ai deployment and model. |
+| model_name | String | Input parameter. The name of the watsonx model to use. Options are dynamically fetched from the API. |
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Default: `1000`. |
+| stop_sequence | String | Input parameter. The sequence where generation should stop. |
+| temperature | Float | Input parameter. Controls randomness in the output. Default: `0.1`. |
+| top_p | Float | Input parameter. Controls nucleus sampling, which limits the model to tokens whose probability is below the `top_p` value. Range: Default: `0.9`. |
+| frequency_penalty | Float | Input parameter. Controls frequency penalty. A positive value decreases the probability of repeating tokens, and a negative value increases the probability. Range: Default: `0.5`. |
+| presence_penalty | Float | Input parameter. Controls presence penalty. A positive value increases the likelihood of new topics being introduced. Default: `0.3`. |
+| seed | Integer | Input parameter. A random seed for the model. Default: `8`. |
+| logprobs | Boolean | Input parameter. Whether to return log probabilities of output tokens or not. Default: `true`. |
+| top_logprobs | Integer | Input parameter. The number of most likely tokens to return at each position. Default: `3`. |
+| logit_bias | String | Input parameter. A JSON string of token IDs to bias or suppress. |
+
+### IBM watsonx.ai output
+
+The **IBM watsonx.ai** component can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use an IBM watsonx.ai model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+For more information, see [Language model components](/components-models).
+
+The `LanguageModel` output from the **IBM watsonx.ai** component is an instance of `[ChatWatsonx](https://docs.langchain.com/oss/python/integrations/chat/ibm_watsonx)` configured according to the [component's parameters](#ibm-watsonxai-parameters).
+
+## IBM watsonx.ai Embeddings
+
+The **IBM watsonx.ai Embeddings** component uses the [supported foundation models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models.html?context=wx) in [IBM watsonx.ai](https://www.ibm.com/products/watsonx-ai) for embedding generation.
+
+The output is [`Embeddings`](/data-types#embeddings) generated with [`WatsonxEmbeddings`](https://python.langchain.com/docs/integrations/text_embedding/ibm_watsonx/).
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+
+
+### IBM watsonx.ai Embeddings parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| url | watsonx API Endpoint | Input parameter. The [watsonx API base URL](https://cloud.ibm.com/apidocs/watsonx-ai#endpoint-url) for your deployment and region. |
+| project_id | watsonx project id | Input parameter. Your [watsonx Project ID](https://www.ibm.com/docs/en/watsonx/saas?topic=projects). |
+| api_key | API Key | Input parameter. A [watsonx API key](https://www.ibm.com/docs/en/watsonx/saas?topic=administration-managing-user-api-key) to authenticate watsonx API access to the specified watsonx.ai deployment and model. |
+| model_name | Model Name | Input parameter. The name of the embedding model to use. Supports [default embedding models](#default-embedding-models) and automatically updates after connecting to your watsonx.ai deployment. |
+| truncate_input_tokens | Truncate Input Tokens | Input parameter. The maximum number of tokens to process. Default: `200`. |
+| input_text | Include the original text in the output | Input parameter. Determines if the original text is included in the output. Default: `true`. |
+
+### Default embedding models
+
+By default, the **IBM watsonx.ai Embeddings** component supports the following default models:
+
+- `sentence-transformers/all-minilm-l12-v2`: 384-dimensional embeddings
+- `ibm/slate-125m-english-rtrvr-v2`: 768-dimensional embeddings
+- `ibm/slate-30m-english-rtrvr-v2`: 768-dimensional embeddings
+- `intfloat/multilingual-e5-large`: 1024-dimensional embeddings
+
+After entering your API endpoint and credentials, the component automatically fetches the list of available models from your watsonx.ai deployment.
+
+## See also
+
+* [IBM documentation](https://cloud.ibm.com/docs)
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diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-icosacomputing.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-icosacomputing.mdx
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+---
+title: Icosa Computing
+slug: /bundles-icosacomputing
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+The **Icosa Computing** components require access to Icosa Computing services.
+For more information and to request access, see the [Icosa Computing site](https://www.icosacomputing.com/).
+
+## Combinatorial Reasoner
+
+The **Combinatorial Reasoner** component runs Icosa's Combinatorial Reasoning (CR) pipeline on an input to create an optimized prompt with embedded reasons.
+
+### Combinatorial Reasoner parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| prompt | String | Input parameter. The input to run CR on. |
+| openai_api_key | SecretString | Input parameter. An OpenAI API key for authentication to OpenAI, which is used for reason generation. |
+| model_name | String | Input parameter. The OpenAI LLM to use for reason generation. |
+| username | String | Input parameter. A username for authentication to the Icosa API. |
+| password | SecretString | Input parameter. A password for authentication to the Icosa API. |
+
+### Combinatorial Reasoner outputs
+
+The **Combinatorial Reasoner** component outputs one of the following:
+
+* **Optimized Prompt (`optimized_prompt`)**: A [`Message`](/data-types#message) object containing the optimized prompt with embedded reasons.
+* **Selected Reasons (`reasons`)**: A [`JSON`](/data-types#json) object containing a list of strings where each string is a reason that was selected and embedded in the optimized prompt.
+
+You can toggle the output type near the component's output port.
+
+## See also
+
+* [**Prompt Template** component](/components-prompts)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-langchain.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-langchain.mdx
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+---
+title: LangChain
+slug: /bundles-langchain
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **LangChain** bundle.
+
+## CSV Agent
+
+:::warning Security
+Setting `allow_dangerous_code` to `true` allows the agent to execute arbitrary, LLM-generated Python code at runtime on the Langflow server.
+This code can do anything that Python can do, so use this option only with trusted data and in a secure environment.
+For more information, see [CVE-2024-21513](https://nvd.nist.gov/vuln/detail/CVE-2024-21513).
+:::
+
+This component is based on the [**Agent** core component](/agents).
+
+This component creates a CSV agent from a CSV file and LLM.
+For more information, see the [LangChain CSV agent documentation](https://python.langchain.com/api_reference/experimental/agents/langchain_experimental.agents.agent_toolkits.csv.base.create_csv_agent.html).
+
+### CSV Agent parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| allow_dangerous_code | Input parameter. | Default: `false`. Set to `true` to allow the agent to execute arbitrary Python code. For more information, see the [CSV Agent security warning](#csv-agent).
+| llm | LanguageModel | Input parameter. The language model to use for the agent. |
+| path | File | Input parameter. The path to the CSV file. |
+| agent_type | String | Input parameter. The type of agent to create. |
+| agent | AgentExecutor | Output parameter. The CSV agent instance. |
+
+## OpenAI Tools Agent
+
+This component is based on the [**Agent** core component](/agents).
+
+This component creates an OpenAI Tools Agent.
+For more information, see the [LangChain OpenAI agent documentation](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.openai_functions_agent.base.create_openai_functions_agent.html).
+
+### OpenAI Tools Agent parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| llm | LanguageModel | Input parameter. The language model to use. |
+| tools | List of Tools | Input parameter. The tools to give the agent access to. |
+| system_prompt | String | Input parameter. The system prompt to provide context to the agent. |
+| input_value | String | Input parameter. The user's input to the agent. |
+| memory | Memory | Input parameter. The memory for the agent to use for context persistence. |
+| max_iterations | Integer | Input parameter. The maximum number of iterations to allow the agent to execute. |
+| verbose | Boolean | Input parameter. This determines whether to print out the agent's intermediate steps. |
+| handle_parsing_errors | Boolean | Input parameter. This determines whether to handle parsing errors in the agent. |
+| agent | AgentExecutor | Output parameter. The OpenAI Tools agent instance. |
+| output | String | Output parameter. The output from executing the agent on the input. |
+
+## OpenAPI Agent
+
+This component is based on the [**Agent** core component](/agents).
+
+This component creates an agent for interacting with OpenAPI services.
+For more information, see the [LangChain OpenAPI toolkit documentation](https://docs.langchain.com/oss/python/integrations/tools/openapi).
+
+### OpenAPI Agent parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| llm | LanguageModel | Input parameter. The language model to use. |
+| openapi_spec | String | Input parameter. The OpenAPI specification for the service. |
+| base_url | String | Input parameter. The base URL for the API. |
+| headers | Dict | Input parameter. The optional headers for API requests. |
+| agent_executor_kwargs | Dict | Input parameter. The optional parameters for the agent executor. |
+| agent | AgentExecutor | Output parameter.The OpenAPI agent instance. |
+
+## Prompt Hub
+
+This component fetches prompts from the [LangChain Hub](https://docs.langchain.com/langsmith/manage-prompts#public-prompt-hub).
+
+Like the [**Prompt Template** core component](/components-prompts), additional fields are added to the component for each variable in the prompt.
+For example, the default prompt `efriis/my-first-prompt` adds fields for `profession` and `question`.
+
+### Prompt Hub parameters
+
+| Name | Display Name | Description |
+|---------------------|---------------------------|------------------------------------------|
+| langchain_api_key | Your LangChain API Key | Input parameter. The LangChain API Key to use. |
+| langchain_hub_prompt| LangChain Hub Prompt | Input parameter. The LangChain Hub prompt to use. |
+| prompt | Build Prompt | Output parameter. The built prompt message returned by the `build_prompt` method. |
+
+## SQL Agent
+
+This component is based on the [**Agent** core component](/agents).
+
+This component creates an agent for interacting with SQL databases.
+For more information, see the [LangChain SQL agent documentation](https://docs.langchain.com/oss/python/langchain/sql-agent).
+
+### SQL Agent parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| llm | LanguageModel | Input parameter. The language model to use. |
+| database | Database | Input parameter. The SQL database connection. |
+| top_k | Integer | Input parameter. The number of results to return from a SELECT query. |
+| use_tools | Boolean | Input parameter. This determines whether to use tools for query execution. |
+| return_intermediate_steps | Boolean | Input parameter. This determines whether to return the agent's intermediate steps. |
+| max_iterations | Integer | Input parameter. The maximum number of iterations to run the agent. |
+| max_execution_time | Integer | Input parameter. The maximum execution time in seconds. |
+| early_stopping_method | String | Input parameter. The method to use for early stopping. |
+| verbose | Boolean | Input parameter. This determines whether to print the agent's thoughts. |
+| agent | AgentExecutor | Output parameter. The SQL agent instance. |
+
+## SQL Database
+
+The LangChain **SQL Database** component establishes a connection to an SQL database.
+
+This component is different from the [**SQL Database** core component](/sql-database), which executes SQL queries on SQLAlchemy-compatible databases.
+
+## Text Splitters
+
+The **LangChain** bundle includes the following text splitter components:
+
+- **Character Text Splitter**
+- **Language Recursive Text Splitter**
+- **Natural Language Text Splitter**
+- **Recursive Character Text Splitter**
+- **Semantic Text Splitter**
+
+## Tool Calling Agent
+
+This component is based on the [**Agent** core component](/agents).
+
+This component creates an agent for structured tool calling with various language models.
+For more information, see the [LangChain tool calling documentation](https://docs.langchain.com/oss/python/langchain/agents#tools).
+
+### Tool Calling Agent parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| llm | LanguageModel | Input parameter. The language model to use. |
+| tools | List[Tool] | Input parameter. The list of tools available to the agent. |
+| system_message | String | Input parameter. The system message to use for the agent. |
+| return_intermediate_steps | Boolean | Input parameter. This determines whether to return the agent's intermediate steps. |
+| max_iterations | Integer | Input parameter. The maximum number of iterations to run the agent. |
+| max_execution_time | Integer | Input parameter. The maximum execution time in seconds. |
+| early_stopping_method | String | Input parameter. The method to use for early stopping. |
+| verbose | Boolean | Input parameter. This determines whether to print the agent's thoughts. |
+| agent | AgentExecutor | Output parameter. The tool calling agent instance. |
+
+## XML Agent
+
+This component is based on the [**Agent** core component](/agents).
+
+This component creates an XML Agent using LangChain.
+The agent uses XML formatting for tool instructions to the LLM.
+For more information, see the [LangChain XML Agent documentation](https://python.langchain.com/api_reference/langchain/agents/langchain.agents.xml.base.create_xml_agent.html).
+
+### XML Agent parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| llm | LanguageModel | Input parameter. The language model to use for the agent. |
+| user_prompt | String | Input parameter. The custom prompt template for the agent with XML formatting instructions. |
+| tools | List[Tool] | Input parameter. The list of tools available to the agent. |
+| agent | AgentExecutor | Output parameter. The XML Agent instance. |
+
+## Other LangChain components
+
+Other components in the **LangChain** bundle include the following:
+
+- **Fake Embeddings**
+- **HTML Link Extractor**
+- **Runnable Executor**
+- **Spider Web Crawler & Scraper**
+
+## Legacy LangChain components
+
+import PartialLegacy from '@site/docs/_partial-legacy.mdx';
+
+
+
+The following LangChain components are in legacy status:
+
+* **Conversation Chain**
+* **LLM Checker Chain**
+* **LLM Math Chain**
+* **Natural Language to SQL**
+* **Retrieval QA**
+* **Self Query Retriever**
+* **JSON Agent**
+* **Vector Store Info/Agent**
+* **VectorStoreRouterAgent**
+
+To replace these components, consider other components in the **LangChain** bundle or general Langflow components, such as the [**Agent** component](/components-agents) or the [**SQL Database** component](/sql-database).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-lite-llm.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-lite-llm.mdx
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+---
+title: LiteLLM
+slug: /bundles-lite-llm
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+The **LiteLLM** bundle component connects to models through a LiteLLM proxy, which routes requests to multiple LLM providers.
+Using a proxy lets you change model providers without changing credentials in your flows.
+You authenticate to the proxy using a single key, and the proxy then uses its own configured credentials to call providers.
+Virtual keys are created by the proxy administrator. For more information on managing virtual keys, see [Virtual Keys](https://docs.litellm.ai/docs/proxy/virtual_keys) in the LiteLLM documentation.
+
+## LiteLLM Proxy text generation
+
+The **LiteLLM Proxy** component generates text using an LLM provider.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a LiteLLM proxy-backed model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### LiteLLM Proxy parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| api_base | String | Input parameter. Base URL of the LiteLLM proxy. Default: `"http://localhost:4000/v1"`. |
+| api_key | String | Input parameter. Virtual key for authentication with the LiteLLM proxy. |
+| model_name | String | Input parameter. Model name to use, such as `gpt-4o`, `claude-3-opus`. |
+| temperature | Float | Input parameter. Controls randomness. Lower values are more deterministic. Range: `[0.0, 2.0]`. Default: `0.7`. |
+| max_tokens | Integer | Input parameter. Maximum number of tokens to generate. Set to `0` for no limit. Range: `[0, 128000]`. Advanced. |
+| timeout | Integer | Input parameter. Request timeout in seconds. Default: `60`. |
+| max_retries | Integer | Input parameter. Maximum number of retries on failure. Default: `2`. |
+| stream | Boolean | Input parameter. Whether to stream the response. |
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-lmstudio.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-lmstudio.mdx
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+---
+title: LM Studio
+slug: /bundles-lmstudio
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+The components in the **LM Studio** bundle let you use models from a local or hosted instance of LM Studio.
+Components can require authentication with an LM Studio API key. For information about LM Studio models, connections, and credentials, see the [LM Studio documentation](https://lmstudio.ai/docs).
+
+## LM Studio text generation
+
+The **LM Studio** component generates text using LM Studio's local language models.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use an LM Studio model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### LM Studio text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| base_url | String | Input parameter. The URL where LM Studio is running. Default: `"http://localhost:1234"`. |
+| api_key | LM Studio API Key | Input parameter. The API key for authentication with LM Studio, if required. |
+| max_tokens | Integer | Input parameter. Maximum number of tokens to generate in the response. Default: `512`. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: `[0.0, 2.0]`. Default: `0.7`. |
+| top_p | Float | Input parameter. Controls diversity with nucleus sampling. Range: `[0.0, 1.0]`. Default: `1.0`. |
+| stop | List[String] | Input parameter. List of strings that stop generation when encountered. |
+| stream | Boolean | Input parameter. Whether to stream the response. Default: `false`. |
+| presence_penalty | Float | Input parameter. Penalizes repeated tokens. Range: `[-2.0, 2.0]`. Default: `0.0`. |
+| frequency_penalty | Float | Input parameter. Penalizes frequent tokens. Range: `[-2.0, 2.0]`. Default: `0.0`. |
+
+## LM Studio Embeddings
+
+The **LM Studio Embeddings** component generates embeddings using LM Studio models.
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### LM Studio Embeddings parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| model | Model | Input parameter. The LM Studio model to use for generating embeddings. |
+| base_url | LM Studio Base URL | Input parameter. The base URL for the LM Studio API. |
+| api_key | LM Studio API Key | Input parameter. The API key for authentication with LM Studio. |
+| temperature | Model Temperature | Input parameter. The temperature setting for the model. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-maritalk.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-maritalk.mdx
new file mode 100644
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+---
+title: MariTalk
+slug: /bundles-maritalk
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **MariTalk** bundle.
+
+For more information, see the [MariTalk documentation](https://www.maritalk.com/).
+
+## MariTalk text generation
+
+The **MariTalk** component generates text using MariTalk LLMs.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a MariTalk model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### MariTalk text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to `0` for unlimited tokens. Default: `512`. |
+| model_name | String | Input parameter. The name of the MariTalk model to use. Options: `sabia-2-small`, `sabia-2-medium`. Default: `sabia-2-small`. |
+| api_key | SecretString | Input parameter. The MariTalk API Key to use for authentication. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: `[0.0, 1.0]`. Default: `0.5`. |
+| endpoint_url | String | Input parameter. The MariTalk API endpoint. Default: `https://api.maritalk.com`. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-mem0.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-mem0.mdx
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+---
+title: Mem0
+slug: /bundles-mem0
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Mem0** bundle.
+
+## Mem0 Chat Memory
+
+The **Mem0 Chat Memory** component retrieves and stores chat messages using Mem0 memory storage.
+
+### Mem0 Chat Memory parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| mem0_config | Mem0 Configuration | Input parameter. The configuration dictionary for initializing the Mem0 memory instance. |
+| ingest_message | Message to Ingest | Input parameter. The message content to be ingested into Mem0 memory. |
+| existing_memory | Existing Memory Instance | Input parameter. An optional existing Mem0 memory instance. |
+| user_id | User ID | Input parameter. The identifier for the user associated with the messages. |
+| search_query | Search Query | Input parameter. The input text for searching related memories in Mem0. |
+| mem0_api_key | Mem0 API Key | Input parameter. The API key for the Mem0 platform. Leave empty to use the local version. |
+| metadata | Metadata | Input parameter. The additional metadata to associate with the ingested message. |
+| openai_api_key | OpenAI API Key | Input parameter. The API key for OpenAI. Required when using OpenAI embeddings without a provided configuration. |
+
+### Mem0 Chat Memory output
+
+The **Mem0 Chat Memory** component can output either **Mem0 Memory** ([`Memory`](/data-types#memory)) or **Search Results** ([`JSON`](/data-types#json)).
+You can select the output type near the component's output port.
+
+Use **Mem0 Chat Memory** for memory storage and retrieval operations with the [**Message History** component](/message-history).
+
+Use the **Search Results** output to retrieve specific memories based on a search query.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-milvus.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-milvus.mdx
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@@ -0,0 +1,60 @@
+---
+title: Milvus
+slug: /bundles-milvus
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Milvus** bundle.
+
+## Milvus vector store
+
+The **Milvus** component reads and writes to Milvus vector stores using an instance of `Milvus` vector store.
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### Milvus vector store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Milvus documentation](https://milvus.io/docs) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+|-------------------------|---------------|--------------------------------------------------|
+| collection_name | String | Input parameter. Name of the Milvus collection. |
+| collection_description | String | Input parameter. Description of the Milvus collection. |
+| uri | String | Input parameter. Connection URI for Milvus. |
+| password | SecretString | Input parameter. Password for Milvus. |
+| username | SecretString | Input parameter. Username for Milvus. |
+| batch_size | Integer | Input parameter. Number of data to process in a single batch. |
+| search_query | String | Input parameter. Query for similarity search. |
+| ingest_data | JSON | Input parameter. Data to be ingested into the vector store. |
+| embedding | Embeddings | Input parameter. Embedding function to use. |
+| number_of_results | Integer | Input parameter. Number of results to return in search. |
+| search_type | String | Input parameter. Type of search to perform. |
+| search_score_threshold | Float | Input parameter. Minimum similarity score for search results. |
+| search_filter | Dict | Input parameter. Metadata filters for search query. |
+| setup_mode | String | Input parameter. Configuration mode for setting up the vector store. |
+| vector_dimensions | Integer | Input parameter. Number of dimensions of the vectors. |
+| pre_delete_collection | Boolean | Input parameter. Whether to delete the collection before creating a new one. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-mistralai.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-mistralai.mdx
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-mistralai.mdx
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+---
+title: MistralAI
+slug: /bundles-mistralai
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **MistralAI** bundle.
+
+For more information about MistralAI features and functionality used by MistralAI components, see the [MistralAI documentation](https://docs.mistral.ai).
+
+## MistralAI text generation
+
+The **MistralAI** component generates text using MistralAI LLMs.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a MistralAI model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### MistralAI text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to 0 for unlimited tokens. |
+| model_name | String | Input parameter. The name of the Mistral AI model to use. Options include `open-mixtral-8x7b`, `open-mixtral-8x22b`, `mistral-small-latest`, `mistral-medium-latest`, `mistral-large-latest`, and `codestral-latest`. Default: `codestral-latest`. |
+| mistral_api_base | String | Input parameter. The base URL of the Mistral API. Defaults to `https://api.mistral.ai/v1`. |
+| api_key | SecretString | Input parameter. The Mistral API Key to use for authentication. |
+| temperature | Float | Input parameter. Controls randomness in the output. Default: 0.5. |
+| max_retries | Integer | Input parameter. Maximum number of retries for API calls. Default: 5. |
+| timeout | Integer | Input parameter. Timeout for API calls in seconds. Default: 60. |
+| max_concurrent_requests | Integer | Input parameter. Maximum number of concurrent API requests. Default: 3. |
+| top_p | Float | Input parameter. Nucleus sampling parameter. Default: 1. |
+| random_seed | Integer | Input parameter. Seed for random number generation. Default: 1. |
+| safe_mode | Boolean | Input parameter. Enables safe mode for content generation. |
+
+## MistralAI Embeddings
+
+The **MistralAI Embeddings** component generates embeddings using MistralAI models.
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### MistralAI Embeddings parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| model | String | Input parameter. The MistralAI model to use. Default: "mistral-embed". |
+| mistral_api_key | SecretString | Input parameter. The API key for authenticating with MistralAI. |
+| max_concurrent_requests | Integer | Input parameter. The maximum number of concurrent API requests. Default: 64. |
+| max_retries | Integer | Input parameter. The maximum number of retry attempts for failed requests. Default: 5. |
+| timeout | Integer | Input parameter. The request timeout in seconds. Default: 120. |
+| endpoint | String | Input parameter. The custom API endpoint URL. Default: `https://api.mistral.ai/v1/`. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-mongodb.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-mongodb.mdx
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-mongodb.mdx
@@ -0,0 +1,54 @@
+---
+title: MongoDB
+slug: /bundles-mongodb
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **MongoDB** bundle.
+
+## MongoDB Atlas
+
+The **MongoDB Atlas** component reads and writes to MongoDB Atlas vector stores using an instance of [`MongoDBAtlasVectorSearch`](https://docs.langchain.com/oss/python/integrations/vectorstores/mongodb_atlas).
+
+
+About vector store instances
+
+
+
+
+
+
+
+### MongoDB Atlas parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [MongoDB Atlas documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/tutorials/vector-search-quick-start/) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+| ------------------------- | ------------ | ----------------------------------------- |
+| mongodb_atlas_cluster_uri | SecretString | Input parameter. The connection URI for your MongoDB Atlas cluster. Required. |
+| enable_mtls | Boolean | Input parameter. Enable mutual TLS authentication. Default: `false`. |
+| mongodb_atlas_client_cert | SecretString | Input parameter. Client certificate combined with private key for mTLS authentication. Required if mTLS is enabled. |
+| db_name | String | Input parameter. The name of the database to use. Required. |
+| collection_name | String | Input parameter. The name of the collection to use. Required. |
+| index_name | String | Input parameter. The name of the Atlas Search index, it should be a Vector Search. Required. |
+| insert_mode | String | Input parameter. How to insert new documents into the collection. The options are "append" or "overwrite". Default: "append". |
+| embedding | Embeddings | Input parameter. The embedding model to use. |
+| number_of_results | Integer | Input parameter. Number of results to return in similarity search. Default: 4. |
+| index_field | String | Input parameter. The field to index. Default: "embedding". |
+| filter_field | String | Input parameter. The field to filter the index. |
+| number_dimensions | Integer | Input parameter. Embedding vector dimension count. Default: 1536. |
+| similarity | String | Input parameter. The method used to measure similarity between vectors. The options are "cosine", "euclidean", or "dotProduct". Default: "cosine". |
+| quantization | String | Input parameter. Quantization reduces memory costs by converting 32-bit floats to smaller data types. The options are "scalar" or "binary". |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-notion.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-notion.mdx
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-notion.mdx
@@ -0,0 +1,276 @@
+---
+title: Notion
+slug: /bundles-notion
+---
+
+import Icon from "@site/src/components/icon";
+
+To use Notion components in Langflow, you first need to create a Notion integration and configure it with the necessary capabilities. This guide will walk you through the process of setting up a Notion integration and granting it access to your Notion databases, and then provide two examples of flows with Notion components.
+
+## Set up a Notion app
+
+To create a Notion app, you need the following:
+
+- A Notion account with access to the workspace where you want to use the integration.
+- Admin permissions in the Notion workspace to create and manage integrations.
+
+### Create a Notion Integration
+
+1. Go to the [Notion Integrations](https://www.notion.com/my-integrations) page.
+2. Click **New Integration**.
+3. Enter an integration name, and then select the workspace where you want to use it.
+4. Optional: Enable the [necessary Notion integration capabilities](https://developers.notion.com/reference/capabilities) based on your requirements.
+5. Click **Submit** to create the integration.
+
+### Configure Integration Capabilities
+
+After creating the integration, you need to configure its capabilities to define what actions it can perform and what data it can access.
+
+1. In the integration settings page, go to the **Capabilities** tab.
+2. Enable the required capabilities for your integration. For example:
+ - If your integration needs to read data from Notion, enable the "Read content" capability.
+ - If your integration needs to create new content in Notion, enable the "Insert content" capability.
+ - If your integration needs to update existing content in Notion, enable the "Update content" capability.
+3. Configure the user information access level based on your integration's requirements.
+4. Save the changes.
+
+### Obtain Integration Token
+
+:::warning
+Your integration token is a sensitive piece of information. Make sure to keep it secure and never share it publicly. Store it safely in your Langflow configuration or environment variables.
+:::
+
+To authenticate your integration with Notion, you need to obtain an integration token.
+
+1. In the integration settings page, go to the "Secrets" tab.
+2. Copy the "Internal Integration Token" value. This token will be used to authenticate your integration with Notion.
+
+### Grant Integration Access to Notion Databases
+
+For your integration to interact with Notion databases, you need to grant it access to the specific databases it must work with:
+
+1. Open the Notion database that you want your integration to access.
+2. Click **Share**.
+3. In the **Invite** section, select your integration from the list.
+4. Click **Invite** to grant the integration access to the database.
+
+ If your database contains references to other databases, you need to grant the integration access to those referenced databases as well.
+ Repeat this step for each referenced database that your integration must access.
+
+## Build with Notion components in Langflow
+
+Once you have set up your Notion integration and granted it access to the required databases, you can start using the Notion components in Langflow:
+
+- **Add Content to Page**: Converts markdown text to Notion blocks and appends them to a specified Notion page.
+- **Create Page**: Creates a new page in a specified Notion database with the provided properties.
+- **List Database Properties**: Retrieves the properties of a specified Notion database.
+- **List Pages**: Queries a Notion database with filtering and sorting options.
+- **List Users**: Retrieves a list of users from the Notion workspace.
+- **Page Content Viewer**: Retrieves the content of a Notion page as plain text.
+- **Search**: Searches all pages and databases that have been shared with the integration. You can filter results to either pages or databases and specify the sort direction.
+- **Update Page Property**: Updates the properties of an existing Notion page.
+
+Each of these components can output `JSON` and `Tool` [data types](/data-types).
+
+## Notion Meeting Notes Agent
+
+The Notion Agent for Meeting Notes is an AI-powered tool that automatically processes meeting transcripts and updates your Notion workspace. It identifies tasks, action items, and key points from your meetings, then creates new tasks or updates existing ones in Notion without manual input.
+
+To build this flow, you need the following:
+
+- [A Notion App](#set-up-a-notion-app)
+- [A Notion API key](https://www.notion.so/my-integrations)
+- [An OpenAI API key](https://platform.openai.com/account/api-keys)
+- Recommended: [Download the Meeting Agent Flow JSON](/files/Meeting_Notes_Agent.json), and then [import the flow](/concepts-flows-import) into Langflow.
+
+:::warning
+Treat all keys and other credentials as sensitive information. Use secure references, and don't share them publicly.
+:::
+
+### Components used in this flow
+
+
+
+* Meeting Transcript (text input): This component allows users to input the meeting transcript directly into the flow.
+* List Users (Notion component): Retrieves a list of users from the Notion workspace. Requires a Notion Secret (API key).
+* List Databases (Notion component): Searches and lists all databases in the Notion workspace. Requires the following inputs:
+ - Notion Secret (API key)
+ - Query (optional)
+ - Filter Type (default: database)
+ - Sort Direction
+* Prompt Template: This component creates a dynamic prompt template using the following inputs:
+ - Meeting Transcript
+ - List of Users
+ - List of Databases
+ - Current Date
+* Meeting Summarizer (Tool Calling Agent): Analyzes the meeting transcript and identifies tasks and action items. Requires the following inputs:
+ - System Prompt (from the **Prompt Template** component)
+ - Language Model (OpenAI)
+ - Tools:
+ - Notion Search
+ - List Database Properties: Retrieves the properties of a specified Notion database. Requires a Database ID and a Notion Secret (API key) as input.
+ - Create Page: Creates a new page in a Notion database. Requires a Database ID, Properties (JSON), and a Notion Secret (API key) as input.
+ - Update Page Property: Updates the properties of an existing Notion page. Requires a Page ID, Notion Secret (API key), and properties to update as input.
+ - Add Content to Page: Converts markdown text to Notion blocks and appends them to a specified Notion page. Requires a Page/Block ID, Notion Secret (API key), and Markdown text as input.
+
+
+
+* Notion Agent (Tool Calling Agent): Executes actions in Notion based on the meeting summary/ Requires the following inputs:
+ - System Prompt (from the second **Prompt Template** component)
+ - Language Model (OpenAI)
+ - Tools:
+ - List Database Properties: Retrieves the properties of a specified Notion database. Requires a Database ID and a Notion Secret (API key) as input.
+ - Create Page: Creates a new page in a Notion database. Requires a Database ID, Properties (JSON), and a Notion Secret (API key) as input.
+ - Update Page Property: Updates the properties of an existing Notion page. Requires a Page ID, Notion Secret (API key), and properties to update as input.
+ - Add Content to Page: Converts markdown text to Notion blocks and appends them to a specified Notion page. Requires a Page/Block ID, Notion Secret (API key), and Markdown text as input.
+* Chat Output: Displays the final output of the Notion Agent in the Playground.
+
+### Flow Process
+
+1. The user inputs a meeting transcript.
+2. The flow retrieves the list of Notion users and databases.
+3. A prompt is generated using the transcript, user list, database list, and current date.
+4. The Meeting Summarizer analyzes the transcript and identifies tasks and action items.
+5. The Notion Agent uses the meeting summary to:
+ - Create new pages for new tasks
+ - Update existing pages for existing tasks
+ - Add content to pages with meeting notes
+6. The Chat Output displays a summary of actions taken in Notion.
+
+### Run the Notion Meeting Notes flow
+
+1. Create a flow manually or import a pre-built flow JSON file:
+
+ * Recommended: [Download the Meeting Agent flow JSON](/files/Meeting_Notes_Agent.json) and then [import the flow](/concepts-flows-import) into Langflow.
+ * Create a blank flow, and then add the previously described components to your flow, connecting them as shown in the flow diagram.
+
+2. Input the Notion and OpenAI API keys in their respective components.
+3. Paste your meeting transcript into the **Meeting Transcript** component.
+
+ For optimal results, use detailed meeting transcripts. The quality of the output depends on the comprehensiveness of the input provided.
+
+4. Run the flow by clicking **Run component** on the **Chat Output** component or open the **Playground**.
+5. Review the output summarizing the actions taken in your Notion workspace.
+6. Optional: The flow can be customized to meet your team's specific needs.
+Customize this flow by:
+ * Adjusting the system prompt to change the agent's behavior or context.
+ * Adding or removing Notion tools based on your specific needs.
+ * Modifying the OpenAI model parameters (e.g., temperature) to adjust the agent's response style.
+
+## Notion Conversational Agent
+
+The Notion Conversational Agent is an AI-powered assistant that interacts with your Notion workspace through natural language conversations. This flow performs Notion-related tasks like creating pages, searching for information, and managing content, all through a chat interface.
+
+
+
+To build this flow, you need the following:
+
+- [A Notion App](#set-up-a-notion-app)
+- [A Notion account and API key](https://www.notion.so/my-integrations)
+- [An OpenAI API key](https://platform.openai.com/account/api-keys)
+- Recommended: [Download the Conversation Agent Flow JSON](/files/Conversational_Notion_Agent.json), and then [import the flow](/concepts-flows-import) into Langflow.
+
+### Components used in this flow
+
+- **Chat Input**: Accepts user queries and commands
+- **Chat Output**: Displays the agent's responses
+- **Language Model**: Processes user input and generates responses with an OpenAI model
+- **Tool Calling Agent**: Coordinates the use of various Notion tools based on user input
+- **Toolkit**: Combines multiple Notion-specific tools into a single toolkit
+- **Notion Tools**: Various components for interacting with Notion, including:
+ - List Users
+ - List Database Properties
+ - List Pages
+ - Page Content Viewer
+ - Create Page
+ - Update Page Property
+ - Add Content to Page
+ - Search
+- **Message History**: Stores conversation history
+- **Prompt Template**: Provides system instructions and context for the agent
+- **Current Date**: Supplies the current date and time for context
+
+### Run the Conversational Notion Agent
+
+1. Open Langflow and create a new flow.
+2. Add the components listed above to your flow canvas, or Download the [Conversation Agent Flow](/files/Conversational_Notion_Agent.json)(Download link) and **Import** the JSON file into Langflow.
+3. Connect the components as shown in the flow diagram.
+4. Input the Notion and OpenAI API keys in their respective components.
+5. To start the flow, click the "Play" button in Langflow.
+6. In the chat interface, enter a Notion-related request or question. For example:
+ - "Create a new page in my task database"
+ - "Search for pages about project management"
+ - "List all users in my Notion workspace"
+7. The agent will process your request, use the appropriate Notion tools, and provide a response.
+8. Continue the conversation, asking follow-up questions or giving new commands as needed.
+
+
+ Example Conversation
+
+ ```
+ User: List all the users in my Notion workspace.
+
+ Agent: Certainly! I'll use the Notion User List tool to retrieve that information for you. One moment please.
+
+ [Agent uses the Notion User List tool]
+
+ Here's a list of users in your Notion workspace:
+
+ 1. John Doe (john@example.com)
+ 2. Jane Smith (jane@example.com)
+ 3. Alex Johnson (alex@example.com)
+
+ Is there anything specific you'd like to know about these users or any other Notion-related task I can help you with?
+
+ User: Create a new page in my Projects database with the title "Website Redesign".
+
+ Agent: Certainly! I'd be happy to create a new page in your Projects database with the title "Website Redesign". I'll use the Notion Page Creator tool to do this. First, let me check the properties of your Projects database to ensure we include all necessary information.
+
+ [Agent uses the Notion Database Properties tool]
+
+ I've retrieved the properties for your Projects database. Now, I'll create the new page with the title "Website Redesign". One moment please.
+
+ [Agent uses the Notion Page Creator tool]
+
+ Great news! I've successfully created a new page in your Projects database with the title "Website Redesign". Here are the details:
+
+ - Title: Website Redesign
+ - Page ID: [page_id_here]
+ - URL: [page_url_here]
+
+ Is there any additional information you'd like me to add to this new project page, such as a description, due date, or assigned team members?
+
+ User: Yes, please add a description: "Redesign company website to improve user experience and modernize the look."
+
+ Agent: Certainly! I'd be happy to add that description to the "Website Redesign" project page. I'll use the Add Content to Page tool to update the page with this information.
+
+ [Agent uses the Add Content to Page tool]
+
+ I've successfully added the description to your "Website Redesign" project page. Here's a summary of what I've done:
+
+ 1. Located the "Website Redesign" page using the page ID from our previous interaction.
+ 2. Added the following content to the page:
+
+ Description: Redesign company website to improve user experience and modernize the look.
+
+ The description has been added as a new text block on the page. Is there anything else you'd like me to add or modify on this project page?
+ ```
+
+
+
+9. Optional: The flow can be customized to meet your team's specific needs.
+For example:
+ * Adjust the system prompt to change the agent's behavior or context.
+ * Add or remove Notion tools based on your specific needs.
+ * Modify the OpenAI model parameters (e.g., temperature) to adjust the agent's response style.
+
+## Troubleshooting
+
+If you encounter issues:
+
+1. Ensure all API keys are correctly set and have the necessary permissions.
+2. Check that your Notion integration has access to the relevant pages and databases.
+3. Verify that all components are properly connected in the flow.
+4. Review the Langflow logs for any error messages.
+
+For more advanced usage and integration options, refer to the [Notion API documentation](https://developers.notion.com/) and [Langflow documentation](/).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-novita.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-novita.mdx
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+---
+title: Novita
+slug: /bundles-novita
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Novita** bundle.
+
+## Novita AI
+
+This component generates text using [Novita's language models](https://novita.ai/docs/guides/llm-api).
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a Novita model as the LLM for another LLM-driven component, such as a **Language Model** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### Novita AI parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| api_key | SecretString | Input parameter. Your Novita AI API Key. |
+| model | String | Input parameter. The id of the Novita AI model to use. |
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to 0 for unlimited tokens. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7. |
+| top_p | Float | Input parameter. Controls the nucleus sampling. Range: [0.0, 1.0]. Default: 1.0. |
+| frequency_penalty | Float | Input parameter. Controls the frequency penalty. Range: [0.0, 2.0]. Default: 0.0. |
+| presence_penalty | Float | Input parameter. Controls the presence penalty. Range: [0.0, 2.0]. Default: 0.0. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-nvidia.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-nvidia.mdx
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+---
+title: NVIDIA
+slug: /bundles-nvidia
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **NVIDIA** bundle.
+
+## NVIDIA
+
+This component generates text using NVIDIA LLMs.
+For more information about NVIDIA LLMs, see the [NVIDIA AI documentation](https://developer.nvidia.com/generative-ai).
+
+### NVIDIA parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to `0` for unlimited tokens. |
+| model_name | String | Input parameter. The name of the NVIDIA model to use. Default: `mistralai/mixtral-8x7b-instruct-v0.1`. |
+| base_url | String | Input parameter. The base URL of the NVIDIA API. Default: `https://integrate.api.nvidia.com/v1`. |
+| nvidia_api_key | SecretString | Input parameter. The NVIDIA API Key for authentication. |
+| temperature | Float | Input parameter. Controls randomness in the output. Default: `0.1`. |
+| seed | Integer | Input parameter. The seed controls the reproducibility of the job. Default: `1`. |
+| model | LanguageModel | Output parameter. An instance of ChatNVIDIA configured with the specified parameters. |
+
+### NVIDIA NIM on WSL2
+
+[NVIDIA NIM (NVIDIA Inference Microservices)](https://docs.nvidia.com/nim/index.html) provides containers to self-host GPU-accelerated inferencing microservices.
+
+You can use the **NVIDIA** component to connect Langflow with NVIDIA NIM on an RTX Windows system with [Windows Subsystem for Linux 2 (WSL2)](https://learn.microsoft.com/en-us/windows/wsl/install) installed.
+The following example connects an **NVIDIA** language model component in Langflow to a deployed `mistral-nemo-12b-instruct` NIM on an **RTX Windows system** with **WSL2**.
+
+1. Prepare your system:
+
+ * [NVIDIA NIM WSL2 installed](https://docs.nvidia.com/nim/wsl2/latest/getting-started.html)
+ * A NIM container deployed according to the model's instructions
+
+ Prerequisites vary between models.
+ For example, to deploy the `mistral-nemo-12b-instruct` NIM, follow the instructions for **Windows on RTX AI PCs (Beta)** on your [model's deployment overview](https://build.nvidia.com/nv-mistralai/mistral-nemo-12b-instruct/deploy?environment=wsl2.md).
+
+ * Windows 11 build 23H2 or later
+ * At least 12 GB of RAM
+
+2. Create a flow based on the **Basic Prompting** template.
+3. Replace the **OpenAI** model component with the **NVIDIA** component.
+4. In the **NVIDIA** component's **Base URL** field, add the URL where your NIM is accessible. If you followed your model's [deployment instructions](https://build.nvidia.com/nv-mistralai/mistral-nemo-12b-instruct/deploy?environment=wsl2.md), the value is `http://localhost:8000/v1`.
+5. In the **NVIDIA** component's **NVIDIA API Key** field, add your NVIDIA API Key.
+6. Select your model from the **Model Name** field.
+7. Open the **Playground** and chat with your **NIM** model.
+
+## NVIDIA Embeddings
+
+The **NVIDIA Embeddings** component generates embeddings using [NVIDIA models](https://docs.nvidia.com).
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### NVIDIA Embeddings parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| model | String | Input parameter. The NVIDIA model to use for embeddings, such as `nvidia/nv-embed-v1`. |
+| base_url | String | Input parameter. The base URL for the NVIDIA API. Default: `https://integrate.api.nvidia.com/v1`. |
+| nvidia_api_key | SecretString | Input parameter. The API key for authenticating with NVIDIA's service. |
+| temperature | Float | Input parameter. The model temperature for embedding generation. Default: `0.1`. |
+| embeddings | Embeddings | Output parameter. An `NVIDIAEmbeddings` instance for generating embeddings. |
+
+:::tip Tokenization considerations
+Be aware of your embedding model's chunk size limit.
+Tokenization errors can occur if your text chunks are too large.
+For more information, see [Tokenization errors due to chunk size](/split-text#chunk-size).
+:::
+
+## NVIDIA Rerank
+
+This component finds and reranks documents using the NVIDIA API.
+
+## NVIDIA Retriever Extraction
+
+The **NVIDIA Retriever Extraction** component integrates with the [NVIDIA nv-ingest](https://github.com/NVIDIA/nv-ingest) microservice for data ingestion, processing, and extraction of text files.
+
+The `nv-ingest` service supports multiple extraction methods for PDF, DOCX, and PPTX file types, and includes pre- and post-processing services like splitting, chunking, and embedding generation. The extractor service's High Resolution mode uses the `nemoretriever-parse` extraction method for better quality extraction from scanned PDF documents. This feature is only available for PDF files.
+
+The **NVIDIA Retriever Extraction** component imports the NVIDIA `Ingestor` client, ingests files with requests to the NVIDIA ingest endpoint, and outputs the processed content as a list of [`JSON`](/data-types#json) objects. `Ingestor` accepts additional configuration options for data extraction from other text formats. To configure these options, see the [parameters](#parameters).
+
+:::tip
+NVIDIA Retriever Extraction is also known as NV-Ingest and NeMo Retriever Extraction.
+:::
+
+### Use the NVIDIA Retriever Extraction component in a flow
+
+The **NVIDIA Retriever Extraction** component accepts `Message` inputs, and then outputs `JSON`. The component calls an NVIDIA Ingest microservice's endpoint to ingest a local file and extract the text.
+
+To use the **NVIDIA Retriever Extraction** component in your flow, follow these steps:
+
+1. Prepare your system:
+
+ * An NVIDIA Ingest endpoint. For more information on setting up an NVIDIA Ingest endpoint, see the [NVIDIA Ingest quickstart](https://github.com/NVIDIA/nv-ingest?tab=readme-ov-file#quickstart).
+
+ * The **NVIDIA Retriever Extraction** component requires the installation of additional dependencies to your Langflow environment. To install the dependencies in a virtual environment, run the following commands.
+
+ * If you have the Langflow repository cloned and installed from source:
+ ```bash
+ source **YOUR_LANGFLOW_VENV**/bin/activate
+ uv sync --extra nv-ingest
+ uv run langflow run
+ ```
+
+ * If you are installing Langflow from the Python Package Index:
+ ```bash
+ source **YOUR_LANGFLOW_VENV**/bin/activate
+ uv pip install --prerelease=allow 'langflow[nv-ingest]'
+ uv run langflow run
+ ```
+
+2. Add the **NVIDIA Retriever Extraction** component to your flow.
+3. In the **Base URL** field, enter the URL of the NVIDIA Ingest endpoint.
+You can also store the URL as a [global variable](/configuration-global-variables) to reuse it in multiple components and flows.
+4. Click **Select Files** to select a file to ingest.
+5. Select which text type to extract from the file: text, charts, tables, images, or infographics.
+6. Optional: For PDF files, enable **High Resolution Mode** for better quality extraction from scanned documents.
+7. Select whether to split the text into chunks.
+
+
+
+8. Click **Run component** to ingest the file, and then click **Logs** or **Inspect output** to confirm the component ingested the file.
+9. To store the processed data in a vector database, add a vector store component to your flow, and then connect the **NVIDIA Retriever Extraction** component's `JSON` output to the vector store component's input.
+
+ When you run the flow with a vector store component, the processed data is stored in the vector database.
+ You can query your database to retrieve the uploaded data.
+
+ 
+
+### NVIDIA Retriever Extraction parameters {#parameters}
+
+The **NVIDIA Retriever Extraction** component has the following parameters.
+
+For more information, see the [NV-Ingest documentation](https://nvidia.github.io/nv-ingest/user-guide/).
+
+#### Inputs
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| base_url | NVIDIA Ingestion URL | The URL of the NVIDIA Ingestion API. |
+| path | Path | File path to process. |
+| extract_text | Extract Text | Extract text from documents. Default: `true`. |
+| extract_charts | Extract Charts | Extract text from charts. Default: `false`. |
+| extract_tables | Extract Tables | Extract text from tables. Default: `true`. |
+| extract_images | Extract Images | Extract images from document. Default: `true`. |
+| extract_infographics | Extract Infographics | Extract infographics from document. Default: `false`. |
+| text_depth | Text Depth | The level at which text is extracted. Options: 'document', 'page', 'block', 'line', 'span'. Default: `page`. |
+| split_text | Split Text | Split text into smaller chunks. Default: `true`. |
+| chunk_size | Chunk Size | The number of tokens per chunk. Default: `500`. Make sure the chunk size is compatible with your embedding model. For more information, see [Tokenization errors due to chunk size](/split-text#chunk-size). |
+| chunk_overlap | Chunk Overlap | Number of tokens to overlap from previous chunk. Default: `150`. |
+| filter_images | Filter Images | Filter images (see advanced options for filtering criteria). Default: `false`. |
+| min_image_size | Minimum Image Size Filter | Minimum image width/length in pixels. Default: `128`. |
+| min_aspect_ratio | Minimum Aspect Ratio Filter | Minimum allowed aspect ratio (width / height). Default: `0.2`. |
+| max_aspect_ratio | Maximum Aspect Ratio Filter | Maximum allowed aspect ratio (width / height). Default: `5.0`. |
+| dedup_images | Deduplicate Images | Filter duplicated images. Default: `true`. |
+| caption_images | Caption Images | Generate captions for images using the NVIDIA captioning model. Default: `true`. |
+| high_resolution | High Resolution (PDF only) | Process PDF in high-resolution mode for better quality extraction from scanned PDF. Default: `false`. |
+
+#### Outputs
+
+The **NVIDIA Retriever Extraction** component outputs a list of [`JSON`](/data-types#json) objects where each object contains:
+- `text`: The extracted content.
+ - For text documents: The extracted text content.
+ - For tables and charts: The extracted table/chart content.
+ - For images: The image caption.
+ - For infographics: The extracted infographic content.
+- `file_path`: The source file name and path.
+- `document_type`: The type of the document, which can be `text`, `structured`, or `image`.
+- `description`: Additional description of the content.
+
+The output varies based on the `document_type`:
+
+- Documents with `document_type: "text"` contain:
+ - Raw text content extracted from documents, for example, paragraphs from PDFs or DOCX files.
+ - Content stored directly in the `text` field.
+ - Content extracted using the `extract_text` parameter.
+
+- Documents with `document_type: "structured"` contain:
+ - Text extracted from tables, charts, and infographics and processed to preserve structural information.
+ - Content extracted using the `extract_tables`, `extract_charts`, and `extract_infographics` parameters.
+ - Content stored in the `text` field after being processed from the `table_content` metadata.
+
+- Documents with `document_type: "image"` contain:
+ - Image content extracted from documents.
+ - Caption text stored in the `text` field when `caption_images` is enabled.
+ - Content extracted using the `extract_images` parameter.
+
+## NVIDIA System-Assist
+
+The **NVIDIA System-Assist** component integrates your flows with NVIDIA G-Assist, enabling interaction with NVIDIA GPU drivers through natural language prompts.
+For example, prompt G-Assist with `"What is my current GPU temperature?"` or `"Show me the available GPU memory"` to get information, and then tell G-Assist to modify your GPU settings.
+For more information, see the [NVIDIA G-Assist repository](https://github.com/NVIDIA/g-assist).
+
+1. Prepare your system:
+
+ * The **NVIDIA System-Assist** component requires an NVIDIA GPU on a Windows operating system.
+ * It uses the `gassist.rise` package, which is installed with all Langflow versions that include this component.
+
+2. Create a flow with a **Chat Input** component, **NVIDIA System-Assist** component, and **Chat Output** components.
+
+ This is a simplified example that uses only three components.
+ Depending on your use case, your flow might use more components or different inputs and outputs.
+
+3. Connect the **Chat Input** component to the **NVIDIA System-Assist** component's **Prompt** input.
+
+ The **Prompt** parameter accepts a natural language prompt that is processed by the NVIDIA G-Assist AI Assistant.
+ In this example, you'll provide the prompt as chat input.
+ You could also enter a prompt directly in the **Prompt** input or connect another input component.
+
+4. Connect the **NVIDIA System-Assist** component's output to the **Chat Output** component.
+
+5. To test the flow, open the **Playground**, and then ask a question about your GPU.
+For example, `"What is my current GPU temperature?"`.
+
+ Through the **NVIDIA System-Assist** component, NVIDIA G-Assist queries your GPU based on the prompt, and then prints the response to the **Playground**.
+
+ The component's output is a [`Message`](/data-types#message) containing the NVIDIA G-Assist response.
+ The string response with the completed operation result is available in the `text` key in the `Message` object.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-ollama.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-ollama.mdx
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+---
+title: Ollama
+slug: /bundles-ollama
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Ollama** bundle.
+
+For more information about Ollama features and functionality used by Ollama components, see the [Ollama documentation](https://ollama.com/).
+
+## Ollama text generation
+
+This component generates text using [Ollama's language models](https://ollama.com/library).
+
+To use the **Ollama** component in a flow, connect Langflow to your locally running Ollama server and select a model:
+
+1. Add the **Ollama** component to your flow.
+
+2. In the **Base URL** field, enter the address for your locally running Ollama server.
+
+ This value is set as the `OLLAMA_HOST` environment variable in Ollama.
+ The default base URL is `http://127.0.0.1:11434`.
+
+3. Once the connection is established, select a model in the **Model Name** field, such as `llama3.2:latest`.
+
+ To refresh the server's list of models, click **Refresh**.
+
+4. Optional: To configure additional parameters, such as temperature or max tokens, click the component to open the [component inspection panel](/concepts-components#component-menus).
+
+5. Connect the **Ollama** component to other components in the flow, depending on how you want to use the model.
+
+ Language model components can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)). Use the **Language Model** output when you want to use an Ollama model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component. For more information, see [Language model components](/components-models).
+
+ In the following example, the flow uses `LanguageModel` output to use an Ollama model as the LLM for an [**Agent** component](/components-agents).
+
+ 
+
+## Ollama Embeddings
+
+The **Ollama Embeddings** component generates embeddings using [Ollama embedding models](https://ollama.com/search?c=embedding).
+
+To use this component in a flow, connect Langflow to your locally running Ollama server and select an embeddings model:
+
+1. Add the **Ollama Embeddings** component to your flow.
+
+2. In the **Ollama Base URL** field, enter the address for your locally running Ollama server.
+
+ This value is set as the `OLLAMA_HOST` environment variable in Ollama.
+ The default base URL is `http://127.0.0.1:11434`.
+
+3. Once the connection is established, select a model in the **Ollama Model** field, such as `all-minilm:latest`.
+
+ To refresh the server's list of models, click **Refresh**.
+
+4. Optional: To configure additional parameters, such as temperature or max tokens, click the component to open the [component inspection panel](/concepts-components#component-menus).
+Available parameters depend on the selected model.
+
+5. Connect the **Ollama Embeddings** component to other components in the flow.
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+ This example connects the **Ollama Embeddings** component to generate embeddings for text chunks extracted from a PDF file, and then stores the embeddings and chunks in a Chroma DB vector store.
+
+ 
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-openai.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-openai.mdx
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+---
+title: OpenAI
+slug: /bundles-openai
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **OpenAI** bundle.
+
+For more information about OpenAI features and functionality used by OpenAI components, see the [OpenAI documentation](https://platform.openai.com/docs/overview).
+
+## OpenAI text generation
+
+The **OpenAI** component generates text using [OpenAI's language models](https://platform.openai.com/docs/models).
+
+It provides access to the same OpenAI models that are available in the core **Language Model** component, but the **OpenAI** component provides additional parameters for customizing the request to the OpenAI API.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a specific OpenAI model configuration as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### OpenAI text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| api_key | SecretString | Input parameter. Your OpenAI API Key. |
+| model | String | Input parameter. The name of the OpenAI model to use. Options include "gpt-3.5-turbo" and "gpt-4". |
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to 0 for unlimited tokens. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.7. |
+| top_p | Float | Input parameter. Controls the nucleus sampling. Range: [0.0, 1.0]. Default: 1.0. |
+| frequency_penalty | Float | Input parameter. Controls the frequency penalty. Range: [0.0, 2.0]. Default: 0.0. |
+| presence_penalty | Float | Input parameter. Controls the presence penalty. Range: [0.0, 2.0]. Default: 0.0. |
+
+## OpenAI Embeddings
+
+The **OpenAI Embeddings** component uses [OpenAI embedding models](https://platform.openai.com/docs/guides/embeddings) for embedding generation.
+
+It provides access to the same OpenAI models that are available in the core **Embedding Model** component, but the **OpenAI Embeddings** component provides additional parameters for customizing the request to the OpenAI embedding API.
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### OpenAI Embeddings parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| OpenAI API Key | String | Input parameter. The API key to use for accessing the OpenAI API. |
+| Default Headers | Dict | Input parameter. The default headers for the HTTP requests. |
+| Default Query | NestedDict | Input parameter. The default query parameters for the HTTP requests. |
+| Allowed Special | List | Input parameter. The special tokens allowed for processing. Default: `[]`. |
+| Disallowed Special | List | Input parameter. The special tokens disallowed for processing. Default: `["all"]`. |
+| Chunk Size | Integer | Input parameter. The chunk size for processing. Default: `1000`. |
+| Client | Any | Input parameter. The HTTP client for making requests. |
+| Deployment | String | Input parameter. The deployment name for the model. Default: `text-embedding-3-small`. |
+| Embedding Context Length | Integer | Input parameter. The length of embedding context. Default: `8191`. |
+| Max Retries | Integer | Input parameter. The maximum number of retries for failed requests. Default: `6`. |
+| Model | String | Input parameter. The name of the model to use. Default: `text-embedding-3-small`. |
+| Model Kwargs | NestedDict | Input parameter. Additional keyword arguments for the model. |
+| OpenAI API Base | String | Input parameter. The base URL of the OpenAI API. |
+| OpenAI API Type | String | Input parameter. The type of the OpenAI API. |
+| OpenAI API Version | String | Input parameter. The version of the OpenAI API. |
+| OpenAI Organization | String | Input parameter. The organization associated with the API key. |
+| OpenAI Proxy | String | Input parameter. The proxy server for the requests. |
+| Request Timeout | Float | Input parameter. The timeout for the HTTP requests. |
+| Show Progress Bar | Boolean | Input parameter. Whether to show a progress bar for processing. Default: `false`. |
+| Skip Empty | Boolean | Input parameter. Whether to skip empty inputs. Default: `false`. |
+| TikToken Enable | Boolean | Input parameter. Whether to enable TikToken. Default: `true`. |
+| TikToken Model Name | String | Input parameter. The name of the TikToken model. |
+
+## See also
+
+* [**Agent** component](/components-agents)
+* [LangChain **OpenAI Tools Agent** component](/bundles-langchain#openai-tools-agent)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-openrouter.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-openrouter.mdx
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+---
+title: OpenRouter
+slug: /bundles-openrouter
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **OpenRouter** bundle.
+
+For more information about OpenRouter features and functionality used by OpenRouter components, see the [OpenRouter documentation](https://openrouter.ai/docs).
+
+## OpenRouter text generation
+
+This component generates text using OpenRouter's unified API for multiple AI models from different providers.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use an OpenRouter model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### OpenRouter text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| api_key | SecretString | Input parameter. Your OpenRouter API key for authentication. |
+| site_url | String | Input parameter. Your site URL for OpenRouter rankings. |
+| app_name | String | Input parameter. Your app name for OpenRouter rankings. |
+| provider | String | Input parameter. The AI model provider to use. |
+| model_name | String | Input parameter. The specific model to use for chat completion. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: [0.0, 2.0]. Default: 0.7. |
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-perplexity.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-perplexity.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-perplexity.mdx
@@ -0,0 +1,36 @@
+---
+title: Perplexity
+slug: /bundles-perplexity
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Perplexity** bundle.
+
+For more information about Perplexity features and functionality used by Perplexity components, see the [Perplexity documentation](https://perplexity.ai/).
+
+## Perplexity text generation
+
+This component generates text using Perplexity's language models.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a Perplexity model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### Perplexity text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| model_name | String | Input parameter. The name of the Perplexity model to use. Options include various Llama 3.1 models. |
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. |
+| api_key | SecretString | Input parameter. The Perplexity API Key for authentication. |
+| temperature | Float | Input parameter. Controls randomness in the output. Default: 0.75. |
+| top_p | Float | Input parameter. The maximum cumulative probability of tokens to consider when sampling. |
+| n | Integer | Input parameter. Number of chat completions to generate for each prompt. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-pgvector.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-pgvector.mdx
new file mode 100644
index 0000000000..31303d8954
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-pgvector.mdx
@@ -0,0 +1,50 @@
+---
+title: pgvector
+slug: /bundles-pgvector
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **pgvector** bundle.
+
+## pgvector vector store
+
+The **PGVector** component reads and writes to PostgreSQL vector stores using an instance of [`PGVector`](https://docs.langchain.com/oss/python/integrations/vectorstores/pgvector).
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### pgvector vector store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [PGVector documentation](https://github.com/pgvector/pgvector) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+| --------------- | ------------ | ----------------------------------------- |
+| pg_server_url | SecretString | Input parameter. The PostgreSQL server connection string. |
+| collection_name | String | Input parameter. The table name for the vector store. |
+| search_query | String | Input parameter. The query for similarity search. |
+| ingest_data | JSON | Input parameter. The data to be ingested into the vector store. |
+| embedding | Embeddings | Input parameter. The embedding function to use. |
+| number_of_results | Integer | Input parameter. The number of results to return in search. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-pinecone.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-pinecone.mdx
new file mode 100644
index 0000000000..6340f789cf
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-pinecone.mdx
@@ -0,0 +1,53 @@
+---
+title: Pinecone
+slug: /bundles-pinecone
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Pinecone** bundle.
+
+## Pinecone vector store
+
+The **Pinecone** component reads and writes to Pinecone vector stores using an instance of `PineconeVectorStore`.
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### Pinecone vector store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Pinecone documentation](https://docs.pinecone.io/home) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+| ----------------- | ------------ | ----------------------------------------- |
+| index_name | String | Input parameter. The name of the Pinecone index. |
+| namespace | String | Input parameter. The namespace for the index. |
+| distance_strategy | String | Input parameter. The strategy for calculating distance between vectors. |
+| pinecone_api_key | SecretString | Input parameter. The API key for Pinecone. |
+| text_key | String | Input parameter. The key in the record to use as text. |
+| search_query | String | Input parameter. The query for similarity search. |
+| ingest_data | JSON | Input parameter. The data to be ingested into the vector store. |
+| embedding | Embeddings | Input parameter. The embedding function to use. |
+| number_of_results | Integer | Input parameter. The number of results to return in search. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-qdrant.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-qdrant.mdx
new file mode 100644
index 0000000000..89241e527f
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-qdrant.mdx
@@ -0,0 +1,60 @@
+---
+title: Qdrant
+slug: /bundles-qdrant
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Qdrant** bundle.
+
+## Qdrant vector store
+
+The **Qdrant** component reads and writes to Qdrant vector stores using an instance of `QdrantVectorStore`.
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### Qdrant vector store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Qdrant documentation](https://qdrant.tech/documentation/) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+| -------------------- | ------------ | ----------------------------------------- |
+| collection_name | String | Input parameter. The name of the Qdrant collection. |
+| host | String | Input parameter. The Qdrant server host. |
+| port | Integer | Input parameter. The Qdrant server port. |
+| grpc_port | Integer | Input parameter. The Qdrant gRPC port. |
+| api_key | SecretString | Input parameter. The API key for Qdrant. |
+| prefix | String | Input parameter. The prefix for Qdrant. |
+| timeout | Integer | Input parameter. The timeout for Qdrant operations. |
+| path | String | Input parameter. The path for Qdrant. |
+| url | String | Input parameter. The URL for Qdrant. |
+| distance_func | String | Input parameter. The distance function for vector similarity. |
+| content_payload_key | String | Input parameter. The content payload key. |
+| metadata_payload_key | String | Input parameter. The metadata payload key. |
+| search_query | String | Input parameter. The query for similarity search. |
+| ingest_data | JSON | Input parameter. The data to be ingested into the vector store. |
+| embedding | Embeddings | Input parameter. The embedding function to use. |
+| number_of_results | Integer | Input parameter. The number of results to return in search. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-redis.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-redis.mdx
new file mode 100644
index 0000000000..11e05684c9
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-redis.mdx
@@ -0,0 +1,77 @@
+---
+title: Redis
+slug: /bundles-redis
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Redis** bundle.
+
+## Redis Chat Memory
+
+The **Redis Chat Memory** component retrieves and stores chat messages using Redis memory storage.
+
+Chat memories are passed between memory storage components as the [`Memory`](/data-types#memory) data type.
+
+For more information about using external chat memory in flows, see the [**Message History** component](/message-history).
+
+### Redis Chat Memory parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| host | hostname | Input parameter. The IP address or hostname. |
+| port | port | Input parameter. The Redis Port Number. |
+| database | database | Input parameter. The Redis database. |
+| username | Username | Input parameter. The Redis username. |
+| password | Password | Input parameter. The password for the username. |
+| key_prefix | Key prefix | Input parameter. The key prefix. |
+| session_id | Session ID | Input parameter. The unique session identifier for the message. |
+
+## Redis vector store
+
+The **Redis** vector store component reads and writes to Redis vector stores using an instance of [`RedisVectorStore`](https://python.langchain.com/docs/integrations/vectorstores/redis/).
+
+
+About vector store instances
+
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+
+
+
+
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### Redis vector store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+
+
+
+For information about accepted values and functionality, see the [Redis documentation](https://redis.io/docs/latest/develop/interact/search-and-query/advanced-concepts/vectors/) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+| ----------------- | ------------ | ----------------------------------------- |
+| redis_server_url | SecretString | Input parameter. The Redis server connection string. |
+| redis_index_name | String | Input parameter. The name of the Redis index. |
+| code | String | Input parameter. Additional custom code for Redis, if supported. |
+| schema | String | Input parameter. The schema for Redis index. |
+| ingest_data | JSON | Input parameter. The data to be ingested into the vector store. |
+| search_query | String | Input parameter. The query for similarity search. |
+| embedding | Embeddings | Input parameter. The embedding function to use. |
+| number_of_results | Integer | Input parameter. The number of results to return in search. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-sambanova.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-sambanova.mdx
new file mode 100644
index 0000000000..37baeaca6b
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-sambanova.mdx
@@ -0,0 +1,35 @@
+---
+title: SambaNova
+slug: /bundles-sambanova
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **SambaNova** bundle.
+
+For more information about SambaNova features and functionality used by SambaNova components, see the [SambaNova Cloud documentation](https://cloud.sambanova.ai/).
+
+## SambaNova text generation
+
+This component generates text using SambaNova LLMs.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a SambaNova model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### SambaNova text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| sambanova_url | String | Input parameter. Base URL path for API requests. Default: `https://api.sambanova.ai/v1/chat/completions`. |
+| sambanova_api_key | SecretString | Input parameter. Your SambaNova API Key. |
+| model_name | String | Input parameter. The name of the SambaNova model to use. Options include various Llama models. |
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to 0 for unlimited tokens. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.07. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-searchapi.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-searchapi.mdx
new file mode 100644
index 0000000000..e9a0391beb
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-searchapi.mdx
@@ -0,0 +1,39 @@
+---
+title: SearchApi
+slug: /bundles-searchapi
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **SearchApi** bundle.
+
+For more information, see the [SearchApi documentation](https://www.searchapi.io/docs/google).
+
+## SearchApi web search
+
+This component calls the SearchApi API to run Google, Bing, and DuckDuckGo web searches.
+
+It returns a list of search results as a [`Table`](/data-types#table).
+
+### SearchApi web search parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| engine | String | Input parameter. The search engine to use. Default: `google`. |
+| api_key | SecretString | Input parameter. The API key for authenticating with SearchApi. |
+| input_value | String | Input parameter. The search query or input for the API call. |
+| max_results | Integer | Input parameter. The maximum number of search results to return. Default: `5`. |
+| max_snippet_length | Integer | Input parameter. The maximum length of the snippet to return. Default: `100`. |
+| search_params | Dict | Input parameter. Additional key-value pairs to customize the request. |
+
+## See also
+
+* [**Web Search** component](/web-search)
+* [**Google** bundle](/bundles-google)
+* [**Bing** bundle](/bundles-bing)
+* [**DuckDuckGo** bundle](/bundles-duckduckgo)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-serper.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-serper.mdx
new file mode 100644
index 0000000000..32abc82e93
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-serper.mdx
@@ -0,0 +1,33 @@
+---
+title: Serper
+slug: /bundles-serper
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Serper** bundle.
+
+For more information, see the [Serper documentation](https://serper.dev/).
+
+## Serper Google Search API
+
+This component allows you to call the Serper.dev Google Search API.
+
+It returns a list of search results as a [`Table`](/data-types#table).
+
+### Google Serper API parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| **Serper API Key** (`serper_api_key`) | SecretString | Input parameter. An API key for Serper.dev API authentication. |
+| **Input Value** (`input_value`) | String | Input parameter. The search query input. |
+| **Number of Results** (`k`) | Integer | Input parameter. The number of search results to return. |
+
+## See also
+
+* [**Web Search** component](/web-search)
+* [**Google** bundle](/bundles-google)
+* [**Bing** bundle](/bundles-bing)
+* [**DuckDuckGo** bundle](/bundles-duckduckgo)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-supabase.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-supabase.mdx
new file mode 100644
index 0000000000..fb0fbf0344
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-supabase.mdx
@@ -0,0 +1,52 @@
+---
+title: Supabase
+slug: /bundles-supabase
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Supabase** bundle.
+
+## Supabase vector store
+
+The **Supabase** component reads and writes to Supabase vector stores using an instance of `SupabaseVectorStore`.
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### Supabase vector store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Supabase documentation](https://supabase.com/docs/guides/ai) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+| ------------------- | ------------ | ----------------------------------------- |
+| supabase_url | String | Input parameter. The URL of the Supabase instance. |
+| supabase_service_key| SecretString | Input parameter. The service key for Supabase authentication. |
+| table_name | String | Input parameter. The name of the table in Supabase. |
+| query_name | String | Input parameter. The name of the query to use. |
+| search_query | String | Input parameter. The query for similarity search. |
+| ingest_data | JSON | Input parameter. The data to be ingested into the vector store. |
+| embedding | Embeddings | Input parameter. The embedding function to use. |
+| number_of_results | Integer | Input parameter. The number of results to return in search. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-upstash.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-upstash.mdx
new file mode 100644
index 0000000000..912dd72a05
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-upstash.mdx
@@ -0,0 +1,53 @@
+---
+title: Upstash
+slug: /bundles-upstash
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Upstash** bundle.
+
+## Upstash vector store
+
+The **Upstash** component reads and writes to Upstash vector stores using an instance of `UpstashVectorStore`.
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### Upstash vector store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Upstash documentation](https://upstash.com/docs/introduction) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+| --------------- | ------------ | ----------------------------------------- |
+| index_url | String | Input parameter. The URL of the Upstash index. |
+| index_token | SecretString | Input parameter. The token for the Upstash index. |
+| text_key | String | Input parameter. The key in the record to use as text. |
+| namespace | String | Input parameter. The namespace for the index. |
+| search_query | String | Input parameter. The query for similarity search. |
+| metadata_filter | String | Input parameter. Filter documents by metadata. |
+| ingest_data | JSON | Input parameter. The data to be ingested into the vector store. |
+| embedding | Embeddings | Input parameter. The embedding function to use. |
+| number_of_results | Integer | Input parameter. The number of results to return in search. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-vectara.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-vectara.mdx
new file mode 100644
index 0000000000..66f3a1e853
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-vectara.mdx
@@ -0,0 +1,60 @@
+---
+title: Vectara
+slug: /bundles-vectara
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Vectara** bundle.
+
+## Vectara vector store
+
+The **Vectara** component reads and writes to Vectara vector stores using an instance of `Vectara` vector store.
+
+
+About vector store instances
+
+
+
+
+
+
+
+### Vectara vector store parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Vectara documentation](https://docs.vectara.com/docs/) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+| ---------------- | ------------ | ----------------------------------------- |
+| vectara_customer_id | String | Input parameter. The Vectara customer ID. |
+| vectara_corpus_id | String | Input parameter. The Vectara corpus ID. |
+| vectara_api_key | SecretString | Input parameter. The Vectara API key. |
+| embedding | Embeddings | Input parameter. The embedding function to use (optional). |
+| ingest_data | List[Document/Data] | Input parameter. The data to be ingested into the vector store. |
+| search_query | String | Input parameter. The query for similarity search. |
+| number_of_results | Integer | Input parameter. The number of results to return in search. |
+
+## Vectara RAG
+
+This component enables Vectara's full end-to-end RAG capabilities with reranking options.
+
+This component uses a `Vectara` vector store to execute the vector search and reranking functions, and then outputs an **Answer** string in [`Message`](/data-types#message) format.
+
+
+About vector store instances
+
+
+
+
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-vertexai.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-vertexai.mdx
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--- /dev/null
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+---
+title: Vertex AI
+slug: /bundles-vertexai
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Vertex AI** bundle.
+
+For more information about Vertex AI features and functionality used by Vertex AI components, see the [Vertex AI documentation](https://cloud.google.com/vertex-ai).
+
+For other Google components, see the [**Google** bundle](/bundles-google).
+
+## Vertex AI text generation
+
+The **Vertex AI** component generates text using Google Vertex AI models, including the Gemini 1.5, 2.0, 2.5, and 3.0 series. The latest Gemini 3.0 models (`gemini-3-pro-preview`, `gemini-3-flash-preview`, and `gemini-3-pro-image-preview`) offer advanced reasoning and multimodal capabilities.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a Vertex AI model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### Vertex AI text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| credentials | File | Input parameter. JSON credentials file. Leave empty to fall back to environment variables. File type: JSON. For more information, see [Create a service account for Vertex AI authentication](#vertexai-credentials). |
+| model_name | String | Input parameter. The name of the Vertex AI model to use, such as `"gemini-1.5-pro"` or `"gemini-3-pro-preview"`. Default: "gemini-1.5-pro". |
+| project | String | Input parameter. The project ID. |
+| location | String | Input parameter. The location for the Vertex AI API. Default: "us-central1". |
+| max_output_tokens | Integer | Input parameter. The maximum number of tokens to generate. |
+| max_retries | Integer | Input parameter. Maximum number of retries for API calls. Default: 1. |
+| temperature | Float | Input parameter. Controls randomness in the output. Default: 0.0. |
+| top_k | Integer | Input parameter. The number of highest-probability vocabulary tokens to keep for top-k-filtering. |
+| top_p | Float | Input parameter. The cumulative probability of all highest-probability vocabulary tokens that are kept for nucleus sampling. Default: 0.95. |
+| verbose | Boolean | Input parameter. Whether to print verbose output. Default: `false`. |
+
+For more information about Vertex AI text generation parameters, see the [Vertex AI content generation parameters documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/content-generation-parameters#token-sampling-parameters).
+
+## Vertex AI Embeddings
+
+The **Vertex AI Embeddings** component is a wrapper around the [Google Vertex AI Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings) for embedding generation.
+
+For more information about using embedding model components in flows, see [Embedding model components](/components-embedding-models).
+
+### Vertex AI Embeddings parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| credentials | Credentials | Input parameter. JSON credentials file. Leave empty to fall back to environment variables. File type: JSON. For more information, see [Create a service account for Vertex AI authentication](#vertexai-credentials). |
+| location | String | Input parameter. The default location to use when making API calls. Default: `us-central1`. |
+| max_output_tokens | Integer | Input parameter. The token limit determines the maximum amount of text output from one prompt. Default: `128`. |
+| model_name | String | Input parameter. The name of the Vertex AI large language model. Default: `text-bison`. |
+| project | String | Input parameter. The default GCP project to use when making Vertex API calls. |
+| request_parallelism | Integer | Input parameter. The amount of parallelism allowed for requests issued to Vertex AI models. Default: `5`. |
+| temperature | Float | Input parameter. Tunes the degree of randomness in text generations. Should be a non-negative value. Default: `0`. |
+| top_k | Integer | Input parameter. How the model selects tokens for output. The next token is selected from the top `k` tokens. Default: `40`. |
+| top_p | Float | Input parameter. Tokens are selected from the most probable to least until the sum of their probabilities exceeds the top `p` value. Default: `0.95`. |
+| tuned_model_name | String | Input parameter. The name of a tuned model. If provided, `model_name` is ignored. |
+| verbose | Boolean | Input parameter. This parameter controls the level of detail in the output. When set to `true`, it prints internal states of the chain to help debug. Default: `false`. |
+| embeddings | Embeddings | Output parameter. An instance for generating embeddings using Vertex AI. |
+
+## Create a service account for Vertex AI authentication {#vertexai-credentials}
+
+The **Vertex AI Embeddings** and **Vertex AI** components authenticate with the [Google Vertex AI API](https://console.cloud.google.com/marketplace/product/google/aiplatform.googleapis.com) using a service account JSON file.
+
+These components require that you provide a service account JSON file in the **Credentials** parameter (`credentials`).
+You can either provide the file directly in your component's settings or you can use a [`GOOGLE_APPLICATION_CREDENTIALS` environment variable](https://cloud.google.com/docs/authentication/application-default-credentials) to reference the path to your JSON file.
+
+The following steps explain how to create a service account for Langflow, create a service account JSON key file, and then use the JSON key file in Langflow:
+
+1. If you haven't done so already, [enable the Vertex AI APIs in your Google Cloud project](https://cloud.google.com/vertex-ai/docs/start/cloud-environment).
+
+2. Create a [service account](https://cloud.google.com/iam/docs/service-account-overview) in your Google Cloud project.
+
+ It is recommended that you [create a custom service account for Vertex AI](https://cloud.google.com/vertex-ai/docs/general/custom-service-account) because Langflow uses this account to access the Vertex AI APIs.
+
+3. Assign the **Vertex AI Service Agent** role to the service account.
+
+ This role allows Langflow to access Vertex AI resources.
+ For more information, see [Vertex AI access control with IAM](https://cloud.google.com/vertex-ai/docs/general/access-control).
+
+4. [Create a service account key](https://cloud.google.com/iam/docs/keys-create-delete#console) in JSON format for your Langflow Vertex AI service account.
+
+ When you click **Create**, the JSON key file is downloaded.
+
+5. In Langflow, create or open a flow with the **Vertex AI** or **Vertex AI Embeddings** component.
+
+6. In the component's **Credentials** field, do one of the following:
+
+ * Select your service account JSON file directly.
+
+ * Leave the **Credentials** field empty if you want to pull the key file from an environment variable.
+ Then, you must provide the path to your JSON file in a `GOOGLE_APPLICATION_CREDENTIALS` environment variable that is set somewhere relative to your Langflow instance.
+ For example, you can set the environment variable in your terminal, your Langflow `.env` file, or otherwise in the environment where your Langflow server or application runs.
+
+ 
+
+7. Run your flow to verify that Langflow can use the service account credentials successfully.
+
+ For example, in a flow with **Chat Input**, **Chat Output**, and **Vertex AI** components, you can open the **Playground** and try chatting with the LLM.
+ If the LLM responds, then the credentials are configured correctly.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-vllm.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-vllm.mdx
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@@ -0,0 +1,63 @@
+---
+title: vLLM
+slug: /bundles-vllm
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **vLLM** bundle.
+
+For more information about vLLM features and functionality used by vLLM components, see the [vLLM documentation](https://docs.vllm.ai/).
+
+## vLLM text generation
+
+The **vLLM** component generates text using [vLLM models](https://docs.vllm.ai/en/latest/models/supported_models.html) via an OpenAI-compatible API.
+
+vLLM is a fast and easy-to-use library for LLM inference and serving. It provides high-throughput serving with efficient attention and PagedAttention, making it ideal for self-hosted model deployments.
+
+The component connects to a vLLM server running locally or remotely and uses the OpenAI-compatible API endpoint to generate text responses.
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use a vLLM model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Function** component.
+
+For more information, see [Language model components](/components-models).
+
+### vLLM text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| api_key | SecretString | Input parameter. The API Key to use for the vLLM model (optional for local servers). |
+| model_name | String | Input parameter. The name of the vLLM model to use (e.g., 'ibm-granite/granite-3.3-8b-instruct'). |
+| api_base | String | Input parameter. The base URL of the vLLM API server. Defaults to http://localhost:8000/v1 for local vLLM server. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: [0.0, 1.0]. Default: 0.1. |
+| max_tokens | Integer | Input parameter. The maximum number of tokens to generate. Set to 0 for unlimited tokens. |
+| seed | Integer | Input parameter. The seed controls the reproducibility of the job. Default: 1. |
+| max_retries | Integer | Input parameter. The maximum number of retries to make when generating. Default: 5. |
+| timeout | Integer | Input parameter. The timeout for requests to vLLM completion API. Default: 700. |
+| model_kwargs | Dict | Input parameter. Additional keyword arguments to pass to the model. |
+| json_mode | Boolean | Input parameter. If True, it will output JSON regardless of passing a schema. |
+
+## Setting up vLLM
+
+To use the vLLM component, you need to have a vLLM server running. Here are the basic steps:
+
+1. **Install vLLM**: `pip install vllm`
+2. **Start a vLLM server**:
+ ```bash
+ python -m vllm.entrypoints.openai.api_server --model --port 8000
+ ```
+3. **Configure the component**: Set the `api_base` to your vLLM server URL (e.g., `http://localhost:8000/v1`)
+
+For more detailed setup instructions, see the [vLLM documentation](https://docs.vllm.ai/en/latest/getting_started/quickstart.html).
+
+## See also
+
+* [**Agent** component](/components-agents)
+* [**Language Model** component](/components-models)
+* [vLLM GitHub repository](https://github.com/vllm-project/vllm)
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-weaviate.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-weaviate.mdx
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--- /dev/null
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+---
+title: Weaviate
+slug: /bundles-weaviate
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialConditionalParams from '@site/docs/_partial-conditional-params.mdx';
+import PartialVectorSearchResults from '@site/docs/_partial-vector-search-results.mdx';
+import PartialVectorStoreInstance from '@site/docs/_partial-vector-store-instance.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Weaviate** bundle.
+
+## Weaviate vector store
+
+The **Weaviate** component reads and writes to Weaviate vector stores using an instance of `Weaviate` vector store.
+
+
+About vector store instances
+
+
+
+
+
+
+
+:::tip
+For a tutorial using a vector database in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+### Weaviate parameters
+
+You can inspect a vector store component's parameters to learn more about the inputs it accepts, the features it supports, and how to configure it.
+
+
+
+
+
+For information about accepted values and functionality, see the [Weaviate documentation](https://weaviate.io/developers/weaviate) or inspect [component code](/concepts-components#component-code).
+
+| Name | Type | Description |
+|---------------|--------------|-------------------------------------------|
+| weaviate_url | String | Input parameter. The default instance URL. |
+| api_key | SecretString | Input parameter. The optional API key for authentication. |
+| index_name | String | Input parameter. The optional index name. |
+| text_key | String | Input parameter. The default text extraction key. |
+| input | JSON or Table | Input parameter. The document or record. |
+| cache_vector_store | Cache Vector Store | Input parameter. If `true`, the component caches the vector store in memory for faster reads. Default: Enabled (`true`). |
+| embedding | Embeddings | Input parameter. Connect an [embedding model component](/components-embedding-models). |
+| number_of_results | Integer | Input parameter. The number of search results to return. Default: `4`. |
+| search_by_text | Boolean | Input parameter. Indicates whether to search by text. Default: Disabled (`false`). |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-wikipedia.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-wikipedia.mdx
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@@ -0,0 +1,43 @@
+---
+title: Wikipedia
+slug: /bundles-wikipedia
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **Wikipedia** bundle.
+
+:::tip
+Wikipedia components are useful [tools for agents](/agents-tools) that need reliable sources for information retrieval, such as tutoring chatbots or generic research assistants.
+
+Rather than broad internet searches or recall from internal model data, these components provide specific data returned from Wikipedia API requests.
+:::
+
+## Wikidata API
+
+This component performs a similarity search using the [Wikidata API](https://www.wikidata.org/wiki/Wikidata:REST_API).
+
+It accepts a string text query, and it outputs a [`Table`](/data-types#table) containing the search results.
+
+## Wikipedia API
+
+This component searches and retrieves information from Wikipedia with the [WikiMedia API](https://www.mediawiki.org/wiki/API:Main_page) search endpoints, and then outputs the response as a [`Table`](/data-types#table).
+
+### Wikipedia API parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| input_value | String | Input parameter. The search query input. |
+| lang | String | Input parameter. The language code for Wikipedia. Default: `en`. |
+| k | Integer | Input parameter. The number of results to return. |
+| load_all_available_meta | Boolean | Input parameter. Whether to load all available metadata. |
+| doc_content_chars_max | Integer | Input parameter. The maximum number of characters for document content. |
+
+## See also
+
+* [**API Request** component](/api-request)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/bundles-xai.mdx b/docs/versioned_docs/version-1.9.0/Components/bundles-xai.mdx
new file mode 100644
index 0000000000..01bc99026a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/bundles-xai.mdx
@@ -0,0 +1,38 @@
+---
+title: xAI
+slug: /bundles-xai
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+ [**Bundles**](/components-bundle-components) contain custom components that support specific third-party integrations with Langflow.
+
+This page describes the components that are available in the **xAI** bundle.
+
+For more information about xAI features and functionality used by xAI components, see the [xAI documentation](https://x.ai/).
+
+## xAI text generation
+
+The **xAI** component generates text using xAI models like [Grok](https://x.ai/grok).
+
+It can output either a **Model Response** ([`Message`](/data-types#message)) or a **Language Model** ([`LanguageModel`](/data-types#languagemodel)).
+
+Use the **Language Model** output when you want to use an xAI model as the LLM for another LLM-driven component, such as an **Agent** or **Smart Transform** component.
+
+For more information, see [Language model components](/components-models).
+
+### xAI text generation parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| max_tokens | Integer | Input parameter. Maximum number of tokens to generate. Set to `0` for unlimited. Range: `0-128000`. |
+| model_kwargs | Dictionary | Input parameter. Additional keyword arguments for the model. |
+| json_mode | Boolean | Input parameter. If `true`, outputs JSON regardless of passing a schema. |
+| model_name | String | Input parameter. The xAI model to use. Default: `grok-2-latest`. |
+| base_url | String | Input parameter. Base URL for API requests. Default: `https://api.x.ai/v1`. |
+| api_key | SecretString | Input parameter. Your xAI API key for authentication. |
+| temperature | Float | Input parameter. Controls randomness in the output. Range: `[0.0, 2.0]`. Default: `0.1`. |
+| seed | Integer | Input parameter. Controls reproducibility of the job. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/calculator.mdx b/docs/versioned_docs/version-1.9.0/Components/calculator.mdx
new file mode 100644
index 0000000000..25e1d4ca15
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/calculator.mdx
@@ -0,0 +1,20 @@
+---
+title: Calculator
+slug: /calculator
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+The **Calculator** component performs basic arithmetic operations on mathematical expressions.
+It supports addition, subtraction, multiplication, division, and exponentiation operations.
+
+For an example of using this component in a flow, see the [**Python Interpreter** component](/python-interpreter).
+
+## Calculator parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| expression | String | Input parameter. The arithmetic expression to evaluate, such as `4*4*(33/22)+12-20`. |
+| result | JSON | Output parameter. The calculation result as a [`JSON` object](/data-types) containing the evaluated expression. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/chat-input-and-output.mdx b/docs/versioned_docs/version-1.9.0/Components/chat-input-and-output.mdx
new file mode 100644
index 0000000000..8d286be7ca
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/chat-input-and-output.mdx
@@ -0,0 +1,146 @@
+---
+title: Chat Input and Output
+slug: /chat-input-and-output
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+:::warning
+**Chat Input and Output** components are required to chat with your flow in the **Playground**.
+For more information, see [Test flows in the Playground](/concepts-playground).
+:::
+
+**Chat Input and Output** components are designed to handle conversational interactions in Langflow.
+
+## Chat Input
+
+The **Chat Input** component accepts text and file input, such as a chat message or a file.
+This data is passed to other components as [`Message` data](/data-types) containing the provided input as well as associated chat metadata, such as the sender, session ID, timestamp, and file attachments.
+
+Initial input should _not_ be provided as a complete `Message` object because the **Chat Input** component constructs the `Message` object that is then passed to other components in the flow.
+
+### Chat Input parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+|input_value|Input Text| Input parameter. The message text string to be passed as input. |
+|sender|Sender Type| Input parameter. Identifies the sender as either `User` or `Language Model`.|
+|sender_name|Sender Name| Input parameter. The name of the sender. If unspecified, defaults to `User` or `Language Model`. |
+|session_id|Session ID| Input parameter. The unique identifier for the chat session. If empty, the current session ID parameter is used.|
+|files|Files| Input parameter. The files to be sent with the message.|
+|background_color|Background Color| Input parameter. The background color of the icon.|
+|chat_icon|Icon| Input parameter. The icon of the message.|
+|should_store_message|Store Messages| Input parameter. Whether to store the message in chat history.|
+|text_color|Text Color| Input parameter. The text color of the name.|
+
+For information about the resulting `Message` object, including input parameters that are directly mapped to `Message` attributes, see [`Message` data](/data-types#message).
+
+
+Message method for Chat Input
+
+The `ChatInput` class provides an asynchronous method to create and store a `Message` object based on the input parameters.
+The `Message` object is created in the `message_response` method of the `ChatInput` class using the `Message.create()` factory method.
+
+```python
+message = await Message.create(
+ text=self.input_value,
+ sender=self.sender,
+ sender_name=self.sender_name,
+ session_id=self.session_id,
+ files=self.files,
+ properties={
+ "background_color": background_color,
+ "text_color": text_color,
+ "icon": icon,
+ },
+)
+```
+
+
+
+## Chat Output
+
+The **Chat Output** component ingests `Message`, `JSON`, or `Table` data from other components, transforms it into `Message` data if needed, and then emits the final output as a chat message.
+For information about these data types, see [Use Langflow data types](/data-types).
+
+In the **Playground**, chat output is limited to the parts of the `Message` object that are relevant to the chat interface, such as the text response, sender name, and file attachments.
+To see the metadata associated with a chat message, inspect the message logs in the **Playground**.
+
+When using the Langflow API, the API response includes the **Chat Output** `Message` object along with other response data from the flow run.
+Langflow API responses can be extremely verbose, so your applications must include code to extract relevant data from the response to return to the user.
+For an example, see the [Langflow quickstart](/get-started-quickstart).
+
+### Chat Output parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+|input_value|Inputs| Input parameter. The message text string to be passed as output. |
+|should_store_message|Store Messages| Input parameter. Whether to store the message in chat history.|
+|sender|Sender Type| Input parameter. Identifies the sender as either `User` or `Language Model`.|
+|sender_name|Sender Name| Input parameter. The name of the sender. If unspecified, defaults to `User` or `Language Model`. |
+|session_id|Session ID| Input parameter. The unique identifier for the chat session. If empty, the current session ID parameter is used.|
+|data_template|Data Template| Input parameter. The template to convert [`JSON` input](/data-types#json) to `text`. If empty, it is dynamically set to the `JSON` object's `text` key.|
+|background_color|Background Color| Input parameter. The background color of the icon.|
+|chat_icon|Icon| Input parameter. The icon of the message.|
+|text_color|Text Color| Input parameter. The text color of the name.|
+|clean_data|Basic Clean Data| Input parameter. When enabled, [`Table` input](/data-types#table) is cleaned when converted to text. Cleaning removes empty rows, empty lines in cells, and multiple newlines.|
+
+For information about the resulting `Message` object, including input parameters that are directly mapped to `Message` attributes, see [`Message` data](/data-types#message).
+
+## Use Chat Input and Output components in a flow
+
+To use the **Chat Input** and **Chat Output** components in a flow, connect them to components that accept or emit [`Message` data](/data-types#message).
+
+For example, the following flow connects **Chat Input** and **Chat Output** to a **Language Model** component, creating a simple LLM-based chat flow.
+
+
+
+:::tip
+For detailed examples of **Chat Input and Output** components in flows, see the following:
+
+* [Langflow quickstart](/get-started-quickstart): Create and run a basic agent flow.
+* **Basic Prompting** template: Create an LLM-based chat flow that accepts chat input as well as a prompt with additional instructions for the LLM. Many other Langflow templates also use **Chat Input and Output** components.
+* [Connect applications to agents](/agent-tutorial): Explore more advanced concepts around agent flows and prompting, including triggering agent flows from external applications.
+:::
+
+### Send chat input with the Langflow API
+
+You can use the Langflow API to run a flow by sending input to a **Chat Input** component:
+
+```bash
+curl --request POST \
+ --url "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID" \
+ --header "Content-Type: application/json" \
+ --header "x-api-key: $LANGFLOW_API_KEY" \
+ --data '{
+ "input_value": "What's the recommended way to install Docker on Mac M1?",
+ "output_type": "chat",
+ "input_type": "chat"
+}'
+```
+
+When triggering flows with the Langflow API, the payload must contain the values for the **Chat Input** component's input parameters, such as `input_value`.
+
+Not all parameters need to be specified in the request.
+For example, `session_id` uses the flow's default session ID if omitted.
+If you want to use a custom session ID, include `session_id` in your request:
+
+```bash
+curl --request POST \
+ --url "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID" \
+ --header "Content-Type: application/json" \
+ --header "x-api-key: $LANGFLOW_API_KEY" \
+ --data '{
+ "input_value": "Whats the recommended way to install Docker on Mac M1",
+ "session_id": "$USER_ID",
+ "output_type": "chat",
+ "input_type": "chat"
+}'
+```
+
+For more information, see [Trigger flows with the Langflow API](/concepts-publish).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/components-agents.mdx b/docs/versioned_docs/version-1.9.0/Components/components-agents.mdx
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--- /dev/null
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@@ -0,0 +1,42 @@
+---
+title: Agents
+slug: /components-agents
+---
+
+import PartialAgentsWork from '@site/docs/_partial-agents-work.mdx';
+
+Langflow's **Agent** component is critical for building agent flows.
+This component defines the behavior and capabilities of AI agents in your flows.
+
+
+
+## Examples of agent flows
+
+For examples of flows using the **Agent** component, see the following:
+
+* [Langflow quickstart](/get-started-quickstart): Start with the **Simple Agent** template, modify its tools, and then learn how to use an agent flow in an application.
+
+ The **Simple Agent** template creates a basic agent flow with an **Agent** component that can use two other Langflow components as tools.
+ The LLM specified in the **Agent** component's settings can use its own built-in functionality as well as the functionality provided by the connected tools when generating responses.
+
+* [Use an agent as a tool](/agents-tools#use-an-agent-as-a-tool): Create a multi-agent flow.
+
+* [Use Langflow as an MCP client](/mcp-client) and [Use Langflow as an MCP server](/mcp-server): Use the **Agent** and [**MCP Tools** component](/mcp-tools) to implement the Model Context Protocol (MCP) in your flows.
+
+## Agent component {#agent-component}
+
+The **Agent** component is the primary agent actor in your agent flows.
+This component uses an LLM integration to respond to input, such as a chat message or file upload.
+
+The agent can use the tools already available in the base LLM as well as additional tools that you connect to the **Agent** component's **Tools** port.
+You can connect any Langflow component as a tool, including other **Agent** components and MCP servers through the [**MCP Tools** component](/mcp-tools).
+
+For more information about using this component, see [Use Langflow agents](/agents).
+
+## See also
+
+* [**MCP Tools** component](/mcp-tools)
+* [**Message History** component](/message-history)
+* [Store chat memory](/memory#store-chat-memory)
+* [Bundles](/components-bundle-components)
+* [Legacy LangChain components](/bundles-langchain#legacy-langchain-components)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/components-bundles.mdx b/docs/versioned_docs/version-1.9.0/Components/components-bundles.mdx
new file mode 100644
index 0000000000..8a244b5c8e
--- /dev/null
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@@ -0,0 +1,269 @@
+---
+title: About bundles
+slug: /components-bundle-components
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Bundles contain custom components that support specific third-party integrations with Langflow.
+You add them to your flows and configure them in the same way as Langflow's core components.
+
+To browse bundles, click **Bundles** in the visual editor.
+
+## Bundle maintenance and documentation
+
+Many bundled components are developed by third-party contributors to the Langflow codebase.
+
+Some providers contribute documentation with their bundles, whereas others document their bundles in their own documentation.
+Some bundles have no documentation.
+
+To find documentation for a specific bundled component, browse the Langflow docs and your provider's documentation.
+If available, you can also find links to relevant documentation, such as API endpoints, through the component itself:
+
+1. Click the component to expose the [component inspection panel](/concepts-components#component-menus).
+2. Click **More**.
+3. Select **Docs**.
+
+The Langflow documentation focuses on using bundles within flows.
+For that reason, it focuses on the Langflow-specific configuration steps for bundled components.
+For information about provider-specific features or APIs, see the provider's documentation.
+
+## Component parameters
+
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+
+
+## Core components and bundles
+
+:::tip
+The Langflow documentation doesn't list all bundles or components in bundles.
+For the most accurate and up-to-date list of bundles and components for your version of Langflow, check **Bundles** in the visual editor.
+
+If you can't find a component that you used in an earlier version of Langflow, it may have been removed or marked as a [legacy component](#legacy-bundles).
+:::
+
+Langflow offers generic **Core components** in addition to third-party, provider-specific bundles.
+
+If you are looking for a specific service or integration, you can **Search** components in the visual editor.
+
+If all else fails, you can always create your own [custom components](/components-custom-components).
+
+## Legacy bundles
+
+import PartialLegacy from '@site/docs/_partial-legacy.mdx';
+
+
+
+The following bundles include only legacy components.
+
+### CrewAI bundle
+
+Replace the following legacy CrewAI components with other agentic components, such as the [**Agent** component](/components-agents).
+
+
+CrewAI Agent
+
+This component represents CrewAI agents, allowing for the creation of specialized AI agents with defined roles goals and capabilities within a crew.
+For more information, see the [CrewAI agents documentation](https://docs.crewai.com/core-concepts/Agents/).
+
+This component accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| role | Role | Input parameter. The role of the agent. |
+| goal | Goal | Input parameter. The objective of the agent. |
+| backstory | Backstory | Input parameter. The backstory of the agent. |
+| tools | Tools | Input parameter. The tools at the agent's disposal. |
+| llm | Language Model | Input parameter. The language model that runs the agent. |
+| memory | Memory | Input parameter. This determines whether the agent should have memory or not. |
+| verbose | Verbose | Input parameter. This enables verbose output. |
+| allow_delegation | Allow Delegation | Input parameter. This determines whether the agent is allowed to delegate tasks to other agents. |
+| allow_code_execution | Allow Code Execution | Input parameter. This determines whether the agent is allowed to execute code. |
+| kwargs | kwargs | Input parameter. Additional keyword arguments for the agent. |
+| output | Agent | Output parameter. The constructed CrewAI Agent object. |
+
+
+
+
+CrewAI Hierarchical Crew, CrewAI Hierarchical Task
+
+The **CrewAI Hierarchical Crew** component represents a group of agents managing how they should collaborate and the tasks they should perform in a hierarchical structure. This component allows for the creation of a crew with a manager overseeing the task execution.
+For more information, see the [CrewAI hierarchical crew documentation](https://docs.crewai.com/how-to/Hierarchical/).
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| agents | Agents | Input parameter. The list of Agent objects representing the crew members. |
+| tasks | Tasks | Input parameter. The list of HierarchicalTask objects representing the tasks to be executed. |
+| manager_llm | Manager LLM | Input parameter. The language model for the manager agent. |
+| manager_agent | Manager Agent | Input parameter. The specific agent to act as the manager. |
+| verbose | Verbose | Input parameter. This enables verbose output for detailed logging. |
+| memory | Memory | Input parameter. The memory configuration for the crew. |
+| use_cache | Use Cache | Input parameter. This enables caching of results. |
+| max_rpm | Max RPM | Input parameter. This sets the maximum requests per minute. |
+| share_crew | Share Crew | Input parameter. This determines if the crew information is shared among agents. |
+| function_calling_llm | Function Calling LLM | Input parameter. The language model for function calling. |
+| crew | Crew | Output parameter. The constructed Crew object with hierarchical task execution. |
+
+
+
+
+CrewAI Sequential Crew, CrewAI Sequential Task
+
+The **CrewAI Sequential Crew** component represents a group of agents with tasks that are executed sequentially. This component allows for the creation of a crew that performs tasks in a specific order.
+For more information, see the [CrewAI sequential crew documentation](https://docs.crewai.com/how-to/Sequential/).
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| tasks | Tasks | Input parameter. The list of SequentialTask objects representing the tasks to be executed. |
+| verbose | Verbose | Input parameter. This enables verbose output for detailed logging. |
+| memory | Memory | Input parameter. The memory configuration for the crew. |
+| use_cache | Use Cache | Input parameter. This enables caching of results. |
+| max_rpm | Max RPM | Input parameter. This sets the maximum requests per minute. |
+| share_crew | Share Crew | Input parameter. This determines if the crew information is shared among agents. |
+| function_calling_llm | Function Calling LLM | Input parameter. The language model for function calling. |
+| crew | Crew | Output parameter. The constructed Crew object with sequential task execution. |
+
+
+
+
+CrewAI Sequential Task Agent
+
+This component creates a CrewAI Task and its associated agent allowing for the definition of sequential tasks with specific agent roles and capabilities.
+For more information, see the [CrewAI sequential agents documentation](https://docs.crewai.com/how-to/Sequential/).
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| role | Role | Input parameter. The role of the agent. |
+| goal | Goal | Input parameter. The objective of the agent. |
+| backstory | Backstory | Input parameter. The backstory of the agent. |
+| tools | Tools | Input parameter. The tools at the agent's disposal. |
+| llm | Language Model | Input parameter. The language model that runs the agent. |
+| memory | Memory | Input parameter. This determines whether the agent should have memory or not. |
+| verbose | Verbose | Input parameter. This enables verbose output. |
+| allow_delegation | Allow Delegation | Input parameter. This determines whether the agent is allowed to delegate tasks to other agents. |
+| allow_code_execution | Allow Code Execution | Input parameter. This determines whether the agent is allowed to execute code. |
+| agent_kwargs | Agent kwargs | Input parameter. The additional kwargs for the agent. |
+| task_description | Task Description | Input parameter. The descriptive text detailing the task's purpose and execution. |
+| expected_output | Expected Task Output | Input parameter. The clear definition of the expected task outcome. |
+| async_execution | Async Execution | Input parameter. Boolean flag indicating asynchronous task execution. |
+| previous_task | Previous Task | Input parameter. The previous task in the sequence for chaining. |
+| task_output | Sequential Task | Output parameter. The list of SequentialTask objects representing the created tasks. |
+
+
+
+### Embeddings bundle
+
+* **Embedding Similarity**: Replaced by built-in similarity search functionality in vector store components.
+* **Text Embedder**: Replaced by the embedding model components.
+
+### Vector Stores bundle
+
+This bundle contains only the legacy **Local DB** component.
+All other vector store components can be found within their respective provider-specific bundles, such as the [**DataStax** bundle](/bundles-datastax).
+
+
+Local DB
+
+Replace the **Local DB** component with the **Chroma DB** vector store component (in the **Chroma** bundle) or another vector store component.
+
+The **Local DB** component reads and writes to a persistent, in-memory Chroma DB instance intended for use with Langflow.
+It has separate modes for reads and writes, automatic collection management, and default persistence in your Langflow cache directory.
+
+Set the **Mode** parameter to reflect the operation you want the component to perform, and then configure the other parameters accordingly.
+Some parameters are only available for one mode.
+
+
+
+
+To create or write to your local Chroma vector store, use **Ingest** mode.
+
+The following parameters are available in **Ingest** mode:
+
+| Name | Type | Description |
+|------|------|-------------|
+| **Name Your Collection** (`collection_name`) | String | Input parameter. The name for your Chroma vector store collection. Default: `langflow`. Only available in **Ingest** mode. |
+| **Persist Directory** (`persist_directory`) | String | Input parameter. The base directory where you want to create and persist the vector store. If you use the **Local DB** component in multiple flows or to create multiple collections, collections are stored at `$PERSISTENT_DIRECTORY/vector_stores/$COLLECTION_NAME`. If not specified, the default location is your Langflow configuration directory. For more information, see [Memory management options](/memory). |
+| **Embedding** (`embedding`) | Embeddings | Input parameter. The embedding function to use for the vector store. |
+| **Allow Duplicates** (`allow_duplicates`) | Boolean | Input parameter. If `true` (default), writes don't check for existing duplicates in the collection, allowing you to store multiple copies of the same content. If `false`, writes won't add documents that match existing documents already present in the collection. If `false`, it can strictly enforce deduplication by searching the entire collection or only search the number of records, specified in `limit`. Only available in **Ingest** mode. |
+| **Ingest Data** (`ingest_data`) | JSON or Table | Input parameter. The records to write to the collection. Records are embedded and indexed for semantic search. Only available in **Ingest** mode. |
+| **Limit** (`limit`) | Integer | Input parameter. Limit the number of records to compare when **Allow Duplicates** is `false`. This can help improve performance when writing to large collections, but it can result in some duplicate records. Only available in **Ingest** mode. |
+
+
+
+
+To read from your local Chroma vector store, use **Retrieve** mode.
+
+The following parameters are available in **Retrieve** mode:
+
+| Name | Type | Description |
+|------|------|-------------|
+| **Persist Directory** (`persist_directory`) | String | Input parameter. The base directory where you want to create and persist the vector store. If you use the **Local DB** component in multiple flows or to create multiple collections, collections are stored at `$PERSISTENT_DIRECTORY/vector_stores/$COLLECTION_NAME`. If not specified, the default location is your Langflow configuration directory. For more information, see [Memory management options](/memory). |
+| **Existing Collections** (`existing_collections`) | String | Input parameter. Select a previously-created collection to search. Only available in **Retrieve** mode. |
+| **Embedding** (`embedding`) | Embeddings | Input parameter. The embedding function to use for the vector store. |
+| **Search Type** (`search_type`) | String | Input parameter. The type of search to perform, either `Similarity` or `MMR`. Only available in **Retrieve** mode. |
+| **Search Query** (`search_query`) | String | Input parameter. Enter a query for similarity search. Only available in **Retrieve** mode. |
+| **Number of Results** (`number_of_results`) | Integer | Input parameter. Number of search results to return. Default: 10. Only available in **Retrieve** mode. |
+
+
+
+
+
+
+### Zep bundle
+
+
+Zep Chat Memory
+
+The **Zep Chat Memory** component is a legacy component.
+Replace this component with the [**Message History** component](/message-history).
+
+This component creates a `ZepChatMessageHistory` instance, enabling storage and retrieval of chat messages using Zep, a memory server for LLMs.
+
+It accepts the following parameters:
+
+| Name | Type | Description |
+|---------------|---------------|-----------------------------------------------------------|
+| url | MessageText | Input parameter. The URL of the Zep instance. Required. |
+| api_key | SecretString | Input parameter. The API Key for authentication with the Zep instance. |
+| api_base_path | Dropdown | Input parameter. The API version to use. Options include api/v1 or api/v2. |
+| session_id | MessageText | Input parameter. The unique identifier for the chat session. Optional. |
+| message_history | BaseChatMessageHistory | Output parameter. An instance of ZepChatMessageHistory for the session. |
+
+
+
+## See also
+
+* [LangWatch observability and evaluation](/integrations-langwatch)
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/components-custom-components.mdx b/docs/versioned_docs/version-1.9.0/Components/components-custom-components.mdx
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,589 @@
+---
+title: Create custom Python components
+slug: /components-custom-components
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialBasicComponentStructure from '../_partial-basic-component-structure.mdx';
+
+Create your own custom components to add any functionality you need to Langflow, from API integrations to data processing.
+
+In Langflow's node-based environment, each node is a "component" that performs discrete functions.
+Custom components in Langflow are built upon:
+
+* The Python class that inherits from `Component`.
+* Class-level attributes that identify and describe the component.
+* [Input and output lists](#inputs-and-outputs) that determine data flow.
+* Methods that define the component's behavior and logic.
+* Internal variables for [Error handling and logging](#error-handling-and-logging)
+
+Use the [Custom component quickstart](#quickstart) to add an example component to Langflow, and then use the reference guide that follows for more advanced component customization.
+
+## Custom component quickstart {#quickstart}
+
+Create a custom `DataFrameProcessor` component by creating a Python file, saving it in the correct folder, including an `__init__.py` file, and loading it into Langflow.
+
+### Create a Python file
+
+
+
+### Save the custom component {#custom-component-path}
+
+Save the custom component in the Langflow directory where the UI will discover and load it.
+
+By default, Langflow looks for custom components in the `src/lfx/src/lfx/components` directory.
+
+When saving components in the default directory, components must be organized in a specific directory structure to be properly loaded and displayed in the visual editor.
+
+Components must be placed inside category folders, not directly in the base directory.
+
+The category folder name determines where the component appears in the Langflow **Core components** menu.
+For example, to add the example `DataFrameProcessor` component to the **Data** category, place it in the `data` subfolder:
+
+```
+src/lfx/src/lfx/components/
+ └── data/ # Category folder (determines menu location)
+ ├── __init__.py # Required - makes it a Python package
+ └── dataframe_processor.py # Your custom component file
+```
+
+If you're creating custom components in a different location using the `LANGFLOW_COMPONENTS_PATH` [environment variable](/environment-variables), components must be similarly organized in a specific directory structure to be displayed in the visual editor.
+
+```
+/your/custom/components/path/ # Base directory set by LANGFLOW_COMPONENTS_PATH
+ └── category_name/
+ ├── __init__.py
+ └── custom_component.py
+```
+
+You can have multiple category folders to organize components into different categories, with multiple components inside each folder:
+```
+/app/custom_components/
+ ├── data/
+ │ ├── __init__.py
+ │ ├── custom_component.py
+ │ └── dataframe_processor.py
+ └── tools/
+ ├── __init__.py
+ └── custom_tool.py
+```
+
+### Create the `__init__.py` file
+
+Each category directory **must** contain an `__init__.py` file for Langflow to properly recognize and load the components.
+This is a Python package requirement that ensures the directory is treated as a module.
+
+To include the `DataFrameProcessor` component, create a file named `__init__.py` in your component's directory with the following content.
+
+```python
+from .dataframe_processor import DataFrameProcessor
+
+__all__ = ["DataFrameProcessor"]
+```
+
+
+Lazy load the DataFrameProcessor component
+
+Alternatively, you can load your component **lazily**, which is better for performance but a little more complex.
+
+```python
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
+from lfx.components._importing import import_mod
+
+if TYPE_CHECKING:
+ from lfx.components.data.dataframe_processor import DataFrameProcessor
+
+_dynamic_imports = {
+ "DataFrameProcessor": "dataframe_processor",
+}
+
+__all__ = [
+ "DataFrameProcessor",
+]
+
+def __getattr__(attr_name: str) -> Any:
+ """Lazily import data components on attribute access."""
+ if attr_name not in _dynamic_imports:
+ msg = f"module '{__name__}' has no attribute '{attr_name}'"
+ raise AttributeError(msg)
+ try:
+ result = import_mod(attr_name, _dynamic_imports[attr_name], __spec__.parent)
+ except (ModuleNotFoundError, ImportError, AttributeError) as e:
+ msg = f"Could not import '{attr_name}' from '{__name__}': {e}"
+ raise AttributeError(msg) from e
+ globals()[attr_name] = result
+ return result
+
+def __dir__() -> list[str]:
+ return list(__all__)
+```
+
+For an additional example of lazy loading, see the [FAISS component](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/components/FAISS/__init__.py).
+
+
+
+### Load your component
+
+Ensure the application builds your component.
+
+1. To rebuild the backend and frontend, run `make install_frontend && make build_frontend && make install_backend && uv run langflow run --port 7860`.
+
+2. Refresh the frontend application.
+Your new `DataFrameProcessor` component is available in the **Core components** menu under the **Data** category in the visual editor.
+
+### Docker deployment
+
+When running Langflow in Docker, mount your custom components directory and set the `LANGFLOW_COMPONENTS_PATH` environment variable in the `docker run` command to point to the custom components directory.
+
+```bash
+docker run -d \
+ --name langflow \
+ -p 7860:7860 \
+ -v ./custom_components:/app/custom_components \
+ -e LANGFLOW_COMPONENTS_PATH=/app/custom_components \
+ langflowai/langflow:latest
+```
+
+Create the same custom components directory structure as the example in [Save the custom component](#custom-component-path).
+
+```
+/app/custom_components/ # LANGFLOW_COMPONENTS_PATH
+ └── data/
+ ├── __init__.py
+ └── dataframe_processor.py
+```
+
+## How components execute
+
+Langflow's engine manages:
+
+1. **Instantiation**: A component is created and internal structures are initialized.
+2. **Assigning Inputs**: Values from the visual editor or connections are assigned to component fields.
+3. **Validation and Setup**: Optional hooks like `_pre_run_setup`.
+4. **Outputs Generation**: `run()` or `build_results()` triggers output methods.
+
+You can customize execution by overriding these optional hooks in your custom component code.
+
+* **`_pre_run_setup()`** - Used during **Validation and Setup**.
+ Add this method inside your component class to initialize component state before execution begins:
+ ```python
+ class MyComponent(Component):
+ # ... your inputs, outputs, and other attributes ...
+
+ def _pre_run_setup(self):
+ if not hasattr(self, "_initialized"):
+ self._initialized = True
+ self.iteration = 0
+ ```
+
+* **Override `run` or `_run`** - Used during **Outputs Generation**.
+ Add this method inside your component class to customize the main execution logic:
+ ```python
+ class MyComponent(Component):
+
+ async def_run(self):
+ # Custom execution logic here
+ # This runs instead of the default output method calls
+ pass
+ ```
+
+* **Store data in `self.ctx`**.
+ Use `self.ctx` in any of your component methods to share data between method calls.
+ ```python
+ class MyComponent(Component):
+
+ def _pre_run_setup(self):
+ # Initialize counter in setup
+ self.ctx["processed_items"] = 0
+
+ def process_data(self) -> Data:
+ # Increment counter during processing
+ self.ctx["processed_items"] += 1
+ return Data(data={"item": f"processed {self.ctx['processed_items']}"})
+
+ def get_summary(self) -> Data:
+ # Access counter in different method
+ total = self.ctx["processed_items"]
+ return Data(data={"summary": f"Processed {total} items total"})
+ ```
+
+## Inputs and outputs
+
+Inputs and outputs are **class-level configurations** that define how data flows through the component, how it appears in the visual editor, and how connections to other components are validated.
+
+### Inputs
+
+Inputs are defined in a class-level `inputs` list. When Langflow loads the component, it uses this list to render component fields and [ports](/concepts-components#component-ports) in the visual editor. Users or other components provide values or connections to fill these inputs.
+
+An input is usually an instance of a class from `lfx.io` (such as `StrInput`, `DataInput`, or `MessageTextInput`).
+
+For example, this component has three inputs: a text field (`StrInput`), a Boolean toggle (`BoolInput`), and a dropdown selection (`DropdownInput`).
+
+```python
+from lfx.io import StrInput, BoolInput, DropdownInput
+
+inputs = [
+ StrInput(name="title", display_name="Title"),
+ BoolInput(name="enabled", display_name="Enabled", value=True),
+ DropdownInput(name="mode", display_name="Mode", options=["Fast", "Safe", "Experimental"], value="Safe")
+]
+```
+
+The `StrInput` creates a single-line text field for entering text. The `name="title"` parameter means you access this value in your component methods with `self.title`, while `display_name="Title"` shows "Title" as the label in the visual editor.
+
+The `BoolInput` creates a boolean toggle that's enabled by default with `value=True`. Users can turn this on or off, and you access the current state with `self.enabled`.
+
+The `DropdownInput` provides a selection menu with three predefined options: "Fast", "Safe", and "Experimental".
+The `value="Safe"` sets "Safe" as the default selection, and you access the user's choice with `self.mode`.
+
+For a list of all available parameters, see the [BaseInputMixin definition](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/inputs/input_mixin.py) in the Langflow codebase.
+
+For a list of all available input types, see the [input type definitions](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/inputs/inputs.py) in the Langflow codebase.
+
+```python
+from lfx.io import StrInput, DataInput, MultilineInput, IntInput, BoolInput, DropdownInput, FileInput, CodeInput, ModelInput, HandleInput, Output
+```
+
+### Outputs
+
+Outputs are defined in a class-level `outputs` list. When Langflow renders a component, each output becomes a connector point in the visual editor. When you connect something to an output, Langflow automatically calls the corresponding method and passes the returned object to the next component.
+
+An output is usually an instance of `Output` from `lfx.io`.
+
+For example, this component has one `output` that returns a `Table`:
+
+```python
+from lfx.io import Output
+from lfx.schema import DataFrame
+
+outputs = [
+ Output(
+ name="df_out",
+ display_name="DataFrame Output",
+ method="build_df"
+ )
+]
+
+def build_df(self) -> DataFrame:
+ # Process data and return DataFrame
+ df = DataFrame({"col1": [1, 2], "col2": [3, 4]})
+ self.status = f"Built DataFrame with {len(df)} rows."
+ return df
+```
+
+The `Output` creates a connector point in the visual editor labeled **DataFrame Output**. The `name="df_out"` parameter identifies this output, while `display_name="DataFrame Output"` shows the label in the UI. The `method="build_df"` parameter tells Langflow to call the `build_df` method when this output is connected to another component.
+
+The `build_df` method processes data and returns a `Table`. The `-> DataFrame` type annotation helps Langflow validate connections and provides color-coding in the visual editor. You can also set `self.status` to show progress messages in the UI.
+
+For a complete list of all available parameters, see the [Output class definition](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/template/field/base.py) in the Langflow codebase. Common parameters include:
+
+**Additional return types:**
+* **`Message`**: Structured chat messages
+* **`JSON`**: Flexible object with `.data` and optional `.text`
+* **`Table`**: Tabular data (pandas DataFrame subclass)
+* **Primitive types**: `str`, `int`, `bool`, not recommended for type consistency
+
+#### Associated methods
+
+Each output is linked to a method where the output method name must match the method name. The method typically returns objects like `Message`, `JSON`, or `Table`, and can use inputs with `self.`.
+
+For example, the `Output` defines a connector point called `file_contents` that will call the `read_file` method when connected. The `read_file` method accesses the filename input with `self.filename`, reads the file content, sets a status message, and returns the content wrapped in a `JSON` object.
+
+```python
+Output(
+ name="file_contents",
+ display_name="File Contents",
+ method="read_file"
+)
+
+def read_file(self) -> Data:
+ path = self.filename
+ with open(path, "r") as f:
+ content = f.read()
+ self.status = f"Read {len(content)} chars from {path}"
+ return Data(data={"content": content})
+```
+
+
+#### Components with multiple outputs
+
+A component can define multiple outputs.
+Each output can have a different corresponding method.
+
+For example:
+```python
+outputs = [
+ Output(display_name="Processed Data", name="processed_data", method="process_data"),
+ Output(display_name="Debug Info", name="debug_info", method="provide_debug_info"),
+]
+```
+
+By default, components in Langflow that produce multiple outputs only allow one output selection in the visual editor.
+The component will have only one output port where the user can select the preferred output type.
+
+This behavior is controlled by the `group_outputs` parameter:
+
+- **`group_outputs=False` (default)**: When a component has more than one output and `group_outputs` is `false` or not set, the outputs are grouped in the visual editor, and the user must select one.
+
+ Use this option when the component is expected to return only one type of output when used in a flow.
+
+- **`group_outputs=True`**: All outputs are available simultaneously in the visual editor. The component has one output port for each output, and the user can connect zero or more outputs to other components.
+
+ Use this option when the component is expected to return multiple values that are used in parallel by downstream components or processes.
+
+
+
+
+In this example, the visual editor provides a single output port, and the user can select one of the outputs.
+Since `group_outputs=False` is the default behavior, it doesn't need to be explicitly set in the component, as shown in this example.
+
+```python
+outputs = [
+ Output(
+ name="structured_output",
+ display_name="Structured Output",
+ method="build_structured_output",
+ ),
+ Output(
+ name="dataframe_output",
+ display_name="DataFrame Output",
+ method="build_structured_dataframe",
+ ),
+]
+```
+
+
+
+
+In this example, all outputs are available simultaneously in the visual editor.
+
+```python
+outputs = [
+ Output(
+ name="true_result",
+ display_name="True",
+ method="true_response",
+ group_outputs=True,
+ ),
+ Output(
+ name="false_result",
+ display_name="False",
+ method="false_response",
+ group_outputs=True,
+ ),
+]
+```
+
+
+
+
+### Tool mode
+
+Components that support **Tool Mode** can be used as standalone components (when _not_ in **Tool Mode**) or as tools for other components with a **Tools** input, such as **Agent** components.
+
+You can allow a custom component to support **Tool Mode** by setting `tool_mode=True`:
+
+```python
+inputs = [
+ MessageTextInput(
+ name="message",
+ display_name="Mensage",
+ info="Enter the message that will be processed directly by the tool",
+ tool_mode=True,
+ ),
+]
+```
+
+## Typed annotations
+
+In Langflow, typed annotations allow Langflow to visually guide users and maintain flow consistency.
+Always annotate your output methods with return types like `-> Data`, `-> Message`, or `-> DataFrame` to enable proper visual editor color-coding and validation.
+Use `JSON`, `Message`, or `Table` wrappers instead of returning plain structures for better consistency. Stay consistent with types across your components to make flows predictable and easier to build.
+
+Typed annotations provide color-coding where outputs like `-> Data` or `-> Message` get distinct colors, automatic validation that blocks incompatible connections, and improved readability for users to quickly understand data flow between components.
+
+### Common return types
+
+
+
+
+For chat-style outputs. Connects to any of several `Message`-compatible inputs.
+
+```python
+def produce_message(self) -> Message:
+ return Message(text="Hello! from typed method!", sender="System")
+```
+
+
+
+
+For structured data like dicts or partial texts. Connects only to `DataInput` (ports that accept `JSON`).
+
+```python
+def get_processed_data(self) -> Data:
+ processed = {"key1": "value1", "key2": 123}
+ return Data(data=processed)
+```
+
+
+
+
+For tabular data. Connects only to `DataFrameInput` (ports that accept `Table`).
+
+```python
+def build_df(self) -> DataFrame:
+ pdf = pd.DataFrame({"A": [1, 2], "B": [3, 4]})
+ return DataFrame(pdf)
+```
+
+
+
+
+Returning primitives is allowed, but wrapping in `JSON` or `Message` is recommended for better consistency in the visual editor.
+
+```python
+def compute_sum(self) -> int:
+ return sum(self.numbers)
+```
+
+
+
+
+
+## Enable dynamic fields
+
+In **Langflow**, dynamic fields allow inputs to change or appear based on user interactions. You can make an input dynamic by setting `dynamic=True`. Optionally, setting `real_time_refresh=True` triggers the `update_build_config` method to adjust the input's visibility or properties in real time, creating a contextual visual editor experience that only exposes relevant fields based on the user's choices.
+
+In this example, the operator field triggers updates with `real_time_refresh=True`.
+The `regex_pattern` field is initially hidden and controlled with `dynamic=True`.
+
+```python
+from lfx.custom import Component
+from lfx.io import DropdownInput, StrInput
+
+class RegexRouter(Component):
+ display_name = "Regex Router"
+ description = "Demonstrates dynamic fields for regex input."
+
+ inputs = [
+ DropdownInput(
+ name="operator",
+ display_name="Operator",
+ options=["equals", "contains", "regex"],
+ value="equals",
+ real_time_refresh=True,
+ ),
+ StrInput(
+ name="regex_pattern",
+ display_name="Regex Pattern",
+ info="Used if operator='regex'",
+ dynamic=True,
+ show=False,
+ ),
+ ]
+```
+
+### Show or hide fields based on user selections
+
+When a user changes a field with `real_time_refresh=True`, Langflow calls your `update_build_config` method.
+
+This method lets you show, hide, or modify other fields based on what the user selected.
+
+This example shows the `regex_pattern` field only when the user selects "regex" from the operator dropdown.
+
+```python
+def update_build_config(self, build_config: dict, field_value: str, field_name: str | None = None) -> dict:
+ if field_name == "operator":
+ if field_value == "regex":
+ build_config["regex_pattern"]["show"] = True
+ else:
+ build_config["regex_pattern"]["show"] = False
+ return build_config
+```
+
+You can modify additional field properties in `update_build_config` other than just `show` and `hide`.
+
+* **`required`**: Make fields required or optional dynamically
+ ```python
+ if field_value == "regex":
+ build_config["regex_pattern"]["required"] = True
+ else:
+ build_config["regex_pattern"]["required"] = False
+ ```
+
+* **`advanced`**: Move fields to the "Advanced" section
+ ```python
+ if field_value == "experimental":
+ build_config["regex_pattern"]["advanced"] = False # Show in main section
+ else:
+ build_config["regex_pattern"]["advanced"] = True # Hide in advanced
+ ```
+
+* **`options`**: Change dropdown options based on other selections
+ ```python
+ if field_value == "regex":
+ build_config["operator"]["options"] = ["regex", "contains", "starts_with"]
+ else:
+ build_config["operator"]["options"] = ["equals", "contains", "not_equals"]
+ ```
+
+## Error handling and logging
+
+You can raise standard Python exceptions such as `ValueError` or specialized exceptions like `ToolException` when validation fails. Langflow automatically catches these and displays appropriate error messages in the visual editor, helping users quickly identify what went wrong.
+
+```python
+def compute_result(self) -> str:
+ if not self.user_input:
+ raise ValueError("No input provided.")
+ # ...
+```
+
+Alternatively, instead of stopping a flow abruptly, you can return a `JSON` object containing an `"error"` field. This approach allows the flow to continue operating and enables downstream components to detect and handle the error gracefully.
+
+```python
+def run_model(self) -> Data:
+ try:
+ # ...
+ except Exception as e:
+ return Data(data={"error": str(e)})
+```
+
+Langflow provides several tools to help you debug and manage component execution. You can use `self.status` to display short messages about execution results directly in the visual editor, making troubleshooting easier for users.
+
+```python
+def parse_data(self) -> Data:
+# ...
+self.status = f"Parsed {len(rows)} rows successfully."
+return Data(data={"rows": rows})
+```
+
+You can halt individual output paths when certain conditions fail using `self.stop()`, without stopping other outputs from the same component.
+
+This example stops the output if the user input is empty, preventing the component from processing invalid data.
+
+```python
+def some_output(self) -> Data:
+if not self.user_input or len(self.user_input.strip()) == 0:
+ self.stop("some_output")
+ return Data(data={"error": "Empty input provided"})
+```
+
+You can log key execution details inside components using `self.log()`. These logs are stored as structured data and displayed in the "Logs" or "Events" section of the component's detail view, and can be accessed later through the **Logs** button in the visual editor or exported files.
+
+Component logs are distinct from Langflow's main application logging system. `self.log()` creates component-specific logs that appear in the UI, while Langflow's main logging system uses [structlog](https://www.structlog.org) for application-level logging that outputs to `langflow.log` files. For more information, see [Logs](/logging).
+
+This example logs a message when the component starts processing a file.
+
+```python
+def process_file(self, file_path: str):
+self.log(f"Processing file {file_path}")
+```
+
+## Contribute custom components to Langflow
+
+To contribute your custom component to the Langflow project, see [Contribute components](/contributing-components).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/components-embedding-models.mdx b/docs/versioned_docs/version-1.9.0/Components/components-embedding-models.mdx
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+---
+title: Embedding Model
+slug: /components-embedding-models
+---
+
+import Icon from "@site/src/components/icon";
+import PartialGlobalModelProviders from '@site/docs/_partial-global-model-providers.mdx';
+
+Embedding model components in Langflow generate text embeddings using a specified Large Language Model (LLM).
+
+Langflow includes an **Embedding Model** core component that has built-in support for some LLMs.
+Alternatively, you can use any [additional embedding model](#additional-embedding-models) in place of the **Embedding Model** core component.
+
+## Use embedding model components in a flow
+
+Use embedding model components anywhere you need to generate embeddings in a flow.
+
+This example shows how to use an embedding model component in a flow to create a semantic search system.
+This flow loads a text file, splits the text into chunks, generates embeddings for each chunk, and then loads the chunks and embeddings into a vector store. The input and output components allow a user to query the vector store through a chat interface.
+
+
+
+1. Create a flow, add a **Read File** component, and then select a file containing text data, such as a PDF, that you can use to test the flow.
+
+2.
+
+ :::tip My preferred provider or model isn't listed
+ If your preferred embedding model provider or model isn't available in Langflow's global **Models**, you can use any [additional embedding models](#additional-embedding-models) in place of the core component.
+
+ Browse [**Bundles**](/components-bundle-components) or **Search** for your preferred provider to find additional embedding models, such as the [**Hugging Face Embeddings Inference** component](/bundles-huggingface#hugging-face-embeddings-inference).
+ :::
+
+3. Add the **Embedding Model** core component to your flow, and then select your configured embedding model from the **Embedding Model** dropdown.
+
+4. Add a [**Split Text** component](/split-text) to your flow.
+This component splits text input into smaller chunks to be processed into embeddings.
+
+5. Add a vector store component, such as the **Chroma DB** component, to your flow, and then configure the component to connect to your vector database.
+This component stores the generated embeddings so they can be used for similarity search.
+
+6. Connect the components:
+
+ * Connect the **Read File** component's **Loaded Files** output to the **Split Text** component's **JSON or Table** input.
+ * Connect the **Split Text** component's **Chunks** output to the vector store component's **Ingest Data** input.
+ * Connect the **Embedding Model** component's **Embeddings** output to the vector store component's **Embedding** input.
+
+7. To query the vector store, add [**Chat Input and Output** components](/chat-input-and-output):
+
+ * Connect the **Chat Input** component to the vector store component's **Search Query** input.
+ * Connect the vector store component's **Search Results** output to the **Chat Output** component.
+
+8. Click **Playground**, and then enter a search query to retrieve text chunks that are most semantically similar to your query.
+
+## Embedding Model parameters
+
+The following parameters are for the **Embedding Model** core component.
+Other embedding model components can have additional or different parameters.
+
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+
+
+| Name | Display Name | Type | Description |
+|------|--------------|------|-------------|
+| provider | Model Provider | List | Input parameter. Select the embedding model provider. Models are configured globally in the **Models** pane. |
+| model | Model Name | List | Input parameter. Select the embedding model to use. Options depend on the selected provider and are configured globally in the **Models** pane. |
+| api_base | API Base URL | String | Input parameter. Base URL for the API. Leave empty for default. |
+| dimensions | Dimensions | Integer | Input parameter. The number of dimensions for the output embeddings. |
+| chunk_size | Chunk Size | Integer | Input parameter. The size of text chunks to process. Default: `1000`. |
+| request_timeout | Request Timeout | Float | Input parameter. Timeout for API requests. |
+| max_retries | Max Retries | Integer | Input parameter. Maximum number of retry attempts. Default: `3`. |
+| show_progress_bar | Show Progress Bar | Boolean | Input parameter. Whether to display a progress bar during embedding generation. |
+| model_kwargs | Model Kwargs | Dictionary | Input parameter. Additional keyword arguments to pass to the model. |
+| embeddings | Embeddings | Embeddings | Output parameter. An instance for generating embeddings using the selected provider. |
+
+## Additional embedding models
+
+If your provider or model isn't available in Langflow's global **Models**, you can replace the **Embedding Model** core component with any other component that generates embeddings.
+
+To find additional embedding model components, browse [**Bundles**](/components-bundle-components) or **Search** for your preferred provider.
+
+## Pair models with vector stores
+
+import PartialVectorRagBlurb from '@site/docs/_partial-vector-rag-blurb.mdx';
+
+
+
+
+Example: Vector search flow
+
+import PartialVectorRagFlow from '@site/docs/_partial-vector-rag-flow.mdx';
+
+
+
+
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/components-models.mdx b/docs/versioned_docs/version-1.9.0/Components/components-models.mdx
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+---
+title: Language Model
+slug: /components-models
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialGlobalModelProviders from '@site/docs/_partial-global-model-providers.mdx';
+
+Language model components in Langflow generate text using a specified Large Language Model (LLM).
+These components accept inputs like chat messages, files, and instructions in order to generate a text response.
+
+Langflow includes a **Language Model** core component that has built-in support for many LLMs.
+Alternatively, you can use any [additional language model](#additional-language-models) in place of the **Language Model** core component.
+
+## Use language model components in flows
+
+Use language model components anywhere you would use an LLM in a flow.
+
+
+
+
+One of the most common use cases of language model components is to chat with LLMs in your flows.
+
+The following example uses a language model component in a chatbot flow similar to the **Basic Prompting** template.
+
+1.
+
+
+ :::tip My preferred provider or model isn't listed
+ If you want to use a provider or model that isn't built-in to Langflow's global **Models**, you can replace the **Language Model** component with any [additional language model component](#additional-language-models).
+
+ Browse [**Bundles**](/components-bundle-components) or **Search** for your preferred provider to find additional language models.
+
+ Alternatively, you can use Ollama to host your preferred model, and then configure your Ollama service in Langflow's global **Models**.
+ Or, create your own custom component to support any provider and model of your choice, and then use your custom component in place of the **Language Model** core component. As a shortcut, use an existing language model component as the basis for your custom component.
+ :::
+
+2. Add the **Language Model** core component to your flow, and then select your model from the **Language Model** field.
+
+ Optionally, to configure API keys and enable or disable models, click **Manage Model Providers** to open the **Model Providers** pane.
+
+3. In the [component inspection panel](/concepts-components#component-menus), enable the **System Message** parameter.
+
+4. Add a [**Prompt Template** component](/components-prompts) to your flow.
+
+5. In the **Template** field, enter some instructions for the LLM, such as `You are an expert in geography who is tutoring high school students`.
+
+6. Connect the **Prompt Template** component's output to the **Language Model** component's **System Message** input.
+
+7. Add [**Chat Input** and **Chat Output** components](/chat-input-and-output) to your flow.
+These components are required for direct chat interaction with an LLM.
+
+8. Connect the **Chat Input** component to the **Language Model** component's **Input**, and then connect the **Language Model** component's **Message** output to the **Chat Output** component.
+
+ 
+
+9. Open the **Playground**, and ask a question to chat with the LLM and test the flow, such as `What is the capital of Utah?`.
+
+
+ Result
+
+ The following response is an example of an OpenAI model's response.
+ Your actual response may vary based on the model version at the time of your request, your template, and input.
+
+ ```
+ The capital of Utah is Salt Lake City. It is not only the largest city in the state but also serves as the cultural and economic center of Utah. Salt Lake City was founded in 1847 by Mormon pioneers and is known for its proximity to the Great Salt Lake and its role in the history of the Church of Jesus Christ of Latter-day Saints. For more information, you can refer to sources such as the U.S. Geological Survey or the official state website of Utah.
+ ```
+
+
+
+10. Optional: Try a different model or provider to see how the response changes.
+
+ If you enabled multiple models in Langflow's global **Model Providers** pane, select a different model in the **Language Model** field. To open the **Model Providers** pane, click your profile icon, select **Settings**, and then click **Model Providers**.
+ Then, open the **Playground**, ask the same question as you did before, and then compare the content and format of the responses.
+
+ This helps you understand how different models handle the same request so you can choose the best model for your use case.
+ You can also learn more about different models in each model provider's documentation.
+
+
+ Result
+
+ The following response is an example of an Anthropic model's response.
+ Your actual response may vary based on the model version at the time of your request, your template, and input.
+
+ Note that this response is shorter and includes sources, whereas the previous OpenAI response was more encyclopedic and didn't cite sources.
+
+ ```
+ The capital of Utah is Salt Lake City. It is also the most populous city in the state. Salt Lake City has been the capital of Utah since 1896, when Utah became a state.
+ Sources:
+ Utah State Government Official Website (utah.gov)
+ U.S. Census Bureau
+ Encyclopedia Britannica
+ ```
+
+
+
+
+
+
+Some components use a language model component to perform LLM-driven actions.
+Typically, these components prepare data for further processing by downstream components, rather than emitting direct chat output.
+For an example, see the [**Smart Transform** component](/smart-transform).
+
+A component must accept a `LanguageModel` input to use a language model component as a driver, and you must set the language model component's output type to `LanguageModel`.
+For more information, see [Language Model output types](#language-model-output-types).
+
+
+
+
+If you don't want to use the **Agent** component's built-in LLM, you can use a language model component to connect your preferred model:
+
+1. Add a language model component to your flow.
+
+ You can use the **Language Model** core component or browse [**Bundles**](/components-bundle-components) to find additional language models.
+ Components in bundles may not have `language model` in the name.
+ For example, Azure OpenAI LLMs are provided through the [**Azure OpenAI** component](/bundles-azure#azure-openai).
+
+2. Select your preferred model from the **Language Model** dropdown. The model must be configured globally in the **Models** pane.
+
+3. Change the language model component's output type from **Model Response** to **Language Model**.
+The output port changes to a `LanguageModel` port.
+This is required to connect the language model component to the **Agent** component.
+For more information, see [Language Model output types](#language-model-output-types).
+
+4. Add an **Agent** component to the flow.
+5. Connect the language model component's output to the **Agent** component's **Language Model** input.
+The **Agent** component now inherits the language model settings from the connected language model component instead of using any of the built-in models.
+
+
+
+
+## Language model parameters
+
+The following parameters are for the **Language Model** core component.
+Other language model components can have additional or different parameters.
+
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| provider | String | Input parameter. The model provider to use. Options depend on your global **Models** configuration. |
+| model_name | String | Input parameter. The name of the model to use. Options depend on the selected provider and your global **Models** configuration. |
+| input_value | String | Input parameter. The input text to send to the model. |
+| system_message | String | Input parameter. A system message that helps set the behavior of the assistant. |
+| stream | Boolean | Input parameter. Whether to stream the response. Default: `false`. |
+| temperature | Float | Input parameter. Controls randomness in responses. Range: `[0.0, 1.0]`. Default: `0.1`. |
+| model | LanguageModel | Output parameter. Alternative output type to the default `Message` output. Produces an instance of Chat configured with the specified parameters. See [Language Model output types](#language-model-output-types). |
+
+## Language model output types
+
+Language model components, including the core component and bundled components, can produce two types of output:
+
+* **Model Response**: The default output type emits the model's generated response as [`Message` data](/data-types#message).
+Use this output type when you want the typical LLM interaction where the LLM produces a text response based on given input.
+
+* **Language Model**: Change the language model component's output type to [`LanguageModel`](/data-types#languagemodel) when you need to attach an LLM to another component in your flow, such as an **Agent** or **Smart Transform** component.
+
+ With this configuration, the language model component supports an action completed by another component, rather than a direct chat interaction.
+ For an example, the **Smart Transform** component uses an LLM to create a function from natural language input.
+
+## Additional language models
+
+If your provider or model isn't supported by the **Language Model** core component, additional language model components are available in [**Bundles**](/components-bundle-components).
+
+You can use these components in the same way that you use the core **Language Model** component, as explained in [Use language model components in flows](#use-language-model-components-in-flows).
+
+## Pair models with vector stores
+
+import PartialVectorRagBlurb from '@site/docs/_partial-vector-rag-blurb.mdx';
+
+
+
+
+Example: Vector search flow
+
+import PartialVectorRagFlow from '@site/docs/_partial-vector-rag-flow.mdx';
+
+
+
+
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/components-prompts.mdx b/docs/versioned_docs/version-1.9.0/Components/components-prompts.mdx
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--- /dev/null
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+---
+title: Prompt Template
+slug: /components-prompts
+---
+
+import PartialCurlyBraces from '@site/docs/_partial-escape-curly-braces.mdx';
+
+Use the **Prompt Template** core component to create a _prompt_ that supplies instructions and context to an LLM or agent, separate from other input like chat messages and file uploads.
+
+Prompts are structured input that use natural language, fixed values, and dynamic variables to provide baseline context for the LLM.
+For example:
+
+* Define a consistent structure for user queries, making it easier for the LLM to understand and respond appropriately.
+* Define a specific output format for the LLM, such as JSON or structured text.
+* Define a role for the LLM, such as `You are a helpful assistant` or `You are an expert in microbiology`.
+* Allow the LLM to reference chat memory.
+
+The **Prompt Template** component can also output variable instructions to other components later in the flow.
+
+## Prompt Template parameters
+
+| Name | Display Name | Description |
+|---------------------|---------------------|-------------------------------------------------------------------|
+| template | Template | Input parameter. Create a prompt template with dynamic variables in curly braces, such as `{VARIABLE_NAME}`. |
+| use_double_brackets | Use Double Brackets | When enabled, use Mustache syntax `{{variable}}` instead of f-string syntax `{variable}`. For more information, see [Use Mustache templating in prompt templates](#use-mustache-templating-in-prompt-templates). |
+
+## Define variables in prompts
+
+Variables in a **Prompt Template** component dynamically add fields to the **Prompt Template** component so that your flow can receive definitions for those values from other components, Langflow global variables, or fixed input.
+
+For example, with the [**Message History** component](/message-history), you can use a `{memory}` variable to pass chat history to the prompt.
+However, the **Agent** component includes built-in chat memory that is enabled by default.
+For more information, see [Memory management options](/memory).
+
+The following steps demonstrate how to add variables to a **Prompt Template** component:
+
+1. Create a flow based on the **Basic prompting** template.
+
+ This template already has a **Prompt Template** component, but the template only contains natural language instructions: `Answer the user as if you were a GenAI expert, enthusiastic about helping them get started building something fresh.`
+
+ This prompt defines a role for the LLM's chat interactions, but it doesn't include variables that help you create prompts that adapt dynamically to changing contexts, such as different users and environments.
+
+2. Click the **Prompt Template** component, and then add some variables to the **Template** field.
+
+ Variables are declared by wrapping the variable name in curly braces, like `{variable_name}`.
+ For example, the following template creates `context` and `user_question` variables:
+
+ ```text
+ Given the context:
+
+ {context}
+
+ Answer the question:
+
+ {user_question}
+ ```
+
+
+
+4. Click **Check & Save** to save the template.
+
+ After adding the variables to the template, new fields are added to the **Prompt Template** component for each variable.
+
+5. Provide input for the variable fields:
+
+ * Connect the fields to other components to pass the output from those components to the variables.
+ * Use Langflow global variables.
+ * Enter fixed values directly into the fields.
+
+You can add as many variables as you like in your template.
+For example, you could add variables for `{references}` and `{instructions}`, and then feed that information in from other components, such as **Text Input**, **URL**, or **Read File** components.
+
+### Use Mustache templating in prompt templates
+
+F-string escaping can become confusing when you mix escaped braces with variables in the same template.
+For example:
+
+```text
+Generate a response in this JSON format:
+{{"name": "{name}", "age": {age}, "city": "{city}"}}
+
+The user's name is {name}, age is {age}, and they live in {city}.
+```
+
+The characters `{{` and `}}` are escaped literal braces for the JSON structure, but `{name}` is a variable.
+This can make prompts error-prone and difficult to parse.
+Use [Mustache](https://mustache.github.io) in your prompt templates to make the differences clearer.
+
+To enable Mustache templating, do the following:
+
+1. In the **Prompt Template** component, enable **Use Double Brackets**.
+2. In your prompt template, change the variables from `{variable}` to `{{variable}}`.
+ Mustache uses `{` `}` for literal braces and `{{variable}}` for variables.
+
+ ```text
+ Generate a response in this JSON format:
+ {"name": "{{name}}", "age": {{age}}, "city": "{{city}}"}
+
+ The user's name is {{name}}, age is {{age}}, and they live in {{city}}.
+ ```
+
+3. Click **Check & Save**.
+ The component lints the template code and returns **Prompt is ready** if there are no errors.
+ Your prompt is now ready to use in a flow.
+
+Langflow supports variable replacement with double brackets, but does not support the full Mustache engine.
+The prompt component validation rejects syntax for other Mustache features such as loops and conditionals.
+
+## See also
+
+* [**LangChain Prompt Hub** component](/bundles-langchain#prompt-hub)
+* [Processing components](/concepts-components)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/concepts-components.mdx b/docs/versioned_docs/version-1.9.0/Components/concepts-components.mdx
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--- /dev/null
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+---
+title: Components overview
+slug: /concepts-components
+---
+
+import Icon from "@site/src/components/icon";
+import CodeSnippet from "@site/src/components/CodeSnippet";
+import RecursiveCharacterSource from "!!raw-loader!@langflow/src/lfx/src/lfx/components/langchain_utilities/recursive_character.py";
+
+Components are the building blocks of your flows.
+Like classes in an application, each component is designed for a specific use case or integration.
+
+:::tip
+Langflow provides keyboard shortcuts for the workspace.
+
+In the Langflow header, click your profile icon, select **Settings**, and then click **Shortcuts** to view the available shortcuts.
+:::
+
+## Add a component to a flow {#component-menus}
+
+To add a component to a flow, drag the component from the **Core components** or **Bundles** menu into the [workspace](/concepts-overview#workspace).
+
+Components are grouped by type or provider, and some components are hidden by default:
+
+* **Core components**: Langflow's base components are grouped by purpose, such as **Inputs and Outputs** or **Data**.
+These components either provide generic functionality, like loops and parsing, or they provide single components that support multiple third-party integrations.
+
+* **Bundles**: Bundles contain one or more components that support specific third-party integrations, and they are grouped by service provider.
+
+* **Legacy**: These components are hidden by default.
+For more information, see [Legacy components](#legacy-components).
+
+### Configure a component
+
+After adding a component to a flow, configure the component's parameters and connect it to the other components in your flows.
+
+Each component has inputs, outputs, parameters, and controls related to the component's purpose.
+By default, components show only required and common options.
+To access additional settings and controls, including meta settings, use the [component inspection panel](#component-inspection-panel).
+
+### Component inspection panel {#component-inspection-panel}
+
+When you select a component in the workspace, a component inspection panel appears on the right side of the screen.
+
+The inspection panel displays all of a component's parameters, including hidden or advanced parameters.
+
+### Component header menus
+
+To access a component's header menu, click the component in your workspace.
+
+
+
+The following options are available directly on the header menu:
+
+- **Code**: Modify component settings by directly editing the component's Python code.
+- **Freeze**: Freeze a component and all upstream components to prevent re-running. For more information, see [Freeze a component](#freeze-a-component).
+- **Tool Mode**: Enable this option when combining a component with an **Agent** component.
+
+For all other options, including **Delete** and **Duplicate** controls, click **Show More**.
+
+### Rename a component
+
+To modify a component's name or description, click the component in the workspace, and then click **Edit**.
+Component descriptions accept Markdown syntax.
+
+### Run a component
+
+To run a single component, click **Run component**.
+A **Last Run** value indicates that the component ran successfully.
+
+Running a single component is different from running an entire flow. In a single component run, the `build_vertex` function is called, which builds and runs only the single component with direct inputs provided through the visual editor (the `inputs_dict` parameter). The `VertexBuildResult` data is passed to the `build_and_run` method that calls the component's `build` method and runs it. Unlike running an entire flow, running a single component doesn't automatically execute its upstream dependencies.
+
+### Inspect component output and logs
+
+To view the output and logs for a single component, click **Inspect**.
+
+### Freeze a component
+
+:::info
+Freezing a component also freezes all components upstream of the selected component.
+:::
+
+Use the freeze option if you expect consistent output from a component _and all upstream components_, and you only need to run those components once.
+
+Freezing a component prevents that component and all upstream components from re-running, and it preserves the last output state for those components.
+Any future flow runs use the preserved output.
+
+To freeze a component, click the component in the workspace to expose the component's header menu, and then click **Freeze**.
+
+## Component ports
+
+Around the border of each component, there are circular port icons like .
+These indicate a component _connection point_ or _port_.
+
+Ports either accept input or produce output of a specific data type.
+You can infer the data type from the field the port is attached to or from the [port's color](#port-colors).
+For example, the **System Message** field accepts [message data](/data-types#message), as illustrated by the blue port icon: .
+
+
+
+When building flows, connect output ports to input ports of the same type (color) to transfer that type of data between two components.
+For information about the programmatic representation of each data type, see [Langflow data types](/data-types).
+
+:::tip
+* In the workspace, hover over a port to see connection details for that port.
+Click a port to **Search** for compatible components.
+
+* If two components have incompatible data types, you can use a processing component like the [**Type Convert** component](/type-convert) to convert the data between components.
+:::
+
+### Dynamic ports
+
+Some components have ports that are dynamically added or removed.
+For example, the **Prompt Template** component accepts [inputs wrapped in curly braces](/components-prompts#define-variables-in-prompts), and new ports are opened when a value wrapped in curly braces is detected in the **Template** field.
+
+
+
+### Output type selection
+
+All components produce output that is either sent to another component in the flow or returned as the final flow result.
+
+Some components can produce multiple types of output:
+
+* If the component emits all types at once, the component has multiple output ports in the visual editor. In component code, this is represented by `group_outputs=True`
+
+* If the component emits only one type, you must select the output type by clicking the output label near the output port, and then selecting the desired output type. In component code, this is represented by `group_outputs=False` or omitting the `group_outputs` parameter.
+
+For example, a language model component can output _either_ a **Model Response** or **Language Model**.
+The **Model Response** output produces [`Message`](/data-types#message) data that can be passed to another component's `Message` port.
+The **Language Model** output must be connected to a component with a **Language Model** input, such as the [**Structured Output** component](/structured-output), that uses the attached LLM to power the receiving component's reasoning.
+
+
+
+### Port colors
+
+Component port colors indicate the data type ingested or emitted by the port.
+For example, a **Message** port either accepts or emits `Message` data.
+
+The following table lists the component data types and their corresponding port colors:
+
+| Data type | Port color | Port icon example |
+|-----------|--------------|----------|
+| JSON | Red | |
+| Table | Pink | |
+| Embeddings | Emerald | |
+| LanguageModel | Fuchsia | |
+| Memory | Orange | |
+| Message | Indigo | |
+| Tool | Cyan | |
+| Unknown or multiple types | Gray | |
+
+## Component code
+
+You can edit components in the [workspace](/concepts-overview#workspace) and in code. When editing a flow, select a component, and then click **Code** to see and edit the component's underlying Python code.
+
+All components have underlying code that determines how you configure them and what actions they can perform.
+In the context of creating and running flows, component code does the following:
+
+* Determines what configuration options to show in the visual editor.
+* Validates inputs based on the component's defined input types.
+* Processes data using the configured parameters, methods, and functions.
+* Passes results to the next component in the flow.
+
+All components inherit from a base `Component` class that defines the component's interface and behavior.
+For example, the [**Recursive Character Text Splitter** component](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/components/langchain_utilities/recursive_character.py) is a child of the [`LCTextSplitterComponent`](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/base/textsplitters/model.py) class.
+
+Each component's code includes definitions for inputs and outputs, which are represented in the workspace as [component ports](#component-ports).
+For example, the `RecursiveCharacterTextSplitter` has four inputs. Each input definition specifies the input type, such as `IntInput`, as well as the encoded name, display name, description, and other parameters for that specific input.
+These values determine the component settings, such as display names and tooltips in the visual editor.
+
+
+
+Additionally, components have methods or functions that handle their functionality.
+For example, the `RecursiveCharacterTextSplitter` has two methods:
+
+
+
+The `get_data_input` method retrieves the text to be split from the component's input, which makes the data available to the class.
+The `build_text_splitter` method creates a `RecursiveCharacterTextSplitter` object by calling its parent class's `build` method. Then, the text is split with the created splitter and passed to the next component.
+
+## Component versions
+
+Component versions and states are stored in an internal Langflow database. When you add a component to a flow, you create a detached copy of the component based on the information in the Langflow database.
+These copies are detached from the primary Langflow database, and they don't synchronize with any updates that can occur when you upgrade your Langflow version.
+
+In other words, an individual instance of a component retains the version number and state from the moment you add it to a specific flow. For example, if a component is at version 1.0 when you add it to a flow, it remains at version 1.0 _in that flow_ unless you update it.
+
+### Update component versions
+
+When editing a flow in the workspace, Langflow notifies you if a component's workspace version is behind the database version so you can update the component's workspace version:
+
+* **Update ready**: This notification means the component update contains no breaking changes.
+* **Update available**: This notification means the component update might contain breaking changes.
+
+ Breaking changes modify component inputs and outputs, causing the components to be disconnected and break the flow. After updating the component, you might need to edit the component settings or reconnect component ports.
+
+There are two ways to update components:
+
+* Click **Update** to update a single component. This is recommended for updates without breaking changes.
+* Click **Review** to view all available updates and create a snapshot before updating. This is recommended for updates with breaking changes.
+
+ To save a snapshot of your flow before updating the components, enable **Create backup flow before updating**. Backup flows are stored in the same project folder as the original flow with the suffix `(backup)`.
+
+ To update specific components, select the components you want to update, and then click **Update Components**.
+
+Components are updated to the latest available version, based on the version of Langflow you are running.
+
+## Group components
+
+Multiple components can be grouped into a single component for reuse. This is useful for organizing large flows by combining related components together, such as a RAG **Agent** component and it's associated tools or vector store components.
+
+1. Hold Shift, and then click and drag to highlight all components you want to merge. Components must be completely within the selection area to be merged.
+
+ Alternatively, to select components for merging one by one, hold Ctrl on Windows or Cmd on Mac, and then click each component to add them to the group.
+
+2. Release the mouse and keyboard, and then click **Group** to merge the components into a single, group component.
+
+Grouped components are configured and managed as a single component, including the component name, code, and settings.
+
+To ungroup the components, click the component in the workspace to expose the component's header menu, click **Show More**, and then select **Ungroup**.
+
+If you want to reuse this grouping in other flows, click the component in the workspace to expose the component's header menu, click **Show More**, and then select **Save** to save the component to the **Core components** menu as a custom component.
+
+## Legacy components
+
+import PartialLegacy from '@site/docs/_partial-legacy.mdx';
+
+
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/current-date.mdx b/docs/versioned_docs/version-1.9.0/Components/current-date.mdx
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+---
+title: Current Date
+slug: /current-date
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+The **Current Date** component returns the current date and time in a selected timezone. This component provides a flexible way to obtain timezone-specific date and time information within a Langflow pipeline.
+
+## Current Date parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| timezone | String | Input parameter. The timezone for the current date and time. |
+| current_date | String | Output parameter. The resulting current date and time in the selected timezone. |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/data-operations.mdx b/docs/versioned_docs/version-1.9.0/Components/data-operations.mdx
new file mode 100644
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@@ -0,0 +1,167 @@
+---
+title: JSON Operations
+slug: /data-operations
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialCurlyBraces from '@site/docs/_partial-escape-curly-braces.mdx';
+
+:::tip
+Prior to Langflow 1.9.0, this component was named **Data Operations**.
+:::
+
+The **JSON Operations** component performs operations on [`JSON`](/data-types#json) objects, including extracting, filtering, and editing keys and values in the `JSON`.
+For all options, see [Available data operations](#available-data-operations).
+The output is a new `JSON` object containing the modified data after running the selected operation.
+
+## Use the JSON Operations component in a flow
+
+The following example demonstrates how to use a **JSON Operations** component in a flow using data from a webhook payload:
+
+1. Create a flow with a **Webhook** component and a **JSON Operations** component, and then connect the **Webhook** component's output to the **JSON Operations** component's **JSON** input.
+
+ All operations in the **JSON Operations** component require at least one `JSON` input from another component.
+ If the preceding component doesn't produce `JSON` output, you can use another component, such as the [**Type Convert** component](/type-convert), to reformat the data before passing it to the **JSON Operations** component.
+ Alternatively, you could consider using a component that is designed to process the original data type, such as the [**Parser** component](/parser) or [**Table Operations** component](/dataframe-operations).
+
+2. In the **Operations** field, select the operation you want to perform on the incoming `JSON`.
+For this example, select the **Select Keys** operation.
+
+ :::tip
+ You can select only one operation.
+ If you need to perform multiple operations on the data, you can chain multiple **JSON Operations** components together to execute each operation in sequence.
+ For more complex multi-step operations, consider using a component like the [**Smart Transform** component](/smart-transform).
+ :::
+
+3. Under **Select Keys**, add keys for `name`, `username`, and `email`.
+Click **Add more** to add a field for each key.
+
+ For this example, assume that the webhook will receive consistent payloads that always contain `name`, `username`, and `email` keys.
+ The **Select Keys** operation extracts the value of these keys from each incoming payload.
+
+4. Optional: If you want to view the output in the **Playground**, connect the **JSON Operations** component's output to a **Chat Output** component.
+
+ 
+
+5. To test the flow, send the following request to your flow's webhook endpoint.
+For more information about the webhook endpoint, see [Trigger flows with webhooks](/webhook).
+
+ ```bash
+ curl -X POST "http://$LANGFLOW_SERVER_URL/api/v1/webhook/$FLOW_ID" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "id": 1,
+ "name": "Leanne Graham",
+ "username": "Bret",
+ "email": "Sincere@april.biz",
+ "address": {
+ "street": "Main Street",
+ "suite": "Apt. 556",
+ "city": "Springfield",
+ "zipcode": "92998-3874",
+ "geo": {
+ "lat": "-37.3159",
+ "lng": "81.1496"
+ }
+ },
+ "phone": "1-770-736-8031 x56442",
+ "website": "hildegard.org",
+ "company": {
+ "name": "Acme-Corp",
+ "catchPhrase": "Multi-layered client-server neural-net",
+ "bs": "harness real-time e-markets"
+ }
+ }'
+ ```
+
+6. To view the `JSON` resulting from the **Select Keys** operation, do one of the following:
+
+ * If you attached a **Chat Output** component, open the **Playground** to see the result as a chat message.
+ * Click **Inspect output** on the **JSON Operations** component.
+
+## JSON Operations parameters
+
+Many parameters are conditional based on the selected **Operation** (`operation`).
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| data | JSON | Input parameter. The `JSON` object to operate on. Must be provided as `JSON` data type input generated by another component. If the preceding component doesn't produce `JSON` output, use the [**Type Convert** component](/type-convert) to reformat the data before passing it to the **JSON Operations** component. |
+| operation | Operation | Input parameter. The operation to perform on the data. See [Available data operations](#available-data-operations) |
+| select_keys_input | Select Keys | Input parameter. A list of keys to select from the data. |
+| filter_key | Filter Key | Input parameter. The key to filter by. |
+| operator | Comparison Operator | Input parameter. The operator to apply for comparing values. |
+| filter_values | Filter Values | Input parameter. A list of values to filter by. |
+| append_update_data | Append or Update | Input parameter. The data to append or update the existing data with. |
+| remove_keys_input | Remove Keys | Input parameter. A list of keys to remove from the data. |
+| rename_keys_input | Rename Keys | Input parameter. A list of keys to rename in the data. |
+| mapped_json_display | JSON to Map | Input parameter. JSON structure to explore for path selection. Only applies to the **Path Selection** operation. For more information, see [Path Selection operation examples](#path-selection-operation-examples). |
+| selected_key | Select Path | Input parameter. The JSON path expression to extract values. Only applies to the **Path Selection** operation. For more information, see [Path Selection operation examples](#path-selection-operation-examples). |
+| query | JQ Expression | Input parameter. The [`jq`](https://jqlang.org/manual/) expression for advanced JSON filtering and transformation. Only applies to the **JQ Expression** operation. For more information, see [JQ Expression operation examples](#jq-expression-operation-examples). |
+
+#### Available data operations
+
+Options for the `operations` input parameter are as follows.
+All operations act on an incoming `JSON` object.
+
+| Name | Required Inputs | Process |
+|-----------|----------------|-------------|
+| Select Keys | `select_keys_input` | Selects specific keys from the data. |
+| Literal Eval | None | Evaluates string values as Python literals. |
+| Combine | None | Combines multiple data objects into one. |
+| Filter Values | `filter_key`, `filter_values`, `operator` | Filters data based on key-value pair. |
+| Append or Update | `append_update_data` | Adds or updates key-value pairs. |
+| Remove Keys | `remove_keys_input` | Removes specified keys from the data. |
+| Rename Keys | `rename_keys_input` | Renames keys in the data. |
+| Path Selection | `mapped_json_display`, `selected_key` | Extracts values from nested JSON structures using path expressions. |
+| JQ Expression | `query` | Performs advanced JSON queries using [`jq`](https://jqlang.org/manual/) syntax for filtering, projections, and transformations. |
+
+## Path Selection operation examples
+
+Use the Path Selection operation to extract values from nested JSON structures with dot notation paths.
+
+1. In the **Operations** dropdown, select **Path Selection**.
+2. In the **JSON to Map** field, enter your JSON structure.
+
+ This example uses the following JSON structure.
+ ```json
+ {
+ "user": {
+ "profile": {
+ "name": "John Doe",
+ "email": "john@example.com"
+ },
+ "settings": {
+ "theme": "dark"
+ }
+ }
+ }
+ ```
+ The **Select Path** dropdown auto-populates with available paths.
+3. In the **Select Paths** dropdown, select the path.
+ You can select paths such as `.user.profile.name` to extract "John Doe", or select `.user.settings.theme` to extract "dark".
+
+## JQ Expression operation example {#jq-expression-operation-examples}
+
+Use the **JQ Expressions** operation to use the [jq](https://jqlang.org/) query language to perform more advanced JSON filtering.
+1. In the **Operations** dropdown, select **JQ Expression**.
+2. In the **JQ Expression** field, enter a `jq` filter to query against the **JSON Operations** component's **JSON** input.
+
+ For this example JSON structure, enter expressions like `.user.profile.name` to extract "John Doe", `.user.profile | {name, email}` to project fields to a new object, or `.user.profile | tostring` to convert the field to a string.
+ ```json
+ {
+ "user": {
+ "profile": {
+ "name": "John Doe",
+ "email": "john@example.com"
+ },
+ "settings": {
+ "theme": "dark"
+ }
+ }
+ }
+ ```
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/dataframe-operations.mdx b/docs/versioned_docs/version-1.9.0/Components/dataframe-operations.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/dataframe-operations.mdx
@@ -0,0 +1,197 @@
+---
+title: Table Operations
+slug: /dataframe-operations
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialCurlyBraces from '@site/docs/_partial-escape-curly-braces.mdx';
+
+:::tip
+Prior to Langflow 1.9.0, this component was named **DataFrame Operations**.
+:::
+
+The **Table Operations** component performs operations on [`Table`](/data-types#table) rows and columns, including schema changes, record changes, sorting, and filtering.
+For all options, see [Table Operations parameters](#table-operations-parameters).
+
+The output is a new `Table` containing the modified data after running the selected operation.
+
+## Use the Table Operations component in a flow
+
+The following steps explain how to configure a **Table Operations** component in a flow.
+You can follow along with an example or use your own flow.
+The only requirement is that the preceding component must create `Table` output that you can pass to the **Table Operations** component.
+
+1. Create a new flow or use an existing flow.
+
+
+ Example: API response extraction flow
+
+ The following example flow uses five components to extract `JSON` from an API response, transform it to a `Table`, and then perform further processing on tabular data using a **Table Operations** component.
+ The sixth component, **Chat Output**, is optional in this example.
+ It only serves as a convenient way for you to view the final output in the **Playground**, rather than inspecting the component logs.
+
+ 
+
+ If you want to use this example to test the **Table Operations** component, do the following:
+
+ 1. Create a flow with the following components:
+
+ * **API Request**
+ * **Language Model**
+ * **Smart Transform**
+ * **Type Convert**
+
+ 2. Configure the [**Smart Transform** component](/smart-transform) and its dependencies:
+
+ * **API Request**: Configure the [**API Request** component](/api-request) to get JSON data from an endpoint of your choice, and then connect the **API Response** output to the **Smart Transform** component's **Data** input.
+ * **Language Model**: Select your preferred provider and model, and then enter a valid API key.
+ Change the output to **Language Model**, and then connect the `LanguageModel` output to the **Smart Transform** component's **Language Model** input.
+ * **Smart Transform**: In the **Instructions** field, enter natural language instructions to extract data from the API response.
+ Your instructions depend on the response content and desired outcome.
+ For example, if the response contains a large `result` field, you might provide instructions like `explode the result field out into a Data object`.
+
+ 3. Convert the **Smart Transform** component's output from `JSON` to `Table`:
+
+ 1. Connect the **Filtered Data** output to the **Type Convert** component's **JSON** input.
+ 2. Set the **Type Convert** component's **Output Type** to **Table**.
+
+ Now the flow is ready for you to add the **Table Operations** component.
+
+
+
+2. Add a **Table Operations** component to the flow, and then connect `Table` output from another component to the **Table** input.
+
+ All operations in the **Table Operations** component require at least one `Table` input from another component.
+ If a component doesn't produce `Table` output, you can use another component, such as the [**Type Convert** component](/type-convert), to reformat the data before passing it to the **Table Operations** component.
+ Alternatively, you could consider using a component that is designed to process the original data type, such as the [**Parser** component](/parser) or [**JSON Operations** component](/data-operations).
+
+ If you are following along with the example flow, connect the **Type Convert** component's **Table Output** port to the **Table** input.
+
+3. In the **Operations** field, select the operation you want to perform on the incoming `Table`.
+For example, the **Filter** operation filters the rows based on a specified column and value.
+
+ :::tip
+ You can select only one operation.
+ If you need to perform multiple operations on the data, you can chain multiple **Table Operations** components together to execute each operation in sequence.
+ For more complex multi-step operations, like dramatic schema changes or pivots, consider using an LLM-powered component, like the [**Structured Output** component](/structured-output) or [**Smart Transform** component](/smart-transform), as a replacement or preparation for the **Table Operations** component.
+ :::
+
+ If you're following along with the example flow, select any operation that you want to apply to the data that was extracted by the **Smart Transform** component.
+ To view the contents of the incoming `Table`, click **Run component** on the **Type Convert** component, and then **Inspect output**.
+ If the `Table` seems malformed, click **Inspect output** on each upstream component to determine where the error occurs, and then modify your flow's configuration as needed.
+ For example, if the **Smart Transform** component didn't extract the expected fields, modify your instructions or verify that the given fields are present in the **API Response** output.
+
+4. Configure the operation's parameters.
+The specific parameters depend on the selected operation.
+For example, if you select the **Filter** operation, you must define a filter condition using the **Column Name**, **Filter Value**, and **Filter Operator** parameters.
+For more information, see [Table Operations parameters](#table-operations-parameters)
+
+5. To test the flow, click **Run component** on the **Table Operations** component, and then click **Inspect output** to view the new `Table` created from the **Filter** operation.
+
+ If you want to view the output in the **Playground**, connect the **Table Operations** component's output to a **Chat Output** component, rerun the **Table Operations** component, and then click **Playground**.
+
+For another example, see [Conditional looping](/loop#conditional-looping).
+
+## Table Operations parameters
+
+Most **Table Operations** parameters are conditional because they only apply to specific operations.
+
+The only permanent parameters are **Table** (`df`), which is the `Table` input, and **Operation** (`operation`), which is the operation to perform on the `Table`.
+Once you select an operation, the conditional parameters for that operation appear on the **Table Operations** component.
+
+
+
+
+The **Add Column** operation allows you to add a new column to the `Table` with a constant value.
+
+The parameters are **New Column Name** (`new_column_name`) and **New Column Value** (`new_column_value`).
+
+
+
+
+The **Drop Column** operation allows you to remove a column from the `Table`, specified by **Column Name** (`column_name`).
+
+
+
+
+The **Filter** operation allows you to filter the `Table` based on a specified condition.
+The output is a `Table` containing only the rows that matched the filter condition.
+
+Provide the following parameters:
+
+* **Column Name** (`column_name`): The name of the column to filter on.
+* **Filter Value** (`filter_value`): The value to filter on.
+* **Filter Operator** (`filter_operator`): The operator to use for filtering, one of `equals` (default), `not equals`, `contains`, `not contains`, `starts with`, `ends with`, `greater than`, or `less than`.
+
+
+
+
+The **Head** operation allows you to retrieve the first `n` rows of the `Table`, where `n` is set in **Number of Rows** (`num_rows`).
+The default is `5`.
+
+The output is a `Table` containing only the selected rows.
+
+
+
+
+The **Rename Column** operation allows you to rename an existing column in the `Table`.
+
+The parameters are **Column Name** (`column_name`), which is the current name, and **New Column Name** (`new_column_name`).
+
+
+
+
+The **Replace Value** operation allows you to replace values in a specific column of the `Table`.
+This operation replaces a target value with a new value.
+All cells matching the target value are replaced with the new value in the new `Table` output.
+
+Provide the following parameters:
+
+* **Column Name** (`column_name`): The name of the column to modify.
+* **Value to Replace** (`replace_value`): The value that you want to replace.
+* **Replacement Value** (`replacement_value`): The new value to use.
+
+
+
+
+The **Select Columns** operation allows you to select one or more specific columns from the `Table`.
+
+Provide a list of column names in **Columns to Select** (`columns_to_select`).
+In the visual editor, click **Add More** to add multiple fields, and then enter one column name in each field.
+
+The output is a `Table` containing only the specified columns.
+
+
+
+
+The **Sort** operation allows you to sort the `Table` on a specific column in ascending or descending order.
+
+Provide the following parameters:
+
+* **Column Name** (`column_name`): The name of the column to sort on.
+* **Sort Ascending** (`ascending`): Whether to sort in ascending or descending order. If enabled (`true`), sorts in ascending order; if disabled (`false`), sorts in descending order. Default: Enabled (`true`)
+
+
+
+
+The **Tail** operation allows you to retrieve the last `n` rows of the `Table`, where `n` is set in **Number of Rows** (`num_rows`).
+The default is `5`.
+
+The output is a `Table` containing only the selected rows.
+
+
+
+
+The **Drop Duplicates** operation removes rows from the `Table` by identifying all duplicate values within a single column.
+
+The only parameter is the **Column Name** (`column_name`).
+
+When the flow runs, all rows with duplicate values in the given column are removed.
+The output is a `Table` containing all columns from the original `Table`, but only rows with non-duplicate values.
+
+
+
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/directory.mdx b/docs/versioned_docs/version-1.9.0/Components/directory.mdx
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,32 @@
+---
+title: Directory
+slug: /directory
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+The **Directory** component recursively loads files from a directory, with options for file types, depth, and concurrency.
+
+Files must be of a [supported type and size](/read-file#file-type-and-size-limits) to be loaded.
+
+Outputs either a [`JSON`](/data-types#json) or [`Table`](/data-types#table) object, depending on the directory contents.
+
+## Directory parameters
+
+
+
+| Name | Type | Description |
+| ------------------ | ---------------- | -------------------------------------------------- |
+| path | MessageTextInput | Input parameter. The path to the directory to load files from. Default: Current directory (`.`) |
+| types | MessageTextInput | Input parameter. The file types to load. Select one or more, or leave empty to attempt to load all files. |
+| depth | IntInput | Input parameter. The depth to search for files. |
+| max_concurrency | IntInput | Input parameter. The maximum concurrency for loading multiple files. |
+| load_hidden | BoolInput | Input parameter. If `true`, hidden files are loaded. |
+| recursive | BoolInput | Input parameter. If `true`, the search is recursive. |
+| silent_errors | BoolInput | Input parameter. If `true`, errors don't raise an exception. |
+| use_multithreading | BoolInput | Input parameter. If `true`, multithreading is used. |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/dynamic-create-data.mdx b/docs/versioned_docs/version-1.9.0/Components/dynamic-create-data.mdx
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/dynamic-create-data.mdx
@@ -0,0 +1,51 @@
+---
+title: Dynamic Create Data
+slug: /dynamic-create-data
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialCurlyBraces from '@site/docs/_partial-escape-curly-braces.mdx';
+
+The **Dynamic Create Data** component creates a [`JSON`](/data-types#json) object or [`Message`](/data-types#message) with configurable fields.
+Define the table in the **Input Configuration** field, and the component creates corresponding input or output handles in the component.
+
+## Use the Dynamic Create Data component in a flow
+
+The following example demonstrates how to use a **Dynamic Create Data** component to create a structured `JSON` or `Message` object from multiple sources.
+
+1. Add the **Dynamic Create Data** component to your flow.
+
+2. To define your data's fields, in the **Input Configuration** field, click **Open table**.
+
+3. To add rows to your table, click **Add a new row**.
+ Adding a new row creates input and output handles for the **Field Type**.
+ For example, if you add a `Text` type field, then`Text` input and output handles are added to the component.
+ For each new row, configure the **Field Name** and **Field Type**.
+
+ * **Field Name**: The name of the field used as both the internal key and display label.
+ * **Field Type**: The type of input field to create. The type options are:
+ * Text: Accepts direct text input or accepts `Text` or `Message` output from other components.
+ * Data: Accepts `JSON` input from other components.
+ * Number: Accepts direct numeric input or accepts `Text` or `Message` outputs from other components.
+ * Handle: Accepts `Text`, `JSON`, or `Message` output from other components.
+ * Boolean: Accepts Boolean values. Cannot accept input from another component.
+
+ For more information, see [Langflow data types](/data-types).
+4. Depending on your **Field Type** selections, either connect output from other components to dynamically populate the inputs, or enter values manually in the **Dynamic Create Data** component's fields.
+
+5. Select the desired output type at the component's output port. The component outputs either a [`JSON`](/data-types#json) object containing all field values from the component's inputs, or a [`Message`](/data-types#message) containing all field values formatted as a text string.
+
+## Dynamic Create Data parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| `form_fields` | **Input Configuration** | Input parameter. A table that defines the dynamic form fields. |
+| `include_metadata` | **Include Metadata** | Input parameter. Whether to include form configuration metadata in the output.|
+| `form_data` | **Data** | Output parameter. A `JSON` object containing all field values from the dynamic inputs. |
+| `message` | **Message** | Output parameter. A formatted `Text` message containing all field values in a human-readable format. |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/guardrails.mdx b/docs/versioned_docs/version-1.9.0/Components/guardrails.mdx
new file mode 100644
index 0000000000..f17781e334
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/guardrails.mdx
@@ -0,0 +1,64 @@
+---
+title: Guardrails
+slug: /guardrails
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+The **Guardrails** component validates input text against security and safety guardrails by issuing prompts to a language model (LLM) to check for violations.
+
+The following guardrails can be validated against:
+
+- **PII**: Detects personal identifiable information such as names, addresses, phone numbers, email addresses, social security numbers, credit card numbers, or other personal data.
+- **Tokens/Passwords**: Detects API tokens, passwords, API keys, access keys, secret keys, authentication credentials, or other sensitive credentials.
+- **Jailbreak**: Detects attempts to bypass AI safety guidelines, manipulate the model's behavior, or make it ignore its instructions.
+- **Offensive Content**: Detects offensive, hateful, discriminatory, violent, or inappropriate content.
+- **Malicious Code**: Detects potentially malicious code, scripts, exploits, or harmful commands.
+- **Prompt Injection**: Detects attempts to inject malicious prompts, override system instructions, or manipulate the AI's behavior through embedded instructions.
+
+When validation passes, the input continues through the **Pass** output.
+When validation fails, the input is blocked and sent through the **Fail** output with a justification explaining why it failed.
+
+The **Jailbreak** and **Prompt Injection** guardrails include additional heuristic detection first, and then fall back to LLM validation if needed. This additional stage identifies obvious patterns quickly and reduces API costs by avoiding unnecessary LLM calls for clear violations.
+
+The **Guardrails** component uses a language model to analyze input and can produce false positives or miss some violations.
+Use this component **in addition to** other data-sanitization best practices, such as personnel training and scripts that check for literal values or regex patterns, rather than as a sole safeguard.
+
+## Use the Guardrails component in a flow
+
+1. Connect a **Chat Input** or other text source to the **Guardrails** component's **Input Text** port.
+2. Select a **Language Model** to use for validation. The component uses the connected LLM to analyze the input text against the enabled guardrails.
+3. From the **Guardrails** dropdown, select one or more guardrails to enable.
+ For example, select **Tokens/Passwords** to block API keys and credentials.
+4. Connect the **Pass** output to components to receive validated input.
+5. Optionally, connect the **Fail** output to handle blocked inputs, such as a [**Chat Output** component](/chat-input-and-output) or [**Write File** component](/write-file).
+
+## Create custom guardrails
+
+Use the **Enable Custom Guardrail** parameter to create your own, specific guardrail validations.
+In the *Custom Guardrail Description** field, enter a natural language guardrail description of disallowed data that you want to detect.
+
+Custom guardrails can work simultaneously with the built-in guardrails, and follow the same validation process.
+
+For example, to block inputs that mention competitor names or products, enter the following in the **Custom Guardrail Description** field:
+
+```
+competitor company names, competitor product names, or references to competing services
+```
+
+When this custom guardrail is enabled, the LLM analyzes the input text against your criteria. If it detects content matching your description, such as mentions of competitors, validation fails and the input is blocked. Otherwise, validation passes and the input continues through the **Pass** output.
+
+## Guardrails parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| Language Model (`model`) | `LanguageModel` | Input parameter. Connect a **Language Model** component to use as the driver for this component. The model reviews the data, compares it against the guardrails, and determines if any data is in violation of the guardrails. |
+| API Key (`api_key`) | Secret String | Input parameter. Model provider API key. Required if the model provider needs authentication. |
+| Guardrails (`enabled_guardrails`) | Multiselect | Input parameter. Select one or more security guardrails to validate the input against. Options: `PII`, `Tokens/Passwords`, `Jailbreak`, `Offensive Content`, `Malicious Code`, `Prompt Injection`. Default: `["PII", "Tokens/Passwords", "Jailbreak"]`. |
+| Input Text (`input_text`) | Multiline String | Input parameter. The text to validate against guardrails. Accepts `Message` input types. |
+| Enable Custom Guardrail (`enable_custom_guardrail`) | Boolean | Input parameter. Enable a custom guardrail with your own validation criteria. Default: `false`. |
+| Custom Guardrail Description (`custom_guardrail_explanation`) | Multiline String | Input parameter. Describe what the custom guardrail should check for. This description is used by the LLM to validate the input. Be specific and clear about what you want to detect. Only used when `enable_custom_guardrail` is `true`. |
+| Heuristic Detection Threshold (`heuristic_threshold`) | Slider | Input parameter. Score threshold (0.0-1.0) for heuristic jailbreak/prompt injection detection. Strong patterns such as "ignore instructions" and "jailbreak" have high weights, while weak patterns such as "bypass" and "act as" have low weights. If the cumulative score meets or exceeds this threshold, the input fails immediately. Lower values are more strict. Higher values defer more cases to LLM validation. Default: `0.7`. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/if-else.mdx b/docs/versioned_docs/version-1.9.0/Components/if-else.mdx
new file mode 100644
index 0000000000..03a2c8e806
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/if-else.mdx
@@ -0,0 +1,94 @@
+---
+title: If-Else
+slug: /if-else
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+The **If-Else** component is a conditional router that routes messages by comparing two strings.
+It evaluates a condition by comparing two text inputs using the specified operator, and then routes the message to `true_result` or `false_result` depending on the evaluation.
+
+The operator looks for single strings in the input (`input_text`) based on an operator and match text (`match_text`), but it can also search for multiple words by matching a regex.
+Available operators include:
+
+- **equals**: Exact match comparison
+- **not equals**: Inverse of exact match
+- **contains**: Checks if the `match_text` is found within `input_text`
+- **starts with**: Checks if `input_text` begins with `match_text`
+- **ends with**: Checks if `input_text` ends with `match_text`
+- **regex**: Matches on a case-sensitive pattern
+
+By default, all operators are case insensitive except **regex**.
+**regex** is always case sensitive, and you can enable case sensitivity for all other operators in the [If-Else parameters](#if-else-parameters).
+
+## Use the If-Else component in a flow
+
+The following example uses the **If-Else** component to check incoming chat messages with regex matching, and then output a different response depending on whether the match evaluated to true or false.
+
+
+
+1. Add an **If-Else** component to your flow, and then configure it as follows:
+
+ * **Text Input**: Connect the **Text Input** port to a **Chat Input** component or another `Message` input.
+
+ If your input isn't in `Message` format, you can use another component to transform it, such as the [**Type Convert** component](/type-convert) or [**Parser** component](/parser).
+ If your input isn't appropriate for `Message` format, consider using another component for conditional routing, such as the [**JSON Operations** component](/data-operations).
+
+ * **Match Text**: Enter `.*(urgent|warning|caution).*` so the component looks for these values in incoming input. The regex match is case sensitive, so if you need to look for all permutations of `warning`, enter `warning|Warning|WARNING`.
+
+ * **Operator**: Select **regex**.
+
+ * **Case True**: In the [component inspection panel](/concepts-components#component-menus), enable the **Case True** parameter, click **Close**, and then enter `New Message Detected` in the field.
+
+ The **Case True** message is sent from the **True** output port when the match condition evaluates to true.
+
+ No message is set for **Case False** so the component doesn't emit a message when the condition evaluates to false.
+
+3. Depending on what you want to happen when the outcome is **True**, add components to your flow to execute that logic:
+
+ 1. Add a **Language Model**, **Prompt Template**, and **Chat Output** component to your flow.
+
+ 2. In the **Language Model** component, enter your OpenAI API key or select a different provider and model.
+
+ 3. Connect the **If-Else** component's **True** output port to the **Language Model** component's **Input** port.
+
+ 4. In the **Prompt Template** component, enter instructions for the model when the evaluation is true, such as `Send a message that a new warning, caution, or urgent message was received`.
+
+ 5. Connect the **Prompt Template** component to the **Language Model** component's **System Message** port.
+
+ 6. Connect the **Language Model** component's output to the **Chat Output** component.
+
+4. Repeat the same process with another set of **Language Model**, **Prompt Template**, and **Chat Output** components for the **False** outcome.
+
+ Connect the **If-Else** component's **False** output port to the second **Language Model** component's **Input** port.
+ In the second **Prompt Template**, enter instructions for the model when the evaluation is false, such as `Send a message that a new low-priority message was received`.
+
+5. To test the flow, open the **Playground**, and then send the flow some messages with and without your regex strings.
+The chat output should reflect the instructions in your prompts based on the regex evaluation.
+
+ ```text
+ User: A new user was created.
+
+ AI: A new low-priority message was received.
+
+ User: Sign-in warning: new user locked out.
+
+ AI: A new warning, caution, or urgent message was received. Please review it at your earliest convenience.
+ ```
+
+## If-Else parameters
+
+
+
+| Name | Type | Description |
+|----------------|----------|-------------------------------------------------------------------|
+| input_text | String | Input parameter. The primary text input for the operation. |
+| match_text | String | Input parameter. The text to compare against. |
+| operator | Dropdown | Input parameter. The operator used to compare texts. Options include `equals`, `not equals`, `contains`, `starts with`, `ends with`, and `regex`. The default is `equals`. |
+| case_sensitive | Boolean | Input parameter. When `true`, the comparison is case sensitive. The default is `false`. This setting doesn't apply to regex comparisons. |
+| max_iterations | Integer | Input parameter. The maximum number of iterations allowed for the conditional router. The default is 10. |
+| default_route | Dropdown | Input parameter. The route to take when max iterations are reached. Options include `true_result` or `false_result`. The default is `false_result`. |
+| true_result | Message | Output parameter. The output produced when the condition is true. |
+| false_result | Message | Output parameter. The output produced when the condition is false. |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/knowledge-base.mdx b/docs/versioned_docs/version-1.9.0/Components/knowledge-base.mdx
new file mode 100644
index 0000000000..fa61e0c413
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/knowledge-base.mdx
@@ -0,0 +1,46 @@
+---
+title: Knowledge Base
+slug: /knowledge-base
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialKbSummary from '@site/docs/_partial-kb-summary.mdx';
+
+
+
+The **Knowledge Base** component reads from an existing knowledge base using semantic search.
+
+The output is a [`Table`](/data-types#table) containing the top matching results from the queried knowledge base.
+
+## Knowledge Base parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| knowledge_base | Knowledge | Input parameter. Select the knowledge base to retrieve data from. |
+| api_key | Embedding Provider API Key | Input parameter. Optional API key for the embedding provider to override a previously-provided key. The embedding provider and model are chosen when you create a knowledge base. |
+| search_query | Search Query | Input parameter. Optional search query to filter knowledge base data using semantic similarity. If omitted, the top results are returned from an arbitrary sort. |
+| top_k | Top K Results | Input parameter. Number of search results to return. Default: `5`. |
+| include_metadata | Include Metadata | Input parameter. Whether to include all metadata and embeddings in the output. If enabled, each output row includes all metadata, embeddings, and content. If disabled, only the content is returned. Default: Enabled (true). |
+
+## Use the Knowledge Base component in a flow
+
+After you create and load data to a [knowledge base](/knowledge), you can use the **Knowledge Base** component in any flow to retrieve data from your knowledge base using semantic search:
+
+1. Add a **Knowledge Base** component to your flow.
+
+2. In the **Knowledge** field, select the knowledge base you want to search, such as the customer sales data knowledge base created in the previous steps.
+
+3. To view the search results as chat messages, connect the **Results** output to a **Chat Output** component.
+
+4. In **Search query**, enter a query that relates to your embedded data.
+
+ For the customer sales data example, enter a product name like `laptop` or `wireless devices`.
+
+5. Click **Run component** on the **Knowledge Base** component, and then open the **Playground** to view the output.
+
+## See also
+
+* [Manage vector data](/knowledge)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/legacy-core-components.mdx b/docs/versioned_docs/version-1.9.0/Components/legacy-core-components.mdx
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--- /dev/null
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@@ -0,0 +1,344 @@
+---
+title: Legacy core components
+slug: /legacy-core-components
+---
+
+import Icon from "@site/src/components/icon";
+import PartialLegacy from '@site/docs/_partial-legacy.mdx';
+
+
+
+## Legacy Data components
+
+The following Data components are in legacy status:
+
+* **Load CSV**
+* **Load JSON**
+
+Replace these components with the [**Read File** component](/read-file), which supports loading CSV and JSON files, as well as many other file types.
+
+## Legacy Helper components
+
+The following Helper components are in legacy status:
+
+* **Message Store**: Replaced by the [**Message History** component](/message-history)
+* **Create List**: Replace with [Processing components](/concepts-components)
+* **ID Generator**: Replace with a component that executes arbitrary code to generate an ID or embed an ID generator script your application code (external to your Langflow flows).
+* **Output Parser**: Replace with the [**Structured Output** component](/structured-output) and [**Parser** component](/parser).
+The components you need depend on the data types and complexity of the parsing task.
+
+ The **Output Parser** component transformed the output of a language model into comma-separated values (CSV) format, such as `["item1", "item2", "item3"]`, using LangChain's `CommaSeparatedListOutputParser`.
+ The **Structured Output** component is a good alternative for this component because it also formats LLM responses with support for custom schemas and more complex parsing.
+
+ **Parsing** components only provide formatting instructions and parsing functionality.
+ _They don't include prompts._
+ You must connect parsers to **Prompt Template** components to create prompts that LLMs can use.
+
+## Legacy Logic components
+
+The following Logic components are in legacy status:
+
+
+Condition
+
+As an alternative to this legacy component, see the [**If-Else** component](/if-else).
+
+The **Condition** component routes `JSON` objects based on a condition applied to a specified key, including Boolean validation.
+It supports `true_output` and `false_output` for routing the results based on the condition evaluation.
+
+This component is useful in workflows that require conditional routing of complex data structures, enabling dynamic decision-making based on data content.
+
+It can process either a single `JSON` object or a list of `JSON` objects.
+The following actions occur when processing a list of `JSON` objects:
+
+- Each object in the list is evaluated individually.
+- Objects meeting the condition go to `true_output`.
+- Objects not meeting the condition go to `false_output`.
+- If all objects go to one output, the other output is empty.
+
+The **Condition** component accepts the following parameters:
+
+| Name | Type | Description |
+|---------------|----------|---------------------------------------------|
+| data_input | JSON | Input parameter. The Data object or list of Data objects to process. This input can handle both single items and lists. |
+| key_name | String | Input parameter. The name of the key in the Data object to check. |
+| operator | Dropdown | Input parameter. The operator to apply. Options: `equals`, `not equals`, `contains`, `starts with`, `ends with`, `boolean validator`. Default: `equals`. |
+| compare_value | String | Input parameter. The value to compare against. Not shown/used when operator is `boolean validator`. |
+
+The `operator` options have the following behaviors:
+
+- `equals`: Exact match comparison between the key's value and compare_value.
+- `not equals`: Inverse of exact match.
+- `contains`: Checks if compare_value is found within the key's value.
+- `starts with`: Checks if the key's value begins with compare_value.
+- `ends with`: Checks if the key's value ends with compare_value.
+- `boolean validator`: Treats the key's value as a Boolean. The following values are considered true:
+ - Boolean `true`.
+ - Strings: `true`, `1`, `yes`, `y`, `on` (case-insensitive)
+ - Any other value is converted using Python's `bool()` function
+
+
+
+
+Pass
+
+As an alternative to this legacy component, use the [**If-Else** component](/if-else) to pass a message without modification.
+
+The **Pass** component forwards the input message without modification.
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| input_message | Input Message | Input parameter. The message to forward. |
+| ignored_message | Ignored Message | Input parameter. A second message that is ignored. Used as a workaround for continuity. |
+| output_message | Output Message | Output parameter. The forwarded message from the input. |
+
+
+
+
+Flow As Tool
+
+This component constructed a tool from a function that ran a loaded flow.
+
+It was deprecated in Langflow version 1.1.2 and replaced by the [**Run Flow** component](/run-flow).
+
+
+
+
+Sub Flow
+
+This component integrated entire flows as components within a larger workflow.
+It dynamically generated inputs based on the selected flow and executed the flow with provided parameters.
+
+It was deprecated in Langflow version 1.1.2 and replaced by the [**Run Flow** component](/run-flow).
+
+
+
+## Legacy Processing components
+
+The following Processing components are in legacy status:
+
+
+Alter Metadata
+
+Replace this legacy component with the [**JSON Operations** component](/data-operations).
+
+This component modifies metadata of input objects. It can add new metadata, update existing metadata, and remove specified metadata fields. The component works with both `Message` and `JSON` objects, and can also create a new `JSON` object from user-provided text.
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| input_value | Input | Input parameter. Objects to which Metadata should be added. |
+| text_in | User Text | Input parameter. Text input; the value is contained in the 'text' attribute of the `JSON` object. Empty text entries are ignored. |
+| metadata | Metadata | Input parameter. Metadata to add to each object. |
+| remove_fields | Fields to Remove | Input parameter. Metadata fields to remove. |
+| data | JSON | Output parameter. List of Input objects, each with added metadata. |
+
+
+
+
+Combine Data
+
+Replace this legacy component with the [**JSON Operations** component](/data-operations) or the [**Loop** component](/loop).
+
+This component combines multiple data sources into a single unified `JSON` object.
+
+The component iterates through a list of `JSON` objects, merging them into a single `JSON` object (`merged_data`).
+If the input list is empty, it returns an empty data object.
+If there's only one input data object, it returns that object unchanged.
+
+The merging process uses the addition operator to combine data objects.
+
+
+
+
+Combine Text
+
+Replace this legacy component with the [**JSON Operations** component](/data-operations).
+
+This component concatenates two text inputs into a single text chunk using a specified delimiter, outputting a `Message` object with the combined text.
+
+
+
+
+Create Data
+
+Replace this legacy component with the [**Dynamic Create Data** component](/dynamic-create-data).
+This component dynamically creates a `JSON` object with a specified number of fields and a text key.
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| number_of_fields | Number of Fields | Input parameter. The number of fields to be added to the record. |
+| text_key | Text Key | Input parameter. Key that identifies the field to be used as the text content. |
+| text_key_validator | Text Key Validator | Input parameter. If enabled, checks if the given `Text Key` is present in the given `JSON`. |
+
+
+
+
+Data to DataFrame/Data to Message
+
+Replace these legacy components with newer Processing components, such as the [**JSON Operations** component](/data-operations) and [**Type Convert** component](/type-convert).
+
+These components converted one or more `JSON` objects into a `Table` or `Message` object.
+
+For the **Data to DataFrame** component, each `JSON` object corresponds to one row in the resulting `Table`.
+Fields from the `.data` attribute become columns, and the `.text` field (if present) is placed in a `text` column.
+
+
+
+
+Extract Key
+
+Replace this legacy component with the [**JSON Operations** component](/data-operations).
+
+This component extracts a specific key from a `JSON` object and returns the value associated with that key.
+
+
+
+
+Filter Data
+
+Replace this legacy component with the [**JSON Operations** component](/data-operations).
+
+This component filters a `JSON` object based on a list of keys (`filter_criteria`), returning a new `JSON` object (`filtered_data`) that contains only the key-value pairs that match the filter criteria.
+
+
+
+
+Filter Values
+
+Replace this legacy component with the [**JSON Operations** component](/data-operations).
+
+The Filter values component filters a list of data items based on a specified key, filter value, and comparison operator.
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| input_data | Input data | Input parameter. The list of data items to filter. |
+| filter_key | Filter Key | Input parameter. The key to filter on. |
+| filter_value | Filter Value | Input parameter. The value to filter by. |
+| operator | Comparison Operator | Input parameter. The operator to apply for comparing the values. |
+| filtered_data | Filtered data | Output parameter. The resulting list of filtered data items. |
+
+
+
+
+JSON Cleaner
+
+Replace this legacy component with the [**Parser** component](/parser).
+
+This component cleans JSON strings to ensure they are fully compliant with the JSON specification.
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| json_str | JSON String | Input parameter. The JSON string to be cleaned. This can be a raw, potentially malformed JSON string produced by language models or other sources that may not fully comply with JSON specifications. |
+| remove_control_chars | Remove Control Characters | Input parameter. If set to `True`, this option removes control characters (ASCII characters 0-31 and 127) from the JSON string. This can help eliminate invisible characters that might cause parsing issues or make the JSON invalid. |
+| normalize_unicode | Normalize Unicode | Input parameter. When enabled, this option normalizes Unicode characters in the JSON string to their canonical composition form (NFC). This ensures consistent representation of Unicode characters across different systems and prevents potential issues with character encoding. |
+| validate_json | Validate JSON | Input parameter. If set to `True`, this option attempts to parse the JSON string to ensure it is well-formed before applying the final repair operation. It raises a ValueError if the JSON is invalid, allowing for early detection of major structural issues in the JSON. |
+| output | Cleaned JSON String | Output parameter. The resulting cleaned, repaired, and validated JSON string that fully complies with the JSON specification. |
+
+
+
+
+Message to Data
+
+Replace this legacy component with the [**Type Convert** component](/type-convert).
+
+This component converts `Message` objects to `JSON` objects.
+
+
+
+
+Parse DataFrame
+
+Replace this legacy component with the [**Table Operations** component](/dataframe-operations) or [**Parser** component](/parser).
+
+This component converts `Table` objects into plain text using templates.
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| df | Table | Input parameter. The DataFrame to convert to text rows. |
+| template | Template | Input parameter. Template for formatting (use `{column_name}` placeholders). |
+| sep | Separator | Input parameter. String to join rows in output. |
+| text | Text | Output parameter. All rows combined into single text. |
+
+
+
+
+Parse JSON
+
+Replace this legacy component with the [**Parser** component](/parser).
+
+This component converts and extracts JSON fields in `Message` and `JSON` objects using JQ queries, then returns `filtered_data`, which is a list of `JSON` objects.
+
+
+
+
+Regex Extractor
+
+Replace this legacy component with the [**Parser** component](/parser).
+
+This component extracts patterns in text using regular expressions. It can be used to find and extract specific patterns or information in text.
+
+
+
+
+Select Data
+
+Replace this legacy component with the [**JSON Operations** component](/data-operations).
+
+This component selects a single `JSON` object from a list.
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| data_list | Data List | Input parameter. List of data to select from |
+| data_index | Data Index | Input parameter. Index of the data to select |
+| selected_data | Selected Data | Output parameter. The selected `JSON` object. |
+
+
+
+
+Update Data
+
+Replace this legacy component with the [**JSON Operations** component](/data-operations).
+
+This component dynamically updates or appends data with specified fields.
+
+It accepts the following parameters:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| old_data | JSON | Input parameter. The records to update. |
+| number_of_fields | Number of Fields | Input parameter. The number of fields to add. The maximum is 15. |
+| text_key | Text Key | Input parameter. The key for text content. |
+| text_key_validator | Text Key Validator | Input parameter. Validates the text key presence. |
+| data | JSON | Output parameter. The updated Data objects. |
+
+
+
+## Legacy Tools components
+
+The following Tools components are in legacy status:
+
+* **Calculator Tool**: Replaced by the [**Calculator** component](/calculator).
+* **Python Code Structured**: Replaced by the [**Python Interpreter** component](/python-interpreter).
+* **Python REPL**: Replaced by the [**Python Interpreter** component](/python-interpreter).
+* **Search API**: Replaced by the [**SearchApi** bundle](/bundles-searchapi).
+* **SearXNG Search**: No direct replacement. Use another provider's search component, create a custom component, or use a core component like the [**API Request** component](/api-request).
+* **Serp Search API**: Replace by the **SerpApi** bundle.
+* **Tavily Search API**: Replaced by the **Tavily** bundle.
+* **Wikidata API**: Replaced by the [**Wikipedia** bundle](/bundles-wikipedia).
+* **Wikipedia API**: Replaced by the [**Wikipedia** bundle](/bundles-wikipedia).
+* **Yahoo! Finance**: Replaced by the **Yahoo! Search** bundle.
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/llm-selector.mdx b/docs/versioned_docs/version-1.9.0/Components/llm-selector.mdx
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+---
+title: LLM Selector
+slug: /llm-selector
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+:::tip
+Prior to Langflow 1.7, this component was called the **LLM Router**.
+:::
+
+The **LLM Selector** component routes requests to the most appropriate LLM based on [OpenRouter](https://openrouter.ai/docs/quickstart) model specifications.
+
+To use the component in a flow, you connect multiple language model components to the **LLM Selector** components.
+One model is the judge LLM that analyzes input messages to understand the evaluation context, selects the most appropriate model from the other attached LLMs, and then routes the input to the selected model.
+The selected model processes the input, and then returns the generated response.
+
+The following example flow has three language model components.
+One is the judge LLM, and the other two are in the LLM pool for request routing.
+The input and output components create a seamless chat interaction where you send a message and receive a response without any user awareness of the underlying routing.
+
+
+
+## LLM Selector parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| `models` | **Language Models** | Input parameter. Connect [`LanguageModel`](/data-types#languagemodel) output from multiple [language model components](/components-models) to create a pool of models. The `judge_llm` selects models from this pool when routing requests. The first model you connect is the default model if there is a problem with model selection or routing. |
+| `input_value` | **Input** | Input parameter. The incoming query to be routed to the model selected by the judge LLM. |
+| `judge_llm` | **Judge LLM** | Input parameter. Connect `LanguageModel` output from _one_ **Language Model** component to serve as the judge LLM for request routing. |
+| `optimization` | **Optimization** | Input parameter. Set a preferred characteristic for model selection by the judge LLM. The options are `quality` (highest response quality), `speed` (fastest response time), `cost` (most cost-effective model), or `balanced` (equal weight for quality, speed, and cost). Default: `balanced` |
+| `use_openrouter_specs` | **Use OpenRouter Specs** | Input parameter. Whether to fetch model specifications from the OpenRouter API. If `false`, only the model name is provided to the judge LLM. Default: Enabled (`true`) |
+| `timeout` | **API Timeout** | Input parameter. Set a timeout duration in seconds for API requests made by the router. Default: `10` |
+| `fallback_to_first` | **Fallback to First Model** | Input parameter. Whether to use the first LLM in `models` as a backup if routing fails to reach the selected model. Default: Enabled (`true`) |
+
+## LLM Selector outputs
+
+The **LLM Selector** component provides three output options.
+You can set the desired output type near the component's output port.
+
+* **Output**: A `Message` containing the response to the original query as generated by the selected LLM.
+Use this output for regular chat interactions.
+
+* **Selected Model Info**: A `JSON` object containing information about the selected model, such as its name and version.
+
+* **Routing Decision**: A `Message` containing the judge model's reasoning for selecting a particular model, including input query length and number of models considered.
+For example:
+
+ ```text
+ Model Selection Decision:
+ - Selected Model Index: 0
+ - Selected Langflow Model Name: gpt-4o-mini
+ - Selected API Model ID (if resolved): openai/gpt-4o-mini
+ - Optimization Preference: cost
+ - Input Query Length: 27 characters (~5 tokens)
+ - Number of Models Considered: 2
+ - Specifications Source: OpenRouter API
+ ```
+
+ This is useful for debugging if you feel the judge model isn't selecting the best model.
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/loop.mdx b/docs/versioned_docs/version-1.9.0/Components/loop.mdx
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@@ -0,0 +1,71 @@
+---
+title: Loop
+slug: /loop
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+The **Loop** component iterates over a list of input by passing individual items to other components attached at the **Item** output port until there are no items left to process.
+Then, the **Loop** component passes the aggregated result of all looping to the component connected to the **Done** port.
+
+## The looping process
+
+The **Loop** component is like a miniature flow within your flow.
+Here's a breakdown of the looping process:
+
+1. Accepts a list of [`JSON`](/data-types#json) or [`Table`](/data-types#table) objects, such as a CSV file, through the **Loop** component's **Inputs** port.
+
+2. Splits the input into individual items. For example, a CSV file is broken down by rows.
+
+ Specifically, the **Loop** component repeatedly extracts items by `text` key in the `JSON` or `Table` objects until there are no more items to extract.
+ Each `item` output is a `JSON` objects.
+
+3. Iterates over each `item` by passing them to the **Item** output port.
+
+ This port connects to one or more components that perform actions on each item.
+ The final component in the **Item** loop connects back to the **Loop** component's **Looping** port to process the next item.
+
+ Only one component connects to the **Item** port, but you can pass the data through as many components as you need, as long as the last component in the chain connects back to the **Looping** port.
+
+ The [**If-Else** component](/if-else) isn't compatible with the **Loop** component.
+ For more information, see [Conditional looping](#conditional-looping).
+
+4. After processing all items, the results are aggregated into a single `JSON` object that is passed from the **Loop** component's **Done** port to the next component in the flow.
+
+The following simplified Python code summarizes how the **Loop** component works.
+This _isn't_ the actual component code; it is only meant to help you understand the general process.
+
+```python
+for i in input: # Receive input data as a list
+ process_item(i) # Process each item through components connected at the Item port
+ if has_more_items():
+ continue # Loop back to Looping port to process the next item
+ else:
+ break # Exit the loop when no more items are left
+
+done = aggregate_results() # Compile all returned items
+
+print(done) # Send the aggregated results from the Done port to another component
+```
+
+## Loop example
+
+In the follow example, the **Loop** component iterates over a CSV file until there are no rows left to process.
+In this case, the **Item** port passes each row to a **Type Convert** component to convert the row into a `Message` object, passes the `Message` to a **Structured Output** component to be processed into structured data that is then passed back to the **Loop** component's **Looping** port.
+After processing all rows, the **Loop** component loads the aggregated list of structured data into a Chroma DB database through the **Chroma DB** component connected to the **Done** port.
+
+
+
+:::tip
+For more examples of the **Loop** component, try the **Research Translation Loop** template in Langflow, or see the video tutorial [Mastering the Loop Component & Agentic RAG in Langflow](https://www.youtube.com/watch?v=9Wx7WODSKTo).
+:::
+
+## Conditional looping
+
+The [**If-Else** component](/if-else) isn't compatible with the **Loop** component.
+If you need conditional loop events, redesign your flow to process conditions before the loop.
+For example, if you are looping over a `Table`, you could use multiple [**Table Operations** components](/dataframe-operations) to conditionally filter data, and then run separate loops on each set of filtered data.
+
+
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/mcp-tools.mdx b/docs/versioned_docs/version-1.9.0/Components/mcp-tools.mdx
new file mode 100644
index 0000000000..38ffd430f7
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+++ b/docs/versioned_docs/version-1.9.0/Components/mcp-tools.mdx
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+---
+title: MCP Tools
+slug: /mcp-tools
+---
+
+import PartialMcpToolsAddSteps from '@site/docs/_partial-mcp-tools-add-steps.mdx';
+
+The **MCP Tools** component connects to a Model Context Protocol (MCP) server and exposes the MCP server's functions as tools for Langflow agents to use to respond to input.
+
+In addition to publicly available MCP servers and your own custom-built MCP servers, you can connect Langflow MCP servers, which allow your agent to use your Langflow flows as tools.
+To do this, use the **MCP Tools** component's [HTTP/SSE mode](/mcp-client#mcp-http-mode) to connect to your Langflow project's MCP server.
+
+
+
+For more information, see [Use Langflow as an MCP client](/mcp-client) and [Use Langflow as an MCP server](/mcp-server).
+
+:::tip Tool mode is missing
+If you're upgrading an existing flow and the **MCP Tools** component loses its **Tool Mode** option, see [MCP Tools component loses Tool Mode option after upgrading flows](/troubleshoot#mcp-tools-component-loses-tool-mode-option-after-upgrading-flows).
+:::
+
+## MCP Tools parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| mcp_server | String | Input parameter. The MCP server to connect to. Select from previously configured servers or add a new one. |
+| tool | String | Input parameter. The specific tool to execute from the connected MCP server. Leave blank to allow access to all tools. |
+| use_cache | Boolean | Input parameter. Enable caching of MCP server and tools to improve performance. Default: `false`. |
+| verify_ssl | Boolean | Input parameter. Enable SSL certificate verification for HTTPS connections. Default: `true`. |
+| response | Table | Output parameter. [`Table`](/data-types#table) containing the response from the executed tool. |
+
+
+Earlier versions of the MCP Tools component
+
+* In Langflow version 1.5, the **MCP Connection** component was renamed to the **MCP Tools** component.
+* In Langflow version 1.3, the **MCP Tools (stdio)** and **MCP Tools (SSE)** components were removed and replaced by the unified **MCP Connection** component, which was later renamed to **MCP Tools**.
+
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/message-history.mdx b/docs/versioned_docs/version-1.9.0/Components/message-history.mdx
new file mode 100644
index 0000000000..43bed71a1b
--- /dev/null
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@@ -0,0 +1,194 @@
+---
+title: Message History
+slug: /message-history
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+The **Message History** component provides combined chat history and message storage functionality.
+It can store and retrieve chat messages from either [Langflow storage](/memory) _or_ a dedicated chat memory database like Mem0 or Redis.
+
+:::tip
+The **Agent** component has built-in chat memory that is enabled by default and uses Langflow storage.
+This built-in chat memory functionality is sufficient for most use cases.
+
+Use the **Message History** component for the following use cases:
+
+* You need to store and retrieve chat memory for a language model component (not an agent).
+* You need to retrieve chat memories outside the chat context, such as a sentiment analysis flow that retrieves and analyzes recently stored memories.
+* You want to store memories in a specific database that is separate from Langflow storage.
+
+For more information, see [Store chat memory](/memory#store-chat-memory).
+:::
+
+## Use the Message History component in a flow
+
+The **Message History** component has two modes, depending on where you want to use it in your flow:
+
+* **Retrieve mode**: The component retrieves chat messages from your Langflow database or external memory.
+* **Store mode**: The component stores chat messages in your Langflow database or external memory.
+
+This means that you need multiple **Message History** components in your flow if you want to both store and retrieve chat messages.
+
+
+
+
+The following steps explain how to create a chat-based flow that uses **Message History** components to store and retrieve chat memory from your Langflow installation's database:
+
+1. Create or edit a flow where you want to use chat memory.
+
+2. At the beginning of the flow, add a **Message History** component, and then set it to **Retrieve** mode.
+
+3. Optional: To enable parameters for memory sorting, filtering, and limits, click the **Message History** component to expose the [component inspection panel](/concepts-components#component-menus).
+
+4. Add a **Prompt Template** component, add a `{memory}` variable to the **Template** field, and then connect the **Message History** output to the **memory** input.
+
+ The **Prompt Template** component supplies instructions and context to LLMs, separate from chat messages passed through a **Chat Input** component.
+ The template can include any text and variables that you want to supply to the LLM, for example:
+
+ ```text
+ You are a helpful assistant that answers questions.
+
+ Use markdown to format your answer, properly embedding images and urls.
+
+ History:
+
+ {memory}
+ ```
+
+ Variables (`{variable}`) in the template dynamically add fields to the **Prompt Template** component so that your flow can receive definitions for those values from elsewhere, such as other components, Langflow global variables, or runtime input.
+ For more information, see [Define variables in prompts](/components-prompts#define-variables-in-prompts).
+
+ In this example, the `{memory}` variable is populated by the retrieved chat memories, which are then passed to a **Language Model** or **Agent** component to provide additional context to the LLM.
+
+5. Connect the **Prompt Template** component's output to a **Language Model** component's **System Message** input.
+
+ This example uses the **Language Model** core component as the central chat driver, but you can also use another language model component or the **Agent** component.
+
+6. Add a **Chat Input** component, and then connect it to the **Language Model** component's **Input** field.
+
+7. Connect the **Language Model** component's output to a **Chat Output** component.
+
+8. At the end of the flow, add another **Message History** component, and then set it to **Store** mode.
+
+ Configure any additional parameters in the second **Message History** component as needed, taking into consideration that this particular component will store chat messages rather than retrieve them.
+
+9. Connect the **Chat Output** component's output to the **Message History** component's **Message** input.
+
+ Each response from the LLM is output from the **Language Model** component to the **Chat Output** component, and then stored in chat memory by the final **Message History** component.
+
+
+
+
+To store and retrieve chat memory from a dedicated, external chat memory database, use the **Message History** component _and_ a provider-specific chat memory component.
+
+The following steps explain how to create a flow that stores and retrieves chat memory from a [**Redis Chat Memory** component](/bundles-redis).
+Other options include the [**Mem0 Chat Memory** component](/bundles-mem0) and [**Cassandra Chat Memory** component](/bundles-cassandra#cassandra-chat-memory).
+
+1. Create or edit a flow where you want to use chat memory.
+
+2. At the beginning of the flow, add a **Message History** component and a **Redis Chat Memory** component:
+
+ 1. Configure the **Redis Chat Memory** component to connect to your Redis database. For more information, see the [Redis documentation](https://redis.io/docs/latest/).
+ 2. Set the **Message History** component to **Retrieve** mode.
+ 3. In the **Message History** [component inspection pane](/concepts-components#component-menus), enable **External Memory**.
+
+ In **Controls**, you can also enable parameters for memory sorting, filtering, and limits.
+
+ 4. Connect the **Redis Chat Memory** component's output to the **Message History** component's **External Memory** input.
+
+3. Add a **Prompt Template** component, add a `{memory}` variable to the **Template** field, and then connect the **Message History** output to the **memory** input.
+
+ The **Prompt Template** component supplies instructions and context to LLMs, separate from chat messages passed through a **Chat Input** component.
+ The template can include any text and variables that you want to supply to the LLM, for example:
+
+ ```text
+ You are a helpful assistant that answers questions.
+
+ Use markdown to format your answer, properly embedding images and urls.
+
+ History:
+
+ {memory}
+ ```
+
+ Variables (`{variable}`) in the template dynamically add fields to the **Prompt Template** component so that your flow can receive definitions for those values from elsewhere, such as other components, Langflow global variables, or runtime input.
+ For more information, see [Define variables in prompts](/components-prompts#define-variables-in-prompts).
+
+ In this example, the `{memory}` variable is populated by the retrieved chat memories, which are then passed to a **Language Model** or **Agent** component to provide additional context to the LLM.
+
+4. Connect the **Prompt Template** component's output to a **Language Model** component's **System Message** input.
+
+ This example uses the **Language Model** core component as the central chat driver, but you can also use another language model component or the **Agent** component.
+
+5. Add a **Chat Input** component, and then connect it to the **Language Model** component's **Input** input.
+
+6. Connect the **Language Model** component's output to a **Chat Output** component.
+
+7. At the end of the flow, add another pair of **Message History** and **Redis Chat Memory** components:
+
+ 1. Configure the **Redis Chat Memory** component to connect to your Redis database.
+ 2. Set the **Message History** component to **Store** mode.
+ 3. In the **Message History** [component inspection pane](/concepts-components#component-menus), enable **External Memory**.
+
+ Configure any additional parameters in this component as needed, taking into consideration that this particular component will store chat messages rather than retrieve them.
+
+ 4. Connect the **Redis Chat Memory** component to the **Message History** component's **External Memory** input.
+
+8. Connect the **Chat Output** component's output to the **Message History** component's **Message** input.
+
+ Each response from the LLM is output from the **Language Model** component to the **Chat Output** component, and then stored in chat memory by passing it to the final **Message History** and **Redis Chat Memory** components.
+
+
+
+
+
+
+## Message History parameters
+
+
+
+The available parameters depend on whether the component is in **Retrieve** or **Store** mode.
+
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| **Template** (`template`) | String | Input parameter. The template to use for formatting the data. It can contain the keys `{text}`, `{sender}` or any other key in the message data. |
+| **External Memory** (`memory`) | External Memory | Input parameter. Retrieve messages from an external memory. If empty, Langflow storage is used. |
+| **Number of Messages** (`n_messages`) | Integer | Input parameter. The number of messages to retrieve. Default: 100. |
+| **Order** (`order`) | String | Input parameter. The order of the messages. Default: `Ascending`. |
+| **Sender Type** (`sender_type`) | String | Input parameter. Filter by sender type, one of `User`, `Machine`, or `Machine and User` (default). |
+| **Session ID** (`session_id`) | String | Input parameter. The [session ID](/session-id) of the chat memories to retrieve. If omitted or empty, the current session ID for the flow run is used. |
+
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| **Template** (`template`) | String | Input parameter. The template to use for formatting the data. It can contain the keys `{text}`, `{sender}` or any other key in the message data. |
+| **Message** (`message`) | String | Input parameter. The message to store, typically provided by connecting a **Chat Output** component. |
+| **External Memory** (`memory`) | External Memory | Input parameter. Store messages in external memory. If empty, Langflow storage is used. |
+| **Sender** (`sender`) | String | Input parameter. Choose which messages to store based on sender, one of `User`, `Machine`, or `Machine and User` (default). |
+| **Sender Name** (`sender_name`) | String | Input parameter. A backup `sender` label to use if a message doesn't have sender metadata. |
+| **Session ID** (`session_id`) | String | Input parameter. The [session ID](/session-id) of the chat memories to store. If omitted or empty, the current session ID for the flow run is used. Use custom session IDs if you need to segregate chat memory for different users or applications that run the same flow. |
+| **Sender Type** (`sender_type`) | String | Input parameter. Filter by sender type, one of `User`, `Machine`, or `Machine and User` (default). |
+
+
+
+
+## Message History output
+
+Memories can be retrieved in one of two formats:
+
+* **Message**: Retrieve memories as `Message` objects, including `messages_text` containing retrieved chat message text.
+This is the typical output format used to pass memories _as chat messages_ to another component.
+
+* **DataFrame**: Returns memories as a `Table` containing the message data.
+Useful for cases where you need to retrieve memories in a tabular format rather than as chat messages.
+
+You can set the output type near the component's output port.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/mock-data.mdx b/docs/versioned_docs/version-1.9.0/Components/mock-data.mdx
new file mode 100644
index 0000000000..ce136daf43
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/mock-data.mdx
@@ -0,0 +1,18 @@
+---
+title: Mock Data
+slug: /mock-data
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+The **Mock Data** component generates sample data for testing and development.
+You can select these output types:
+
+* `message_output`: A [Message (text)](/data-types#message) output with Lorem Ipsum sample text.
+* `data_output`: A [Data (JSON)](/data-types#json) object containing a JSON structure with one sample record under `records` and a `summary` section.
+* `dataframe_output`: A [DataFrame (tabular)](/data-types#table) with 50 mock records, including columns such as `customer_id`, `first_name`, and `last_name`.
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/notify-and-listen.mdx b/docs/versioned_docs/version-1.9.0/Components/notify-and-listen.mdx
new file mode 100644
index 0000000000..310eb75604
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/notify-and-listen.mdx
@@ -0,0 +1,15 @@
+---
+title: Notify and Listen
+slug: /notify-and-listen
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+The **Notify** and **Listen** components are used together.
+
+The **Notify** component builds a notification from the current flow's context, including specific data content and a status identifier.
+
+The resulting notification is sent to the **Listen** component.
+The notification data can then be passed to other components in the flow, such as the [**If-Else** component](/if-else).
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/parser.mdx b/docs/versioned_docs/version-1.9.0/Components/parser.mdx
new file mode 100644
index 0000000000..99d7b23c5e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/parser.mdx
@@ -0,0 +1,142 @@
+---
+title: Parser
+slug: /parser
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialCurlyBraces from '@site/docs/_partial-escape-curly-braces.mdx';
+
+The **Parser** component extracts text from structured data (`Table` or `JSON`) using a template or direct stringification.
+The output is a `Message` containing the parsed text.
+
+This is a versatile component for data extraction and manipulation in your flows.
+For examples of **Parser** components in flows, see the following:
+
+* [**Batch Run** component example](/batch-run)
+* [**Structured Output** component example](/structured-output)
+* **Financial Report Parser** template
+* [Trigger flows with webhooks](/webhook)
+* [Create a vector RAG chatbot](/chat-with-rag)
+
+
+
+## Parsing modes
+
+The **Parser** component has two modes: **Parser** and **Stringify**.
+
+
+
+
+In **Parser** mode, you create a template for text output that can include literal strings and variables for extracted keys.
+
+Use curly braces to define variables anywhere in the template.
+Variables must match keys in the `Table` or `JSON` input, such as column names.
+For example, `{name}` extracts the value of a `name` key.
+For more information about the content and structure of `Table` and `JSON` objects, see [Langflow data types](/data-types).
+
+
+
+When the flow runs, the **Parser** component iterates over the input, producing a `Message` for each parsed item.
+For example, parsing a `Table` creates a `Message` for each row, populated with the unique values from that row.
+
+
+Employee summary template
+
+This example template extracts employee data into a natural language summary about an employee's hire date and current role:
+
+```text
+{employee_first_name} {employee_last_name} was hired on {start_date}.
+Their current position is {job_title} ({grade}).
+```
+
+The resulting `Message` output replaces the variables with the corresponding extracted values.
+For example:
+
+```text
+Renlo Kai was hired on 11-July-2017.
+Their current position is Software Engineer (Principal).
+```
+
+
+
+
+Employee profile template
+
+This example template uses Markdown syntax and extracted employee data to create an employee profile:
+
+```text
+# Employee Profile
+## Personal Information
+- **Name:** {name}
+- **ID:** {id}
+- **Email:** {email}
+```
+
+When the flow runs, the **Parser** component iterates over each row of the `Table`, populating the template's variables with the appropriate extracted values.
+The resulting text for each row is output as a [`Message`](/data-types#message).
+
+
+
+The following parameters are available in **Parser** mode.
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| input_data | JSON or Table | Input parameter. The `JSON` or `Table` input to parse. |
+| pattern | Template | Input parameter. The formatting template using plaintext and variables for keys (`{KEY_NAME}`). See the preceding examples for more information. |
+| sep | Separator | Input parameter. A string defining the separator for rows or lines. Default: `\n` (new line). |
+| clean_data | Clean Data | Whether to remove empty rows and lines in each cell or key of the `Table` or `JSON` input. Default: Enabled (`true`) |
+
+
+
+
+Use **Stringify** mode to convert the entire input directly to text.
+This mode doesn't support templates or key selection.
+
+The following parameters are available in **Stringify** mode.
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| input_data | JSON or Table | Input parameter. The `JSON` or `Table` input to parse. |
+| sep | Separator | Input parameter. A string defining the separator for rows or lines. Default: `\n` (new line). |
+| clean_data | Clean Data | Whether to remove empty rows and lines in each cell or key of the `Table` or `JSON` input. Default: Enabled (`true`) |
+
+
+
+
+## Test and troubleshoot parsed text
+
+To test the **Parser** component, click **Run component**, and then click **Inspect output** to see the `Message` output with the parsed text.
+You can also connect a **Chat Output** component if you want to view the output in the **Playground**.
+
+If the `Message` output from the **Parser** component has empty or unexpected values, there might be a mapping error between the input and the parsing mode, the input has empty values, or the input isn't suitable for plaintext extraction.
+
+For example, assume you use the following template to parse a `Table`:
+
+```text
+{employee_first_name} {employee_last_name} is a {job_title} ({grade}).
+```
+
+The following `Message` could result from parsing a row where `employee_first_name` was empty and `grade` was `null`:
+
+```text
+ Smith is a Software Engineer (null).
+```
+
+To troubleshoot missing or unexpected values, you can do the following:
+
+* Make sure the variables in your template map to keys in the incoming `JSON` or `Table`.
+To see the data being passed directly to the **Parser** component, click **Inspect output** on the component that is sending data to the **Parser** component.
+
+* Check the source data for missing or incorrect values.
+There are several ways you can address these inconsistencies:
+
+ * Rectify the source data directly.
+ * Use other components to amend or filter anomalies before passing the data to the **Parser** component.
+ There are many components you can use for this depending on your goal, such as the [**JSON Operations** component](/data-operations), [**Structured Output** component](/structured-output), and [**Smart Transform** component](/smart-transform).
+ * Enable the **Parser** component's **Clean Data** parameter to skip empty rows or lines.
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/policies.mdx b/docs/versioned_docs/version-1.9.0/Components/policies.mdx
new file mode 100644
index 0000000000..12809fc966
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/policies.mdx
@@ -0,0 +1,32 @@
+---
+title: Policies (Beta)
+slug: /policies
+---
+
+The **Policies** component leverages [ToolGuard](https://github.com/AgentToolkit/toolguard) to automatically generate guard code that validates tool execution against defined business policies.
+
+With this component, developers define business policies in natural language and integrate policy enforcement into agent workflows. It automatically generates validation code for tools from those policies, protects tool execution by enforcing policy compliance at runtime, and caches generated guard code for better performance.
+
+The component supports **Generate** activity, which runs ToolGuard’s buildtime flow to produce new guard code from policies, and **Guard** activity, which reuses previously generated guard code for faster runs.
+
+## Use the component in a flow
+
+When `enabled` is `true`, you must supply at least one policy before generating guards.
+Generated guard code is written under `tmp_toolguard/{project_name}/`, with **Step 1** producing guard specifications from policies in `Step_1/`, and **Step 2** turning those specifications into executable guard code in `Step_2/`.
+If you switch to **Guard** (apply cached guards), the project directory must already contain valid generated guard code. The component handles module caching and cleanup automatically.
+
+For more information, see the [ToolGuard GitHub repository](https://github.com/AgentToolkit/toolguard).
+
+## Policies parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| enabled | Boolean | Input parameter. If `true`, ToolGuard runs before tool execution; if `false`, policy validation is skipped. |
+| mode | String | Input parameter. **Activity**: **Generate** runs buildtime to create guard code, or **Guard** loads existing guards from the project folder. |
+| project | String | Input parameter. Folder name for generated code under `tmp_toolguard/` (default `my_project`). |
+| in_tools | List[Tool] | Input parameter. Tools the agent can use; they are wrapped with policy guards when enabled. |
+| policies | List[String] | Input parameter. One or more clear, self-contained business policy strings. Required when generating guards. |
+| model | Model | Input parameter. LLM used for Policies buildtime; Anthropic Claude Sonnet is recommended. Required when generating guards. |
+| api_key | String | Input parameter. Model provider API key (advanced). Required when generating guards. |
+| guarded_tools | List[Tool] | Output parameter. Tools with policy enforcement applied. Returns the original tools when the component is disabled. |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/python-interpreter.mdx b/docs/versioned_docs/version-1.9.0/Components/python-interpreter.mdx
new file mode 100644
index 0000000000..88ca480a12
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/python-interpreter.mdx
@@ -0,0 +1,239 @@
+---
+title: Python Interpreter
+slug: /python-interpreter
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+This component allows you to execute Python code with imported packages.
+
+The **Python Interpreter** component can only import packages that are already installed in your Langflow environment.
+If you encounter an `ImportError` when trying to use a package, you need to install it first.
+
+To install custom packages, see [Install custom dependencies](/install-custom-dependencies).
+
+## Use the Python Interpreter in a flow
+
+1. To use this component in a flow, in the **Global Imports** field, add the packages you want to import as a comma-separated list, such as `math,pandas`.
+At least one import is required.
+2. In the **Python Code** field, enter the Python code you want to execute. Use `print()` to see the output.
+3. Optional: Enable **Tool Mode**, and then connect the **Python Interpreter** component to an **Agent** component as a tool.
+For example, connect a **Python Interpreter** component and a [**Calculator** component](/calculator) as tools for an **Agent** component, and then test how it chooses different tools to solve math problems.
+
+4. Ask the agent an easier math question.
+The **Calculator** tool can add, subtract, multiple, divide, or perform exponentiation.
+The agent executes the `evaluate_expression` tool to correctly answer the question.
+
+Result:
+```text
+Executed evaluate_expression
+Input:
+{
+ "expression": "2+5"
+}
+Output:
+{
+ "result": "7"
+}
+```
+
+5. Give the agent complete Python code.
+This example creates a Pandas DataFrame table with the imported `pandas` packages, and returns the square root of the mean squares.
+
+```python
+import pandas as pd
+import math
+
+# Create a simple DataFrame
+df = pd.DataFrame({
+ 'numbers': [1, 2, 3, 4, 5],
+ 'squares': [x**2 for x in range(1, 6)]
+})
+
+# Calculate the square root of the mean
+result = math.sqrt(df['squares'].mean())
+print(f"Square root of mean squares: {result}")
+```
+
+The agent correctly chooses the `run_python_repl` tool to solve the problem.
+
+Result:
+```text
+Executed run_python_repl
+
+Input:
+
+{
+ "python_code": "import pandas as pd\nimport math\n\n# Create a simple DataFrame\ndf = pd.DataFrame({\n 'numbers': [1, 2, 3, 4, 5],\n 'squares': [x**2 for x in range(1, 6)]\n})\n\n# Calculate the square root of the mean\nresult = math.sqrt(df['squares'].mean())\nprint(f\"Square root of mean squares: {result}\")"
+}
+Output:
+
+{
+ "result": "Square root of mean squares: 3.3166247903554"
+}
+```
+
+If you don't include the package imports in the chat, the agent can still create the table using `pd.DataFrame`, because the `pandas` package is imported globally by the **Python Interpreter** component in the **Global Imports** field.
+
+## Pass inputs to the Python Interpreter
+
+To pass inputs to the **Python Interpreter** component, you need to customize the component's code to add input fields.
+After the input field is added to the component code, the port becomes available for connections.
+For example, to connect a [**Text** component](/text-input-and-output) and pass a URL value to the **Python Interpreter** component, do the following:
+
+1. Add a **Python Interpreter** component to your flow.
+2. To modify the **Python Interpreter** component's code, click **Edit Code**.
+3. To pass a URL input to the **Python Interpreter** component, make the following changes to the code:
+
+ a. Add the URL input field to the `inputs` list. This creates the input port that other components can connect to.
+
+ b. Update the `get_globals` method to extract the URL value and add it to the globals dictionary.
+ This makes the `url` variable available in the component's Python code.
+
+ c. Update the default Python code value to use the `url` variable.
+
+ The following example demonstrates these modifications.
+
+
+ Python code example
+
+ ```python
+ import importlib
+
+ from langchain_experimental.utilities import PythonREPL
+ from lfx.custom.custom_component.component import Component
+ from lfx.io import MultilineInput, Output, StrInput
+ from lfx.schema.data import Data
+ from lfx.schema.message import Message # Needed to extract text from Message objects
+
+ class PythonREPLComponent(Component):
+ display_name = "Python Interpreter"
+ description = "Run Python code with optional imports. Use print() to see the output."
+ documentation: str = "https://docs.langflow.org/python-interpreter"
+ icon = "square-terminal"
+
+ inputs = [
+ StrInput(
+ name="global_imports",
+ display_name="Global Imports",
+ info="A comma-separated list of modules to import globally, e.g. 'math,numpy,pandas'.",
+ value="math,pandas",
+ required=True,
+ ),
+ MultilineInput(
+ name="python_code",
+ display_name="Python Code",
+ info="The Python code to execute. Only modules specified in Global Imports can be used. Use 'url' variable if URL input is connected.",
+ value="print(f'URL: {url}')", # Updated to make the URL variable available to the Python code execution
+ input_types=["Message"],
+ tool_mode=True,
+ required=True,
+ ),
+ # Add the URL input field to inputs list
+ StrInput(
+ name="url",
+ display_name="URL",
+ info="URL variable that can be used in Python code. Connect a Text component or enter manually.",
+ value="",
+ input_types=["Text", "Message"],
+ required=False,
+ ),
+ ]
+
+ outputs = [
+ Output(
+ display_name="Results",
+ name="results",
+ type_=Data,
+ method="run_python_repl",
+ ),
+ ]
+
+ def get_globals(self, global_imports: str | list[str]) -> dict:
+ """Create a globals dictionary with only the specified allowed imports and input variables."""
+ global_dict = {}
+
+ try:
+ if isinstance(global_imports, str):
+ modules = [module.strip() for module in global_imports.split(",")]
+ elif isinstance(global_imports, list):
+ modules = global_imports
+ else:
+ msg = "global_imports must be either a string or a list"
+ raise TypeError(msg)
+
+ for module in modules:
+ try:
+ imported_module = importlib.import_module(module)
+ global_dict[imported_module.__name__] = imported_module
+ except ImportError as e:
+ msg = f"Could not import module {module}: {e!s}"
+ raise ImportError(msg) from e
+
+ # Add the URL variable to the component's globals dictionary
+ # Extract from Message object or use the string directly
+ if hasattr(self, "url") and self.url:
+ url_value = self.url.text if isinstance(self.url, Message) else str(self.url)
+ if url_value:
+ global_dict["url"] = url_value # Makes 'url' available in Python code
+ self.log(f"URL variable set: {url_value}")
+
+ except Exception as e:
+ self.log(f"Error in global imports: {e!s}")
+ raise
+ else:
+ self.log(f"Successfully imported modules: {list(global_dict.keys())}")
+ return global_dict
+
+ def run_python_repl(self) -> Data:
+ try:
+ # Extract Python code text if it's a Message object
+ python_code_text = self.python_code
+ if isinstance(python_code_text, Message):
+ python_code_text = python_code_text.text if python_code_text.text else ""
+ elif not isinstance(python_code_text, str):
+ python_code_text = str(python_code_text)
+
+ globals_ = self.get_globals(self.global_imports)
+ python_repl = PythonREPL(_globals=globals_)
+ result = python_repl.run(python_code_text)
+ result = result.strip() if result else ""
+
+ self.log("Code execution completed successfully")
+ return Data(data={"result": result})
+
+ except ImportError as e:
+ error_message = f"Import Error: {e!s}"
+ self.log(error_message)
+ return Data(data={"error": error_message})
+
+ except SyntaxError as e:
+ error_message = f"Syntax Error: {e!s}"
+ self.log(error_message)
+ return Data(data={"error": error_message})
+
+ except (NameError, TypeError, ValueError) as e:
+ error_message = f"Error during execution: {e!s}"
+ self.log(error_message)
+ return Data(data={"error": error_message})
+
+ def build(self):
+ return self.run_python_repl
+ ```
+
+
+4. To save the modifications, click **Check & Save**.
+5. Add a **Text** component to your flow and set its value, such as `google.com`.
+6. Connect the **Text** component's output to the new **URL** input field on your customized **Python Interpreter** component.
+
+The **Python Interpreter** component can now use the `url` variable in the Python code that it executes.
+
+## Python Interpreter parameters
+
+| Name | Type | Description |
+|------|------|-------------|
+| global_imports | String | Input parameter. A comma-separated list of modules to import globally, such as `math,pandas,numpy`. |
+| python_code | Code | Input parameter. The Python code to execute. Only modules specified in Global Imports can be used. |
+| results | JSON | Output parameter. The output of the executed Python code, including any printed results or errors. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/read-file.mdx b/docs/versioned_docs/version-1.9.0/Components/read-file.mdx
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/read-file.mdx
@@ -0,0 +1,175 @@
+---
+title: Read File
+slug: /read-file
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+import PartialDockerDoclingDeps from '@site/docs/_partial-docker-docling-deps.mdx';
+
+In Langflow version 1.7.0, this component was renamed from **File** to **Read File**.
+
+The **Read File** component loads and parses files, converts the content into a `JSON`, `Table`, or `Message` object.
+It supports multiple file types, provides parameters for parallel processing and error handling, and supports advanced parsing with the Docling library.
+
+You can add files to the **Read File** component in the visual editor or at runtime, and you can upload multiple files at once.
+For more information about uploading files and working with files in flows, see [File management](/concepts-file-management) and [Create a chatbot that can ingest files](/chat-with-files).
+
+The **Read File** component can read files from the local Langflow database, **AWS S3**, or **Google Drive**.
+For more information, see [Configure file storage](/concepts-file-management#configure-file-storage).
+
+## File type and size limits
+
+By default, the maximum file size is 1024 MB.
+To modify this value, change the `LANGFLOW_MAX_FILE_SIZE_UPLOAD` [environment variable](/environment-variables).
+
+
+Supported file types
+
+The following file types are supported by the **Read File** component.
+Use archive and compressed formats to bundle multiple files together, or use the [**Directory** component](/directory) to load all files in a directory.
+
+- `.bz2`
+- `.csv`
+- `.docx`
+- `.gz`
+- `.htm`
+- `.html`
+- `.json`
+- `.js`
+- `.md`
+- `.mdx`
+- `.pdf`
+- `.py`
+- `.sh`
+- `.sql`
+- `.tar`
+- `.tgz`
+- `.ts`
+- `.tsx`
+- `.txt`
+- `.xml`
+- `.yaml`
+- `.yml`
+- `.zip`
+
+
+
+If you need to load an unsupported file type, you must use a different component that supports that file type and, potentially, parses it outside Langflow, or you must convert it to a supported type before uploading it.
+
+For images, see [Upload images](/concepts-file-management#upload-images).
+
+For videos, see the **Twelve Labs** and **YouTube** [**Bundles**](/components-bundle-components).
+
+## File parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| path | Files | Input parameter. The path to files to load. Can be local or in [Langflow file management](/concepts-file-management). Supports individual files and bundled archives. |
+| file_path | Server File Path | Input parameter. A `JSON` object with a `file_path` property pointing to a file in [Langflow file management](/concepts-file-management) or a `Message` object with a path to the file. Supersedes **Files** (`path`) but supports the same file types. |
+| separator | Separator | Input parameter. The separator to use between multiple outputs in `Message` format. |
+| silent_errors | Silent Errors | Input parameter. If `true`, errors in the component don't raise an exception. Default: Disabled (`false`). |
+| delete_server_file_after_processing | Delete Server File After Processing | Input parameter. If `true` (default), the **Server File Path** (`file_path`) is deleted after processing. |
+| ignore_unsupported_extensions | Ignore Unsupported Extensions | Input parameter. If enabled (`true`), files with unsupported extensions are accepted but not processed. If disabled (`false`), the **Read File** component either can throw an error if an unsupported file type is provided. The default is `true`. |
+| ignore_unspecified_files | Ignore Unspecified Files | Input parameter. If `true`, `JSON` with no `file_path` property is ignored. If `false` (default), the component errors when a file isn't specified. |
+| concurrency_multithreading | Processing Concurrency | Input parameter. The number of files to process concurrently if multiple files are uploaded. Default is 1. Values greater than 1 enable parallel processing for 2 or more files. Ignored for single-file uploads and advanced parsing. |
+| advanced_parser | Advanced Parser | Input parameter. If `true`, enables [advanced parsing](#advanced-parsing). Only available for single-file uploads of compatible file types. Default: Disabled (`false`). |
+
+## Advanced parsing
+
+Starting in Langflow version 1.6, the **Read File** component supports advanced document parsing using the [Docling](https://docling-project.github.io/docling/) library for supported file types.
+
+To use advanced parsing, do the following:
+
+1. Complete the following prerequisites, if applicable:
+
+ * **Install Langflow version 1.6 or later**: Earlier versions don't support advanced parsing with the **Read File** component. For upgrade guidance, see the [Release notes](/release-notes).
+
+ * **Install Docling dependency on macOS Intel (x86_64)**: The Docling dependency isn't installed by default for macOS Intel (x86_64). Use the [Docling installation guide](https://docling-project.github.io/docling/installation/) to install the Docling dependency.
+
+ For all other operating systems, the Docling dependency is installed by default.
+
+
+
+ * **Enable Developer Mode for Windows**:
+
+
+ Developer Mode isn't required for Langflow OSS on Windows.
+
+2. Add one valid file to the **Read File** component.
+
+ :::info Advanced parsing limitations
+ * Advanced parsing processes only one file.
+ If you select multiple files, the **Read File** component processes the first file only, ignoring any additional files.
+ To process multiple files with advanced parsing, pass each file to a separate **Read File** components, or use the dedicated [**Docling** components](/bundles-docling).
+
+ * Advanced parsing can process any of the **Read File** component's supported file types except `.csv`, `.xlsx`, and `.parquet` files because it is designed for document processing, such as extracting text from PDFs.
+ For structured data analysis, use the [**Parser** component](/parser).
+ :::
+
+3. Enable **Advanced Parsing**.
+
+4. To configure advanced parsing parameters, click the component to open the [component inspection panel](/concepts-components#component-menus).
+
+ | Name | Display Name | Info |
+ |------|--------------|------|
+ | pipeline | Pipeline | Input parameter, advanced parsing. The Docling pipeline to use, either `standard` (default, recommended) or `vlm` (may produce inconsistent results). |
+ | ocr_engine | OCR Engine | Input parameter, advanced parsing. The OCR parser to use if `pipeline` is `standard`. Options are `None` (default) or [`EasyOCR`](https://pypi.org/project/easyocr/). `None` means that no OCR engine is used, and this can produce inconsistent or broken results for some documents. This setting has no effect with the `vlm` pipeline. |
+ | md_image_placeholder | Markdown Image Placeholder | Input parameter, advanced parsing. Defines the placeholder for image files if the output type is **Markdown**. Default: ``. |
+ | md_page_break_placeholder | Markdown Page Break Placeholder | Input parameter, advanced parsing. Defines the placeholder for page breaks if the output type is **Markdown**. Default: `""` (empty string). |
+ | doc_key | Document Key | Input parameter, advanced parsing. The key to use for the `DoclingDocument` column, which holds the structured information extracted from the source document. See [Docling Document](https://docling-project.github.io/docling/concepts/docling_document/) for details. Default: `doc`. |
+
+ :::tip
+ For additional Docling features, including other components and OCR parsers, use the [**Docling** bundle](/bundles-docling).
+ :::
+
+## File output
+
+The output of the **Read File** component depends on the number of files loaded and whether advanced parsing is enabled.
+If multiple options are available, you can set the output type near the component's output port.
+
+
+
+
+If you run the **Read File** component with no file selected, it throws an error, or, if **Silent Errors** is enabled, produces no output.
+
+
+
+
+If advanced parsing is disabled and you upload one file, the following output types are available:
+
+- **Structured Content**: Available only for `.csv`, `.xlsx`, `.parquet`, and `.json` files.
+
+ - For `.csv` files, produces a [`Table`](/data-types#table) representing the table data.
+ - For `.json` files, produces a [`JSON`](/data-types#json) object with the parsed JSON data.
+
+- **Raw Content**: A [`Message`](/data-types#message) containing the file's raw text content.
+
+- **File Path**: A [`Message`](/data-types#message) containing the path to the file in [Langflow file management](/concepts-file-management).
+
+
+
+
+If advanced parsing is enabled and you upload one file, the following output types are available:
+
+- **Structured Output**: A [`Table`](/data-types#table) containing the Docling-processed document data with text elements, page numbers, and metadata.
+
+- **Markdown**: A [`Message`](/data-types#message) containing the uploaded document contents in Markdown format with image placeholders.
+
+- **File Path**: A [`Message`](/data-types#message) containing the path to the file in [Langflow file management](/concepts-file-management).
+
+
+
+
+If you upload multiple files, the component outputs **Files**, which is a [`Table`](/data-types#table) containing the content and metadata of all selected files.
+
+[Advanced parsing](#advanced-parsing) doesn't support multiple files; it processes only the first file.
+
+
+
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/run-flow.mdx b/docs/versioned_docs/version-1.9.0/Components/run-flow.mdx
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+---
+title: Run Flow
+slug: /run-flow
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+The **Run Flow** component runs another Langflow flow as a subprocess of the current flow.
+
+You can use this component to chain flows together, run flows conditionally, and attach flows to [**Agent** components](/components-agents) as [tools for agents](/agents-tools) to run as needed.
+
+When used with an agent, the `name` and `description` metadata that the agent uses to register the tool are created automatically.
+
+When you select a flow for the **Run Flow** component, it uses the target flow's graph structure to dynamically generate input and output fields on the **Run Flow** component.
+
+## Run Flow parameters
+
+
+
+| Name | Type | Description |
+|-------------------|----------|----------------------------------------------------------------|
+| flow_name_selected| Dropdown | Input parameter. The name of the flow to run. |
+| session_id | String | Input parameter. The session ID for the flow run, if you want to pass a custom session ID for the subflow. |
+| flow_tweak_data | Dict | Input parameter. Dictionary of tweaks to customize the flow's behavior. Available tweaks depend on the selected flow. |
+| dynamic inputs | Various | Input parameter. Additional inputs are generated based on the selected flow. |
+| run_outputs | A `List` of types (`JSON`, `Message`, or `Table`) | Output parameter. All outputs are generated from running the flow. |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/smart-router.mdx b/docs/versioned_docs/version-1.9.0/Components/smart-router.mdx
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@@ -0,0 +1,59 @@
+---
+title: Smart Router
+slug: /smart-router
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+The **Smart Router** component is an LLM-powered variation of the [**If-Else** component](/if-else).
+Instead of string matching, the **Smart Router** uses a connected [**Language Model** component](/components-models) to categorize and route incoming messages.
+
+You can use the **Smart Router** component anywhere you would use the **If-Else** component.
+For an example, create the [If-Else component example flow](/if-else#use-the-if-else-component-in-a-flow), then replace the **If-Else** component with a **Smart Router** component.
+Instead of a regex, use the **Routes** table to define the outputs for your messages.
+
+The **Routes** table defines the categories for routing.
+For example, a routes table for sentiment analysis might look like this:
+
+| Route Name | Route Description | Route Message |
+|------------|-------------------|---------------|
+| Positive | Positive feedback, satisfaction, or compliments | |
+| Negative | Complaints, issues, or dissatisfaction | |
+| Neutral | Questions, requests for information, or neutral statements | Thank you for your inquiry! |
+
+This component creates ports for the **Positive**, **Negative**, and **Neutral** routes.
+When the LLM categorizes the input text, it routes to the matching category's output port by route name.
+For the Positive and Negative routes, the original input text is passed through.
+For the Neutral route, the `"Thank you for your inquiry!"` route message is sent instead of the input text.
+
+The **Override Output** parameter sends a single message regardless of which route the LLM matches.
+The override message takes precedence over all other output options, and completely replaces both the original input text and any custom route messages.
+For the sentiment analysis example, if you set the **Override Output** to `"Message received"`, all routes send the same message.
+
+The **Additional Instructions** parameter adds extra guidance to the LLM.
+Use the `{input_text}` placeholder to reference the input text being categorized, and `{routes}` to reference the comma-separated list of route names.
+
+For example, to add domain-specific context for the LLM, include the following as the custom prompt:
+
+```
+The text "{input_text}" is from a customer support context.
+Consider the urgency and emotional tone when choosing from {routes}.
+```
+
+## Smart Router parameters
+
+
+
+| Name | Type | Description |
+|---------------------|----------|-------------------------------------------------------------------|
+| Language Model | [LanguageModel](/data-types#languagemodel) | Input parameter. The language model to use for categorization. The LLM receives the input text and available categories, then returns the exact category name that matches. Required. |
+| Input | String | Input parameter. The primary text input for categorization. Required. |
+| Routes | Table | Input parameter. Table defining categories for routing. Each row contains a route name (required), an optional route description to help LLMs understand the category, and an optional custom output message. The component creates one output port for each route category. Required. |
+| Override Output | Message | Input parameter. An optional override message that takes precedence over all other output options. When provided, this message replaces both the original input text and any custom route messages for all routes. Advanced. |
+| Additional Instructions | String | Input parameter. Additional instructions for LLM-based categorization. These are added to the base classification prompt, which already includes the full Routes table (names and descriptions). Use `{input_text}` for the input text and `{routes}` for a comma-separated list of route names only.|
+| Include Else Output | Boolean | Input parameter. Include an Else output for cases that don't match any route. When disabled, no output is produced if no match is found. Default: false. |
+| Else | Message | Output parameter. The Else output. Only available when **Include Else Output** is `true`. Uses the override message (if provided) or the original input text when no route matches. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/smart-transform.mdx b/docs/versioned_docs/version-1.9.0/Components/smart-transform.mdx
new file mode 100644
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+++ b/docs/versioned_docs/version-1.9.0/Components/smart-transform.mdx
@@ -0,0 +1,44 @@
+---
+title: Smart Transform
+slug: /smart-transform
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+This component has been renamed multiple times.
+Its previous names include **Lambda Filter** and **Smart Function**.
+
+The **Smart Transform** component uses an LLM and natural language instructions to generate a Lambda function that can filter or transform [`JSON`](/data-types#json), [`Table`](/data-types#table), or [`Message`](/data-types#message) input.
+You must connect this component to a [language model component](/components-models), which is used to generate a function based on the natural language instructions you provide in the **Instructions** parameter.
+The LLM runs the function against the input, and then outputs the results as [`JSON`](/data-types#json), [`Table`](/data-types#table), or [`Message`](/data-types#message).
+
+:::tip
+Provide brief, clear instructions, focusing on the desired outcome or specific actions, such as `Filter the data to only include items where the 'status' is 'active'`.
+One sentence or less is preferred because end punctuation, like periods, can cause errors or unexpected behavior.
+
+If you need to provide more details instructions that aren't directly relevant to the Lambda function, you can input them in the **Language Model** component's **Input** field or through a **Prompt Template** component.
+
+For the most reliable results, the **Smart Transform** component's output type must match the input type. For example, select **Message** output for [`Message`](/data-types#message) input.
+:::
+
+The following example uses the **API Request** endpoint to pass JSON data from the `https://jsonplaceholder.typicode.com/users` endpoint to the **Smart Transform** component.
+Then, the **Smart Transform** component passes the data and the instruction `extract emails` to the attached **Language Model** component.
+From there, the LLM generates a filter function that extracts email addresses from the JSON data, returning the filtered data as chat output.
+
+
+
+## Smart Transform parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| data | JSON | Input parameter. The [`JSON`](/data-types#json), [`Table`](/data-types#table), or [`Message`](/data-types#message) input to filter or transform using the generated Lambda function. |
+| model | Language Model | Input parameter. Connect [`LanguageModel`](/data-types#languagemodel) output from a **Language Model** component. |
+| filter_instruction | Instructions | Input parameter. The natural language instructions for how to filter or transform the data. The LLM uses these instructions to create a Lambda function. |
+| sample_size | Sample Size | Input parameter. For large datasets, the number of characters to sample from the dataset head and tail. Only applied if the dataset meets or exceeds `max_size`. Default: `1000`. |
+| max_size | Max Size | Input parameter. The number of characters for the dataset to be considered large, which triggers sampling by the `sample_size` value. Default: `30000`. |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/split-text.mdx b/docs/versioned_docs/version-1.9.0/Components/split-text.mdx
new file mode 100644
index 0000000000..d1e4574157
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/split-text.mdx
@@ -0,0 +1,50 @@
+---
+title: Split Text
+slug: /split-text
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialCurlyBraces from '@site/docs/_partial-escape-curly-braces.mdx';
+
+The **Split Text** component splits data into chunks based on parameters like chunk size and separator.
+It is often used to chunk data to be tokenized and embedded into vector databases.
+For examples, see [Use embedding model components in a flow](/components-embedding-models#use-embedding-model-components-in-a-flow) and [Create a Vector RAG chatbot](/chat-with-rag).
+
+
+
+The component accepts `Message`, `JSON`, or `Table`, and then outputs either **Chunks** or **DataFrame**.
+The **Chunks** output returns a list of [`JSON`](/data-types#json) objects containing individual text chunks.
+The **DataFrame** output returns the list of chunks as a structured [`Table`](/data-types#table) with additional `text` and `metadata` columns.
+
+## Split Text parameters
+
+The **Split Text** component's parameters control how the text is split into chunks, specifically the `chunk_size`, `chunk_overlap`, and `separator` parameters.
+
+To test the chunking behavior, add a **Text Input** or **Read File** component with some sample data to chunk, click **Run component** on the **Split Text** component, and then click **Inspect output** to view the list of chunks and their metadata. The **text** column contains the actual text chunks created from your chunking settings.
+If the chunks aren't split as you expect, adjust the parameters, rerun the component, and then inspect the new output.
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| data_inputs | Input | Input parameter. The data to split. Input must be in `Message`, `JSON`, or `Table` format. |
+| chunk_overlap | Chunk Overlap | Input parameter. The number of characters to overlap between chunks. This helps maintain context across chunks. When a separator is encountered, the overlap is applied at the point of the separator so that the subsequent chunk contains the last _n_ characters of the preceding chunk. Default: `200`. |
+| chunk_size | Chunk Size | Input parameter. The target length for each chunk after splitting. The data is first split by separator, and then chunks smaller than the `chunk_size` are merged up to this limit. However, if the initial separator split produces any chunks larger than the `chunk_size`, those chunks are neither further subdivided nor combined with any smaller chunks; these chunks will be output as-is even though they exceed the `chunk_size`. Default: `1000`. See [Tokenization errors due to chunk size](#chunk-size) for important considerations. |
+| separator | Separator | Input parameter. A string defining a character to split on, such as `\n` to split on new line characters, `\n\n` to split at paragraph breaks, or `},` to split at the end of JSON objects. You can directly provide the separator string, or pass a separator string from another component as `Message` input. |
+| text_key | Text Key | Input parameter. The key to use for the text column that is extracted from the input and then split. Default: `text`. |
+| keep_separator | Keep Separator | Input parameter. Select how to handle separators in output chunks. If `False`, separators are omitted from output chunks. Options include `False` (remove separators), `True` (keep separators in chunks without preference for placement), `Start` (place separators at the beginning of chunks), or `End` (place separators at the end of chunks). Default: `False`. |
+
+### Tokenization errors due to chunk size {#chunk-size}
+
+When using **Split Text** with embedding models (especially NVIDIA models like `nvidia/nv-embed-v1`), you may need to use smaller chunk sizes (`500` or less) even though the model supports larger token limits.
+The **Split Text** component doesn't always enforce the exact chunk size you set, and individual chunks may exceed your specified limit.
+If you encounter tokenization errors, modify your text splitting strategy by reducing the chunk size, changing the overlap length, or using a more common separator.
+Then, test your configuration by running the flow and inspecting the component's output.
+
+### Other text splitters
+
+See [LangChain text splitter components](/bundles-langchain#text-splitters).
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/sql-database.mdx b/docs/versioned_docs/version-1.9.0/Components/sql-database.mdx
new file mode 100644
index 0000000000..521980cc89
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/sql-database.mdx
@@ -0,0 +1,127 @@
+---
+title: SQL Database
+slug: /sql-database
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+The **SQL Database** component executes SQL queries on [SQLAlchemy-compatible databases](https://docs.sqlalchemy.org/en/20/).
+It supports any SQLAlchemy-compatible database, such as PostgreSQL, MySQL, and SQLite.
+
+For CQL queries, see the [**DataStax** bundle](/bundles-datastax).
+
+## Query an SQL database with natural language prompts
+
+The following example demonstrates how to use the **SQL Database** component in a flow, and then modify the component to support natural language queries through an **Agent** component.
+
+This allows you to use the same **SQL Database** component for any query, rather than limiting it to a single manually entered query or requiring the user, application, or another component to provide valid SQL syntax as input.
+Users don't need to master SQL syntax because the **Agent** component translates the users' natural language prompts into SQL queries, passes the query to the **SQL Database** component, and then returns the results to the user.
+
+Additionally, input from applications and other components doesn't have to be extracted and transformed to exact SQL queries.
+Instead, you only need to provide enough context for the agent to understand that it should create and run a SQL query according to the incoming data.
+
+1. Use your own sample database or create a test database.
+
+
+ Create a test SQL database
+
+ 1. Create a database called `test.db`:
+
+ ```shell
+ sqlite3 test.db
+ ```
+
+ 2. Add some values to the database:
+
+ ```shell
+ sqlite3 test.db "
+ CREATE TABLE users (
+ id INTEGER PRIMARY KEY,
+ name TEXT,
+ email TEXT,
+ age INTEGER
+ );
+
+ INSERT INTO users (name, email, age) VALUES
+ ('John Doe', 'john@example.com', 30),
+ ('Jane Smith', 'jane@example.com', 25),
+ ('Bob Johnson', 'bob@example.com', 35);
+ "
+ ```
+
+ 3. Verify that the database has been created and contains your data:
+
+ ```shell
+ sqlite3 test.db "SELECT * FROM users;"
+ ```
+
+ The result should list the text data you entered in the previous step:
+
+ ```shell
+ 1|John Doe|john@example.com
+ 2|Jane Smith|jane@example.com
+ 3|John Doe|john@example.com
+ 4|Jane Smith|jane@example.com
+ ```
+
+
+
+2. Add an **SQL Database** component to your flow.
+
+3. In the **Database URL** field, add the connection string for your database, such as `sqlite:///test.db`.
+
+ At this point, you can enter an SQL query in the **SQL Query** field or use the [port](/concepts-components#component-ports) to pass a query from another component, such as a **Chat Input** component.
+ If you need more space, click **Expand** to open a full-screen text field.
+
+ However, to make this component more dynamic in an agentic context, use an **Agent** component to transform natural language input to SQL queries, as explained in the following steps.
+
+4. Click the **SQL Database** component to expose the [component's header menu](/concepts-components#component-menus), and then enable **Tool Mode**.
+
+ You can now use this component as a tool for an agent.
+ In **Tool Mode**, no query is set in the **SQL Database** component because the agent will generate and send one if it determines that the tool is required to complete the user's request.
+ For more information, see [Configure tools for agents](/agents-tools).
+
+5. Add an **Agent** component to your flow, and then enter your OpenAI API key.
+
+ The default model is an OpenAI model.
+ If you want to use a different model, edit the **Model Provider**, **Model Name**, and **API Key** fields accordingly.
+
+ If you need to execute highly specialized queries, consider selecting a model that is trained for tasks like advanced SQL queries.
+ If your preferred model isn't in the **Agent** component's built-in model list, set **Model Provider** to **Connect other models**, and then connect any [language model component](/components-models).
+
+6. Connect the **SQL Database** component's **Toolset** output to the **Agent** component's **Tools** input.
+
+ 
+
+7. Click **Playground**, and then ask the agent a question about the data in your database, such as `Which users are in my database?`
+
+ The agent determines that it needs to query the database to answer the question, uses the LLM to generate an SQL query, and then uses the **SQL Database** component's `RUN_SQL_QUERY` action to run the query on your database.
+ Finally, it returns the results in a conversational format, unless you provide instructions to return raw results or a different format.
+
+ The following example queried a test database with little data, but with a more robust dataset you could ask more detailed or complex questions.
+
+ ```text
+ Here are the users in your database:
+
+ 1. **John Doe** - Email: john@example.com
+ 2. **Jane Smith** - Email: jane@example.com
+ 3. **John Doe** - Email: john@example.com
+ 4. **Jane Smith** - Email: jane@example.com
+
+ It seems there are duplicate entries for the users.
+ ```
+
+## SQL Database parameters
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| database_url | Database URL | Input parameter. The SQLAlchemy-compatible database connection URL. |
+| query | SQL Query | Input parameter. The SQL query to execute, which can be entered directly, passed in from another component, or, in **Tool Mode**, automatically provided by an **Agent** component. |
+| include_columns | Include Columns | Input parameter. Whether to include column names in the result. The default is enabled (`true`). |
+| add_error | Add Error | Input parameter. If enabled, adds any error messages to the result, if any are returned. The default is disabled (`false`). |
+| run_sql_query | Result Table | Output parameter. The query results as a [`Table`](/data-types#table). |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Components/structured-output.mdx b/docs/versioned_docs/version-1.9.0/Components/structured-output.mdx
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+---
+title: Structured Output
+slug: /structured-output
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+The **Structured Output** component uses an LLM to transform any input into structured data (`JSON` or `Table`) using natural language formatting instructions and an output schema definition.
+For example, you can extract specific details from documents, like email messages or scientific papers.
+
+## Use the Structured Output component in a flow
+
+To use the **Structured Output** component in a flow, do the following:
+
+1. Provide an **Input Message**, which is the source material from which you want to extract structured data.
+This can come from practically any component, but it is typically a **Chat Input**, **Read File**, or other component that provides some unstructured or semi-structured input.
+
+ :::tip
+ Not all source material has to become structured output.
+ The power of the **Structured Output** component is that you can specify the information you want to extract, even if that data isn't explicitly labeled or an exact keyword match.
+ Then, the LLM can use your instructions to analyze the source material, extract the relevant data, and format it according to your specifications.
+ Any irrelevant source material isn't included in the structured output.
+ :::
+
+2. Define **Format Instructions** and an **Output Schema** to specify the data to extract from the source material and how to structure it in the final `JSON` or `Table` output.
+
+ The instructions are a prompt that tell the LLM what data to extract, how to format it, how to handle exceptions, and any other instructions relevant to preparing the structured data.
+
+ The schema is a table that defines the fields (keys) and data types to organize the data extracted by the LLM into a structured `JSON` or `Table` object.
+ For more information, see [Output Schema options](#output-schema-options)
+
+3. Attach a [language model component](/components-models) that is set to emit [`LanguageModel`](/data-types#languagemodel) output.
+
+ The LLM uses the **Input Message** and **Format Instructions** from the **Structured Output** component to extract specific pieces of data from the input text.
+ The output schema is applied to the model's response to produce the final `JSON` or `Table` structured object.
+
+4. Optional: Typically, the structured output is passed to downstream components that use the extracted data for other processes, such as the **Parser** or **JSON Operations** components.
+
+
+
+
+Structured Output example: Financial Report Parser template
+
+The **Financial Report Parser** template provides an example of how the **Structured Output** component can be used to extract structured data from unstructured text.
+
+The template's **Structured Output** component has the following configuration:
+
+* The **Input Message** comes from a **Chat Input** component that is preloaded with quotes from sample financial reports
+
+* The **Format Instructions** are as follows:
+
+ ```text
+ You are an AI that extracts structured JSON objects from unstructured text.
+ Use a predefined schema with expected types (str, int, float, bool, dict).
+ Extract ALL relevant instances that match the schema - if multiple patterns exist, capture them all.
+ Fill missing or ambiguous values with defaults: null for missing values.
+ Remove exact duplicates but keep variations that have different field values.
+ Always return valid JSON in the expected format, never throw errors.
+ If multiple objects can be extracted, return them all in the structured format.
+ ```
+
+* The **Output Schema** includes keys for `EBITDA`, `NET_INCOME`, and `GROSS_PROFIT`.
+
+The structured `JSON` object is passed to a **Parser** component that produces a text string by mapping the schema keys to variables in the parsing template:
+
+```text
+EBITDA: {EBITDA} , Net Income: {NET_INCOME} , GROSS_PROFIT: {GROSS_PROFIT}
+```
+
+When printed to the **Playground**, the resulting `Message` replaces the variables with the actual values extracted by the **Structured Output** component. For example:
+
+```text
+EBITDA: 900 million , Net Income: 500 million , GROSS_PROFIT: 1.2 billion
+```
+
+
+
+## Structured Output parameters
+
+
+
+| Name | Type | Description |
+|------|------|-------------|
+| Language Model (`llm`) | `LanguageModel` | Input parameter. The [`LanguageModel`](/data-types#languagemodel) output from a **Language Model** component that defines the LLM to use to analyze, extract, and prepare the structured output. |
+| Input Message (`input_value`) | String | Input parameter. The input message containing source material for extraction. |
+| Format Instructions (`system_prompt`) | String | Input parameter. The instructions to the language model for extracting and formatting the output. |
+| Schema Name (`schema_name`) | String | Input parameter. An optional title for the **Output Schema**. |
+| Output Schema (`output_schema`)| Table | Input parameter. A table describing the schema of the desired structured output, ultimately determining the content of the `JSON` or `Table` output. See [Output Schema options](#output-schema-options). |
+| Structured Output (`structured_output`) | `JSON` or `Table` | Output parameter. The final structured output produced by the component. Near the component's output port, you can select the output data type as either **Structured Output Data** or **Structured Output DataFrame**. The specific content and structure of the output depends on the input parameters. |
+
+#### Output Schema options {#output-schema-options}
+
+After the LLM extracts the relevant data from the **Input Message** and **Format Instructions**, the data is organized according to the **Output Schema**.
+
+The schema is a table that defines the fields (keys) and data types for the final `JSON` or `Table` output from the **Structured Output** component.
+
+The default schema is a single `field` string.
+
+To add a key to the schema, click **Add a new row**, and then edit each column to define the schema:
+
+* **Name**: The name of the output field. Typically a specific key for which you want to extract a value.
+
+ You can reference these keys as variables in downstream components, such as a **Parser** component's template.
+ For example, the schema key `NET_INCOME` could be referenced by the variable `{NET_INCOME}`.
+
+* **Description**: An optional metadata description of the field's contents and purpose.
+
+* **Type**: The data type of the value stored in the field.
+Supported types are `str` (default), `int`, `float`, `bool`, and `dict`.
+
+* **As List**: Enable this setting if you want the field to contain a list of values rather than a single value.
+
+For simple schemas, you might only extract a few `string` or `int` fields.
+For more complex schemas with lists and dictionaries, it might help to refer to the `JSON` and `Table` structures and attributes, as described in [Langflow data types](/data-types).
+You can also emit a rough `JSON` or `Table`, and then use downstream components for further refinement, such as a **JSON Operations** component.
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/text-input-and-output.mdx b/docs/versioned_docs/version-1.9.0/Components/text-input-and-output.mdx
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@@ -0,0 +1,36 @@
+---
+title: Text Input and Output
+slug: /text-input-and-output
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+:::warning
+**Text Input and Output** components aren't supported in the **Playground**.
+Because the data isn't formatted as a chat message, the data doesn't appear in the **Playground**, and you can't chat with your flow in the **Playground**.
+
+If you want to chat with a flow in the **Playground**, you must use the [**Chat Input and Output** components](/chat-input-and-output).
+:::
+
+**Text Input and Output** components are designed for flows that ingest or emit simple text strings.
+These components don't support full conversational interactions.
+
+Passing chat-like metadata to a **Text Input and Output** component doesn't change the component's behavior; the result is still a simple text string.
+
+## Text Input
+
+The **Text Input** component accepts a text string input that is passed to other components as [`Message` data](/data-types) containing only the provided input text string in the `text` attribute.
+
+It accepts only **Text** (`input_value`), which is the text supplied as input to the component.
+This can be entered directly into the component or passed as `Message` data from other components.
+
+Initial input _shouldn't_ be provided as a complete `Message` object because the **Text Input** component constructs the `Message` object that is then passed to other components in the flow.
+
+## Text Output
+
+The **Text Output** component ingests [`Message` data](/data-types#message) from other components, emitting only the `text` attribute in a simplified `Message` object.
+
+It accepts only **Text** (`input_value`), which is the text to be ingested and output as a string.
+This can be entered directly into the component or passed as `Message` data from other components.
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/type-convert.mdx b/docs/versioned_docs/version-1.9.0/Components/type-convert.mdx
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--- /dev/null
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@@ -0,0 +1,138 @@
+---
+title: Type Convert
+slug: /type-convert
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialCurlyBraces from '@site/docs/_partial-escape-curly-braces.mdx';
+
+The **Type Convert** component converts data from one type to another.
+It supports `JSON`, `Table`, and `Message` data types.
+
+
+
+
+A `JSON` object is a structured object that contains a primary `text` key and other key-value pairs:
+
+```json
+"data": {
+ "text": "User Profile",
+ "name": "Charlie Lastname",
+ "age": 28,
+ "email": "charlie.lastname@example.com"
+},
+```
+
+The larger context associated with a component's `data` dictionary also identifies which key is the primary `text_key`, and it can provide an optional default value if the primary key isn't specified.
+For example:
+
+```json
+{
+ "text_key": "text",
+ "data": {
+ "text": "User Profile",
+ "name": "Charlie Lastname",
+ "age": 28,
+ "email": "charlie.lastname@example.com"
+ },
+ "default_value": ""
+}
+```
+
+
+
+
+A `Table` is an array that represents a tabular data structure with rows and columns.
+
+It consists of a list (array) of dictionary objects, where each dictionary represents a row.
+Each key in the dictionaries corresponds to a column name.
+For example, the following `Table` contains two rows with columns for `name`, `age`, and `email`:
+
+```json
+[
+ {
+ "name": "Charlie Lastname",
+ "age": 28,
+ "email": "charlie.lastname@example.com"
+ },
+ {
+ "name": "Bobby Othername",
+ "age": 25,
+ "email": "bobby.othername@example.com"
+ }
+]
+```
+
+
+
+
+A `Message` is primarily for passing a `text` string, such as`"Name: Charlie Lastname, Age: 28, Email: charlie.lastname@example.com"`.
+However, the entire `Message` object can include metadata about the message, particularly when used as chat input or output.
+
+
+
+
+For more information, see [Langflow data types](/data-types).
+
+## Use the Type Convert component in a flow
+
+The **Type Convert** component is typically used to transform data into a format required by a downstream component.
+For example, if a component outputs a `Message`, but the following component requires `JSON`, then you can use the **Type Convert** component to reformat the `Message` as `JSON` before passing it to the downstream component.
+
+The following example uses the **Type Convert** component to convert the `Table` output from a **Web Search** component into `Message` data that is passed as text input for an LLM:
+
+1. Create a flow based on the **Basic prompting** template.
+
+2. Add a **Web Search** component to the flow, and then enter a search query, such as `environmental news`.
+
+3. In the **Prompt Template** component, replace the contents of the **Template** field with the following text:
+
+ ```text
+ Answer the user's question using the {context}
+ ```
+
+ The curly braces define a [prompt variable](/components-prompts#define-variables-in-prompts) that becomes an input field on the **Prompt Template** component.
+ In this example, you will use the **context** field to pass the search results into the template, as explained in the next steps.
+
+3. Add a **Type Convert** component to the flow, and then set the **Output Type** to **Message**.
+
+ Because the **Web Search** component's `Table` output is incompatible with the **context** variable's `Message` input, you must use the **Type Convert** component to change the `Table` to a `Message` in order to pass the search results to the **Prompt Template** component.
+
+4. Connect the additional components to the rest of the flow:
+
+ * Connect the **Web Search** component's output to the **Type Convert** component's input.
+ * Connect the **Type Convert** component's output to the **Prompt Template** component's **context** input.
+
+ 
+
+5. In the **Language Model** component, add your OpenAI API key.
+
+ If you want to use a different provider or model, edit the **Model Provider**, **Model Name**, and **API Key** fields accordingly.
+
+6. Click **Playground**, and then ask something relevant to your search query, such as `latest news` or `what's the latest research on the environment?`.
+
+
+ Result
+
+ The LLM uses the search results context, your chat message, and it's built-in training data to respond to your question.
+ For example:
+
+ ```text
+ Here are some of the latest news articles related to the environment:
+ Ozone Pollution and Global Warming: A recent study highlights that ozone pollution is a significant global environmental concern, threatening human health and crop production while exacerbating global warming. Read more
+ ...
+ ```
+
+
+
+## Type Convert parameters
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| input_data | Input Data | Input parameter. The data to convert. Accepts `JSON`, `Table`, or `Message` input. |
+| output_type | Output Type | Input parameter. The desired output type, as one of **Data**, **DataFrame** or **Message**. |
+| output | Output | Output parameter. The converted data in the specified format. The output port changes depending on the selected **Output Type**. |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/url.mdx b/docs/versioned_docs/version-1.9.0/Components/url.mdx
new file mode 100644
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@@ -0,0 +1,56 @@
+---
+title: URL
+slug: /url
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+The **URL** component fetches content from one or more URLs, processes the content, and returns it in various formats.
+It follows links recursively to a given depth, and it supports output in plain text, Markdown, or raw HTML.
+
+## URL parameters
+
+
+
+Some of the available parameters include the following:
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| urls | URLs | Input parameter. One or more URLs to crawl recursively. In the visual editor, click **Add URL** to add multiple URLs. |
+| max_depth | Depth | Input parameter. Controls link traversal: how many "clicks" away from the initial page the crawler will go. A depth of 1 limits the crawl to the first page at the given URL only. A depth of 2 means the crawler crawls the first page plus each page directly linked from the first page, then stops. This setting exclusively controls link traversal; it doesn't limit the number of URL path segments or the domain. |
+| prevent_outside | Prevent Outside | Input parameter. If enabled, only crawls URLs within the same domain as the root URL. This prevents the crawler from accessing sites outside the given URL's domain, even if they are linked from one of the crawled pages. |
+| use_async | Use Async | Input parameter. If enabled, uses asynchronous loading which can be significantly faster but might use more system resources. |
+| format | Output Format | Input parameter. Sets the desired output format as **Text**, **Markdown**, or **HTML**. The default is **Text**. For more information, see [URL output](#url-output).|
+| timeout | Timeout | Input parameter. Timeout for the request in seconds. |
+| headers | Headers | Input parameter. The headers to send with the request if needed for authentication or otherwise. |
+
+Additional input parameters are available for error handling and encoding.
+
+## URL output
+
+There are two settings that control the output of the **URL** component at different stages:
+
+* **Output Format**: This optional parameter controls the content extracted from the crawled pages:
+
+ * **Text (default)**: The component extracts only the text from the HTML of the crawled pages.
+ * **Markdown**: The component converts the HTML content to markdown format using [Markitdown](https://github.com/microsoft/markitdown).
+ * **HTML**: The component extracts the entire raw HTML content of the crawled pages.
+
+* **Output data type**: In the component's output field (near the output port) you can select the structure of the outgoing data when it is passed to other components:
+
+ * **Extracted Pages**: Outputs a [`Table`](/data-types#table) that breaks the crawled pages into columns for the entire page content (`text`) and metadata like `url` and `title`.
+ * **Raw Content**: Outputs a [`Message`](/data-types#message) containing the entire text, Markdown, or HTML from the crawled pages, including metadata, in a single block of text.
+
+When used as a standard component in a flow, the **URL** component must be connected to a component that accepts the selected output data type (`Table` or `Message`).
+You can connect the **URL** component directly to a compatible component, or you can use a [**Type Convert** component](/type-convert) to convert the output to another type before passing the data to other components if the data types aren't directly compatible.
+
+Processing components like the **Type Convert** component are useful with the **URL** component because it can extract a large amount of data from the crawled pages.
+For example, if you only want to pass specific fields to other components, you can use a [**Parser** component](/parser) to extract only that data from the crawled pages before passing the data to other components.
+
+When used in **Tool Mode** with an **Agent** component, the **URL** component can be connected directly to the **Agent** component's **Tools** port without converting the data.
+The agent decides whether to use the **URL** component based on the user's query, and it can process the `Table` or `Message` output directly.
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/web-search.mdx b/docs/versioned_docs/version-1.9.0/Components/web-search.mdx
new file mode 100644
index 0000000000..4b448d26b6
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/web-search.mdx
@@ -0,0 +1,139 @@
+---
+title: Web Search
+slug: /web-search
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+The **Web Search** component consolidates the **Web Search**, **News Search**, and **RSS Reader** components into a single component with tabs for different search modes. You can search the web using DuckDuckGo, search Google News, or read RSS feeds, all from one component.
+
+For other search APIs, see [**Bundles**](/components-bundle-components).
+
+:::info
+The **Web Search** component uses web scraping that can be subject to rate limits.
+
+For production use, consider using another search component with more robust API support, such as provider-specific bundles.
+:::
+
+## Use the Web Search component in a flow
+
+The following steps demonstrate one way that you can use a **Web Search** component in a flow:
+
+1. Create a flow based on the **Basic Prompting** template.
+
+2. Add a **Web Search** component, select your desired **Search Mode** (Web, News, or RSS), and then enter a search query or RSS feed URL.
+
+3. Add a [**Type Convert** component](/type-convert), set the **Output Type** to **Message**, and then connect the **Web Search** component's output to the **Type Convert** component's input.
+
+ By default, the **Web Search** component outputs a `Table`.
+ Because the **Prompt Template** component only accepts `Message` data, this conversion is required so that the flow can pass the search results to the **Prompt Template** component.
+ For more information, see [Web Search output](#web-search-output).
+
+4. In the **Prompt Template** component's **Template** field, add a variable like `{searchresults}` or `{context}`.
+
+ This adds a field to the **Prompt Template** component that you can use to pass the converted search results to the prompt.
+ For more information, see [Define variables in prompts](/components-prompts#define-variables-in-prompts).
+
+5. Connect the **Type Convert** component's output to the new variable field on the **Prompt Template** component.
+
+ 
+
+6. In the **Language Model** component, add your OpenAI API key, or select a different provider and model.
+
+7. Click **Playground**, and then enter your query.
+
+ The LLM processes the request, including the context passed through the **Prompt Template** component, and then prints the response in the **Playground** chat interface.
+
+
+ Result
+
+ The following is an example of a possible response.
+ Your response may vary based on the current state of the web, your specific query, the model, and other factors.
+
+ ```text
+ Here are some of the latest news articles related to the environment:
+ Ozone Pollution and Global Warming: A recent study highlights that ozone pollution is a significant global environmental concern, threatening human health and crop production while exacerbating global warming. Read more
+ ...
+ ```
+
+
+
+## Parameters
+
+
+
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| search_mode | Search Mode | Input parameter. Choose search mode: Web (DuckDuckGo), News (Google News), or RSS (Feed Reader). Default: `Web`. |
+| query | Search Query | Input parameter. Keywords to search for. |
+| timeout | Timeout | Input parameter. Timeout for the web search request in seconds. Default: `5`. |
+| results | Results | Output parameter. Returns a `Table` containing `title`, `link`, `snippet`, and `content`. For more information, see [Web Search output](#web-search-output). |
+
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| search_mode | Search Mode | Input parameter. Choose search mode: Web (DuckDuckGo), News (Google News), or RSS (Feed Reader). Default: `Web`. |
+| query | Search Query | Input parameter. Search keywords for news articles. |
+| hl | Language (hl) | Input parameter. Language code, such as en-US, fr, de. Default: `en-US`. |
+| gl | Country (gl) | Input parameter. Country code, such as US, FR, DE. Default: `US`. |
+| ceid | Country:Language (ceid) | Input parameter. Language, such as US:en, FR:fr. Default: `US:en`. |
+| topic | Topic | Input parameter. One of: `WORLD`, `NATION`, `BUSINESS`, `TECHNOLOGY`, `ENTERTAINMENT`, `SCIENCE`, `SPORTS`, `HEALTH`. |
+| location | Location (Geo) | Input parameter. City, state, or country for location-based news. Leave blank for keyword search. |
+| timeout | Timeout | Input parameter. Timeout for the request in seconds. Default: `5`. |
+| results | Results | Output parameter. A `Table` with the key columns `title`, `link`, `published` and `summary`. For more information, see [Web Search output](#web-search-output). |
+
+
+
+
+| Name | Display Name | Info |
+|------|--------------|------|
+| search_mode | Search Mode | Input parameter. Choose search mode: Web (DuckDuckGo), News (Google News), or RSS (Feed Reader). Default: `Web`. |
+| query | RSS Feed URL | Input parameter. URL of the RSS feed to parse, such as `https://rss.nytimes.com/services/xml/rss/nyt/HomePage.xml`. |
+| timeout | Timeout | Input parameter. Timeout for the RSS feed request in seconds. Default: `5`. |
+| results | Results | Output parameter. A `Table` containing the key columns `title`, `link`, `published` and `summary`. For more information, see [Web Search output](#web-search-output). |
+
+
+
+
+## Web Search output
+
+The **Web Search** component outputs a [`Table`](/data-types#table) with different columns depending on the search mode.
+
+
+
+
+When using **Web** search mode, the component returns a `Table` containing:
+- `title`: The title of the search result
+- `link`: The URL of the search result
+- `snippet`: A brief snippet from the search result
+- `content`: The full content of the page (when successfully fetched)
+
+
+
+
+When using **News** search mode, the component returns a `Table` containing:
+- `title`: The title of the news article
+- `link`: The URL of the news article
+- `published`: The publication date of the article
+- `summary`: A summary or description of the article
+
+
+
+
+When using **RSS** search mode, the component returns a `Table` containing:
+- `title`: The title of the RSS feed item
+- `link`: The URL of the RSS feed item
+- `published`: The publication date of the item
+- `summary`: A summary or description of the item
+
+
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/webhook.mdx b/docs/versioned_docs/version-1.9.0/Components/webhook.mdx
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Components/webhook.mdx
@@ -0,0 +1,35 @@
+---
+title: Webhook
+slug: /component-webhook
+---
+
+import Icon from "@site/src/components/icon";
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+
+The **Webhook** component defines a webhook trigger that runs a flow when it receives an HTTP POST request.
+
+## Trigger the webhook
+
+When you add a **Webhook** component to your flow, a **Webhook curl** tab is added to the flow's [**API Access** pane](/concepts-publish#api-access).
+This tab automatically generates an HTTP POST request code snippet that you can use to trigger your flow through the **Webhook** component.
+For example:
+
+```bash
+curl -X POST \
+ "http://$LANGFLOW_SERVER_ADDRESS/api/v1/webhook/$FLOW_ID" \
+ -H 'Content-Type: application/json' \
+ -H 'x-api-key: $LANGFLOW_API_KEY' \
+ -d '{"any": "data"}'
+```
+
+For more information, see [Trigger flows with webhooks](/webhook).
+
+## Webhook parameters
+
+| Name | Display Name | Description |
+|------|--------------|-------------|
+| data | Payload | Input parameter. Receives a payload from external systems through HTTP POST requests. |
+| curl | curl | Input parameter. The curl command template for making requests to this webhook. |
+| endpoint | Endpoint | Input parameter. The endpoint URL where this webhook receives requests. |
+| output_data | JSON | Output parameter. The processed data from the webhook input. Returns an empty [`JSON`](/data-types#json) object if no input is provided. If the input isn't valid JSON, the **Webhook** component wraps it in a `payload` object so that it can be accepted as input to trigger the flow. |
+
diff --git a/docs/versioned_docs/version-1.9.0/Components/write-file.mdx b/docs/versioned_docs/version-1.9.0/Components/write-file.mdx
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@@ -0,0 +1,76 @@
+---
+title: Write File
+slug: /write-file
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialParams from '@site/docs/_partial-hidden-params.mdx';
+import PartialDevModeWindows from '@site/docs/_partial-dev-mode-windows.mdx';
+
+In Langflow version 1.7.0, this component was renamed from **Save File** to **Write File**.
+
+The **Write File** component creates a file containing data produced by another component.
+Several file formats are supported, and you can store files in [Langflow storage](/memory), AWS S3, Google Drive, or the local file system.
+
+To configure the **Write File** component and use it in a flow, do the following:
+
+1. Connect [`Table`](/data-types#table), [`JSON`](/data-types#json), or [`Message`](/data-types#message) output from another component to the **Write File** component's **Input** port.
+
+ You can connect the same output to multiple **Write File** components if you want to create multiple files, save the data in different file formats, or save files to multiple locations.
+
+2. Click **Storage Location**, and then select **Local**, **AWS**, or **Google Drive**.
+ If required, enter your cloud provider's credentials.
+ For more information, see [Configure file storage](/concepts-file-management#configure-file-storage).
+3. In **File Name**, enter a file name and an optional path.
+
+ The **File Name** parameter controls where the file is saved.
+ It can contain a file name or an entire file path:
+
+ * **Default location**: If you only provide a file name, then the file is stored in the Langflow data directory. For example,`~/Library/Caches/langflow/data` on macOS.
+
+ * **Subdirectory**: To store files in subdirectories, add the path to the **File Name** parameter.
+ If a given subdirectory doesn't already exist, Langflow automatically creates it.
+ For example, `files/my_file` creates `my_file` in `/data/files`, and it creates the `files` subdirectory if it doesn't already exist.
+
+ * **Absolute or relative path**: To store files elsewhere in your environment or local file storage, provide the absolute or relative path to the desired location.
+ For example, `~/Desktop/my_file` saves `my_file` to the desktop.
+
+ Don't include an extension in the file name.
+ If you do, the extension is treated as part of the file name; it has no impact on the **File Format** parameter.
+
+4. Select the desired file format, and then click **Close**.
+
+ The available **File Format** options depend on the input data type:
+
+ * `Table` can be saved to CSV (default), Excel (requires `openpyxl` [custom dependency](/install-custom-dependencies)), JSON (fallback default), or Markdown.
+
+ * `JSON` can be saved to CSV, Excel (requires `openpyxl` [custom dependency](/install-custom-dependencies)), JSON (default), or Markdown.
+
+ * `Message` can be saved to TXT, JSON (default), or Markdown.
+
+ :::warning Overwrites allowed
+ If you have multiple **Write File** components, in one or more flows, with the same file name, path, and extension, the file contains the data from the most recent run only.
+ Langflow doesn't block overwrites if a matching file already exists.
+ To avoid unintended overwrites, use unique file names and paths.
+ :::
+
+5. To test the **Write File** component, click **Run component**, and then click **Inspect output** to get the filepath where the file was saved.
+
+ The component's literal output is a `Message` containing the original data type, the file name and extension, and the absolute filepath to the file based on the **File Name** parameter.
+ For example:
+
+ ```text
+ DataFrame saved successfully as 'my_file.csv' at /Users/user.name/Library/Caches/langflow/data/my_file.csv
+ ```
+
+ If the **File Name** contains a subdirectory or other non-default path, this is reflected in the `Message` output.
+ For example, a CSV file with the file name `~/Desktop/my_file` could produce the following output:
+
+ ```text
+ DataFrame saved successfully as '/Users/user.name/Desktop/my_file.csv' at /Users/user.name/Desktop/my_file.csv
+ ```
+
+5. Optional: If you want to use the saved file in a flow, you must use an API call or another component to retrieve the file from the given filepath.
+
diff --git a/docs/versioned_docs/version-1.9.0/Contributing/contributing-bundles.mdx b/docs/versioned_docs/version-1.9.0/Contributing/contributing-bundles.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Contributing/contributing-bundles.mdx
@@ -0,0 +1,146 @@
+---
+title: Contribute component bundles
+slug: /contributing-bundles
+---
+
+import Icon from "@site/src/components/icon";
+
+ [**Bundles**](/components-bundle-components) are groups of components that are related to a specific service provider.
+If you want to contribute your custom components back to the Langflow project, you must put them into a bundle.
+
+Follow these steps to add components to **Bundles** in the Langflow visual editor.
+This example adds a bundle named `DarthVader`.
+
+For more information on creating custom components, see [Create custom Python components](/components-custom-components).
+
+## Add the bundle to the lfx components folder
+
+1. Navigate to the lfx directory in the Langflow repository and create a new folder for your bundle.
+The path for your new component is `src/lfx/src/lfx/components/darth_vader`.
+You can view the [components folder](https://github.com/langflow-ai/langflow/tree/main/src/lfx/src/lfx/components) in the Langflow repository.
+
+2. Within the newly created `darth_vader` folder, add the following files:
+
+ * `darth_vader_component.py` — This file contains the backend logic for the new bundle. Create multiple `.py` files for multiple components.
+ * `__init__.py` — This file initializes the bundle components. You can use any existing `__init__.py` as an example to see how it should be structured.
+
+ For an example of adding multiple components in a bundle, see the [Notion](https://github.com/langflow-ai/langflow/tree/main/src/lfx/src/lfx/components/Notion) bundle.
+
+
+## Add the bundle to the frontend folder
+
+1. Navigate to the frontend directory in the Langflow repository to add your bundle's icon.
+The path for your new component icon is `src/frontend/src/icons/DarthVader`
+You can view the [icons folder](https://github.com/langflow-ai/langflow/tree/main/src/frontend/src/icons) in the Langflow repository.
+To add your icon, create **three** files inside the `icons/darth_vader` folder.
+
+2. In the `icons/darth_vader` folder, add the raw SVG file of your icon, such as `darth_vader-icon.svg`.
+
+ :::tip
+ To convert the SVG file to JSX format, you can use an online tool like SVG to JSX.
+ It's highly recommended to use the original, lighter version of the SVG.
+ :::
+
+3. In the `icons/darth_vader` folder, add the icon as a React component in JSX format, such as `DarthVaderIcon.jsx`.
+
+4. Update the JSX file to include the correct component name and structure.
+Ensure you include the `{...props}` spread operator in your JSX file.
+For example, here is `DarthVaderIcon.jsx`:
+
+ ```javascript
+ const DarthVaderIcon = (props) => (
+
+ );
+
+ export default DarthVaderIcon;
+ ```
+
+5. In the `icons/darth_vader` folder, add the React component itself in TypeScript format, such as `index.tsx`.
+Ensure the icon's React component name corresponds to the JSX component you just created, such as `DarthVaderIcon`:
+
+ ```typescript
+ import { useDarkStore } from "@/stores/darkStore";
+ import React, { forwardRef } from "react";
+ import DarthVaderIconSVG from "./DarthVaderIcon";
+
+ export const DarthVaderIcon = forwardRef<
+ SVGSVGElement,
+ React.PropsWithChildren<{}>
+ >((props, ref) => {
+ const isdark = useDarkStore((state) => state.dark).toString();
+
+ return ;
+ });
+
+ export default DarthVaderIcon;
+ ```
+
+6. To link your new bundle to the frontend, open `/src/frontend/src/icons/lazyIconImports.ts`.
+You can view the [lazyIconImports.ts](https://github.com/langflow-ai/langflow/blob/main/src/frontend/src/icons/lazyIconImports.ts) in the Langflow repository.
+
+7. Add the name of your icon, which should match the icon name you used in the `.tsx` file.
+For example:
+
+ ```typescript
+ CrewAI: () =>
+ import("@/icons/CrewAI").then((mod) => ({ default: mod.CrewAiIcon })),
+ DarthVader: () =>
+ import("@/icons/DarthVader").then((mod) => ({ default: mod.DarthVaderIcon })),
+ DeepSeek: () =>
+ import("@/icons/DeepSeek").then((mod) => ({ default: mod.DeepSeekIcon })),
+ ```
+
+8. To add your bundle to the **Bundles** menu, edit the [`SIDEBAR_BUNDLES` array](https://github.com/langflow-ai/langflow/blob/main/src/frontend/src/utils/styleUtils.ts#L243) in `/src/frontend/src/utils/styleUtils.ts`.
+
+ Add an object to the array with the following keys:
+
+ * `display_name`: The text label shown in the Langflow visual editor
+ * `name`: The name of the folder you created within the `/src/lfx/src/lfx/components` directory
+ * `icon`: The name of the bundle's icon that you defined in the previous steps
+
+ For example:
+
+ ```typescript
+ { display_name: "AssemblyAI", name: "assemblyai", icon: "AssemblyAI" },
+ { display_name: "DarthVader", name: "darth_vader", icon: "DarthVader" },
+ { display_name: "DataStax", name: "datastax", icon: "AstraDB" },
+ ```
+
+## Update bundle components with icons
+
+In your component bundle, associate the icon variable with your new bundle.
+
+In your `darth_vader_component.py` file, in the component class, include the icon that you defined in the frontend.
+The `icon` must point to the directory you created for your icons within the `src/frontend/src/icons` directory.
+For example:
+```
+class DarthVaderAPIComponent(LCToolComponent):
+ display_name: str = "Darth Vader Tools"
+ description: str = "Use the force to run actions with your agent"
+ name = "DarthVaderAPI"
+ icon = "DarthVader"
+```
+
+## Ensure the application builds your component bundle
+
+1. To rebuild the backend and frontend, run `make install_frontend && make build_frontend && make install_backend && uv run langflow run --port 7860`.
+
+2. Refresh the frontend application.
+Your new bundle called `DarthVader` is available in the **Bundles** menu in the visual editor.
diff --git a/docs/versioned_docs/version-1.9.0/Contributing/contributing-community.mdx b/docs/versioned_docs/version-1.9.0/Contributing/contributing-community.mdx
new file mode 100644
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+++ b/docs/versioned_docs/version-1.9.0/Contributing/contributing-community.mdx
@@ -0,0 +1,35 @@
+---
+title: Join the Langflow community
+slug: /contributing-community
+---
+
+There are several ways you can interact with the Langflow community and learn more about the Langflow codebase.
+
+## Join the Langflow Discord server
+
+Join the [Langflow Discord Server](https://discord.gg/EqksyE2EX9) to ask questions and showcase your projects.
+
+## Follow Langflow on X
+
+Follow [@langflow_ai](https://twitter.com/langflow_ai) on X to get the latest news about Langflow.
+
+## Star Langflow on GitHub
+
+If you like Langflow, you can star the [Langflow GitHub repository](https://github.com/langflow-ai/langflow).
+Stars help other users find Langflow more easily, and quickly understand that other users have found it useful.
+
+Because Langflow is open-source, the more visible the repository is, the more likely the codebase is to attract [contributors](/contributing-how-to-contribute).
+
+## Watch the GitHub repository
+
+You can watch the [Langflow GitHub repository](https://github.com/langflow-ai/langflow) to get notified about new releases and other repository activity.
+
+To get release notifications only, select **Releases only**.
+
+If you select **Watching**, you will receive notifications about new releases as well as issues, discussions, and pull requests, if you are interested in that activity.
+For information about customizing repository notifications, see the [GitHub documentation on repository subscriptions](https://docs.github.com/en/account-and-profile/managing-subscriptions-and-notifications-on-github/managing-subscriptions-for-activity-on-github/viewing-your-subscriptions).
+
+## Request enhancements and get help through GitHub
+
+You can also submit feature requests and get help with Langflow through the GitHub repository.
+For more information, see [Get help and request enhancements](/contributing-github-issues).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Contributing/contributing-component-tests.mdx b/docs/versioned_docs/version-1.9.0/Contributing/contributing-component-tests.mdx
new file mode 100644
index 0000000000..6a9e2b0db0
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Contributing/contributing-component-tests.mdx
@@ -0,0 +1,113 @@
+---
+title: Contribute component tests
+slug: /contributing-component-tests
+---
+
+This guide outlines how to structure and implement tests for application components to ensure consistency and adequate coverage.
+
+## File naming
+
+* The test file should follow the same directory structure as the component being tested, but should be placed in the corresponding unit tests folder.
+
+ For example, if the file path for the component is `src/lfx/src/lfx/components/data/`, then the test file should be located at `src/backend/tests/unit/components/data`.
+
+* The test file name should use snake case and follow the pattern `test_.py`.
+
+ For example, if the file to be tested is `FileComponent.py`, then the test file should be named `test_file_component.py`.
+
+## File structure
+
+* Each test file should group tests into classes by component. There should be no standalone test functions in the file— only test methods within classes.
+* Class names should follow the pattern `Test`.
+For example, if the component being tested is `FileComponent`, then the test class should be named `TestFileComponent`.
+
+## Imports, inheritance, and mandatory methods
+
+To standardize component tests, base test classes have been created and should be imported and inherited by all component test classes. These base classes are located in the file `src/backend/tests/unit/base.py`.
+
+To import the base test classes:
+
+```python
+from tests.base import ComponentTestBaseWithClient
+from tests.base import ComponentTestBaseWithoutClient
+```
+
+These base classes enforce mandatory methods that the component test classes must implement. The base classes ensure that components built in previous versions continues to work in the current version. By inheriting from one of these base classes, the developer must define the following methods decorated with `@pytest.fixture`:
+
+* `component_class:` Returns the class of the component to be tested. For example:
+
+ ```python
+ @pytest.fixture
+ def component_class(self):
+ return FileComponent
+ ```
+
+* `default_kwargs:` Returns a dictionary with the default arguments required to instantiate the component. For example:
+
+ ```python
+ @pytest.fixture
+ def default_kwargs(self):
+ return {"file_path": "/tmp/test.txt", "_session_id": "123"}
+ ```
+
+* `file_names_mapping:` Returns a list of dictionaries representing the relationship between `version`, `module`, and `file_name` that the tested component has had over time. This can be left empty if it is an unreleased component. For example:
+
+ ```python
+ @pytest.fixture
+ def file_names_mapping(self):
+ return [
+ {"version": "1.0.15", "module": "data", "file_name": "File"},
+ {"version": "1.0.16", "module": "data", "file_name": "File"},
+ {"version": "1.0.17", "module": "data", "file_name": "File"},
+ {"version": "1.0.18", "module": "data", "file_name": "File"},
+ {"version": "1.0.19", "module": "data", "file_name": "File"},
+ ]
+ ```
+
+## Testing component functionalities
+
+Once the basic structure of the test file is defined, implement test methods for the component's functionalities. The following guidelines must be followed:
+
+1. Test method names should be descriptive, use snake case, and follow the pattern `test_`.
+2. Each test should follow the **Arrange, Act, Assert** pattern:
+ 1. **Arrange**: Prepare the data.
+ 2. **Act**: Execute the component.
+ 3. **Assert**: Verify the result.
+
+### Example
+
+1. **Arrange**: Prepare the data.
+
+ It is recommended, but not mandatory, that you use the fixtures defined in the basic structure.
+
+ ```python
+ def test_post_code_processing(self, component_class, default_kwargs):
+ component = component_class(**default_kwargs)
+ ```
+
+2. **Act**: Execute the component.
+
+ Call the `.to_frontend_node()` method of the component prepared during the **Arrange** step.
+
+ ```python
+ def test_post_code_processing(self, component_class, default_kwargs):
+ component = component_class(**default_kwargs)
+
+ frontend_node = component.to_frontend_node()
+ ```
+
+3. **Assert**: Verify the result.
+
+ After executing the `.to_frontend_node()` method, the resulting data is available for verification in the dictionary `frontend_node["data"]["node"]`. Assertions should be clear and cover the expected outcomes.
+
+ ```python
+ def test_file_component_processing(self, component_class, default_kwargs):
+ component = component_class(**default_kwargs)
+
+ frontend_node = component.to_frontend_node()
+
+ node_data = frontend_node["data"]["node"]
+ assert node_data["template"]["path"]["file_path"] == "/tmp/test.txt"
+ assert "path" in node_data["template"]
+ assert node_data["display_name"] == "File"
+ ```
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Contributing/contributing-components.mdx b/docs/versioned_docs/version-1.9.0/Contributing/contributing-components.mdx
new file mode 100644
index 0000000000..7929ac7ce7
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Contributing/contributing-components.mdx
@@ -0,0 +1,114 @@
+---
+title: Contribute components
+slug: /contributing-components
+---
+
+import PartialBasicComponentStructure from '../_partial-basic-component-structure.mdx';
+
+New components are added as objects of the [`Component`](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/custom/custom_component/component.py) class.
+
+Dependencies are added to the [pyproject.toml](https://github.com/langflow-ai/langflow/blob/main/pyproject.toml) file.
+
+## Contribute an example component to Langflow
+
+Anyone can contribute an example component. For example, to create a new data component called **DataFrame processor**, follow these steps to contribute it to Langflow.
+
+
+
+5. Save the `dataframe_processor.py` to the `src/lfx/src/lfx/components` directory.
+This example adds a data component, so add it to the `/data` directory.
+
+6. Add the component dependency to `src/lfx/src/lfx/components/data/__init__.py` as `from .DataFrameProcessor import DataFrameProcessor`.
+You can view the [/data/__init__.py](https://github.com/langflow-ai/langflow/blob/dev/src/lfx/src/lfx/components/data/__init__.py) in the Langflow repository.
+
+7. Add any new dependencies to the [pyproject.toml](https://github.com/langflow-ai/langflow/blob/main/pyproject.toml#L20) file.
+
+8. Submit documentation for your component. For this example component, you would submit documentation to the [Data components page](https://github.com/langflow-ai/langflow/blob/main/docs/docs/Components/components-data.mdx).
+
+9. Submit your changes as a pull request. The Langflow team will review, suggest changes, and add your component to Langflow.
+
+## Best practices for modifying components
+
+When creating or updating components, follow these best practices to maintain backward compatibility and ensure a smooth experience for users.
+
+### Don't rename the class or `name` attribute
+
+Changing the class name or the `name` attribute breaks the component for all existing users. This happens because the frontend tests the `type` attribute, which is set to the class' name or the `name` attribute. If these names change, the component effectively becomes a new component, and the old component disappears.
+
+Instead, do the following:
+* Change only the display name if the old name is unclear.
+* Change only the display name if functionality changes but remains related.
+* If a new internal name is necessary, mark the old component as `legacy=true` and create a new component.
+
+For example:
+```python
+class MyCustomComponent(BaseComponent):
+ name = "my_custom_component_internal"
+ legacy = True
+```
+
+### Don't remove fields and outputs
+
+Removing fields or outputs can cause edges to disconnect and change the behavior of components.
+
+Instead, mark fields as `deprecated` and keep them in the same location. If removal is absolutely necessary, you must define and document a migration plan. Always clearly communicate any changes in the field's information to users.
+
+### Maintain outdated components as legacy
+
+When updating components, create them as completely separate entities while maintaining the old component as a legacy version. Always ensure backward compatibility and never remove methods and attributes from base classes, such as `LCModelComponent`.
+
+### Favor asynchronous methods
+
+Always favor asynchronous methods and functions in your components. When interacting with files, use `aiofile` and `anyio.Path` for better performance and compatibility.
+
+### Include tests with your component
+
+Include tests for your changes using `ComponentTestBase` classes. For more information, see [Contribute component tests](/contributing-component-tests).
+
+### Documentation
+
+When documenting changes in pull requests, clearly explain *what* changed, such as display name updates or new fields, *why* it changed, such as improvements or bug fixes, and the *impact* on existing users.
+
+For example:
+
+
+Example PR
+
+```markdown
+# Pull request with changes to Notify component
+
+This pull request updates the Notify component.
+
+## What changed
+- Added new `timeout` field to control how long the component waits for a response.
+- Renamed `message` field to `notification_text` for clarity.
+- Added support for async operations.
+- Deprecated the `retry_count` field in favor of `max_retries`.
+
+## Why it changed
+- `timeout` field addresses user requests for better control over wait times.
+- `message` to `notification_text` change makes the field's purpose clearer.
+- Async support improves performance in complex flows.
+- `retry_count` to `max_retries` aligns with common retry pattern terminology.
+
+## Impact on users
+- New `timeout` field is optional (defaults to 30 seconds).
+- Users will see a deprecation warning for `retry_count`.
+ - Migration: Replace `retry_count` with `max_retries` in existing flows.
+ - Both fields will work until version 2.0.
+- No action needed for async support - it's backward compatible.
+```
+
+
+
+## Example pull request flow
+
+1. Create or update a component.
+Maintain the class name and `name` attribute if the purpose remains the same.
+Otherwise, create a new component and move the old component to `legacy`.
+2. Add tests.
+Create tests using one of the `ComponentTestBase` classes.
+For more information, see [Contribute component tests](/contributing-component-tests).
+3. Flag outdated fields and outputs as `deprecated` and keep them in the same location to ensure backward compatibility.
+4. Document your changes.
+Include migration instructions if breaking changes occur.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Contributing/contributing-how-to-contribute.mdx b/docs/versioned_docs/version-1.9.0/Contributing/contributing-how-to-contribute.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Contributing/contributing-how-to-contribute.mdx
@@ -0,0 +1,305 @@
+---
+title: Contribute to Langflow
+slug: /contributing-how-to-contribute
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+This guide is intended to help you start contributing to Langflow.
+As an open-source codebase in a rapidly developing field, Langflow welcomes contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
+
+To contribute code or documentation to Langflow, follow the [pull request guide](#open-a-pull-request).
+
+## Langflow services overview
+
+This overview will help you understand how to set up your development environment.
+
+Langflow consists of two main services:
+- Frontend: A React/TypeScript application that provides the user interface.
+- Backend: A Python/FastAPI service that handles API requests.
+
+During development, the frontend and backend services run separately on different ports:
+- Frontend development server: `http://localhost:3000` (with hot reload).
+- Backend API server: `http://localhost:7860`.
+
+When you install Langflow as an end user, the backend serves the frontend internally, making both services available on a single port (`7860` by default).
+
+## Install Langflow from source
+
+Install Langflow from source by forking the repository and setting up your development environment.
+
+### Prerequisites
+
+* [uv](https://docs.astral.sh/uv/getting-started/installation/) version 0.4 or later
+* [Node.js](https://nodejs.org/en/download/package-manager)
+* [Make](https://www.gnu.org/software/make/#documentation) (Linux and macOS only)
+
+### Clone the Langflow repository
+
+1. Fork the [Langflow GitHub repository](https://github.com/langflow-ai/langflow).
+
+2. Add the new remote to your local repository on your local machine:
+```bash
+git remote add FORK_NAME https://github.com/GIT_USERNAME/langflow.git
+```
+Replace the following:
+* `FORK_NAME`: A name for your fork of the repository
+* `GIT_USERNAME`: Your Git username
+
+### Run Langflow from source
+
+You can run Langflow from source after cloning the repository, even if you aren't contributing to the codebase.
+This builds the frontend and serves it through the backend on port `7860`.
+
+The instructions below are for running Langflow from source. For development with hot reload, see [Set up development environment](#set-up-your-langflow-development-environment).
+
+
+Run from source on macOS/Linux
+
+In your terminal, navigate to the root of the Langflow directory, and then run `make run_cli`.
+
+This command does the following:
+
+- Installs frontend and backend dependencies
+- Builds the frontend static files
+- Starts the application with default settings
+
+The Langflow frontend is served at `http://localhost:7860`.
+
+
+
+Run from source with Windows CMD
+
+To run Langflow from source on Windows, you can use the Langflow repository's included scripts, or run the commands in the terminal.
+
+Do one of the following:
+
+* To install and run Langflow with the included Windows Batch file, navigate to the `scripts/windows` directory, and then run the `build_and_run.bat` file.
+
+* To run Langflow from the Windows Command Line:
+
+ 1. Build the frontend static files:
+
+ ```
+ cd src/frontend
+ npm install
+ npm run build
+ ```
+
+ 2. Copy the contents of the built `src/frontend/build` directory to `src/backend/base/langflow/frontend`.
+
+ 3. Start Langflow:
+
+ ```
+ uv run langflow run
+ ```
+
+The Langflow frontend is served at `http://localhost:7860`.
+
+
+
+
+Run from source with PowerShell
+
+To run Langflow from source on Windows, you can use the Langflow repository's included scripts, or run the commands in the terminal.
+
+Do one of the following:
+
+* To install and run Langflow with the included scripts, navigate to the `scripts/windows` directory, and then run the `build_and_run.ps1` file.
+
+* To run Langflow from a PowerShell terminal:
+
+ 1. Build the frontend static files:
+
+ ```
+ cd src/frontend
+ npm install
+ npm run build
+ ```
+
+ 2. Copy the contents of the built `src/frontend/build` directory to `src/backend/base/langflow/frontend`.
+
+ 3. Start Langflow:
+
+ ```
+ uv run langflow run
+ ```
+
+The Langflow frontend is served at `http://localhost:7860`.
+
+
+
+### Set up your Langflow development environment
+
+:::tip
+Run `make help` to display all available make commands.
+:::
+
+This section is for contributors who want to develop and test code changes with hot reload enabled.
+
+If you just want to run Langflow locally without making code changes, see [Run Langflow from source](#run-langflow-from-source).
+
+
+
+
+1. Set up the Langflow development environment:
+
+ ```bash
+ make init
+ ```
+
+ This command sets up the development environment by doing the following:
+
+ - Checking for uv and npm.
+ - Installing backend and frontend dependencies.
+ - Installing pre-commit hooks.
+
+2. Run the backend and frontend in separate terminals for development:
+
+ ```bash
+ # Run backend in development mode (includes hot reload)
+ make backend
+ ```
+
+ ```bash
+ # In another terminal, run frontend in development mode (includes hot reload)
+ make frontend
+ ```
+
+ The `make backend` and `make frontend` commands automatically install dependencies, so you don't need to run install commands separately.
+
+ The frontend is served at `http://localhost:3000` and the backend at `http://localhost:7860`.
+
+3. Optional: Install pre-commit hooks to help keep your changes clean and well-formatted.
+
+ With pre-commit hooks installed, you must use `uv run git commit` instead of `git commit` directly.
+
+ `make init` installs pre-commit hooks automatically, or you can run the following command to install them manually:
+
+ ```bash
+ uv sync
+ uv run pre-commit install
+ ```
+
+4. To test your changes before pushing commits, run `make lint`, `make format`, and `make unit_tests`.
+To run all tests, including coverage, unit, and integration, tests, run `make tests`.
+
+
+
+
+Since Windows doesn't include `make`, building and running Langflow from source uses `npm` and `uv`.
+
+To set up the Langflow development environment, run the frontend and backend in separate terminals:
+
+1. To run the frontend development server with hot reload, run the following commands:
+
+ ```bash
+ cd src/frontend
+ npm install
+ npm run start
+ ```
+
+2. In a separate terminal, run the following command to run the backend:
+
+ ```bash
+ uv run langflow run --backend-only
+ ```
+
+The frontend is served at `http://localhost:3000` and the backend at `http://localhost:7860`. This setup preserves hot reload for frontend development, with no need to copy build files after every edit.
+
+
+
+
+### Troubleshoot frontend build issues
+
+If you encounter frontend build problems or are upgrading from an older version of Langflow, run `make run_clic` once.
+
+```bash
+make run_clic
+```
+
+This command cleans the build cache and rebuilds everything from scratch, which resolves most frontend-related issues when switching between versions.
+
+### Debug
+
+The repo includes a `.vscode/launch.json` file for debugging the backend in VSCode, which is faster than debugging with Docker Compose.
+
+For more information, see the [VSCode documentation](https://code.visualstudio.com/docs/debugtest/debugging#_start-a-debugging-session).
+
+### Additional contribution guides
+
+- [Contribute bundles](./contributing-bundles.mdx)
+- [Contribute components](./contributing-components.mdx)
+- [Contribute tests](./contributing-component-tests.mdx)
+- [Contribute templates](./contributing-templates.mdx)
+
+## Contribute documentation
+
+The documentation is built using [Docusaurus](https://docusaurus.io/) and written in [Markdown](https://docusaurus.io/docs/markdown-features).
+For style guidance, see the [Google Developer Documentation Style Guide](https://developers.google.com/style).
+
+1. Install [Node.js](https://nodejs.org/en/download/package-manager).
+
+2. Fork the [Langflow GitHub repository](https://github.com/langflow-ai/langflow).
+
+3. Add the new remote to your local repository on your local machine:
+
+ ```bash
+ git remote add FORK_NAME https://github.com/GIT_USERNAME/langflow.git
+ ```
+
+ Replace the following:
+
+ * `FORK_NAME`: A name for your fork of the repository
+ * `GIT_USERNAME`: Your Git username
+
+4. From the root of your local Langflow fork, change to the `/docs` directory:
+
+ ```bash
+ cd docs
+ ```
+
+ If you're using a dev container, run the documentation build from outside the container on your host terminal, not from within the dev container workspace.
+ The documentation build may not work properly when run inside the dev container.
+
+5. Install dependencies and start a local Docusaurus static site with hot reload:
+
+ ```bash
+ npm install
+ npm run start
+ ```
+
+ The documentation is served at `http://localhost:3000`.
+
+6. To edit and create content, work with the `.mdx` files in the `langflow/docs/docs` directory.
+
+ Create new files in `.mdx` format.
+
+ Navigation is defined in `langflow/docs/sidebars.js`.
+
+ Most pages use a `slug` for shorthand cross-referencing, rather than supplying the full or relative directory path.
+ For example, if a page has a `slug` of `/cool-page`, you can link to it with `[Cool page](/cool-page)` from any other `/docs` page.
+
+7. Recommended: After making some changes, run `npm run build` to build the site locally with more robust logging.
+This can help you find broken links before creating a PR.
+
+## Open a pull request
+
+To submit a pull request, do the following:
+
+1. Open a GitHub pull request against the active `release-X.Y.Z` release candidate branch.
+Do not target `main`.
+For example, if the latest released version is `1.8.0`, your pull request should target the `release-1.9.0` branch.
+Include a clear title and description.
+
+2. Ensure the PR title follows semantic commit conventions. For example, features are `feat: add new feature` and fixes are `fix: correct issue with X`.
+
+Some additional guidance on pull request titles:
+* Ensure the pull request description clearly describes the problem and solution. If the PR fixes an issue, include a link to the fixed issue in the PR description with `Fixes #1234`.
+* Pull request titles appear in Langflow's release notes, so they should explain what the PR does as explicitly as possible.
+* Pull requests should strive to fix one thing **only**, and should contain a good description of what is being fixed.
+
+3. A Langflow maintainer will review your pull request and may request changes, so ensure you pay attention to your PRs. Thanks for your contribution!
+
+For more information, see the [Python Developer's Guide](https://devguide.python.org/getting-started/pull-request-lifecycle/index.html#making-good-commits).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Contributing/contributing-templates.mdx b/docs/versioned_docs/version-1.9.0/Contributing/contributing-templates.mdx
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@@ -0,0 +1,91 @@
+---
+title: Contribute templates
+slug: /contributing-templates
+---
+
+import Icon from "@site/src/components/icon";
+
+Follow these best practices when submitting a template to Langflow.
+
+For template formatting examples, see [`/starter_projects`](https://github.com/langflow-ai/langflow/tree/main/src/backend/base/langflow/initial_setup/starter_projects) in the Langflow repository.
+
+## Create a PR to submit your template
+
+Follow these steps to submit your template:
+
+1. Fork the [Langflow repository](https://github.com/langflow-ai/langflow) on GitHub.
+2. On your fork, add your `template.json` file to `/starter_projects`.
+3. Include the [required items for template submission](#required-items-for-template-submission).
+4. Create a Pull Request (PR) from your fork to the main Langflow repository.
+5. Include a screenshot of your template in the PR.
+
+The Langflow team will review your PR, offer feedback, and, if approved, merge the template.
+
+## Required items for template submission
+
+Include the following items and follow these guidelines when submitting your template.
+
+### Name
+
+The template name must be concise and contain no more than three words.
+Capitalize only the first letter of each word.
+For example: **Blog Writer** or **Travel Planning Agent**.
+
+### Description
+
+A brief, informative description that is shown in the visual editor to help users understand the template's purpose and use cases.
+For example:
+
+```json
+ "description": "Auto-generate a customized blog post from instructions and referenced articles.",
+```
+
+### Icons
+
+Use icons from the [Lucide](https://lucide.dev/icons/) icon library.
+
+### Flow
+
+Use only **Core components** and **Bundles**.
+Don't use custom components that aren't part of the Langflow codebase.
+
+Include brief README, quickstart, or other essential details in a **Note**. Notes accept Markdown syntax.
+For example:
+
+```text
+# Financial Assistant Agents
+
+The Financial Assistant Agent retrieves web content and writes reports about finance.
+
+## Prerequisites
+
+* [OpenAI API key](https://platform.openai.com/api-keys)
+* [Tavily AI Search key](https://docs.tavily.com/welcome)
+* [Sambanova API key](https://sambanova.ai/)
+
+## Quickstart
+
+1. In both **Agent** components, add your OpenAI API key.
+2. In the **Model Provider** field, select **Sambanova**, and select a model.
+3. In the **Sambanova** component, add your **Sambanova API key**.
+4. In the **Tavily Search** component, add your **Tavily API key**.
+5. Click the **Playground** and ask `Why did Nvidia stock drop in January?`
+```
+
+### Format
+
+Submit the template in JSON format.
+
+### Tags
+Assign the template to one of the following categories:
+
+- Assistants
+- Classification
+- Coding
+- Content Generation
+- Q&A
+- Prompting
+- RAG
+- Agents
+
+For more information, see the Langflow repository's [template categories](https://github.com/langflow-ai/langflow/blob/main/src/frontend/src/modals/templatesModal/index.tsx#L27-L57).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-architecture.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-architecture.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Deployment/deployment-architecture.mdx
@@ -0,0 +1,72 @@
+---
+title: Langflow architecture on Kubernetes
+slug: /deployment-architecture
+---
+
+There are two broad types of Langflow deployments:
+
+* **Langflow IDE (development)**: Deploy both the Langflow visual editor (frontend) and API (backend). Typically, this is used for development environments where developers use the visual editor to create and manage flows before packaging and serving them through a production runtime deployment.
+
+ The Langflow repository's example [`docker-compose.yml`](https://github.com/langflow-ai/langflow/blob/main/docker_example/docker-compose.yml) file builds a Langflow IDE image.
+
+ For information about IDE deployments on Kubernetes, see [Deploy the Langflow development environment on Kubernetes](/deployment-kubernetes-dev).
+
+
+* **Langflow runtime (production)**: Deploy the Langflow runtime for production flows, which is headless (backend only) service focused on serving the Langflow API. This is used for production environments where flows are executed programmatically without the need for the visual editor. The server exposes your flows as endpoints, and then runs only the processes necessary to serve each flow.
+
+ An external PostgreSQL database is strongly recommended with this deployment type to improve scalability and reliability as compared to the default SQLite database.
+
+ For information about runtime deployments on Kubernetes, see [Deploy the Langflow production environment on Kubernetes](/deployment-kubernetes-prod).
+
+ :::tip
+ You can start Langflow in headless mode with the `LANGFLOW_BACKEND_ONLY` [environment variable](/environment-variables).
+ :::
+
+You can also [deploy the Langflow IDE and runtime on Docker](/deployment-docker).
+
+## Benefits of deploying Langflow on Kubernetes
+
+Deploying on Kubernetes offers the following advantages:
+
+* **Scalability**: Kubernetes allows you to scale the Langflow service to meet the demands of your workload.
+* **Availability and resilience**: Kubernetes provides built-in resilience features, such as automatic failover and self-healing, to ensure that the Langflow service is always available.
+* **Security**: Kubernetes provides security features, such as role-based access control and network isolation, to protect the Langflow service and its data.
+* **Portability**: Kubernetes is a portable platform, which means that you can deploy the Langflow service to any Kubernetes cluster, on-premises or in the cloud.
+
+ Langflow can be deployed on cloud platforms like AWS EKS, Google GKE, or Azure AKS. For more information, see the [Langflow Helm charts repository](https://github.com/langflow-ai/langflow-helm-charts).
+
+## Langflow deployment
+
+A typical Langflow deployment includes:
+
+* **Langflow services**: The Langflow API and, for IDE deployments, the visual editor.
+* **Kubernetes cluster**: The Kubernetes cluster provides a platform for deploying and managing Langflow and its supporting services.
+* **Persistent storage**: Persistent storage is used to store the Langflow service's data, such as models and training data.
+* **Ingress controller**: The ingress controller provides a single entry point for traffic to the Langflow service.
+* **Load balancer**: Balances traffic across multiple Langflow replicas.
+* **Vector database**: If you are using Langflow for RAG, you can integrate with the vector database in Astra Serverless.
+
+
+
+## Environment isolation
+
+It is recommended to deploy and run separate development and production environments for Langflow:
+
+* Deploy the IDE in your development environment where your developers prototype and test new flows.
+* Deploy the runtime in your production environment to serve flows as standalone services.
+
+
+
+This separation is designed to enhance security, support an organized software development pipeline, and optimize infrastructure resource allocation:
+
+* **Isolation**: By separating the development and production environments, you can better isolate different phases of the application lifecycle. This isolation minimizes the risk of development-related issues impacting the production environments.
+* **Access control**: Different security policies and access controls can be applied to each environment. Developers may require broader access in the IDE for testing and debugging, while the runtime environment can be locked down with stricter security measures.
+* **Reduced attack surface**: The runtime environment is configured to include only essential components, reducing the attack surface and potential vulnerabilities.
+* **Optimized resource usage and cost efficiency**: By separating the two environments, you can allocate resources more effectively. Each flow can be deployed independently, providing fine-grained resource control.
+* **Scalability**: The runtime environment can be scaled independently based on application load and performance requirements, without affecting the development environment.
+
+## Next steps
+
+* [Best practices for Langflow on Kubernetes](/deployment-prod-best-practices)
+* [Deploy the Langflow development environment on Kubernetes](/deployment-kubernetes-dev)
+* [Deploy the Langflow production environment on Kubernetes](/deployment-kubernetes-prod)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-block-custom-components.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-block-custom-components.mdx
new file mode 100644
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@@ -0,0 +1,43 @@
+---
+title: Block custom components
+slug: /deployment-block-custom-components
+---
+
+Custom components and the component code editor allow Langflow users to create and execute Python code in the Langflow runtime.
+While this freedom is a feature of Langflow, in some deployments you might want to block the creation of custom Langflow components to prevent arbitrary code execution.
+
+To block custom component creation on a Langflow server, set the following [environment variable](/environment-variables):
+
+```bash
+LANGFLOW_ALLOW_CUSTOM_COMPONENTS=false
+```
+
+When set to `false`, Langflow blocks creating custom components and changing code in the visual editor.
+
+When unset or `true`, Langflow allows custom code.
+Existing Langflow installations will keep the default `true` behavior until you opt in to this restriction.
+
+This environment variable is a beta feature, and should not be your only safeguard in production environments.
+
+In the event that Langflow could execute untrusted or LLM-generated code, run Langflow in an isolated and containerized execution environment.
+
+### Configure a custom component allow-list
+
+`LANGFLOW_ALLOW_CUSTOM_COMPONENTS` works together with optional paths that define which component templates the server loads, and which code hashes are trusted.
+
+Directories listed in the `LANGFLOW_COMPONENTS_PATH` environment variable are loaded as custom components and merged into the server’s template set.
+When `LANGFLOW_ALLOW_CUSTOM_COMPONENTS` is set to `false`, the components in the `LANGFLOW_COMPONENTS_PATH` directory **remain allowed**.
+
+To allow some components while blocking others, include the allowed custom components directory in `LANGFLOW_COMPONENTS_PATH` and set `LANGFLOW_ALLOW_CUSTOM_COMPONENTS` to `false`.
+
+`LANGFLOW_COMPONENTS_INDEX_PATH` points to a prebuilt component index JSON file at a local path or `http://` / `https://` URL.
+This environment variable replaces the default index bundled with the `lfx` package.
+It does **not** import Python modules from a directory on disk.
+To allow-list custom components, use the `LANGFLOW_COMPONENTS_PATH` environment variable.
+
+If both environment variables are set, Langflow builds one combined set of components from the custom index _and_ from `LANGFLOW_COMPONENTS_PATH`. If the same component category name exists in both, `LANGFLOW_COMPONENTS_PATH` replaces that whole category from the custom index.
+
+For more information, see:
+
+* [Environment variables](/environment-variables#visual-editor-and-playground-behavior)
+* [Security](/security)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-caddyfile.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-caddyfile.mdx
new file mode 100644
index 0000000000..1624c2e700
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Deployment/deployment-caddyfile.mdx
@@ -0,0 +1,170 @@
+---
+title: Deploy Langflow on a remote server
+slug: /deployment-caddyfile
+---
+
+Learn how to deploy Langflow on your own remote server with secure web access.
+This guide walks you through setting up Langflow on a remote server using [Docker](https://docs.docker.com/) and configuring secure web access with [Caddy](https://caddyserver.com/docs/).
+
+## Prerequisites
+
+* A server with a dual-core CPU and at least 2 GB of RAM.
+This example uses [Hetzner cloud](https://www.hetzner.com/) for hosting. Your deployment may vary.
+
+## Connect to your remote server with SSH
+
+1. Create an SSH key to connect to your server remotely.
+For example:
+
+ ```bash
+ ssh-keygen -t ed25519 -C "DANA@EXAMPLE.COM"
+ ```
+
+ Replace `DANA@EXAMPLE.COM` with the email address that you want to associate with the SSH key.
+
+2. In your terminal, follow the instructions to create your SSH key pair.
+This creates both a private and public key.
+To copy the public key from your terminal, enter the following command:
+
+ ```bash
+ cat ~/Downloads/host-lf.pub | pbcopy
+ ```
+
+3. In your remote server, add the SSH key you copied in the previous step.
+For example, if you are using a Hetzner cloud server, click **Server**, and then select **SSH keys** to add an SSH key.
+
+4. To connect to your server with SSH, enter the following command.
+
+ ```bash
+ ssh -i PATH_TO_PRIVATE_KEY/PRIVATE_KEY_NAME root@SERVER_IP_ADDRESS
+ ```
+
+ Replace the following:
+
+ * `PATH_TO_PRIVATE_KEY/PRIVATE_KEY_NAME`: The path to your private SSH key file that matches the public key you added to your server
+ * `SERVER_IP_ADDRESS`: Your server's IP address
+
+5. When prompted for a key fingerprint, type `yes`.
+
+The terminal output indicates if the connection succeeds or fails.
+The following response was returned after connecting to a Hetzner cloud server:
+
+```text
+System information as of Mon May 19 04:34:44 PM UTC 2025
+
+System load: 0.0 Processes: 129
+Usage of /: 1.5% of 74.79GB Users logged in: 0
+Memory usage: 5% IPv4 address for eth0: 5.161.250.132
+Swap usage: 0% IPv6 address for eth0: 2a01:4ff:f0:4de7::1
+```
+
+## Deploy Langflow on your server
+
+Now that your local machine is connected to your remote server with SSH, you can install Docker, create a `docker-compose.yml` file, and serve it publicly with a reverse proxy, such as Caddy.
+
+1. Install Docker on your server.
+
+ Since this example server is an Ubuntu server, it can install snap packages.
+ If you aren't using Ubuntu or you prefer a different installation method, see the [official Docker installation guide](https://docs.docker.com/get-started/get-docker/) for instructions for your operating system.
+
+ ```bash
+ snap install docker
+ ```
+
+2. Create a file called `docker-compose.yml`, and then open it in a text editor:
+
+ ```bash
+ touch docker-compose.yml && nano docker-compose.yml
+ ```
+
+3. Add the following values to `docker-compose.yml`, and then save the file.
+
+ The following example defines the Langflow service from the `langflow:latest` image and a Caddy service to expose Langflow through a reverse proxy.
+
+ :::tip
+ The [host-langflow](https://github.com/datastax/host-langflow) repository offers pre-built copies of this `docker-compose.yml` and `Caddyfile`, if you prefer to fork the repository to your server.
+ :::
+
+ ```yml
+ version: "3.8"
+
+ services:
+ langflow:
+ image: langflowai/langflow:latest
+ ports:
+ - "7860:7860"
+ environment:
+ - LANGFLOW_HOST=0.0.0.0
+ - LANGFLOW_PORT=7860
+
+ caddy:
+ image: caddy:latest
+ ports:
+ - "80:80"
+ - "443:443"
+ volumes:
+ - ./Caddyfile:/etc/caddy/Caddyfile
+ - caddy_data:/data
+ - caddy_config:/config
+ depends_on:
+ - langflow
+
+ volumes:
+ caddy_data:
+ caddy_config:
+ ```
+
+4. Create a file called `Caddyfile`:
+
+ ```bash
+ touch Caddyfile && nano Caddyfile
+ ```
+
+5. Add the following values to `Caddyfile`, and then save the file.
+
+ This Caddyfile configures Caddy to listen on port 80, and forward all incoming requests to port 80 to the Langflow service at port 7860.
+
+ ```
+ :80 {
+ reverse_proxy langflow:7860
+ }
+ ```
+
+6. To deploy your server, run `docker-compose up`.
+
+ When the `Welcome to Langflow` message appears, Langflow is running and accessible internally at `http://0.0.0.0:7860` inside the Docker network.
+
+7. To access your Langflow server over the public internet, navigate to your server's public IP address, such as `http://5.161.250.132`.
+This address uses HTTP because HTTPS isn't enabled yet.
+
+8. Recommended: Enable HTTPS:
+
+ 1. Modify your domain's A record to point to your server's IP address. For example:
+
+ ```
+ Type: A
+ Name: langflow
+ Value: 5.161.250.132 # Set to your server's IP address
+ ```
+
+ 2. Stop your server.
+
+ 3. Modify your Caddyfile to include port `443` so Caddy can forward both HTTP (port 80) and HTTPS (port 443) requests to the Langflow service:
+
+ ```
+ :80, :443 {
+ reverse_proxy langflow:7860
+ }
+ ```
+
+ 4. Start your server.
+
+ When users visit your domain, Caddy recognizes the incoming traffic and automatically routes it to your server with a secure, encrypted connection.
+
+9. To exit your SSH session, type `exit`.
+
+## See also
+
+To package your local flows into a custom Docker image, see [Containerize a Langflow application](/develop-application).
+
+For a step-by-step guide to deploying Langflow, including deployments to [fly.io](https://fly.io/) and [Flightcontrol.dev](https://www.flightcontrol.dev/), see [How to Host Langflow Anywhere](https://www.youtube.com/watch?v=q4qt5hSnte4).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-docker.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-docker.mdx
new file mode 100644
index 0000000000..6a58e5ea48
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Deployment/deployment-docker.mdx
@@ -0,0 +1,278 @@
+---
+title: Deploy Langflow on Docker
+slug: /deployment-docker
+---
+
+import PartialPodmanAlt from '@site/docs/_partial-podman-alt.mdx';
+
+
+
+Running applications in Docker containers ensures consistent behavior across different systems and eliminates dependency conflicts.
+
+You can use the Langflow Docker image to start a Langflow container.
+
+This guide demonstrates several ways to deploy Langflow with [Docker](https://docs.docker.com/) and [Docker Compose](https://docs.docker.com/compose/):
+
+* [Quickstart](#quickstart): Start a Langflow container with default values.
+* [Use Docker Compose](#clone): Clone the Langflow repo, and then use Docker Compose to build the Langflow Docker container.
+This option provides more control over the configuration, including a persistent PostgreSQL database service, while still using the base Langflow Docker image.
+* [Create a custom flow image](#package-your-flow-as-a-docker-image): Use a Dockerfile to package a flow as a Docker image.
+* [Create a custom Langflow image](#customize-the-langflow-docker-image): Use a Dockerfile to package a custom Langflow Docker image that includes your own code, custom dependencies, or other modifications.
+* [Upgrade the Langflow Docker image](#upgrade-the-langflow-docker-image): Upgrade to a newer image without losing your database or flows by using persistent volumes and replacing only the container.
+
+## Quickstart: Start a Langflow container with default values {#quickstart}
+
+With Docker installed and running on your system, run the following command:
+
+```shell
+docker run -p 7860:7860 langflowai/langflow:latest
+```
+
+Then, access Langflow at `http://localhost:7860/`.
+
+This container runs a pre-built Docker image with default settings.
+For more control over the configuration, see [Clone the repo and run the Langflow Docker container](#clone).
+
+## Clone the repo and run the Langflow Docker container {#clone}
+
+Cloning the Langflow repository and using Docker Compose gives you more control over your configuration, allowing you to customize environment variables, use a persistent PostgreSQL database service (instead of the default SQLite database), and include custom dependencies.
+
+The default deployment with Docker Compose includes the following:
+
+- **Langflow service**: Runs the latest Langflow image with PostgreSQL as the database.
+- **PostgreSQL service**: Provides persistent data storage for flows, users, and settings.
+- **Persistent volumes**: Ensures your data survives container restarts.
+
+The complete Docker Compose configuration is available in `docker_example/docker-compose.yml`.
+
+1. Clone the Langflow repository:
+
+ ```shell
+ git clone https://github.com/langflow-ai/langflow.git
+ ```
+
+2. Navigate to the `docker_example` directory:
+
+ ```shell
+ cd langflow/docker_example
+ ```
+
+3. Run the Docker Compose file:
+
+ ```shell
+ docker compose up
+ ```
+
+4. Access Langflow at `http://localhost:7860/`.
+
+### Customize your deployment
+
+You can customize the Docker Compose configuration to fit your specific deployment.
+
+For example, to configure the container's database credentials using a `.env` file, do the following:
+
+1. Create a `.env` file with your database credentials in the same directory as `docker-compose.yml`:
+
+ ```text
+ # Database credentials
+ POSTGRES_USER=myuser
+ POSTGRES_PASSWORD=mypassword
+ POSTGRES_DB=langflow
+
+ # Langflow configuration
+ LANGFLOW_DATABASE_URL=postgresql://myuser:mypassword@postgres:5432/langflow
+ LANGFLOW_CONFIG_DIR=/app/langflow
+ ```
+
+2. Modify the `docker-compose.yml` file to reference the `.env` file for both the `langflow` and `postgres` services:
+
+ ```yaml
+ services:
+ langflow:
+ environment:
+ - LANGFLOW_DATABASE_URL=${LANGFLOW_DATABASE_URL}
+ - LANGFLOW_CONFIG_DIR=${LANGFLOW_CONFIG_DIR}
+ postgres:
+ environment:
+ - POSTGRES_USER=${POSTGRES_USER}
+ - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
+ - POSTGRES_DB=${POSTGRES_DB}
+ ```
+
+For a complete list of available environment variables, see [Langflow environment variables](/environment-variables).
+
+For more customization options, see [Customize the Langflow Docker image with your own code](#customize-the-langflow-docker-image).
+
+## Package your flow as a Docker image {#package-your-flow-as-a-docker-image}
+
+This section shows you how to create a Dockerfile that builds a Docker image containing your Langflow flow. This approach is useful when you want to distribute a specific flow as a standalone container or deploy it to environments like Kubernetes.
+
+Unlike the previous sections that use pre-built images, this method builds a custom image with your flow embedded inside it.
+
+1. Create a project directory, and change directory into it.
+
+ ```bash
+ mkdir langflow-custom && cd langflow-custom
+ ```
+
+2. Add your flow's JSON file to the directory. You can download an example, or use your own:
+
+ ```bash
+ # Download an example flow
+ wget https://raw.githubusercontent.com/langflow-ai/langflow-helm-charts/refs/heads/main/examples/flows/basic-prompting-hello-world.json
+
+ # Or copy your own flow file
+ cp /path/to/your/flow.json .
+ ```
+
+3. Create a Dockerfile to build your custom image:
+
+ ```dockerfile
+ FROM langflowai/langflow:latest
+ RUN mkdir /app/flows
+ COPY ./*.json /app/flows/
+ ENV LANGFLOW_LOAD_FLOWS_PATH=/app/flows
+ ```
+
+This Dockerfile uses the official Langflow image as the base, creates a directory for your flows, copies your JSON flow files into the directory, and sets the environment variable to tell Langflow where to find the flows.
+
+4. Build and test your custom image:
+
+ ```bash
+ docker build -t myuser/langflow-custom:1.0.0 .
+ docker run -p 7860:7860 myuser/langflow-custom:1.0.0
+ ```
+
+5. Push your image to Docker Hub (optional):
+
+ ```bash
+ docker push myuser/langflow-custom:1.0.0
+ ```
+
+Your custom image now contains your flow and can be deployed anywhere Docker runs. For Kubernetes deployment, see [Deploy the Langflow production environment on Kubernetes](/deployment-kubernetes-prod).
+
+## Customize the Langflow Docker image with your own code {#customize-the-langflow-docker-image}
+
+While the previous section showed how to package a flow with a Docker image, this section shows how to customize the Langflow application itself. This is useful when you need to add custom Python packages or dependencies, modify Langflow's configuration or settings, include custom components or tools, or add your own code to extend Langflow's functionality.
+
+This example demonstrates how to customize the **Message History** component, but the same approach can be used for any code modifications.
+
+```dockerfile
+FROM langflowai/langflow:latest
+
+# Set working directory
+WORKDIR /app
+
+# Copy your modified memory component
+COPY src/lfx/src/lfx/components/helpers/memory.py /tmp/memory.py
+
+# Find the site-packages directory where langflow is installed
+RUN python -c "import site; print(site.getsitepackages()[0])" > /tmp/site_packages.txt
+
+# Replace the file in the site-packages location
+RUN SITE_PACKAGES=$(cat /tmp/site_packages.txt) && \
+ echo "Site packages at: $SITE_PACKAGES" && \
+ mkdir -p "$SITE_PACKAGES/langflow/components/helpers" && \
+ cp /tmp/memory.py "$SITE_PACKAGES/langflow/components/helpers/"
+
+# Clear Python cache in the site-packages directory only
+RUN SITE_PACKAGES=$(cat /tmp/site_packages.txt) && \
+ find "$SITE_PACKAGES" -name "*.pyc" -delete && \
+ find "$SITE_PACKAGES" -name "__pycache__" -type d -exec rm -rf {} +
+
+# Expose the default Langflow port
+EXPOSE 7860
+
+# Command to run Langflow
+CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"]
+```
+
+To use this custom Dockerfile, do the following:
+
+1. Create a directory for your custom Langflow setup:
+
+ ```bash
+ mkdir langflow-custom && cd langflow-custom
+ ```
+
+2. Create the necessary directory structure for your custom code.
+In this example, Langflow expects `memory.py` to exist in the `/helpers` directory, so you create a directory in that location.
+
+ ```bash
+ mkdir -p src/lfx/src/lfx/components/helpers
+ ```
+
+3. Place your modified `memory.py` file in the `/helpers` directory.
+
+4. Create a new file named `Dockerfile` in your `langflow-custom` directory, and then copy the Dockerfile contents shown above into it.
+
+5. Build and run the image:
+
+ ```bash
+ docker build -t myuser/langflow-custom:1.0.0 .
+ docker run -p 7860:7860 myuser/langflow-custom:1.0.0
+ ```
+
+This approach can be adapted for any other components or custom code you want to add to Langflow by modifying the file paths and component names.
+
+## Upgrade the Langflow Docker image {#upgrade-the-langflow-docker-image}
+
+To upgrade a Langflow Docker deployment without losing your database or flows, do the following:
+
+1. Keep data on persistent volumes, so when you upgrade Langflow, you will replace only the container image.
+Use Docker volumes or bind mounts for Langflow data and the database so they persist outside of the container.
+For example, this Docker Compose file uses a bind mount for Langflow data (`./langflow-data` on the host) and a named volume for the PostgreSQL database (`langflow-postgres`):
+
+ ```yaml
+ services:
+ langflow:
+ image: langflowai/langflow:1.8.0
+ environment:
+ - LANGFLOW_CONFIG_DIR=/app/langflow
+ volumes:
+ - ./langflow-data:/app/langflow
+ postgres:
+ image: postgres:16
+ volumes:
+ - langflow-postgres:/var/lib/postgresql/data
+
+ volumes:
+ langflow-postgres:
+ ```
+
+ For additional examples, see the [Docker Compose configuration](#clone) and the [docker_example compose file](https://github.com/langflow-ai/langflow/blob/main/docker_example/docker-compose.yml).
+
+2. Pull the new image and update the image tag in your `docker-compose.yml` or `docker run` command.
+
+ With Docker Compose, set the image in your compose file, such as `image: langflowai/langflow:1.8.0`, and then pull:
+
+ ```bash
+ docker compose pull
+ ```
+
+ With `docker run`, pull the image:
+
+ ```bash
+ docker pull langflowai/langflow:1.8.0
+ ```
+
+3. Restart the container. The same volumes will be reattached, so your database and flows are preserved.
+
+ With Docker Compose:
+
+ ```bash
+ docker compose up -d
+ ```
+
+ With `docker run`, use the same volume mount and the new image tag:
+
+ ```bash
+ docker run -p 7860:7860 -v langflow-data:/app/langflow langflowai/langflow:1.8.0
+ ```
+
+This approach keeps the persistent volumes separate from the Langflow container, so you can upgrade the Langflow application without losing data.
+
+If you need to upgrade to a custom image based on a Langflow release, such as to add `uv` in `1.8.0`, first build a derived image from the official image, and then follow the same steps above.
+Set the custom image in your compose file or `docker run`, and then pull and restart.
+
+For a minimal Dockerfile that adds `uv` to the 1.8.0 image, see the [release notes](/release-notes) (“Docker image no longer includes uv or uvx”).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-gcp.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-gcp.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Deployment/deployment-gcp.mdx
@@ -0,0 +1,27 @@
+---
+title: Deploy Langflow on Google Cloud Platform
+slug: /deployment-gcp
+---
+
+This guide demonstrates how to deploy Langflow on [Google Cloud Platform](https://console.cloud.google.com/) with a Cloud Shell script that walks through the process of setting up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow development environment in GCP.
+
+To use this script, you need a Google Cloud project with the necessary permissions to create resources.
+
+1. Follow this link to launch the Cloud Shell with the GCP deployment script from the Langflow repository:
+
+ [](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial.md)
+
+2. Click **Trust repo**.
+
+ Some `gcloud` commands may not run in an ephemeral Cloud Shell environment.
+
+3. Click **Start**, and then follow the tutorial to deploy Langflow.
+
+:::info
+This deployment uses a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible) as a cost-effective option to demonstrate how to deploy Langflow on GCP.
+However, due to the nature of spot instances, the VM can be terminated at any time if Google Cloud needs to reclaim the resources.
+
+For a more stable deployment, consider using a regular VM instance instead of a spot instance.
+
+For more information, see the [GCP pricing calculator](https://cloud.google.com/products/calculator?hl=en).
+:::
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-hugging-face-spaces.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-hugging-face-spaces.mdx
new file mode 100644
index 0000000000..f7a615ebde
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Deployment/deployment-hugging-face-spaces.mdx
@@ -0,0 +1,18 @@
+---
+title: Deploy Langflow on Hugging Face Spaces
+slug: /deployment-hugging-face-spaces
+---
+
+This guide explains how to deploy Langflow on [Hugging Face Spaces](https://huggingface.co/spaces/).
+
+1. Go to the [Langflow Space](https://huggingface.co/spaces/Langflow/Langflow?duplicate=true).
+2. Click **Duplicate Space**.
+3. Configure the duplicated Space:
+
+ 1. Enter a name for your Space.
+ 2. Select either public or private visibility.
+ 3. Click **Duplicate Space**.
+
+ 
+
+When setup is complete, you're redirected to your new Space automatically, and your Langflow instance is ready to use.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-kubernetes-dev.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-kubernetes-dev.mdx
new file mode 100644
index 0000000000..4a657e6af5
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Deployment/deployment-kubernetes-dev.mdx
@@ -0,0 +1,186 @@
+---
+title: Deploy the Langflow development environment on Kubernetes
+slug: /deployment-kubernetes-dev
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+The [Langflow integrated development environment (IDE) Helm chart](https://github.com/langflow-ai/langflow-helm-charts/tree/main/charts/langflow-ide) is designed to provide a complete environment for developers to create, test, and debug their flows. It includes both the Langflow API and visual editor.
+
+## Prerequisites
+
+- A [Kubernetes](https://kubernetes.io/docs/setup/) cluster
+- [kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl)
+- [Helm](https://helm.sh/docs/intro/install/)
+
+## Prepare a Kubernetes cluster
+
+This example uses [Minikube](https://minikube.sigs.k8s.io/docs/start/), but you can use any Kubernetes cluster.
+
+1. Create a Kubernetes cluster on Minikube:
+
+ ```shell
+ minikube start
+ ```
+
+2. Set `kubectl` to use Minikube:
+
+ ```shell
+ kubectl config use-context minikube
+ ```
+
+## Install the Langflow IDE Helm chart
+
+1. Add the repository to Helm, and then update it:
+
+ ```shell
+ helm repo add langflow https://langflow-ai.github.io/langflow-helm-charts
+ helm repo update
+ ```
+
+2. Install Langflow with the default options in the `langflow` namespace:
+
+ ```shell
+ helm install langflow-ide langflow/langflow-ide -n langflow --create-namespace
+ ```
+
+3. Check the status of the pods:
+
+ ```shell
+ kubectl get pods -n langflow
+ ```
+
+## Access the Langflow IDE
+
+Enable local port forwarding to access Langflow from your local machine:
+
+1. Make the Langflow API accessible from your local machine at port 7860:
+
+ ```shell
+ kubectl port-forward -n langflow svc/langflow-service-backend 7860:7860
+ ```
+
+2. Make the visual editor accessible from your local machine at port 8080:
+
+ ```shell
+ kubectl port-forward -n langflow svc/langflow-service 8080:8080
+ ```
+
+Now you can do the following:
+
+- Access the Langflow API at `http://localhost:7860`.
+- Access the visual editor at `http://localhost:8080`.
+
+## Modify your Langflow IDE deployment
+
+You can modify the Langflow IDE Helm chart's [`values.yaml`](https://github.com/langflow-ai/langflow-helm-charts/blob/main/charts/langflow-ide/values.yaml) file to customize your deployment.
+The following sections describe some common modifications.
+
+If you need to set secrets, Kubernetes secrets are recommended.
+
+### Deploy a different Langflow version
+
+The Langflow IDE Helm chart deploys the latest Langflow version by default.
+
+To specify a different Langflow version, set the `langflow.backend.image.tag` and `langflow.frontend.image.tag` values to your preferred version.
+For example:
+
+```yaml
+langflow:
+ backend:
+ image:
+ tag: "1.0.0a59"
+ frontend:
+ image:
+ tag: "1.0.0a59"
+```
+
+### Use external storage for the Langflow database
+
+The Langflow IDE Helm chart uses the default Langflow database configuration, specifically a SQLite database stored in a local persistent disk.
+
+If you want to use an [external PostgreSQL database](/configuration-custom-database), use `postgresql` chart or `externalDatabase` to configure the database connection in `values.yaml`.
+
+
+
+
+Use the built-in PostgreSQL chart:
+
+```yaml
+postgresql:
+ enabled: true
+ auth:
+ username: "langflow"
+ password: "langflow-postgres"
+ database: "langflow-db"
+```
+
+
+
+
+If you don't want to use the built-in PostgreSQL chart, set `postgresql.enabled` to `false`, and then configure the database connection in `langflow.backend.externalDatabase`:
+
+```yaml
+postgresql:
+ enabled: false
+
+langflow:
+ backend:
+ externalDatabase:
+ enabled: true
+ driver:
+ value: "postgresql"
+ port:
+ value: "5432"
+ user:
+ value: "langflow"
+ password:
+ valueFrom:
+ secretKeyRef:
+ key: "password"
+ name: "your-secret-name"
+ database:
+ value: "langflow-db"
+ sqlite:
+ enabled: false
+```
+
+
+
+
+### Configure scaling
+
+To configure scaling for the Langflow IDE Helm chart deployment, you must set `replicaCount` (horizontal scaling) and `resources` (vertical scaling) for both the `langflow.backend` and `langflow.frontend`.
+
+If your flows rely on a shared state, such as [built-in chat memory](/memory), you must also set up a shared database when scaling horizontally.
+
+```yaml
+langflow:
+ backend:
+ replicaCount: 1
+ resources:
+ requests:
+ cpu: 0.5
+ memory: 1Gi
+ # limits:
+ # cpu: 0.5
+ # memory: 1Gi
+
+ frontend:
+ enabled: true
+ replicaCount: 1
+ resources:
+ requests:
+ cpu: 0.3
+ memory: 512Mi
+ # limits:
+ # cpu: 0.3
+ # memory: 512Mi
+```
+
+## See also
+
+* [Best practices for Langflow on Kubernetes](/deployment-prod-best-practices)
+* [Deploy the Langflow production environment on Kubernetes](/deployment-kubernetes-prod)
+* [Langflow Helm Charts repository](https://github.com/langflow-ai/langflow-helm-charts)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-kubernetes-prod.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-kubernetes-prod.mdx
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@@ -0,0 +1,208 @@
+---
+title: Deploy the Langflow production environment on Kubernetes
+slug: /deployment-kubernetes-prod
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+The [Langflow runtime Helm chart](https://github.com/langflow-ai/langflow-helm-charts/blob/main/charts/langflow-runtime) is tailored for deploying applications in a production environment. It is focused on stability, performance, isolation, and security to ensure that applications run reliably and efficiently.
+
+:::warning
+For security reasons, the default Langflow runtime Helm chart sets [`readOnlyRootFilesystem: true`](https://github.com/langflow-ai/langflow-helm-charts/blob/main/charts/langflow-runtime/values.yaml#L46). This setting prevents modifications to the container's root filesystem at runtime, which is a recommended security measure in production environments.
+
+If `readOnlyRootFilesystem` is disabled (`false`), it degrades your deployment's security posture. Only disable this setting if you understand the security implications and you have implemented other security measures.
+
+For more information, see the [Kubernetes documentation](https://kubernetes.io/docs/tasks/configure-pod-container/security-context/).
+:::
+
+## Prerequisites
+
+- A [Kubernetes](https://kubernetes.io/docs/setup/) server
+- [kubectl](https://kubernetes.io/docs/tasks/tools/#kubectl)
+- [Helm](https://helm.sh/docs/intro/install/)
+
+## Install the Langflow runtime Helm chart
+
+1. Add the repository to Helm:
+
+ ```shell
+ helm repo add langflow https://langflow-ai.github.io/langflow-helm-charts
+ helm repo update
+ ```
+
+2. Install the Langflow app with the default options in the `langflow` namespace.
+
+
+
+
+ If you have a [custom image with packaged flows](/deployment-docker#package-your-flow-as-a-docker-image), you can deploy Langflow by overriding the default [`values.yaml`](https://github.com/langflow-ai/langflow-helm-charts/blob/main/charts/langflow-runtime/values.yaml) with the `--set` flag:
+
+ ```shell
+ helm install my-langflow-app langflow/langflow-runtime -n langflow --create-namespace --set image.repository=myuser/langflow-hello-world --set image.tag=1.0.0
+ ```
+
+
+
+
+ Install the chart and download flows from a URL with the `--set` flag:
+
+ ```shell
+ helm install my-langflow-app-with-flow langflow/langflow-runtime \
+ -n langflow \
+ --create-namespace \
+ --set 'downloadFlows.flows[0].url=https://raw.githubusercontent.com/langflow-ai/langflow/dev/tests/data/basic_example.json'
+ ```
+
+ If your shell requires escaping square brackets, modify the `--set` path as needed.
+ For example, `--set 'downloadFlows.flows\[0\].url=https://raw.githubusercontent.com/langflow-ai/langflow/dev/tests/data/basic_example.json'`.
+
+
+
+
+3. Check the status of the pods:
+
+ ```shell
+ kubectl get pods -n langflow
+ ```
+
+## Access the Langflow runtime
+
+1. Get your service name:
+
+ ```shell
+ kubectl get svc -n langflow
+ ```
+
+ The service name is your release name suffixed by `-langflow-runtime`. For example, if you used `helm install my-langflow-app-with-flow`, then the service name is `my-langflow-app-with-flow-langflow-runtime`.
+
+2. Enable port forwarding to access Langflow from your local machine:
+
+ ```shell
+ kubectl port-forward -n langflow svc/my-langflow-app-with-flow-langflow-runtime 7860:7860
+ ```
+
+3. Confirm you can access the API by calling `http://localhost:7860/api/v1/flows/`:
+
+ ```shell
+ curl -v http://localhost:7860/api/v1/flows/
+ ```
+
+ A successful request returns a list of flows.
+
+4. Run a packaged flow.
+The following example gets the first flow ID from the flows list, and then runs the flow:
+
+ ```shell
+ # Get flow ID
+ id=$(curl -s "http://localhost:7860/api/v1/flows/" | jq -r '.[0].id')
+
+ # Run flow
+ curl -X POST \
+ "http://localhost:7860/api/v1/run/$id?stream=false" \
+ -H 'Content-Type: application/json' \
+ -d '{
+ "input_value": "Hello!",
+ "output_type": "chat",
+ "input_type": "chat"
+ }'
+ ```
+
+## Configure secrets and environment variables
+
+Use the `.env` section of the Langflow runtime Helm chart's [`values.yaml`](https://github.com/langflow-ai/langflow-helm-charts/blob/main/charts/langflow-runtime/values.yaml) file to define environment variables for your Langflow deployment.
+This includes built-in [Langflow environment variables](/environment-variables), as well as [global variables](/configuration-global-variables) used by your flows.
+
+Langflow can source global variables from your runtime environment, such as Kubernetes secrets referenced in `values.yaml`.
+For example, the Langflow runtime Helm chart's [example flow JSON](https://raw.githubusercontent.com/langflow-ai/langflow-helm-charts/refs/heads/main/examples/flows/basic-prompting-hello-world.json) uses a global variable that is a secret.
+If you want to run this flow in your Langflow deployment on Kubernetes, you need to include the secret in your runtime configuration.
+
+:::tip
+When you export flows as JSON files, it's recommended to omit secrets.
+Whether or not a secret is included depends on how you declare the secret in your flow and whether you use the **Save with my API keys** option.
+For more information, see [Import and export flows](/concepts-flows-import).
+:::
+
+### Set secrets
+
+Kubernetes secrets are the recommended way to store sensitive values and credentials.
+
+Use `secretKeyRef` to reference a Kubernetes secret in `values.yaml`:
+
+```yaml
+env:
+ - name: OPENAI_API_KEY
+ valueFrom:
+ secretKeyRef:
+ name: openai-credentials
+ key: openai-key
+```
+
+
+Create and set secrets with `kubectl` and `helm`
+
+You can use `kubectl` and `helm` commands to create and set secrets:
+
+1. Create a secret:
+
+ ```shell
+ kubectl create secret generic openai-credentials \
+ --namespace langflow \
+ --from-literal=OPENAI_API_KEY=sk...
+ ```
+
+2. Verify the secret exists:
+
+ ```shell
+ kubectl get secrets -n langflow openai-credentials
+ ```
+
+ The result is encrypted.
+
+3. Upgrade the Helm release to use the secret:
+
+ ```shell
+ helm upgrade my-langflow-app-image langflow/langflow-runtime -n langflow \
+ --reuse-values \
+ --set "extraEnv[0].name=OPENAI_API_KEY" \
+ --set "extraEnv[0].valueFrom.secretKeyRef.name=openai-credentials" \
+ --set "extraEnv[0].valueFrom.secretKeyRef.key=OPENAI_API_KEY"
+ ```
+
+ Escape square brackets if required by your shell.
+
+
+
+### Set the log level and other configuration variables
+
+For non-sensitive variables, such as `LANGFLOW_LOG_LEVEL`, you can set the value directly in `values.yaml`:
+
+```yaml
+env:
+ - name: LANGFLOW_LOG_LEVEL
+ value: "INFO"
+```
+
+## Configure scaling
+
+Use `replicaCount` and `resources` in the Langflow runtime Helm chart's [`values.yaml`](https://github.com/langflow-ai/langflow-helm-charts/blob/main/charts/langflow-runtime/values.yaml) file to configure scaling:
+
+* **Horizontal scaling**: Use `replicaCount` to set the number of replicas for your Langflow deployment.
+
+ ```yaml
+ replicaCount: 3
+ ```
+
+* **Vertical scaling**: Use the `resources` section to adjust pod resources depending on your application's needs.
+
+ ```yaml
+ resources:
+ requests:
+ memory: "2Gi"
+ cpu: "1000m"
+ ```
+
+## See also
+
+* [Best practices for Langflow on Kubernetes](/deployment-prod-best-practices)
+* [Langflow Helm Charts repository](https://github.com/langflow-ai/langflow-helm-charts)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-nginx-ssl.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-nginx-ssl.mdx
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--- /dev/null
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@@ -0,0 +1,231 @@
+---
+title: Deploy Langflow with Nginx and SSL
+slug: /deployment-nginx-ssl
+---
+
+import Icon from "@site/src/components/icon";
+
+Deploy Langflow on a Linux-based server using Nginx as a reverse proxy, Let's Encrypt for SSL certificates, and Certbot for automated certificate management.
+
+This setup encrypts all communications between users and your Langflow server. SSL certificates ensure that sensitive data is protected from eavesdropping and tampering, and the automatic certificate management through Certbot eliminates the complexity of manual SSL configuration.
+
+## Prerequisites
+
+- An Ubuntu or Debian-based Linux server with a dual-core CPU and at least 2 GB of RAM
+This example uses [Digital Ocean cloud](https://www.digitalocean.com) for hosting. Your deployment may vary.
+- A domain name with external DNS management access
+- A DNS record configured to point your domain to your server's external IP address
+For example, if your server's IP is `203.0.113.1`, configure your DNS like this:
+ ```bash
+ Type: A
+ Name: langflow.example.com
+ Value: 203.0.113.1
+ ```
+
+## Connect to your server with SSH
+
+1. Create an SSH key to connect to your server remotely.
+For example:
+
+ ```bash
+ ssh-keygen -t ed25519 -C "DANA@EXAMPLE.COM"
+ ```
+
+ Replace `DANA@EXAMPLE.COM` with the email address that you want to associate with the SSH key.
+
+2. In your terminal, follow the instructions to create your SSH key pair.
+This creates both a private and public key.
+To copy the public key from your terminal, enter the following command:
+
+ ```bash
+ cat ~/Downloads/host-lf.pub | pbcopy
+ ```
+
+3. In your server, add the SSH key you copied in the previous step.
+For example, if you are using a Digital Ocean cloud server, add this SSH key when the server is created, or with the [Digital Ocean control panel](https://docs.digitalocean.com/products/droplets/how-to/add-ssh-keys/).
+
+4. To connect to your server with SSH, enter the following command.
+
+ ```bash
+ ssh -i PATH_TO_PRIVATE_KEY/PRIVATE_KEY_NAME root@SERVER_IP_ADDRESS
+ ```
+
+ Replace the following:
+
+ * `PATH_TO_PRIVATE_KEY/PRIVATE_KEY_NAME`: The path to your private SSH key file that matches the public key you added to your server
+ * `SERVER_IP_ADDRESS`: Your server's IP address
+
+5. When prompted for a key fingerprint, type `yes`.
+The terminal output indicates if the connection succeeds or fails.
+The following response was returned after connecting to a Digital Ocean cloud server.
+ ```bash
+ System information as of Wed Oct 8 21:40:43 UTC 2025
+
+ System load: 0.02 Processes: 103
+ Usage of /: 4.1% of 47.35GB Users logged in: 1
+ Memory usage: 10% IPv4 address for eth0: 165.227.176.236
+ Swap usage: 0% IPv4 address for eth0: 10.17.0.5
+ ```
+
+## Install Langflow on your server
+
+To install Langflow on your server, do the following:
+
+1. Update system packages.
+ ```bash
+ sudo apt update && sudo apt upgrade -y
+ ```
+
+2. Install Python and pip.
+ ```bash
+ sudo apt install python3 python3-pip python3-venv -y
+ ```
+
+3. Install uv to manage Python packages.
+ Langflow recommends uv for faster installation.
+ ```bash
+ pip install uv
+ ```
+
+4. Create a virtual environment for Langflow.
+ ```bash
+ uv venv langflow-venv
+ source langflow-venv/bin/activate
+ ```
+
+5. Install Langflow using uv.
+ ```bash
+ uv pip install langflow
+ ```
+
+6. Optionally, start Langflow.
+ ```bash
+ uv run langflow run --host 127.0.0.1 --port 7860 &
+ ```
+
+ Test access at your `http://YOUR_PUBLIC_IP:7860`.
+ Since Langflow is running on `localhost`, you should not be successful.
+ In next steps, you will install Nginx as a reverse proxy to handle external access, and Certbot to configure SSL for secure HTTPS access.
+
+## Install Nginx
+
+Nginx is a reverse proxy that receives external requests and forwards them to your Langflow server. In addition to SSL termination, Nginx includes features for load balancing and security. For more information, see the [Nginx documentation](https://nginx.org/en/docs/).
+
+1. Install Nginx on your server.
+
+ ```bash
+ sudo apt install nginx -y
+ ```
+
+2. Start and enable Nginx on your server.
+ ```bash
+ sudo systemctl start nginx
+ sudo systemctl enable nginx
+ ```
+
+3. Create an Nginx configuration file.
+Replace **DOMAIN_NAME** with your actual domain name, such as `langflow.example.com`.
+ ```bash
+ sudo nano /etc/nginx/sites-available/DOMAIN_NAME
+ ```
+
+4. Paste the following content to the configuration file you created at `/etc/nginx/sites-available/DOMAIN_NAME`.
+
+ Replace **DOMAIN_NAME** with your actual domain name, such as `langflow.example.com`.
+ ```nginx
+ server {
+ listen 80;
+ server_name DOMAIN_NAME;
+
+ # Increase client body size for file uploads
+ client_max_body_size 100M;
+
+ location / {
+ proxy_pass http://127.0.0.1:7860/;
+ proxy_set_header Host $host;
+ proxy_set_header X-Real-IP $remote_addr;
+ proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
+ proxy_set_header X-Forwarded-Proto $scheme;
+
+ # WebSocket support for Langflow
+ proxy_http_version 1.1;
+ proxy_set_header Upgrade $http_upgrade;
+ proxy_set_header Connection "upgrade";
+
+ # Timeout settings for long-running flows
+ proxy_connect_timeout 60s;
+ proxy_send_timeout 60s;
+ proxy_read_timeout 300s;
+
+ # Buffer settings
+ proxy_buffering off;
+ proxy_request_buffering off;
+ }
+ }
+ ```
+ This configuration listens on port 80 for standard HTTP. When you install Certbot in a later step, it will modify this configuration to add port 443 for HTTPS.
+
+5. To enable your site configuration, create a symlink between the `/sites-available` and `/sites-enabled` directories. `/sites-available` stores all site configurations, and `/sites-enabled` stores only active configurations which Nginx reads from. Creating this symlink switches this configuration ON.
+
+ ```bash
+ sudo ln -s /etc/nginx/sites-available/DOMAIN_NAME /etc/nginx/sites-enabled/DOMAIN_NAME
+ ```
+
+6. Check the Nginx configuration file's syntax.
+ ```bash
+ sudo nginx -t
+ ```
+
+7. Restart Nginx.
+ ```bash
+ sudo systemctl restart nginx
+ ```
+
+## Install Certbot and obtain SSL certificates
+
+While Nginx handles SSL termination in encrypting and decrypting traffic, Certbot automatically obtains SSL certificates from Let's Encrypt and configures Nginx to use them.
+
+The Certbot client is [recommended by Let's Encrypt](https://letsencrypt.org/getting-started/#selecting-and-operating-an-acme-client-yourself) for automated certificate management. For more information, see the [Certbot documentation](https://certbot.eff.org/).
+
+Install the Certbot client on your server to manage certificates, and install the [`python3-certbot-nginx` plugin](https://packages.ubuntu.com/source/jammy/python-certbot-nginx) to allow Certbot to modify your server's Nginx configuration.
+
+1. Install Certbot and the `python3-certbot-nginx` plugin.
+ ```bash
+ sudo apt install certbot python3-certbot-nginx -y
+ ```
+
+2. Obtain the SSL certificate for `DOMAIN_NAME` from Let's Encrypt.
+ ```bash
+ sudo certbot --nginx -d DOMAIN_NAME
+ ```
+
+ This result indicates that Certbot succeeded.
+ ```bash
+ Successfully received certificate.
+ Certificate is saved at: /etc/letsencrypt/live/DOMAIN_NAME/fullchain.pem
+ Key is saved at: /etc/letsencrypt/live/DOMAIN_NAME/privkey.pem
+ ```
+
+ When using `--nginx`, Certbot automatically injects the paths into your Nginx configuration as `ssl_certificate` and `ssl_certificate_key`.
+
+3. Start Langflow from the virtual environment.
+
+ a. Activate the virtual environment.
+ ```bash
+ source langflow-venv/bin/activate
+ ```
+
+ b. Start Langflow in the background.
+ ```bash
+ uv run langflow run --host 127.0.0.1 --port 7860 &
+ ```
+
+4. To test the deployment, navigate to `https://DOMAIN_NAME` in your browser.
+5. Verify the SSL certificate is working. Ensure the URL is `https://`, not `http://`.
+Your browser's address bar should display a **Lock** icon.
+Click **Lock** to view your SSL certificate details.
+
+## See also
+
+- [Deploy Langflow on a remote server with Caddy](/deployment-caddyfile)
+- [Deploy Langflow on Docker](/deployment-docker)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-overview.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-overview.mdx
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@@ -0,0 +1,24 @@
+---
+title: Langflow deployment overview
+slug: /deployment-overview
+---
+
+This section includes the different ways to bring your locally-built flows to the world.
+
+* To self-host your local server through an ngrok gateway, see [Deploy a public Langflow server](/deployment-public-server).
+This approach uses [ngrok](https://ngrok.com/docs/getting-started/) to forward traffic and share your local Langflow server over the internet, without deploying to a cloud provider or exposing your network directly.
+
+* To build and deploy a Langflow container that includes your flow files, see [Containerize a Langflow application](develop-application).
+This approach bundles your flows and dependencies into a portable, reproducible Docker image for easy deployment across different environments.
+
+* To deploy a Langflow server on a remote server with Docker and Caddy, see [Deploy Langflow on a remote server](/deployment-caddyfile).
+This approach is good for hosting your own Langflow instance on a remote server with secure web access, using Docker containers and Caddy as a reverse proxy for HTTPS support.
+
+* To deploy Langflow with Nginx and automatic SSL certificates, see [Deploy Langflow with Nginx and Let's Encrypt](/deployment-nginx-ssl).
+This approach uses Nginx as a reverse proxy with Let's Encrypt for automatic HTTPS certificate management to provide a secure deployment _without_ Docker.
+
+* To deploy Langflow on Kubernetes, see [Langflow Kubernetes architecture and best practices](/deployment-prod-best-practices)
+This approach creates production-grade deployments with high availability, scalability, and robust orchestration.
+
+* For cloud provider-specific deployment guides, see your cloud provider's documentation.
+The Langflow documentation provides a few examples, such as [Google Cloud Platform](/deployment-gcp) and [Hugging Face Spaces](/deployment-hugging-face-spaces), to help you get started.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-prod-best-practices.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-prod-best-practices.mdx
new file mode 100644
index 0000000000..79061c888d
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Deployment/deployment-prod-best-practices.mdx
@@ -0,0 +1,127 @@
+---
+title: Best practices for Langflow on Kubernetes
+slug: /deployment-prod-best-practices
+---
+
+This guide provides best practices for deploying Langflow in production environments on Kubernetes.
+
+## Resources and scaling
+
+Langflow's minimum resource requirements vary by deployment type:
+
+* **IDE (development)**: Deploy both the Langflow visual editor (frontend) and API (backend). Typically, this is used for development environments where developers use the visual editor to create and manage flows before packaging and serving them through a production runtime deployment.
+
+ The frontend service requires a minimum of 512Mi RAM and 0.3 CPU per instance with 1 replica.
+
+ The backend service requires a minimum of 1Gi RAM and 0.5 CPU per instance with 1 replica.
+
+* **Runtime (production)**: Deploy the Langflow runtime for production flows, which is headless (backend only) service focused on serving the Langflow API. This is used for production environments where flows are executed programmatically without the need for the visual editor.
+
+ Minimum requirements include 2Gi RAM and 1000m (1 CPU) per instance with 3 replicas.
+
+For more information about Langflow deployment types, see [Langflow architecture on Kubernetes](/deployment-architecture).
+
+### Estimate, test, and adjust
+
+Start with the minimum recommended resources and replicas, then monitor and scale as needed based on your deployment's requirements and performance testing.
+Consider the following factors in your resource estimation and performance testing:
+
+* Flow complexity.
+* Volume of concurrent users and requests.
+
+ For IDE (development) deployments, consider that frontend activity also pings the backend service, so you typically need to scale both the frontend and backend together.
+
+* Request payload content and size, particularly for file uploads in production deployments.
+* Storage requirements for cache, file management, and the Langflow database.
+
+ An [external PostgreSQL database](#use-an-external-postgresql-database) is recommended for production deployments.
+
+* Infrastructure options that might require more resources, such as multi-core CPUs.
+
+### Use an external PostgreSQL database
+
+An external PostgreSQL database is recommended for production deployments to improve scalability and reliability as compared to the default SQLite database.
+
+Your resource allocation and replication strategy must be able to support the PostgreSQL service and storage.
+For example, for a runtime (production) deployment, you might allocate 4Gi RAM, 2 CPU, and multiple replicas for high availability.
+Tune PostgreSQL parameters, such as `work_mem` and `shared_buffers`, as needed based on resource requirements and usage metrics.
+
+Recommended configurations include:
+
+* Persistent storage to prevent data loss on container shutdown
+* High Availability (HA) or Active-Active for automatic failover, scaling, and load balancing
+* Shared databases for multi-instance deployments
+* Shared storage, such as NFS or cloud storage, for multi-instance deployments to access large files stored on disk, such as in `/opt/langflow/data/`.
+
+For more information, see [Configure an external PostgreSQL database](/configuration-custom-database) and [Langflow database guide for enterprise DBAs](/enterprise-database-guide).
+
+### Use HPA for dynamic scaling
+
+Load balancing and dynamic scaling are recommended for runtime (production) deployments.
+
+For example, consider using Horizontal Pod Autoscaler (HPA) in Kubernetes to dynamically scale based on CPU or memory usage.
+The following example shows a Langflow HPA configuration with CPU-based scaling:
+
+```yaml
+apiVersion: autoscaling/v2
+kind: HorizontalPodAutoscaler
+metadata:
+ name: langflow-runtime-hpa
+spec:
+ scaleTargetRef:
+ apiVersion: apps/v1
+ kind: Deployment
+ name: langflow-runtime
+ minReplicas: 1
+ maxReplicas: 10
+ metrics:
+ - type: Resource
+ resource:
+ name: cpu
+ target:
+ type: Utilization
+ averageUtilization: 80
+```
+
+## Failure points
+
+Langflow's reliability in production depends on mitigating key failure points, particularly around the database, file system, and instance availability:
+
+* **Database failures**: See [Langflow database guide for enterprise DBAs](/enterprise-database-guide).
+* **File system failures**: Concurrency issues in file caching, such as `/app/data/.cache`, can cause IO errors in multi-instance setups.
+To avoid this, use a shared, POSIX-compliant file system or cloud storage.
+Use persistent volumes instead of ramdisk solutions that result in data loss on container shutdown.
+* **Instance failures**: Deploy multiple replicas to avoid service disruptions when a single instance fails. Use health checks to detect and replace failed pods.
+* **Network and dependency failures**: External APIs or services used in flows may fail, causing flow errors. Implement retry logic and error handling in flows or application code. Monitor network latency and dependency health.
+
+## Monitoring
+
+Effective monitoring ensures Langflow operates reliably and performs well under varying loads:
+
+* **Database monitoring**: See [Langflow database guide for enterprise DBAs](/enterprise-database-guide).
+* **Application logs**: Collect and analyze logs for errors, warnings, and flow execution issues. Centralize logs using tools like ELK Stack or Fluentd. You can also inspect [Langflow logs](/logging).
+* **Resource usage**: Track CPU, memory, and disk usage of Langflow instances. Use Prometheus and Grafana for real-time metrics collection and monitoring in Kubernetes.
+
+ To expose your Langflow server's Prometheus metrics, set `LANGFLOW_PROMETHEUS_ENABLED=True` (the default is `false`).
+ The default port for the Prometheus metrics is 9090.
+ To change the port, set `LANGFLOW_PROMETHEUS_PORT`.
+
+* **API performance**: Monitor response times, error rates, and request throughput. Set alerts for high latency or error spikes.
+* **Observability tools**: Integrate with [LangWatch](/integrations-langwatch) or [Opik](/integrations-opik) for detailed flow tracing and metrics. Use these tools to debug flow performance and optimize execution.
+
+## Security
+
+Running Langflow in production requires robust security measures to protect the application, data, and users.
+Follow industry best practices and use secure Langflow configurations, such as the following:
+
+* **Container security**: Apply security best practices for containerized applications. For example, set `readOnlyRootFilesystem: true` in runtime (production) containers to prevent unauthorized modifications. Restrict access to files and codebases containing sensitive data and configuration files that shouldn't be exposed to unauthorized users.
+* **Secrets management**: Store sensitive data like API keys and PostgreSQL credentials in Kubernetes secrets or external secrets managers like HashiCorp Vault.
+* **Authentication, authorization, and access control**: Start your Langflow server with authentication enabled, as explained in [API keys and authentication](/api-keys-and-authentication). Restrict network and resource access with firewalls, network policies, network security groups, or VPCs. For example, restrict PostgreSQL database access to Langflow instances.
+* **Encryption and privacy**: Follow industry best practices and legal requirements for data privacy and encryption of data in transit and at rest, including GDPR requirements, HTTPS, TLS, and SSL. For example, configure PostgreSQL with valid SSL certificates and append `?sslmode=require` or `?sslmode=verify-full` to the connection string to enable SSL for database connections.
+* **Security posture maintenance**: Conduct regular security audits, keep current with software updates, and monitor for suspicious activity using intrusion detection systems.
+
+## See also
+
+* [Deploy the Langflow production environment on Kubernetes](/deployment-kubernetes-prod)
+* [Langflow Helm Charts repository](https://github.com/langflow-ai/langflow-helm-charts)
+* [Langflow environment variables](/environment-variables)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-public-server.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-public-server.mdx
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--- /dev/null
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@@ -0,0 +1,123 @@
+---
+title: Deploy a public Langflow server
+slug: /deployment-public-server
+---
+
+By default, your Langflow server at `http://localhost:7860` isn't exposed to the public internet.
+However, you can forward Langflow server traffic with a forwarding platform like [ngrok](https://ngrok.com/docs/getting-started/) or [zrok](https://docs.zrok.io/docs/getting-started) to make your server public.
+
+When your Langflow server is public, you can do things like [deploy your Langflow MCP server externally](#deploy-your-mcp-server-externally), [serve API requests](#serve-api-requests), and [share a flow's **Playground** publicly](#share-a-flows-playground).
+
+## Prerequisites
+
+On the machine where you plan to host your Langflow installation, [install Langflow](/get-started-installation) and a reverse proxy or forwarding service.
+
+This guide uses ngrok, but you can use any similar reverse proxy or forwarding platform.
+
+If you want to follow along with this guide, [install ngrok](https://ngrok.com/docs/getting-started/#1-install-ngrok) and [create an ngrok authtoken](https://dashboard.ngrok.com/get-started/your-authtoken).
+
+## Expose your Langflow server with ngrok
+
+1. Start Langflow:
+
+ ```bash
+ uv run langflow run
+ ```
+
+2. In another terminal window, use your ngrok authtoken to authenticate your local ngrok server:
+
+ ```bash
+ ngrok config add-authtoken NGROK_AUTHTOKEN
+ ```
+
+3. Use ngrok to expose your Langflow server to the public internet:
+
+ ```bash
+ ngrok http http://localhost:7860
+ ```
+
+ This example assumes that you use the default Langflow listening address at `http://localhost:7860`. If you have a different listening address, you must modify this command accordingly.
+
+ The ngrok session starts in your terminal and deploys an ephemeral domain with no authentication.
+ To add authentication or deploy a static domain, see the [ngrok documentation](https://ngrok.com/docs/).
+
+ The `Forwarding` line prints the forwarding address for your Langflow server:
+
+ ```
+ Forwarding https://94b1-76-64-171-14.ngrok-free.app -> http://localhost:7860
+ ```
+
+ The forwarding address acts as a reverse proxy for your Langflow server, and ngrok forwards your local traffic to this domain.
+
+4. To verify that your Langflow server is publicly available, navigate to the forwarding address URL, such as `https://94b1-76-64-171-14.ngrok-free.app`.
+
+## Use a public Langflow server
+
+When your Langflow server is public, you can do things like [deploy your Langflow MCP server externally](#deploy-your-mcp-server-externally), [serve API requests](#serve-api-requests), and [share a flow's **Playground** publicly](#share-a-flows-playground).
+
+### Deploy your MCP server externally
+
+After you deploy a public Langflow server, you can also access your Langflow projects' MCP servers publicly.
+
+To do this, use your server's forwarding address when you [connect a client to a Langflow MCP server](/mcp-server#connect-clients-to-use-the-servers-actions).
+
+### Serve API requests
+
+To send requests to a public Langflow server's [Langflow API](/api-reference-api-examples) endpoints, use the server's domain as the [base URL](/api-reference-api-examples#base-url) for your API requests.
+For example:
+
+```bash
+curl -X POST \
+ "PUBLIC_SERVER_DOMAIN/api/v1/webhook/FLOW_ID" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: LANGFLOW_API_KEY" \
+ -d '{"data": "example-data"}'
+```
+
+:::tip
+When you create flows on public Langflow servers, the code snippets generated in the [**API access** pane](/concepts-publish) automatically use your public server's domain.
+:::
+
+You also use your public domain when making Langflow API calls in scripts, including the code snippets that are automatically generated by Langflow.
+For example, the following code snippet calls an ngrok domain to trigger the specified flow (`d764c4b8...`):
+
+ ```python
+ import requests
+
+ url = "https://3f7c-73-64-93-151.ngrok-free.app/api/v1/run/d764c4b8-5cec-4c0f-9de0-4b419b11901a" # The complete API endpoint URL for this flow
+
+ # Request payload configuration
+ payload = {
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": "Hello"
+ }
+
+ # Request headers
+ headers = {
+ "Content-Type": "application/json",
+ "x-api-key": "LANGFLOW_API_KEY"
+ }
+
+ try:
+ # Send API request
+ response = requests.request("POST", url, json=payload, headers=headers)
+ response.raise_for_status() # Raise exception for bad status codes
+
+ # Print response
+ print(response.text)
+
+ except requests.exceptions.RequestException as e:
+ print(f"Error making API request: {e}")
+ except ValueError as e:
+ print(f"Error parsing response: {e}")
+ ```
+
+For a demo of the Langflow API in a script, see the [Quickstart](/get-started-quickstart).
+
+### Share a flow's Playground
+
+After you deploy a public Langflow server, you can use the **Shareable Playground** option to make a flow's **Playground** available at a public URL.
+If a user accesses this URL, they can interact with the flow's chat input and output and view the results without installing Langflow or generating a Langflow API key.
+
+For more information, see [Share a flow's Playground](/concepts-playground#share-a-flows-playground).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-railway.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-railway.mdx
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+---
+title: Deploy Langflow on Railway
+description: Deploy Langflow to Railway using a one-click template
+slug: /deployment-railway
+---
+
+This guide explains how to [deploy Langflow on Railway](https://railway.com/?utm_medium=integration&utm_source=docs&utm_campaign=langflow), a cloud infrastructure platform that provides auto-deploy, managed databases, and automatic scaling.
+
+1. Create a Railway account.
+
+ A Hobby account on Railway is sufficient for Langflow's dual-core CPU and 2 GB RAM requirements. For more information, see [Railway pricing](https://railway.com/pricing?utm_medium=integration&utm_source=docs&utm_campaign=langflow).
+
+2. Follow this link to deploy the Langflow template on Railway:
+
+ [](https://railway.com/new/template/JMXEWp?referralCode=MnPSdg&utm_medium=integration&utm_source=docs&utm_campaign=langflow)
+
+3. Optional: Add any custom configuration for your Langflow deployment.
+
+ The Langflow Railway template automatically sets up the infrastructure, deploys Langflow, and then starts the application.
+
+4. Wait for the deployment to complete.
+
+5. Navigate to your Langflow instance at your deployment's public URL, such as `https://APP-NAME.up.railway.app`.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/deployment-render.mdx b/docs/versioned_docs/version-1.9.0/Deployment/deployment-render.mdx
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--- /dev/null
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@@ -0,0 +1,20 @@
+---
+title: Deploy Langflow on Render
+slug: /deployment-render
+---
+
+This guide explains how to deploy Langflow on [Render](https://render.com/), a cloud platform for deploying web applications and APIs.
+
+1. Prepare a Render instance that can support Langflow.
+
+ Langflow requires at least 2 GB of RAM to run, so you must use a Render instance type of **Standard** or better.
+ This requires a paid Render account.
+ For more information, see [Render Web Services](https://render.com/docs/web-services) and [Render pricing](https://render.com/pricing).
+
+2. Follow this link to start a Langflow deployment on Render:
+
+ [](https://render.com/deploy?repo=https%3A%2F%2Fgithub.com%2Flangflow-ai%2Flangflow%2Ftree%2Fdev)
+
+3. Enter a blueprint name, select the branch for your `render.yaml` file, and then click **Deploy Blueprint**.
+
+When deployment is complete, your Langflow instance is ready to use.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/develop-application.mdx b/docs/versioned_docs/version-1.9.0/Deployment/develop-application.mdx
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+---
+title: Containerize a Langflow application
+slug: /develop-application
+---
+
+import Icon from "@site/src/components/icon";
+import PartialPodmanAlt from '@site/docs/_partial-podman-alt.mdx';
+
+
+
+Designing flows in the visual editor is only the first step in building an application that uses Langflow.
+
+Once you have a functional flow, you can use that flow in a larger application, such as a website or mobile app.
+Because Langflow is both an IDE and a runtime, you can use Langflow to build and test your flows locally, and then package and serve your flows in a production environment.
+
+This guide introduces application development with Langflow from initial setup through packaging and deployment.
+This documentation doesn't explain how to write a complete application; it only describes how to include Langflow in the context of a larger application.
+
+## Directory structure
+
+The following example describes the directory structure for a minimal Langflow application:
+
+```text
+LANGFLOW-APPLICATION/
+├── docker.env
+├── Dockerfile
+├── flows/
+│ ├── flow1.json
+│ └── flow2.json
+├── langflow-config-dir/
+├── README.md
+```
+
+This directory contains the following:
+
+* [`docker.env`](#docker-env): This file is copied to the Docker image as a `.env` file in the container root.
+* [`Dockerfile`](#dockerfile): This file controls how your Langflow image is built.
+* [`/flows`](#flows): This folder holds the flows you want to host, which are the flows that your application uses.
+* `/langflow-config-dir`: This folder is referenced in the Dockerfile as the location for your Langflow deployment's configuration files, database, and logs.
+* `README.md`: This is a typical README file for your application's documentation.
+
+This is a minimal example of a Langflow application directory.
+Your application might have additional files and folders, such as a `/components` folder for custom components, or a `pyproject.toml` file for additional dependencies.
+
+### Package management
+
+The base Langflow Docker image includes the Langflow core dependencies because it uses `langflowai/langflow:latest` as the parent image.
+
+If your application requires additional dependencies, create a [`pyproject.toml`](https://packaging.python.org/en/latest/guides/writing-pyproject-toml) file for the additional dependencies.
+For more information, see [Install custom dependencies](/install-custom-dependencies).
+
+To deploy an application with additional dependencies to Docker, you must copy the `pyproject.toml` and `uv.lock` files to the Docker image.
+To do this, add the following to your Langflow application's Dockerfile:
+
+```text
+COPY pyproject.toml uv.lock /app/
+```
+
+### Environment variables {#docker-env}
+
+The `docker.env` file is a `.env` file loaded into your Docker image.
+It contains [Langflow environment variables](/environment-variables) that are used in flows or control Langflow's behavior, such as authentication, database storage, API keys, and server configurations.
+For example:
+
+```text
+LANGFLOW_AUTO_LOGIN=True
+LANGFLOW_SAVE_DB_IN_CONFIG_DIR=True
+LANGFLOW_BASE_URL=http://0.0.0.0:7860
+OPENAI_API_KEY=sk-...
+```
+
+You can set environment variables in the Dockerfile as well.
+However, if you set an environment variable in both `docker.env` and the Dockerfile, Langflow uses the value set in `docker.env`.
+
+Langflow can also [create global variables from your environment variables](/configuration-global-variables#add-custom-global-variables-from-the-environment), or [use environment variables as a backup for missing global variables](/configuration-global-variables#use-environment-variables-for-missing-global-variables).
+
+### Secrets
+
+For simplicity, the examples in the Langflow documentation might use direct references to API keys and other sensitive values.
+In your own applications, you should always follow industry best practices for managing secrets, such as using environment variables or secret management tools.
+
+For information about generating authentication keys and managing secrets in Langflow, see [API keys and authentication](/api-keys-and-authentication).
+
+### Storage
+
+By default, Langflow uses an [SQLite](https://www.sqlite.org/) database for storage.
+If you prefer to use PostgreSQL, see [Configure an external PostgreSQL database](/configuration-custom-database).
+
+For more information about storage, including cache and memory, see [Memory management options](/memory).
+
+### Flows {#flows}
+
+Your local Langflow instance might have many flows for different applications.
+When you package Langflow as a dependency of an application, you only want to include the flows your application uses.
+
+1. [Export flows](/concepts-flows-import) that are relevant to your application.
+
+ If you have chained flows (flows that trigger other flows), make sure you export _all_ necessary flows.
+
+2. Add the exported Langflow JSON files to the `/flows` folder in your application directory.
+
+### Components
+
+The **Core components** and **Bundles** that you see in the Langflow visual editor are automatically included in the base Langflow Docker image.
+
+
+If you have [custom components](/components-custom-components) for your application, include them in your application directory.
+Custom components must be placed inside category folders, not directly in the base directory:
+
+1. Create a `/components` folder in your application directory.
+2. Inside `/components`, create category subfolders (for example, `/components/data`, `/components/tools`).
+3. Add your custom component files to the appropriate category folders.
+4. Copy the `/components` folder to your Docker image and set the `LANGFLOW_COMPONENTS_PATH` environment variable:
+
+ ```dockerfile
+ COPY components /app/components
+ ENV LANGFLOW_COMPONENTS_PATH=/app/components
+ ```
+
+## Langflow Dockerfile {#dockerfile}
+
+The Dockerfile determines how your Langflow image is built, including the dependencies, flows, components, and configuration files.
+
+At minimum, you need to specify the base Langflow image, create the necessary folders in the container, copy folders and files to the container, and provide a startup command.
+
+```dockerfile
+# Use the latest version of the base Langflow image
+FROM langflowai/langflow:latest
+
+# Create folders and set the working directory in the container
+RUN mkdir /app/flows
+RUN mkdir /app/langflow-config-dir
+WORKDIR /app
+
+# Copy flows, langflow-config-dir, and docker.env to the container
+COPY flows /app/flows
+COPY langflow-config-dir /app/langflow-config-dir
+COPY docker.env /app/.env
+
+# Optional: Copy custom components to the container
+COPY components /app/components
+
+# Optional: Use custom dependencies
+COPY pyproject.toml uv.lock /app/
+
+# Set environment variables if not set in docker.env
+ENV PYTHONPATH=/app
+ENV LANGFLOW_LOAD_FLOWS_PATH=/app/flows
+ENV LANGFLOW_CONFIG_DIR=/app/langflow-config-dir
+ENV LANGFLOW_COMPONENTS_PATH=/app/components
+ENV LANGFLOW_LOG_ENV=container
+
+# Command to run the Langflow server on port 7860
+EXPOSE 7860
+CMD ["langflow", "run", "--backend-only", "--env-file","/app/.env","--host", "0.0.0.0", "--port", "7860"]
+```
+
+The environment variables set directly in this Dockerfile specify resource paths for Langflow.
+If these variables are also set in `docker.env`, the values in `docker.env` override the values set in the Dockerfile.
+
+In this example, `ENV LANGFLOW_LOG_ENV=container` sets the logging behavior for serialized JSON to `stdout` to track the application's behavior in a containerized environment. For more information, see [Logging](/logging).
+
+### Backend-only mode
+
+The `--backend-only` flag in `CMD` starts Langflow in backend-only mode, which provides programmatic access only.
+This is recommended when running Langflow as a dependency of an application where you don't need access to the visual editor.
+
+If you want to serve the Langflow visual editor _and_ backend, then omit `--backend-only`.
+
+For more information, see [Deploy Langflow on Docker](/deployment-docker).
+
+## Test your Langflow Docker image
+
+Build and run your Langflow Docker image to test it.
+
+This example runs the container locally.
+For information about publishing your image on Docker Hub and running a Langflow container remotely, see [Deploy to Docker Hub and Kubernetes](#deploy-docker).
+
+1. Build the Docker image:
+
+ ```bash
+ docker build -t langflow-pokedex:1.2.0 .
+ ```
+
+2. Run the Docker container to start your Langflow server:
+
+ ```bash
+ docker run -p 7860:7860 langflow-pokedex:1.2.0
+ ```
+
+3. To confirm that the container is serving your flows as expected, use the Langflow API to run a flow:
+
+ 1. Open one of the JSON files in your application's `/flows` folder, and then find the flow's `id` in the [additional metadata and project information](/concepts-flows-import#additional-metadata-and-project-information).
+
+ There are many `id` values; make sure you get the ID for the entire flow, not the ID for an individual component.
+ If your flow is complex, try searching for the flow's name, which is typically near the flow's `id`.
+
+ ```json
+ "name": "Basic Prompting",
+ "description": "Perform basic prompting with an OpenAI model.",
+ "id": "e4167236-938f-4aca-845b-21de3f399858",
+ ```
+
+ 2. Send a POST request to the [`/v1/run/$FLOW_ID`](/api-flows-run#run-flow) endpoint using the flow ID from the previous step .
+
+ The following example runs a simple LLM chat flow that responds to a chat input string.
+ If necessary, modify the payload for your flow.
+ For example, if your flow doesn't have a **Chat Input** component, you must modify the payload to provide the expected input for your flow.
+
+ ```bash
+ curl --request POST \
+ --url 'http://localhost:7860/api/v1/run/e4167236-938f-4aca-845b-21de3f399858?stream=true' \
+ --header 'Content-Type: application/json' \
+ --data '{
+ "input_value": "Tell me about Charizard.",
+ "output_type": "chat",
+ "input_type": "chat",
+ "session_id": "charizard_test_request"
+ }'
+ ```
+
+
+ About this example
+
+ This command runs the **Pokédex** template flow.
+ It provides chat input about a specific Pokémon, uses an optional custom `session_id`, and enables response streaming with `?stream=true`.
+
+ The default [session ID](/session-id) is the flow ID.
+ Custom session IDs can help isolate unique conversation threads to keep the LLM's context clean, and they can help identify specific conversations in flow logs to make debugging easier.
+
+ This command uses response streaming because the Pokédex flow can return a large amount of text.
+ To use batching, set `?stream=false`.
+
+
+
+ 3. Verify that the request succeeds and the response is valid, depending on the specific flow you ran.
+
+ This confirms that your Langflow Docker image is correctly configured and this flow is accessible through the Langflow API server that is hosted on the container.
+ When you build and test your entire application stack, your front-end application can use Langflow API requests to trigger the flows served by your Langflow container in the same way you manually tested the flow in the previous step.
+
+ This example triggered a flow by sending chat input to the `/v1/run/$FLOW_ID` endpoint.
+ For more examples of flow triggers, see [Trigger flows with webhooks](/webhook) and the tutorial to [Create a chatbot that can ingest files](/chat-with-files).
+
+## Deploy to Docker Hub and Kubernetes {#deploy-docker}
+
+When you're ready to share your application with the world, you need to serve Langflow in a production environment.
+For more information about deploying Langflow, see the following:
+
+* [Learn about Langflow deployments](/deployment-overview)
+* [Deploy Langflow on Docker](/deployment-docker)
+* [Deploy the Langflow production environment on Kubernetes](/deployment-kubernetes-prod)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Deployment/security.mdx b/docs/versioned_docs/version-1.9.0/Deployment/security.mdx
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+---
+title: Security
+slug: /security
+---
+
+The Langflow UI is an IDE and code execution platform, which means that Langflow is inherently capable of executing arbitrary, developer-provided code.
+By design, the Langflow UI includes a code editor allowing developers to author and execute arbitrary Python with full access to the host Langflow backend process, filesystem, and network.
+In addition, some components wrap and use code execution, including use of AI models to generate code to be executed based
+on user input.
+
+Langflow neither enforces isolation between users within a single Langflow process, nor restricts access to the local disk or network resources. Flow visibility and user access controls are designed for usability, not security enforcement. Users can access underlying database connections and system resources directly. For multi-tenant deployments, Langflow relies on infrastructure-level security rather than application-level isolation, which means it is your responsibility to enforce tenant isolation.
+
+:::warning
+You are responsible for the following:
+
+* Enforcing comprehensive infrastructure isolation.
+* Running flows safely, particularly flows that use LLM-generated code or user inputs that could contain user-submitted code.
+* Ensuring that end-user interactions with Langflow-backed APIs are secure from end-to-end, including data at rest and in transit.
+:::
+
+## Secure Langflow during local development
+
+Langflow is a code execution platform with full access to your local system.
+You are responsible for ensuring the safety of flows you execute.
+
+In the event that Langflow could execute untrusted or LLM-generated code, consider [blocking custom component execution](./deployment-block-custom-components.mdx) and running Langflow in an isolated and containerized execution environment.
+For more information, see [Containerize a Langflow application](/develop-application).
+
+## Secure first-party deployments
+
+When you are serving APIs backed by flows that you or your organization authored, you are responsible for ensuring Langflow-based APIs provide security to your end users.
+
+Follow industry best practices for APIs:
+
+* Use a secure API gateway to provide authentication and authorization
+* Ensure user data is appropriately isolated
+* Sanitize inputs and outputs against XSS and injection attacks, including regex patterns to prevent ReDoS vulnerabilities
+
+For more information on setting up a reverse proxy, see [Deploy Langflow with Nginx and SSL](/deployment-nginx-ssl).
+For more information on authentication configuration, see [API keys and authentication](/api-keys-and-authentication).
+
+## Secure third-party deployments
+
+If you provide Langflow as a service to third parties, you must assume that any code executed by Langflow could be potentially malicious.
+
+Langflow provides no isolation between tenants, so it's your responsibility to enforce isolation at the infrastructure level.
+
+* Use process-level isolation to prevent tenants from sharing a single Langflow process.
+* Use disk-level isolation to prevent shared access to writable persistent storage.
+* Use network-level isolation to prevent access to private networks.
+* Use database-level isolation to prevent access to or modification of shared database resources.
+
+Authentication and authorization must be provided and enforced outside of the Langflow container.
+For shared services, such as databases, enforce access limitations externally through credentials and security policies.
+
+For more information, see [Best practices for Langflow on Kubernetes](/deployment-prod-best-practices).
+
+## Security bulletin
+
+For more information, see the [Langflow Security Policy](https://github.com/langflow-ai/langflow/blob/main/SECURITY.md).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/api-keys-and-authentication.mdx b/docs/versioned_docs/version-1.9.0/Develop/api-keys-and-authentication.mdx
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,566 @@
+---
+title: API keys and authentication
+slug: /api-keys-and-authentication
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Icon from "@site/src/components/icon";
+
+:::warning
+Never expose Langflow ports directly to the internet without proper security measures.
+Set `LANGFLOW_AUTO_LOGIN=False`, use a non-default `LANGFLOW_SECRET_KEY`, and deploy your Langflow server behind a reverse proxy with authentication enabled.
+For more information, see [Start a Langflow server with authentication enabled](#start-a-langflow-server-with-authentication-enabled).
+:::
+
+Authentication credentials help prevent unauthorized access to your Langflow server, flows, and services connected through components.
+
+There are three types of credentials that you use in Langflow:
+
+* [Langflow API keys](#langflow-api-keys): For authentication with the Langflow API and authorizing server-side Langflow actions like running flows and uploading files.
+* [Component API keys](#component-api-keys): For authentication between Langflow and a service connected through a component, such as a model provider or third-party API.
+* [Authentication environment variables](#authentication-environment-variables): These environment variables configure how Langflow handles user authentication and authorization.
+
+## Langflow API keys {#langflow-api-keys}
+
+You can use Langflow API keys to interact with Langflow programmatically.
+
+By default, most Langflow API endpoints, such as `/v1/run/$FLOW_ID`, require authentication with a Langflow API key.
+
+Langflow validates API keys against keys stored in the database, but you can configure Langflow to validate API keys against an environment variable instead.
+For more information, see [`LANGFLOW_API_KEY_SOURCE`](#langflow-api-key-source).
+
+To require API key authentication for flow webhook endpoints, use the [`LANGFLOW_WEBHOOK_AUTH_ENABLE`](/webhook#require-authentication-for-webhooks) environment variable.
+To configure authentication for Langflow MCP servers, see [Use Langflow as an MCP server](/mcp-server).
+
+### Langflow API key permissions
+
+A Langflow API key adopts the privileges of the user who created it.
+This means that API keys you create have the same permissions and access that you do, including access to your flows, components, and Langflow database.
+A Langflow API key cannot be used to access resources outside of your own Langflow server.
+
+In single-user environments, you are always a superuser, and your Langflow API keys always have superuser privileges.
+
+In multi-user environments, users who aren't superusers cannot use their API keys to access other users' resources.
+Superusers can only run their own flows, and cannot run flows owned by other users.
+You must [start your Langflow server with authentication enabled](#start-a-langflow-server-with-authentication-enabled) to allow superusers to manage users and create non-superuser accounts.
+
+### Create a Langflow API key
+
+You can generate a Langflow API key in your Langflow **Settings** or with the Langflow CLI.
+
+The CLI option is required if your Langflow server is running in `--backend-only` mode.
+
+
+
+
+1. In the Langflow header, click your profile icon, and then select **Settings**.
+2. Click **Langflow API Keys**, and then click **Add New**.
+3. Name your key, and then click **Create API Key**.
+4. Copy the API key and store it securely.
+
+
+
+
+If you're serving your flow with `--backend-only=true`, you can't create API keys in your Langflow **Settings** because the frontend isn't running.
+In this case, you must create API keys with the Langflow CLI.
+
+1. Recommended: [Start your Langflow server with authentication enabled](#start-a-langflow-server-with-authentication-enabled).
+
+ The Langflow team recommends enabling authentication for security reasons to prevent unauthorized creation of API keys and superusers, especially in production environments.
+ If authentication isn't enabled (`LANGFLOW_AUTO_LOGIN=True`), all users are effectively superusers, and they can create API keys with the Langflow CLI.
+
+2. Create an API key with [`langflow api-key`](/configuration-cli#langflow-api-key):
+
+ ```shell
+ uv run langflow api-key
+ ```
+
+ All API keys created with the Langflow CLI have superuser privileges because the command requires superuser authentication, and Langflow API keys adopt the privileges of the user who created them.
+
+
+
+
+### Use a Langflow API key
+
+To authenticate Langflow API requests, pass your Langflow API key an `x-api-key` header or query parameter.
+
+
+
+
+```shell
+curl -X POST \
+ "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID?stream=false" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{"inputs": {"text":""}, "tweaks": {}}'
+```
+
+
+
+
+```shell
+curl -X POST \
+ "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID?x-api-key=$LANGFLOW_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{"inputs": {"text":""}, "tweaks": {}}'
+```
+
+
+
+
+For more information about forming Langflow API requests, see [Get started with the Langflow API](/api-reference-api-examples) and [Trigger flows with the Langflow API](/concepts-publish).
+
+### Track API key usage
+
+By default, Langflow tracks API key usage through `total_uses` and `last_used_at` records in your [Langflow database](/memory).
+
+To disable API key tracking, set `LANGFLOW_DISABLE_TRACK_APIKEY_USAGE=True` in your [Langflow environment variables](/environment-variables).
+This can help avoid database contention during periods of high concurrency.
+
+### Revoke an API key
+
+To revoke and delete an API key, do the following:
+
+1. In the Langflow header, click your profile icon, and then select **Settings**.
+2. Click **Langflow API Keys**.
+3. Select the keys you want to delete, and then click **Delete**.
+
+This action immediately invalidates the key and prevents it from being used again.
+
+## Component API keys {#component-api-keys}
+
+Component API keys authorize access to external services that are called by components in your flows, such as model providers, databases, or third-party APIs.
+These aren't Langflow API keys or general application credentials.
+
+In Langflow, you can store component API keys in global variables in your **Settings** or import them from your runtime environment.
+For more information, see [Global variables](/configuration-global-variables).
+
+You create and manage component API keys within the service provider's platform.
+Langflow only stores the encrypted key value or a secure reference to a key stored elsewhere; it doesn't manage the actual credentials at the source.
+This means that deleting a global variable from Langflow doesn't delete or invalidate the actual API key in the service provider's system.
+You must delete or rotate component API keys directly using the service provider's interface or API.
+
+For added security, you can set `LANGFLOW_REMOVE_API_KEYS=True` to omit API keys and tokens from flow data in your [Langflow database](/memory).
+Additionally, when [exporting flows](/concepts-flows-import), you can choose to omit API keys from the exported flow JSON.
+
+## Authentication environment variables
+
+This section describes the available authentication configuration variables.
+
+You can use the [`.env.example`](https://github.com/langflow-ai/langflow/blob/main/.env.example) file in the Langflow repository as a template for your own `.env` file.
+
+For JWT authentication configuration, including algorithm selection and key management, see [JWT authentication](/jwt-authentication).
+
+### LANGFLOW_AUTO_LOGIN {#langflow-auto-login}
+
+This variable controls whether authentication is required to access your Langflow server, including the visual editor, API, and Langflow CLI:
+
+* If `LANGFLOW_AUTO_LOGIN=False`, automatic login is disabled. Users must sign in to the visual editor, authenticate as a superuser to run certain Langflow CLI commands, and use a Langflow API key for Langflow API requests.
+If `false`, the Langflow team recommends that you also explicitly set [`LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD`](#langflow-superuser) to avoid using the insecure default values.
+
+* If `LANGFLOW_AUTO_LOGIN=True` (default), all API requests require authentication with a Langflow API key, but the visual editor automatically signs in all users as superusers, and Langflow uses _only_ the default [superuser credentials](/api-keys-and-authentication#langflow-superuser).
+All users access the same visual editor environment without password protection, they can run all Langflow CLI commands as superusers, and Langflow automatically authenticates internal requests between the backend and frontend based on the users' superuser privileges.
+If you also want to bypass authentication for Langflow API requests in addition to other bypassed authentication, see [`LANGFLOW_SKIP_AUTH_AUTO_LOGIN`](/api-keys-and-authentication#langflow-skip-auth-auto-login).
+
+Langflow doesn't allow users to simultaneously edit the same flow in real time.
+If two users edit the same flow, Langflow saves only the work of the most recent editor based on the state of that user's [workspace](/concepts-overview#workspace). Any changes made by the other user in the interim are overwritten.
+
+#### Default authentication enforcement and LANGFLOW_SKIP_AUTH_AUTO_LOGIN {#langflow-skip-auth-auto-login}
+
+In Langflow version 1.6, the default settings are `LANGFLOW_AUTO_LOGIN=True` and `LANGFLOW_SKIP_AUTH_AUTO_LOGIN=False`.
+This enforces authentication for API requests only, as explained in the preceding section.
+
+For temporary backwards compatibility, you can revert to the fully unauthenticated behavior from earlier versions by setting both variables to `true`.
+However, a future release will set `LANGFLOW_AUTO_LOGIN=False` and remove `LANGFLOW_SKIP_AUTH_AUTO_LOGIN`.
+At that point, Langflow will strictly enforce API key authentication for API requests, and you can manually disable authentication for some features, like the visual editor, by setting `LANGFLOW_AUTO_LOGIN=True`.
+
+
+Authentication enforcement in earlier versions
+
+Langflow version 1.5 was the first version that could enforce authentication for Langflow API requests, regardless of the value of `LANGFLOW_AUTO_LOGIN`.
+As a temporary bypass for backwards compatibility, this version added the `LANGFLOW_SKIP_AUTH_AUTO_LOGIN` environment variable and set both variables to `true` by default to preserve the fully unauthenticated behavior from earlier versions.
+This allowed users to upgrade to version 1.5 with no change in the authentication behavior.
+
+In Langflow versions earlier than 1.5, Langflow API requests didn't require authentication.
+Additionally, the default setting of `LANGFLOW_AUTO_LOGIN=True` automatically granted all users superuser privileges in the visual editor, and it allowed all users to run all Langflow CLI commands as superusers.
+
+
+### LANGFLOW_ENABLE_SUPERUSER_CLI {#langflow-enable-superuser-cli}
+
+Controls the availability of the `langflow superuser` command in the Langflow CLI.
+The default is `true`, but `false` is recommended to prevent unrestricted superuser creation.
+For more information, see [`langflow superuser`](/configuration-cli#langflow-superuser).
+
+### LANGFLOW_SUPERUSER and LANGFLOW_SUPERUSER_PASSWORD {#langflow-superuser}
+
+These variables specify the username and password for the Langflow server's superuser.
+
+```text
+LANGFLOW_SUPERUSER=administrator
+LANGFLOW_SUPERUSER_PASSWORD=securepassword
+```
+
+They are required if `LANGFLOW_AUTO_LOGIN=False`.
+Otherwise, they aren't relevant.
+
+When you [start a Langflow server with authentication enabled](#start-a-langflow-server-with-authentication-enabled), if these variables are required but _not_ set, then Langflow uses the default values of `langflow` and `langflow`.
+These defaults don't apply when using the Langflow CLI command [`langflow superuser`](/configuration-cli#langflow-superuser).
+
+### LANGFLOW_SECRET_KEY {#langflow-secret-key}
+
+This environment variable stores a secret key used for encrypting sensitive data like API keys and for JWT signing when using the HS256 algorithm.
+Langflow uses the [Fernet](https://pypi.org/project/cryptography/) library for secret key encryption.
+For JWT-specific configuration, see [JWT authentication](/jwt-authentication).
+
+If no secret key is provided, Langflow automatically generates one.
+
+However, you should generate and explicitly set your own key in production environments.
+This is particularly important for multi-instance deployments like Kubernetes to ensure consistent encryption across instances.
+
+To generate a secret encryption key for `LANGFLOW_SECRET_KEY`, do the following:
+
+1. Run the command to generate and copy a secret to the clipboard.
+
+
+
+
+ * **macOS**: Generate a secret key and copy it to the clipboard:
+
+ ```bash
+ python3 -c "from secrets import token_urlsafe; print(f'LANGFLOW_SECRET_KEY={token_urlsafe(32)}')" | pbcopy
+ ```
+
+ * **Linux**: Generate a secret key and copy it to the clipboard:
+
+ ```bash
+ python3 -c "from secrets import token_urlsafe; print(f'LANGFLOW_SECRET_KEY={token_urlsafe(32)}')" | xclip -selection clipboard
+ ```
+
+ * **Unix**: Generate a secret key and print it to the terminal to manually copy it:
+
+ ```bash
+ python3 -c "from secrets import token_urlsafe; print(f'LANGFLOW_SECRET_KEY={token_urlsafe(32)}')"
+ ```
+
+
+
+
+ * Generate a secret key and copy it to the clipboard:
+
+ ```bash
+ python -c "from secrets import token_urlsafe; print(f'LANGFLOW_SECRET_KEY={token_urlsafe(32)}')"
+ ```
+
+ * Generate a secret key and print it to the terminal to manually copy it:
+
+ ```bash
+
+ # Or just print
+ python -c "from secrets import token_urlsafe; print(f'LANGFLOW_SECRET_KEY={token_urlsafe(32)}')"
+ ```
+
+
+
+
+2. Paste the value into your `.env` file:
+
+ ```text
+ LANGFLOW_SECRET_KEY=dBuu...2kM2_fb
+ ```
+
+ If you're running Langflow on Docker, reference the `LANGFLOW_SECRET_KEY` from your `.env` file in the `docker-compose.yml` file like this:
+
+ ```yaml
+ environment:
+ - LANGFLOW_SECRET_KEY=${LANGFLOW_SECRET_KEY}
+ ```
+
+#### Rotate the secret key {#rotating-the-secret-key}
+
+Rotate `LANGFLOW_SECRET_KEY` if the key might have been compromised and as part of your routine credential management practices.
+Langflow provides a migration script that re-encrypts stored credentials and other sensitive data with a new key so you can rotate without losing access.
+
+For more information, see [Secret Key Rotation](https://github.com/langflow-ai/langflow/blob/main/SECURITY.md#secret-key-rotation) in the Langflow Security Policy.
+
+### LANGFLOW_NEW_USER_IS_ACTIVE {#langflow-new-user-is-active}
+
+When `LANGFLOW_NEW_USER_IS_ACTIVE=False` (default), accounts created by superusers are inactive by default and must be explicitly activated before users can sign in to the visual editor.
+The superuser can also deactivate a user's account as needed.
+
+When `LANGFLOW_NEW_USER_IS_ACTIVE=True`, accounts created by superusers are automatically activated.
+
+```text
+LANGFLOW_NEW_USER_IS_ACTIVE=False
+```
+
+Only superusers can manage user accounts for a Langflow server, but user management only matters if your server has authentication enabled.
+For more information, see [Start a Langflow server with authentication enabled](#start-a-langflow-server-with-authentication-enabled).
+
+### LANGFLOW_API_KEY_SOURCE {#langflow-api-key-source}
+
+This variable controls how Langflow validates API keys.
+
+| Value | Description |
+|-------|-------------|
+| `db` (default) | Validates API keys against [Langflow API keys](#langflow-api-keys) stored in the database. This is the standard behavior where users create and manage API keys through the Langflow UI or CLI. |
+| `env` | Validates API keys against the `LANGFLOW_API_KEY` environment variable. Useful for Kubernetes deployments, CI/CD pipelines, or any environment where you want to inject a pre-defined API key without database configuration. |
+
+By default, Langflow validates the `x-api-key` header against the Langflow database with `LANGFLOW_API_KEY_SOURCE=db`.
+When using database-based validation, you can create multiple keys with per-user permissions, track usage, and manage keys through the Langflow UI or CLI.
+
+When `LANGFLOW_API_KEY_SOURCE=env`, Langflow validates the `x-api-key` header against the value of the `LANGFLOW_API_KEY` environment variable.
+This means Langflow runs securely in stateless environments, such as with LFX or Kubernetes secrets.
+
+When `LANGFLOW_API_KEY_SOURCE=env`, only a single API key can be used for the deployment. All authenticated requests use the same API key, and successful authentication grants superuser privileges.
+This mode is designed for single-tenant deployments or automated systems, not multi-user environments where different users need different access levels. To rotate your keys, update the environment variable and restart the Langflow server.
+
+To enable environment-based API key validation:
+
+1. In the Langflow `.env` file, set the API key source to `env`:
+
+ ```text
+ LANGFLOW_API_KEY_SOURCE=env
+ ```
+
+2. In the Langflow `.env` file, set the API key value:
+
+ ```text
+ LANGFLOW_API_KEY=your-secure-api-key
+ ```
+
+3. Use the API key in your requests:
+
+ ```shell
+ curl -X POST \
+ "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: LANGFLOW_API_KEY" \
+ -d '{"inputs": {"text":""}, "tweaks": {}}'
+ ```
+
+ Replace `LANGFLOW_SERVER_ADDRESS`, `FLOW_ID`, and `LANGFLOW_API_KEY` with the values from your deployment.
+
+
+Kubernetes deployment example
+
+To configure an environment-based API key in a Kubernetes Secret, do the following:
+
+1. Create a Kubernetes Secret with your API key:
+
+ ```yaml
+ apiVersion: v1
+ kind: Secret
+ metadata:
+ name: langflow-api-key
+ type: Opaque
+ stringData:
+ api-key: "YOUR_API_KEY"
+ ```
+
+ Replace `YOUR_API_KEY` with the `LANGFLOW_API_KEY` value from the Langflow `.env` file.
+
+2. Reference the `langflow-api-key` Secret in your Kubernetes deployment:
+
+ ```yaml
+ apiVersion: apps/v1
+ kind: Deployment
+ metadata:
+ name: langflow
+ spec:
+ template:
+ spec:
+ containers:
+ - name: langflow
+ image: langflowai/langflow:latest
+ env:
+ - name: LANGFLOW_API_KEY_SOURCE
+ value: "env"
+ - name: LANGFLOW_API_KEY
+ valueFrom:
+ secretKeyRef:
+ name: langflow-api-key
+ key: api-key
+ ```
+
+
+
+
+Docker Compose example
+
+To configure an environment-based API key in Docker Compose, do the following:
+
+1. Set the API key in your Langflow `.env` file.
+
+ ```text
+ LANGFLOW_API_KEY=your-secure-api-key
+ ```
+
+ Replace `YOUR_API_KEY` with your actual Langflow API key value.
+
+2. Create or update your `docker-compose.yml` file to set `LANGFLOW_API_KEY_SOURCE=env` and reference the `LANGFLOW_API_KEY`.
+
+ ```yaml
+ services:
+ langflow:
+ image: langflowai/langflow:latest
+ environment:
+ - LANGFLOW_API_KEY_SOURCE=env
+ - LANGFLOW_API_KEY=${LANGFLOW_API_KEY}
+ ports:
+ - "7860:7860"
+ ```
+
+
+
+### LANGFLOW_CORS_* {#cors-configuration-for-authentication}
+
+Cross-Origin Resource Sharing (CORS) configuration controls how authentication credentials are handled when your Langflow frontend and backend are served from different origins.
+The following `LANGFLOW_CORS_*` environment variables are available:
+
+| Variable | Format | Default | Description |
+|----------|--------|---------|-------------|
+| `LANGFLOW_CORS_ALLOW_CREDENTIALS` | Boolean | `True` | Whether to allow credentials, such as cookies and authorization headers, in CORS requests. |
+| `LANGFLOW_CORS_ALLOW_HEADERS` | List[String] or String | `*` | The allowed headers for CORS requests. Provide a comma-separated list of headers or use `*` to allow all headers. |
+| `LANGFLOW_CORS_ALLOW_METHODS` | List[String] or String | `*` | The allowed HTTP methods for CORS requests. Provide a comma-separated list of methods or use `*` to allow all methods. |
+| `LANGFLOW_CORS_ORIGINS` | String | `*` | The allowed CORS origins. Provide a comma-separated list of origins or use `*` for all origins. |
+
+The default configuration enables CORS credentials and uses wildcards (`*`) to allow all origins, headers, and methods:
+
+```text
+LANGFLOW_CORS_ORIGINS=*
+LANGFLOW_CORS_ALLOW_CREDENTIALS=True
+LANGFLOW_CORS_ALLOW_HEADERS=*
+LANGFLOW_CORS_ALLOW_METHODS=*
+```
+
+:::danger
+Langflow's default CORS settings can be a security risk in production environments because any website can make requests to your Langflow API, and any website can include credentials in cross-origin requests, including authentication cookies and authorization headers.
+
+In production deployments, specify exact origins in `LANGFLOW_CORS_ORIGINS`.
+You can also specify allowed headers and methods, if needed.
+For example:
+
+```text
+LANGFLOW_CORS_ORIGINS=["https://yourdomain.com","https://app.yourdomain.com"]
+LANGFLOW_CORS_ALLOW_CREDENTIALS=True
+LANGFLOW_CORS_ALLOW_HEADERS=["Content-Type","Authorization"]
+LANGFLOW_CORS_ALLOW_METHODS=["GET","POST","PUT"]
+```
+:::
+
+### SSRF protection {#ssrf-protection}
+
+The following environment variables configure Server-Side Request Forgery (SSRF) protection for the [**API Request** component](/api-request).
+SSRF protection prevents requests to internal or private network resources, such as private IP ranges, loopback addresses, and cloud metadata endpoints.
+
+| Variable | Format | Default | Description |
+|----------|--------|---------|-------------|
+| `LANGFLOW_SSRF_PROTECTION_ENABLED` | Boolean | `False` | Enable SSRF protection for the **API Request** component. When enabled, the component blocks requests to private IP addresses. When disabled, requests are not blocked. |
+| `LANGFLOW_SSRF_ALLOWED_HOSTS` | List[String] | Not set | A comma-separated list of allowed hosts, IP addresses, or CIDR ranges that can bypass SSRF protection checks. For example: `192.168.1.0/24,10.0.0.5,*.internal.company.local`.|
+
+### LANGFLOW_WEBHOOK_AUTH_ENABLE {#langflow-webhook-auth-enable}
+
+This variable controls whether API key authentication is required for webhook endpoints.
+
+| Variable | Format | Default | Description |
+|----------|--------|---------|-------------|
+| `LANGFLOW_WEBHOOK_AUTH_ENABLE` | Boolean | `False` | When `True`, webhook endpoints require API key authentication and validate that the authenticated user owns the flow being executed. When `False`, no Langflow API key is required and all requests to the webhook endpoint are treated as being sent by the flow owner. |
+
+By default, webhooks run as the flow owner without authentication with `LANGFLOW_WEBHOOK_AUTH_ENABLE=False`.
+
+To require API key authentication for webhooks, in your Langflow `.env` file, set `LANGFLOW_WEBHOOK_AUTH_ENABLE=True`.
+
+When webhook authentication is enabled, you must provide a Langflow API key with each webhook request as an HTTP header or query parameter. For more information, see [Require authentication for webhooks](/webhook#require-authentication-for-webhooks).
+
+## Start a Langflow server with authentication enabled
+
+This section shows you how to use the [authentication environment variables](/api-keys-and-authentication#authentication-environment-variables) to deploy a Langflow server with authentication enabled.
+This involves disabling automatic login, setting superuser credentials, generating a secret encryption key, and enabling user management.
+
+This configuration is recommended for any deployment where Langflow is exposed to a shared or public network, or where multiple users access the same Langflow server.
+
+With authentication enabled, all users must sign in to the visual editor with valid credentials, and API requests require authentication with a Langflow API key.
+Additionally, you must sign in as a superuser to manage users and [create a Langflow API key](#create-a-langflow-api-key) with superuser privileges.
+
+### Start the Langflow server
+
+1. Create a `.env` file with the following variables:
+
+ ```text
+ LANGFLOW_AUTO_LOGIN=False
+ LANGFLOW_SUPERUSER=
+ LANGFLOW_SUPERUSER_PASSWORD=
+ LANGFLOW_SECRET_KEY=
+ LANGFLOW_NEW_USER_IS_ACTIVE=False
+ LANGFLOW_ENABLE_SUPERUSER_CLI=False
+ ```
+
+ Your `.env` file can have other environment variables.
+ This example focuses on authentication variables.
+
+2. Set `LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD` to your desired superuser credentials.
+
+ For a one-time test, you can use basic credentials like `administrator` and `password`.
+ Strong, securely-stored credentials are recommended in genuine development and production environments.
+
+3. Recommended: Generate and set a `LANGFLOW_SECRET_KEY` for encrypting sensitive data.
+
+ If you don't set a secret key, Langflow generates one automatically, but this isn't recommended for production environments.
+
+ For instructions on generating and setting a secret key, see [`LANGFLOW_SECRET_KEY`](#langflow-secret-key).
+
+4. Save your `.env` file with the populated variables. For example:
+
+ ```text
+ LANGFLOW_AUTO_LOGIN=False
+ LANGFLOW_SUPERUSER=administrator
+ LANGFLOW_SUPERUSER_PASSWORD=securepassword
+ LANGFLOW_SECRET_KEY=dBuu...2kM2_fb
+ LANGFLOW_NEW_USER_IS_ACTIVE=False
+ LANGFLOW_ENABLE_SUPERUSER_CLI=False
+ ```
+
+5. Start Langflow with the configuration from your `.env` file:
+
+ ```text
+ uv run langflow run --env-file .env
+ ```
+
+ Starting Langflow with a `.env` file automatically authenticates you as the superuser set in `LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD`.
+ If you don't explicitly set these variables, the default values are `langflow` and `langflow` for system auto-login.
+
+6. Verify the server is running. The default location is `http://localhost:7860`.
+
+Next, you can add users to your Langflow server to collaborate with others on flows.
+
+### Manage users as an administrator
+
+1. To complete your first-time login as a superuser, go to `http://localhost:7860/login`.
+
+ If you aren't using the default location, replace `localhost:7860` with your server's address.
+
+2. Log in with the superuser credentials you set in your `.env` (`LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD`).
+
+3. To manage users on your server, navigate to `/admin`, such as `http://localhost:7860/admin`, click your profile icon, and then click **Admin Page**.
+
+ As a superuser, you can add users, set permissions, reset passwords, and delete accounts.
+
+4. To add a user, click **New User**, and then complete the user account form:
+
+ 1. Enter a username and password.
+ 2. To activate the account immediately, select **Active**. Inactive users cannot sign in or access flows they created before becoming inactive.
+ 3. Deselect **Superuser** if you don't want the user to have full administrative privileges.
+ 4. Click **Save**. The new user appears in the **Admin Page**.
+
+5. Send the credentials to the user so they can sign in to Langflow. The superuser sets the initial password when creating the account, so users must receive their login credentials from the superuser.
+
+6. To test the new user's access, sign out of Langflow, and then sign in with the new user's credentials.
+
+ Try to access the `/admin` page.
+ You are redirected to the `/flows` page if the new user isn't a superuser.
+
+## See also
+
+* [Langflow environment variables](/environment-variables)
+* [Langflow Security Policy](https://github.com/langflow-ai/langflow/blob/main/SECURITY.md) — reporting vulnerabilities, security configuration, and [secret key rotation](https://github.com/langflow-ai/langflow/blob/main/SECURITY.md#secret-key-rotation)
diff --git a/docs/versioned_docs/version-1.9.0/Develop/concepts-file-management.mdx b/docs/versioned_docs/version-1.9.0/Develop/concepts-file-management.mdx
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--- /dev/null
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+---
+title: Manage files
+slug: /concepts-file-management
+---
+
+import Icon from "@site/src/components/icon";
+
+Each Langflow server has a file management system where you can store files that you want to use in your flows.
+
+Files uploaded to Langflow file management are stored in Langflow's [storage backend (local or AWS S3)](/concepts-file-management#configure-file-storage), and they are available to all of your flows.
+
+Uploading files to Langflow file management keeps your files in a central location, and allows you to reuse files across flows without repeated manual uploads.
+
+## Use the file management UI
+
+You can use the file management UI to upload files from your local machine to your own Langflow server.
+You can also manage all files that have been uploaded to your Langflow server.
+
+1. Navigate to Langflow file management:
+
+ * **Langflow Desktop**: In Langflow, on the [**Projects** page](/concepts-flows#projects) page, click **My Files** below the list of projects.
+ * **Langflow OSS**: From a browser, navigate to your Langflow server's `/files` endpoint, such as `http://localhost:7860/files`. Modify the base URL as needed for your Langflow server.
+ * **Backend-only**: For programmatic file management, use the [Langflow API files endpoints](/api-files). However, the following steps assume you're using the file management UI.
+
+2. On the **My Files** page, click **Upload**.
+
+3. Select one or more files to upload.
+
+After uploading files, you can rename, download, copy, or delete files within the file management UI.
+To delete a file, hover over a file's icon, select it, and then click **Delete**.
+You can delete multiple files in a single action.
+To download a file, hover over a file's icon, select it, and then click **Download**.
+If you download multiple files in a single action, they are saved together in a zip file.
+
+## Upload and manage files with the Langflow API
+
+With the Langflow API, you can upload and manage files in Langflow file management, and you can send files to flows programmatically at runtime.
+
+For more information and examples, see [Files endpoints](/api-files) and [Create a chatbot that can ingest files](/chat-with-files).
+
+## Set the maximum file size
+
+By default, the maximum file size is 1024 MB.
+To modify this value, change the `LANGFLOW_MAX_FILE_SIZE_UPLOAD` [environment variable](/environment-variables).
+
+## Use files in a flow
+
+To use files in your Langflow file management system in a flow, add a component that accepts file input to your flow, such as the **Read File** component.
+
+For example, add a **Read File** component to your flow, click **Select files**, and then select files from the **My Files** list.
+
+This list includes all files in your server's file management system, but you can only select [file types that are supported by the **Read File** component](/read-file).
+If you need another file type, you must use a different component that supports that file type, or you need to convert it to a supported type before uploading it.
+
+For more information about the **Read File** component and other data loading components, see the [**Read file** component](/read-file).
+
+### Load files at runtime
+
+You can use preloaded files in your flows, and you can load files at runtime, if your flow accepts file input.
+To enable file input in your flow, do the following:
+
+1. Add a [**Read File** component](/read-file) to your flow.
+
+2. Click **Share**, select **API access**, and then click **Input Schema** to add [`tweaks`](/concepts-publish#input-schema) to the request payload in the flow's automatically generated code snippets.
+
+3. Expand the **File** section, find the **Files** row, and then enable **Expose Input** to allow the parameter to be set at runtime through the Langflow API.
+
+4. Close the **Input Schema** pane to return to the **API access** pane.
+The payload in each code snippet now includes `tweaks` with your **Read File** component's ID and the `path` key that you enabled in **Input Schema**:
+
+ ```json
+ "tweaks": {
+ "File-qYD5w": {
+ "path": []
+ }
+ }
+ ```
+
+5. When you run this flow programmatically, your script must upload a file to Langflow file management, and then pass the returned `file_path` to the `path` tweak in the `/run` request:
+
+ ```json
+ "tweaks": {
+ "FILE_COMPONENT_ID": {
+ "path": [ "file_path" ]
+ }
+ }
+ ```
+
+ For a complete example see [Create a chatbot that can ingest files](/chat-with-files) and [Files endpoints](/api-files).
+
+ If you want to upload multiple files, you can pass multiple `file_path` values in the `path` array, such as `[ "path1", "path2" ]`.
+
+## Upload images
+
+Langflow supports base64 images in the following formats:
+
+* PNG
+* JPG/JPEG
+* GIF
+* BMP
+* WebP
+
+You can upload images to the **Playground** chat interface and as runtime input with the Langflow API.
+
+* In the **Playground**, you can drag-and-drop images into the chat input area, or you can click the **Attach image** icon to select an image to upload.
+
+* When you trigger a flow with the `/api/v1/run/$FLOW_ID` endpoint, you can use the `files` parameter to attach image data as a base64-encoded string:
+
+ ```bash
+ curl -X POST "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY" \
+ -d '{
+ "session_id": "custom_session_123",
+ "input_value": "What is in this image?",
+ "input_type": "chat",
+ "output_type": "chat",
+ "files": ["data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAA..."]
+ }'
+ ```
+
+For more specialized image processing, browse [**Bundles**] or [create your own components](/components-custom-components).
+
+## Work with video files
+
+For videos, see the **Twelve Labs** and **YouTube** [**Bundles**](/components-bundle-components).
+
+## Configure file storage
+
+Langflow supports two storage backends for file management.
+
+* **Local storage**: Langflow's default storage backend. Files are stored locally in your [Langflow configuration directory](/memory). Set `LANGFLOW_STORAGE_TYPE=local` or leave it unset to use local storage.
+
+* **S3 storage**: Files are stored in an AWS S3 bucket.
+Langflow uses the [boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) library to interact with S3.
+
+To use S3 as your file storage backend, add the following configuration to your `.env` file:
+
+```text
+# S3 Storage Configuration
+LANGFLOW_STORAGE_TYPE=s3
+LANGFLOW_OBJECT_STORAGE_BUCKET_NAME=S3_BUCKET_NAME
+LANGFLOW_OBJECT_STORAGE_PREFIX=S3_BUCKET_DIRECTORY
+
+# AWS Credentials (required for S3)
+AWS_ACCESS_KEY_ID=S3_ACCESS_KEY
+AWS_SECRET_ACCESS_KEY=S3_ACCESS_SECRET_KEY
+AWS_DEFAULT_REGION=S3_REGION
+```
+
+Replace the following placeholders with the actual values for your S3 instance:
+
+* `S3_BUCKET_NAME`: The name of your S3 bucket.
+* `S3_BUCKET_DIRECTORY`: An optional folder path within the bucket where files are stored, such as `s3://S3_BUCKET_NAME/S3_BUCKET_DIRECTORY`.
+* `S3_ACCESS_KEY`: Your AWS Access Key ID.
+* `S3_ACCESS_SECRET_KEY`: Your AWS Secret Access Key.
+* `S3_REGION`: The AWS region where your bucket is located, such as `us-east-2`.
+
+Your AWS credentials must have the necessary permissions to perform the required S3 operations for your use case, such as reading, writing, and deleting files in S3.
+This example policy allows basic CRUD operations on S3 objects.
+
+```json
+{
+ "Version": "2012-10-17",
+ "Statement": [
+ {
+ "Sid": "LangflowS3StorageAccess",
+ "Effect": "Allow",
+ "Action": [
+ "s3:PutObject",
+ "s3:GetObject",
+ "s3:DeleteObject",
+ "s3:ListBucket",
+ "s3:PutObjectTagging",
+ ],
+ "Resource": [
+ "arn:aws:s3:::S3_BUCKET_NAME",
+ "arn:aws:s3:::S3_BUCKET_NAME/S3_BUCKET_DIRECTORY/*"
+ ]
+ }
+ ]
+}
+```
+
+Replace the following placeholders with the actual values for your IAM policy and S3 instance:
+
+* `S3_BUCKET_NAME`: The name of your S3 bucket.
+* `S3_BUCKET_DIRECTORY`: An optional folder path within the bucket where files are stored, such as `s3://S3_BUCKET_NAME/S3_BUCKET_DIRECTORY`.
+
+For more information, see the [AWS documentation](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_change-permissions.html).
+
+**Google Drive** storage is available through the [**Read File**](/read-file) and [**Write file**](/write-file) components, but you cannot use environment variables to configure it.
+
+## File storage environment variables {#file-storage-environment-variables}
+
+The following environment variables configure file storage backends for Langflow's file management system:
+
+| Variable | Format | Default | Description |
+|----------|--------|---------|-------------|
+| `LANGFLOW_STORAGE_TYPE` | String | `local` | Set the file storage backend. Supported values: `local` (files stored in the Langflow configuration directory) or `s3` (files stored in AWS S3). For S3 storage, you must also configure AWS credentials and bucket settings. |
+| `LANGFLOW_OBJECT_STORAGE_BUCKET_NAME` | String | Not set | The name of the S3 bucket to use for file storage. Required when `LANGFLOW_STORAGE_TYPE=s3`. |
+| `LANGFLOW_OBJECT_STORAGE_PREFIX` | String | Not set | Optional prefix/folder path within the S3 bucket where files will be stored. If not set, files are stored at the bucket root. |
+| `LANGFLOW_OBJECT_STORAGE_TAGS` | JSON object | Not set | Optional S3 object tags applied to stored files when `LANGFLOW_STORAGE_TYPE=s3`. Ignored for local storage. Provided as a JSON map of string keys to string values, such as `{"env": "prod", "owner": "data-team"}`. |
+
+## See also
+
+* [Components reference](/concepts-components)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/concepts-voice-mode.mdx b/docs/versioned_docs/version-1.9.0/Develop/concepts-voice-mode.mdx
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+---
+title: Use voice mode
+slug: /concepts-voice-mode
+---
+
+import Icon from "@site/src/components/icon";
+
+:::info
+Voice mode is not available in Langflow Desktop.
+To use voice mode, [Install the Langflow OSS Python package](/get-started-installation#install-and-run-the-langflow-oss-python-package).
+:::
+
+You can use Langflow's voice mode to interact with your flows verbally through a microphone and speakers.
+
+## Prerequisites
+
+Voice mode requires the following:
+
+* A flow with **Chat Input**, **Language Model**, and **Chat Output** components.
+
+ If your flow has an **Agent** component, make sure the tools in your flow have accurate names and descriptions to help the agent choose which tools to use.
+
+ Additionally, be aware that voice mode overrides typed instructions in the **Agent** component's **Agent Instructions** field.
+
+* An [OpenAI](https://platform.openai.com/) account and an OpenAI API key because Langflow uses the OpenAI API to process voice input and generate responses.
+
+* Optional: An [ElevenLabs](https://elevenlabs.io) API key to enable more voice options for the LLM's response.
+
+* A microphone and speakers.
+
+ A high quality microphone and minimal background noise are recommended for optimal voice comprehension.
+
+## Test voice mode in the Playground
+
+In the **Playground**, click the **Microphone** to enable voice mode and verbally interact with your flows through a microphone and speakers.
+
+The following steps use the **Simple Agent** template to demonstrate how to enable voice mode:
+
+1. Create a flow based on the **Simple Agent** template.
+
+2. Add your **OpenAI API key** credentials to the **Agent** component.
+
+3. Click **Playground**.
+
+4. Click the **Microphone** icon to open the **Voice mode** dialog.
+
+5. Enter your OpenAI API key, and then click **Save**. Langflow saves the key as a [global variable](/configuration-global-variables).
+
+6. If you are prompted to grant microphone access, you must allow microphone access to use voice mode.
+If microphone access is blocked, you won't be able to provide verbal input.
+
+7. For **Audio Input**, select the input device to use with voice mode.
+
+8. Optional: Add an ElevenLabs API key to enable more voices for the LLM's response.
+Langflow saves this key as a global variable.
+
+9. For **Preferred Language**, select the language you want to use for your conversations with the LLM.
+This option changes both the expected input language and the response language.
+
+10. Speak into your microphone to start the chat.
+
+ If configured correctly, the waveform registers your input, and then the agent's logic and response are described verbally and in the **Playground**.
+
+## Develop applications with websockets endpoints
+
+Langflow exposes two OpenAI Realtime API-compatible websocket endpoints for your flows.
+You can build applications against these endpoints the same way you would build against [OpenAI Realtime API websockets](https://platform.openai.com/docs/guides/realtime#connect-with-websockets).
+
+The Langflow API's websockets endpoints require an [OpenAI API key](https://platform.openai.com/docs/overview) for authentication, and they support an optional [ElevenLabs](https://elevenlabs.io) integration with an ElevenLabs API key.
+
+Additionally, both endpoints require that you provide the flow ID in the endpoint path.
+
+### Voice-to-voice audio streaming
+
+The `/ws/flow_as_tool/$FLOW_ID` endpoint establishes a connection to OpenAI Realtime voice, and then invokes the specified flow as a tool according to the [OpenAI Realtime model](https://platform.openai.com/docs/guides/realtime-conversations#handling-audio-with-websockets).
+
+This approach is ideal for low latency applications, but it is less deterministic because the OpenAI voice-to-voice model determines when to call your flow.
+
+### Speech-to-text audio transcription
+
+The `/ws/flow_tts/$FLOW_ID` endpoint converts audio to text using [OpenAI Realtime voice transcription](https://platform.openai.com/docs/guides/realtime-transcription), and then directly invokes the specified flow for each transcript.
+
+This approach is more deterministic but has higher latency.
+
+This is the mode used in the Langflow **Playground**.
+
+### Session IDs for websockets endpoints
+
+Both endpoints accept an optional `/$SESSION_ID` path parameter to provide a unique ID for the conversation.
+If omitted, Langflow uses the flow ID as the [session ID](/session-id).
+
+However, be aware that voice mode only maintains context within the current conversation instance.
+When you close the **Playground** or end a chat, verbal chat history is discarded and not available for future chat sessions.
+
+## See also
+
+* [Test flows in the Playground](/concepts-playground)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/configuration-cli.mdx b/docs/versioned_docs/version-1.9.0/Develop/configuration-cli.mdx
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+---
+title: Langflow CLI
+slug: /configuration-cli
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Icon from "@site/src/components/icon";
+
+The Langflow command line interface is the main interface for managing and running the Langflow server.
+
+The Langflow CLI is automatically installed when you [install the Langflow package](/get-started-installation).
+It isn't available for Langflow Desktop.
+
+## How to use the CLI
+
+The Langflow CLI can be invoked in several ways, depending on your installation method and environment.
+
+The recommended approach is to run the CLI with `uv run` from within a virtual environment where Langflow is installed.
+
+For example, to start Langflow on the default port, run the following command:
+
+ ```bash
+ uv run langflow run
+ ```
+
+If Langflow is installed globally or added to your PATH, you can execute the CLI directly with `langflow`.
+
+ ```bash
+ langflow run
+ ```
+
+## Precedence
+
+Langflow CLI options override the values of [environment variables](/environment-variables) set in your terminal or primary `.env` file.
+
+For example, if you have `LANGFLOW_PORT=7860` defined as an environment variable, and you run the CLI with `--port 7880`, then Langflow sets the port to `7880` because the CLI option overrides the environment variable.
+
+This also applies to Boolean environment variables.
+For example, if you set `LANGFLOW_REMOVE_API_KEYS=True` in your `.env` file, you can change it to `False` at runtime by running the CLI with `--no-remove-api-keys`.
+
+## Langflow CLI options
+
+All Langflow CLI commands support options that modify the command's behavior or set environment variables.
+
+To set values for options, you can use either of the following syntax styles:
+
+* `--option value`
+* `--option=value`
+
+Values with spaces must be surrounded by quotation marks:
+
+* `--option 'Value with Spaces'`
+* `--option="Value with Spaces"`
+
+### Boolean options
+
+Boolean options enable and disable settings.
+They have a true (enabled) and false (disabled) form:
+
+* Enabled (true): `--option`
+* Disabled (false): `--no-option`
+
+The following examples compare Boolean option forms for `REMOVE_API_KEYS`.
+
+
+
+
+`--remove-api-keys` is equivalent to setting `LANGFLOW_REMOVE_API_KEYS=True` in `.env`:
+
+```bash
+uv run langflow run --remove-api-keys
+```
+
+
+
+
+`--no-remove-api-keys` is equivalent to `LANGFLOW_REMOVE_API_KEYS=False` in `.env`:
+
+```bash
+uv run langflow run --no-remove-api-keys
+```
+
+
+
+
+In the following command references, default values for Booleans include both the CLI flag and the equivalent Boolean evaluation, such as "`--option` (true)" and "`--no-option` (false)".
+
+### Universal options
+
+The following options are available for all Langflow CLI commands:
+
+* `--version`, `-v`: Show the version and exit.
+* `--install-completion`: Install auto-completion for the current shell.
+* `--show-completion`: Show the location of the auto-completion config file, if installed.
+* `--help`: Print information about command usage, options, and arguments.
+
+## CLI commands
+
+The following sections describe the available CLI commands and any additional options (beyond the [universal options](#universal-options)) available for each command.
+
+### langflow
+
+Running the CLI without any arguments prints a list of available options and commands.
+
+
+
+
+```bash
+uv run langflow
+```
+
+
+
+
+```bash
+langflow
+```
+
+
+
+
+### langflow api-key {#langflow-api-key}
+
+Creates a Langflow API key.
+
+You must be a superuser to create API keys with the CLI.
+For more information, see [Langflow API keys](/api-keys-and-authentication#langflow-api-keys).
+
+
+
+
+```bash
+uv run langflow api-key
+```
+
+
+
+
+```bash
+langflow api-key
+```
+
+
+
+
+#### Options
+
+| Option | Default | Type | Description |
+|--------|---------|--------|-------------|
+| `--log-level` | `error` | String | The logging level. One of `debug`, `info`, `warning`, `error`, or `critical`. |
+
+### langflow copy-db
+
+Copies the Langflow database files from the cache directory to the current Langflow installation directory, which is the directory containing `__main__.py`.
+You can find the copy target directory by running `which langflow`.
+
+The following files are copied if they exist in the cache directory:
+
+* `langflow.db`: The main Langflow database, stored in the user cache directory
+* `langflow-pre.db`: The pre-release database, if it exists
+
+
+
+
+```bash
+uv run langflow copy-db
+```
+
+
+
+
+```bash
+langflow copy-db
+```
+
+
+
+
+### langflow migration
+
+Manages Langflow database schema changes using [Alembic](https://alembic.sqlalchemy.org/en/latest/), a database migration tool for SQLAlchemy.
+
+The `migration` command has two modes:
+
+* **Test mode (default)**: Checks if migrations can be applied safely without actually running the migrations.
+Use this mode to previews the changes that would be made to the database schema before proceeding with the migrations.
+
+* **Fix mode**: Applies the migrations to update the database schema.
+
+ :::warning
+ `langflow migration --fix` is a destructive operation that can delete data.
+ Always run `langflow migration` first to preview the changes.
+ :::
+
+
+
+
+1. Run test mode:
+
+ ```bash
+ uv run langflow migration
+ ```
+
+2. Preview the changes returned by the test to determine if it's safe to proceed with the migration.
+
+3. Run fix mode to apply the changes:
+
+ ```bash
+ uv run langflow migration --fix
+ ```
+
+
+
+
+1. Run test mode:
+
+ ```bash
+ langflow migration
+ ```
+
+2. Preview the changes returned by the test to determine if it's safe to proceed with the migration.
+
+3. Run fix mode to apply the changes:
+
+ ```bash
+ langflow migration --fix
+ ```
+
+
+
+
+### langflow run {#langflow-run}
+
+Starts the Langflow server.
+
+
+
+
+```bash
+uv run langflow run [OPTIONS]
+```
+
+
+
+
+```bash
+langflow run [OPTIONS]
+```
+
+
+
+
+#### Options
+
+This command supports some common and non-sensitive configuration options for your Langflow server.
+Other options must be set in the `.env` or your terminal.
+For more information Langflow configuration options, see [Langflow environment variables](/environment-variables).
+
+| Option | Default | Type | Description |
+|--------|---------|--------|-------------|
+| `--auto-saving` | `--auto-saving` (true) | Boolean | Whether to enable flow auto-saving in the visual editor. Use `--no-auto-saving` to disable flow auto-saving. |
+| `--auto-saving-interval` | `1000` | Integer | The interval for flow auto-saving in milliseconds. |
+| `--backend-only` | `--no-backend-only` (false) | Boolean | Whether to run Langflow's backend service only (no frontend). Omit or use `--no-backend-only` to start both the frontend and backend. See [Start Langflow in headless mode](#start-langflow-in-headless-mode). |
+| `--cache` | `async` | String | The type of [cache storage](/memory) to use. One of `async`, `redis`, `memory`, or `disk`. |
+| `--components-path` | Not set | String | The path to the directory containing your custom components. |
+| `--dev` | `--no-dev` (false) | Boolean | Whether to run in development mode (may contain bugs). |
+| `--env-file` | Not set | String | The path to the `.env` file containing Langflow environment variables. See [Start Langflow with a specific .env file](#start-langflow-with-a-specific-env-file). |
+| `--frontend-path` | Not set | String | The path to the frontend directory containing build files. This is only used when [contributing to the Langflow codebase](/contributing-how-to-contribute) or developing a custom Langflow image that includes customized frontend code. |
+| `--health-check-max-retries` | `5` | Integer | The maximum number of retries for your Langflow server's health check. |
+| `--host` | `localhost` | String | The host on which the Langflow server will run. |
+| `--log-file` | `logs/langflow.log` | String | The path to the log file for Langflow. |
+| `--log-level` | `critical` | String | The logging level as one of `debug`, `info`, `warning`, `error`, or `critical`. |
+| `--log-rotation` | Not set | String | The log rotation interval, either a time duration or file size. |
+| `--max-file-size-upload` | `1024` | Integer | The maximum size in megabytes for file uploads. |
+| `--open-browser` | `--no-open-browser` (false) | Boolean | Whether to open the system web browser on startup. Use `--open-browser` to open the system's default web browser when Langflow starts. |
+| `--port` | `7860` | Integer | The port on which the Langflow server will run. The server automatically selects a free port if the specified port is in use. |
+| `--remove-api-keys` | `--no-remove-api-keys` (false) | Boolean | Whether to remove API keys and tokens from flows saved in the Langflow database. |
+| `--ssl-cert-file-path` | Not set | String | The path to the SSL certificate file on the local system for SSL-encrypted connections. |
+| `--ssl-key-file-path` | Not set | String | The path to the SSL key file on the local system for SSL-encrypted connections. |
+| `--worker-timeout` | `300` | Integer | The Langflow server worker timeout in seconds. |
+| `--workers` | `1` | Integer | The number of Langflow server worker processes. |
+
+#### Start Langflow with a specific .env file {#start-langflow-with-a-specific-env-file}
+
+The `--env-file` option starts Langflow using the configuration defined in the given `.env` file.
+Additional options appended to this command override the values in the `.env` file if there are duplicates.
+
+If `--env-file` is omitted or doesn't include all required variables, Langflow uses the default values for those variables.
+
+
+
+
+```bash
+uv run langflow run --env-file PATH/TO/LANGFLOW/.env
+```
+
+
+
+
+```bash
+langflow run --env-file PATH/TO/LANGFLOW/.env
+```
+
+
+
+
+#### Start Langflow in headless mode {#start-langflow-in-headless-mode}
+
+The `--backend-only` option starts Langflow's backend service only.
+This headless mode has no frontend (visual editor), and you can only access the server programmatically with the Langflow API and CLI.
+
+
+
+
+```bash
+uv run langflow run --backend-only
+```
+
+
+
+
+```bash
+langflow run --backend-only
+```
+
+
+
+
+### langflow superuser {#langflow-superuser}
+
+Creates a superuser account with the given username and password.
+
+
+
+
+```bash
+uv run langflow superuser --username [NAME] --password [PASSWORD] [OPTIONS]
+```
+
+
+
+
+```bash
+langflow superuser --username [NAME] --password [PASSWORD] [OPTIONS]
+```
+
+
+
+
+#### Options
+
+| Option | Default | Type | Description |
+|--------|---------|--------|-------------|
+| `--log-level` | `error` | String | The logging level. One of `debug`, `info`, `warning`, `error`, or `critical`. |
+
+For this command, `--username` and `--password` aren't optional, and they have no default value.
+The command fails if you don't provide these arguments.
+For more information, see [`LANGFLOW_SUPERUSER` and `LANGFLOW_SUPERUSER_PASSWORD`](/api-keys-and-authentication#langflow-superuser).
+
+#### Disable CLI superuser creation
+
+The `langflow superuser` command is controlled by the [`LANGFLOW_ENABLE_SUPERUSER_CLI`](/api-keys-and-authentication#langflow-enable-superuser-cli) environment variable:
+
+* **`LANGFLOW_ENABLE_SUPERUSER_CLI=True` (default)**: The `langflow superuser` command is available, and superuser creation is unrestricted.
+* **`LANGFLOW_ENABLE_SUPERUSER_CLI=False` (recommended)**: Disables the `langflow superuser` command.
+For security reasons, this is recommended to prevent unauthorized superuser creation, especially in production environments.
+
+To disable the `langflow superuser` command, you must set `LANGFLOW_ENABLE_SUPERUSER_CLI=False` in your Langflow `.env` file, and then [start Langflow with your `.env` file](#start-langflow-with-a-specific-env-file).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/configuration-custom-database.mdx b/docs/versioned_docs/version-1.9.0/Develop/configuration-custom-database.mdx
new file mode 100644
index 0000000000..5ac46ccad0
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/configuration-custom-database.mdx
@@ -0,0 +1,198 @@
+---
+title: Configure an external PostgreSQL database
+slug: /configuration-custom-database
+---
+
+Langflow's default database is [SQLite](https://www.sqlite.org/docs.html), but you can configure Langflow to use PostgreSQL instead.
+
+This guide walks you through setting up an external database for Langflow by replacing the default SQLite connection string `sqlite:///./langflow.db` with PostgreSQL, both in local and containerized environments.
+
+In this configuration, all structured application data from Langflow, including flows, message history, and logs, is instead managed by PostgreSQL.
+PostgreSQL is better suited for production environments due to its robust support for concurrent users, advanced data integrity features, and scalability.
+Langflow can more efficiently handle multiple users and larger workloads by using PostgreSQL as the database.
+
+## Prerequisites
+
+- A [PostgreSQL](https://www.pgadmin.org/download/) database version 15 or later
+
+## Connect Langflow to a local PostgreSQL database
+
+1. If Langflow is running, stop Langflow with Ctrl+C.
+
+2. Find your PostgreSQL database's connection string in the format `postgresql://user:password@host:port/dbname`.
+
+ The hostname in your connection string depends on how you're running PostgreSQL:
+
+ - If you're running PostgreSQL directly on your machine, use `localhost`.
+ - If you're running PostgreSQL in Docker Compose, use the service name, such as `postgres`.
+ - If you're running PostgreSQL in a separate Docker container with `docker run`, use the container's IP address or network alias.
+ - If you're running a cloud-hosted PostgreSQL, your provider will share your connection string, which includes a username and password.
+
+3. Edit or create a Langflow `.env` file:
+
+ ```
+ touch .env
+ ```
+
+ You can use the [`.env.example`](https://github.com/langflow-ai/langflow/blob/main/.env.example) file in the Langflow repository as a template for your own `.env` file.
+
+4. In your `.env` file, set `LANGFLOW_DATABASE_URL` to your PostgreSQL connection string:
+
+ ```text
+ LANGFLOW_DATABASE_URL="postgresql://user:password@localhost:5432/dbname"
+ ```
+
+ Langflow uses [SQLAlchemy](https://www.sqlalchemy.org/) with the [psycopg](https://www.psycopg.org/) driver to pass SSL parameters directly to the PostgreSQL connection.
+
+ :::warning PostgreSQL driver compatibility
+ Langflow requires `psycopg2-binary` or `psycopg[binary]` as the PostgreSQL driver. The `asyncpg` driver is not compatible with Langflow's current database schema due to stricter timezone handling requirements.
+ :::
+
+ The following SSL modes are supported:
+
+ - **`sslmode=require`**: Requires SSL connection but doesn't verify server certificate. This option is the least secure, but acceptable for most use cases.
+ ```bash
+ LANGFLOW_DATABASE_URL="postgresql://user:password@localhost:5432/dbname?sslmode=require"
+ ```
+
+ - **`sslmode=verify-ca`**: Requires SSL and verifies the server certificate against the Certificate Authority (CA). Add the certificate paths to your connection string:
+ ```bash
+ LANGFLOW_DATABASE_URL="postgresql://user@localhost:5432/dbname?sslmode=verify-ca&sslcert=/path/to/client.crt&sslkey=/path/to/client.key&sslrootcert=/path/to/ca.crt"
+ ```
+
+ - **`sslmode=verify-full`**: Requires SSL, verifies the server certificate, and checks the request hostname against the certificate hostname. The `db.example.com` hostname in this example must match the server certificate's CN. This option is the most secure.
+ ```bash
+ LANGFLOW_DATABASE_URL="postgresql://user@db.example.com:5432/dbname?sslmode=verify-full&sslcert=/path/to/client.crt&sslkey=/path/to/client.key&sslrootcert=/path/to/ca.crt"
+ ```
+
+ Do not use the Langflow environment variables [`LANGFLOW_SSL_CERT_FILE`](/environment-variables#server) and [`LANGFLOW_SSL_KEY_FILE`](/environment-variables#server) for your PostgreSQL certificates: these variables are for enabling HTTPS on the Langflow server, not for PostgreSQL database connections.
+
+ For more on managing SSL certificates in PostgreSQL, see the [PostgreSQL documentation](https://www.postgresql.org/docs/9.1/ssl-tcp.html).
+
+5. Save your changes, and then start Langflow with your `.env` file:
+
+ ```bash
+ uv run langflow run --env-file .env
+ ```
+
+ For optional connection pooling and timeout settings, see [Configure external memory](/memory#configure-external-memory).
+
+6. In Langflow, run any flow to create traffic.
+
+7. Inspect your PostgreSQL database's tables and activity to verify that new tables and traffic were created after you ran a flow.
+
+## Deploy Langflow and PostgreSQL containers with docker-compose.yml
+
+Launching Langflow and PostgreSQL containers in the same Docker network ensures proper connectivity between services.
+For an example, see the [`docker-compose.yml`](https://github.com/langflow-ai/langflow/blob/main/docker_example/docker-compose.yml) file in the Langflow repository.
+
+The configuration in the example `docker-compose.yml` also sets up persistent volumes for both Langflow and PostgreSQL data.
+Persistent volumes map directories inside of containers to storage on the host machine, so data persists through container restarts.
+
+Docker Compose creates an isolated network for all services defined in `docker-compose.yml`. This ensures that the services can communicate with each other using their service names as hostnames, such as `postgres` in the database URL.
+In contrast, if you run PostgreSQL separately with `docker run`, it launches in a different network than the Langflow container, and this prevents Langflow from connecting to PostgreSQL using the service name.
+
+To start the Langflow and PostgreSQL services with the example Docker Compose file, navigate to the `langflow/docker_example` directory, and then run `docker-compose up`.
+If you're using a different `docker-compose.yml` file, run the `docker-compose up` command from the same directory as your `docker-compose.yml` file.
+
+## Deploy multiple Langflow instances with a shared PostgreSQL database
+
+To configure multiple Langflow instances that share the same PostgreSQL database, modify your `docker-compose.yml` file to include multiple Langflow services.
+
+This example populates the values in `docker-compose.yml` with values from your Langflow `.env` file.
+This approach means you only have to manage deployment variables in one file, instead of copying values across multiple files.
+
+1. Update your `.env` file with values for your PostgreSQL database:
+
+ ```text
+ POSTGRES_USER=langflow
+ POSTGRES_PASSWORD=your_secure_password
+ POSTGRES_DB=langflow
+ POSTGRES_HOST=postgres
+ POSTGRES_PORT=5432
+ LANGFLOW_CONFIG_DIR=app/langflow
+ LANGFLOW_PORT_1=7860
+ LANGFLOW_PORT_2=7861
+ LANGFLOW_HOST=0.0.0.0
+ ```
+
+ For optional connection pooling and timeout settings, see [Configure external memory](/memory#configure-external-memory).
+
+2. Reference these variables in your `docker-compose.yml`.
+For example:
+
+ ```yaml
+ services:
+ postgres:
+ image: postgres:16
+ environment:
+ - POSTGRES_USER=${POSTGRES_USER}
+ - POSTGRES_PASSWORD=${POSTGRES_PASSWORD}
+ - POSTGRES_DB=${POSTGRES_DB}
+ ports:
+ - "${POSTGRES_PORT}:5432"
+ volumes:
+ - langflow-postgres:/var/lib/postgresql/data
+
+ langflow-1:
+ image: langflowai/langflow:latest
+ pull_policy: always
+ ports:
+ - "${LANGFLOW_PORT_1}:7860"
+ depends_on:
+ - postgres
+ environment:
+ - LANGFLOW_DATABASE_URL=postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@${POSTGRES_HOST}:${POSTGRES_PORT}/${POSTGRES_DB}
+ - LANGFLOW_CONFIG_DIR=${LANGFLOW_CONFIG_DIR}
+ - LANGFLOW_HOST=${LANGFLOW_HOST}
+ - PORT=7860
+ volumes:
+ - langflow-data-1:/app/langflow
+
+ langflow-2:
+ image: langflowai/langflow:latest
+ pull_policy: always
+ ports:
+ - "${LANGFLOW_PORT_2}:7860"
+ depends_on:
+ - postgres
+ environment:
+ - LANGFLOW_DATABASE_URL=postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@${POSTGRES_HOST}:${POSTGRES_PORT}/${POSTGRES_DB}
+ - LANGFLOW_CONFIG_DIR=${LANGFLOW_CONFIG_DIR}
+ - LANGFLOW_HOST=${LANGFLOW_HOST}
+ - PORT=7860
+ volumes:
+ - langflow-data-2:/app/langflow
+
+ volumes:
+ langflow-postgres:
+ langflow-data-1:
+ langflow-data-2:
+ ```
+
+3. Deploy the file with `docker-compose up`.
+You can access the first Langflow instance at `http://localhost:7860`, and the second Langflow instance at `http://localhost:7861`.
+
+4. To confirm both instances are using the same database, run the `docker exec` command to start `psql` in your PostgreSQL container.
+Your container name may vary.
+
+ ```bash
+ docker exec -it docker-test-postgres-1 psql -U langflow -d langflow
+ ```
+
+5. Query the database for active connections:
+
+ ```sql
+ langflow=# SELECT * FROM pg_stat_activity WHERE datname = 'langflow';
+ ```
+
+6. Examine the query results for multiple connections with different `client_addr` values, for example `172.21.0.3` and `172.21.0.4`.
+Since each Langflow instance runs in its own container on the Docker network, using different incoming IP addresses confirms that both instances are actively connected to the PostgreSQL database.
+
+7. To quit `psql`, type `quit`.
+
+## See also
+
+* [Langflow database guide for enterprise DBAs](/enterprise-database-guide)
+* [Memory management options](/memory)
+* [Logs](/logging)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/configuration-global-variables.mdx b/docs/versioned_docs/version-1.9.0/Develop/configuration-global-variables.mdx
new file mode 100644
index 0000000000..6342effe67
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/configuration-global-variables.mdx
@@ -0,0 +1,185 @@
+---
+title: Global variables
+slug: /configuration-global-variables
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Use global variables to store and reuse credentials and generic values across all of your flows.
+Global variables are typically used by components in flow, and you can use them in any field with the global variable icon.
+
+In contrast, [environment variables](/environment-variables), like `LANGFLOW_PORT` or `LANGFLOW_LOG_LEVEL`, are generally for broader settings that configure how Langflow runs.
+However, Langflow can also source global variables from environment variables.
+
+Langflow stores global variables in its internal database, and it encrypts the values using a secret key.
+
+## Create a global variable
+
+To create a new global variable, follow these steps.
+
+1. In the Langflow header, click your profile icon, and then select **Settings**.
+2. Click **Global Variables**.
+
+3. Click **Add New**.
+
+4. In the **Create Variable** dialog, enter a name for your variable in the **Variable Name** field.
+
+5. Optional: Select a **Type** for your global variable. The available types are **Generic** (default) and **Credential**.
+
+ Langflow encrypts both **Generic** and **Credential** type global variables.
+ However, **Generic** variables aren't masked in the visual editor, whereas **Credential** variables are masked.
+ **Session ID** fields don't accept **Credential** (masked) variables.
+
+6. Enter the **Value** for your global variable.
+
+7. Optional: Use the **Apply To Fields** menu to select one or more fields that you want Langflow to automatically apply your global variable to. For example, if you select **OpenAI API Key**, Langflow automatically applies the variable to any **OpenAI API Key** field.
+
+8. Click **Save Variable**.
+
+You can now select your global variable from any text input field that displays the **Globe** icon.
+
+## Edit a global variable
+
+1. In the Langflow header, click your profile icon, and then select **Settings**.
+
+2. Click **Global Variables**.
+
+3. Click on the global variable you want to edit.
+
+4. In the **Update Variable** dialog, you can edit the following fields: **Variable Name**, **Value**, and **Apply To Fields**.
+
+5. Click **Update Variable**.
+
+## Delete a global variable
+
+Deleting a global variable permanently deletes the value from the database.
+Flows that reference the deleted global variable will fail.
+
+1. In the Langflow header, click your profile icon, and then select **Settings**.
+
+2. Click **Global Variables**.
+
+3. Click the checkbox next to the global variable that you want to delete.
+
+4. Click **Delete**.
+
+The global variable is deleted from the database.
+
+## Add custom global variables from the environment {#add-custom-global-variables-from-the-environment}
+
+Langflow can source custom global variables from your runtime environment.
+For information about how Langflow detects and applies environment variables, see [Langflow environment variables](/environment-variables).
+
+Langflow automatically generates global variables based on [`constants.py`](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/services/settings/constants.py) if it detects any matching environment variables.
+For example, if you set `OPENAI_API_KEY` in your runtime environment, Langflow automatically generates a global variable using that value.
+
+You can declare additional variables in `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`.
+For example, `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=WATSONX_PROJECT_ID,WATSONX_API_KEY` creates global variables named `WATSONX_PROJECT_ID` and `WATSONX_API_KEY` in Langflow's database.
+Then, you can use these variables wherever they are needed in your component settings.
+
+
+
+
+If you installed Langflow locally, set `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT` in your Langflow `.env` file:
+
+1. Create or edit your Langflow `.env` file.
+
+2. Add the `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT` environment variable as follows:
+
+ You can specify the variables either as a comma-separated string with no spaces, or as a JSON list:
+
+ ```text
+ # Option 1: Comma-separated string (no spaces)
+ LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=VARIABLE1,VARIABLE2
+
+ # Option 2: JSON list format
+ LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT=["VARIABLE1", "VARIABLE2"]
+ ```
+
+ Replace `VARIABLE1,VARIABLE2` with your additional variables that you want Langflow to source from the environment, such as `CUSTOM_API_KEY,INTERNAL_SERVICE_URL` or `["CUSTOM_API_KEY", "INTERNAL_SERVICE_URL"]`.
+
+3. Save and close the file.
+
+4. Start Langflow with the `.env` file:
+
+ ```bash
+ uv run langflow run --env-file .env
+ ```
+
+ Alternatively, you can set environment variables directly in the command line:
+
+ ```bash
+ VARIABLE1="VALUE1" VARIABLE2="VALUE2" uv run langflow run --env-file .env
+ ```
+
+ The command-line variables override matching variables in the `.env` file.
+ Expose your environment variables to Langflow in a manner that best suits your own environment.
+
+5. Confirm that Langflow successfully sourced the global variables from the environment:
+
+ 1. In the Langflow header, click your profile icon, and then select **Settings**.
+
+ 2. Click **Global Variables**, and then make sure that your environment variables appear in the **Global Variables** list.
+
+
+
+
+If you're using Docker, there are two ways that you can set `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`:
+
+* On the command line:
+
+ ```bash
+ docker run -it --rm \
+ -p 7860:7860 \
+ -e LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT="VARIABLE1,VARIABLE2" \
+ -e VARIABLE1="VALUE1" \
+ -e VARIABLE2="VALUE2" \
+ langflowai/langflow:latest
+ ```
+
+* In your `.env` file:
+
+ ```bash
+ docker run -it --rm \
+ -p 7860:7860 \
+ --env-file .env \
+ -e VARIABLE1="VALUE1" \
+ -e VARIABLE2="VALUE2" \
+ langflowai/langflow:latest
+ ```
+
+The list in `LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT` includes only the variable names.
+You must ensure that these environment variables are defined in your Docker environment, such as with `-e` or otherwise.
+
+
+
+
+After starting Langflow, go to your Langflow **Settings** to confirm that the variables were created.
+
+Only the **Name** and **Value** are taken from the environment.
+You can edit the variables in your Langflow **Settings** if you want to configure additional options, such as the **Apply To Fields** option.
+
+Global variables sourced from the environment are assigned the **Credential** type, which masks the values in the visual editor.
+However, Langflow automatically encrypts _all_ global variables stored in the database.
+
+## Disallow global variables from the environment
+
+If you want to explicitly prevent Langflow from sourcing global variables from the environment, set `LANGFLOW_STORE_ENVIRONMENT_VARIABLES=False` in your `.env` file.
+
+## Use environment variables for missing global variables {#use-environment-variables-for-missing-global-variables}
+
+If you want to automatically set fallback values for your global variables to environment variables, set `LANGFLOW_FALLBACK_TO_ENV_VAR=True` in your `.env` file.
+When this setting is enabled, if a global variable isn't found, Langflow attempts to use an environment variable with the same name as a backup.
+
+For example, assume you have the following Langflow `.env` configuration, and your flow has a component that expects a `WATSONX_API_KEY` global variable:
+
+```text
+LANGFLOW_FALLBACK_TO_ENV_VAR=True
+WATSONX_PROJECT_ID=your_project_id
+WATSONX_API_KEY=your_api_key
+```
+
+When you run the flow, if there is no global variable named `WATSONX_API_KEY`, Langflow looks for an environment variable named `WATSONX_API_KEY`.
+In this example, Langflow uses the `WATSONX_API_KEY` value from the `.env` to run the flow.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/contributing-telemetry.mdx b/docs/versioned_docs/version-1.9.0/Develop/contributing-telemetry.mdx
new file mode 100644
index 0000000000..691d8aa023
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/contributing-telemetry.mdx
@@ -0,0 +1,93 @@
+---
+title: Telemetry
+slug: /contributing-telemetry
+---
+
+Langflow uses anonymous telemetry to collect statistics about feature usage and performance.
+The Langflow team uses this data to identify popular features and areas that need improvement based on actual usage patterns.
+This helps prioritize development efforts on the most impactful changes and popular features.
+
+## Privacy
+
+The Langflow team respects your privacy, and the team is committed to protecting your data.
+
+Langflow telemetry doesn't collect any personal information or sensitive data.
+All telemetry data is anonymized and used solely for improving Langflow.
+
+## Opt out of telemetry
+
+To opt out of telemetry, set `DO_NOT_TRACK=True` in your [Langflow environment variables](/environment-variables) before starting Langflow. This disables telemetry data collection.
+
+## Data that Langflow collects
+
+Langflow telemetry collects data on flow runs, your environment, and component usage.
+
+### Run
+
+This telemetry event is sent every time a flow is executed.
+
+- **IsWebhook**: Indicates whether the operation was triggered with a webhook.
+- **Seconds**: Duration in seconds for how long the operation lasted, providing insights into performance.
+- **Success**: Boolean value indicating whether the operation was successful, helping identify potential errors or issues.
+- **ErrorMessage**: Provides error message details if the operation was unsuccessful, aiding in troubleshooting and enhancements.
+
+### Shutdown
+
+This telemetry event captures information about application lifecycle and runtime duration.
+
+- **TimeRunning**: Total runtime before shutdown, which is useful for understanding the application lifecycle and optimizing uptime.
+
+### Version
+
+This telemetry event is sent once when the telemetry service starts.
+
+- **Version**: The specific version of Langflow used, which helps in tracking feature adoption and compatibility.
+- **Platform**: Operating system of the host machine, which helps determine the most popular platforms for development and testing efforts.
+- **Python**: The version of Python used, assisting in maintaining compatibility and support for various Python versions.
+- **Arch**: Architecture of the system, such as x86 or ARM, which helps prioritize hardware optimization and testing in the Langflow codebase.
+- **AutoLogin**: Indicates whether the auto-login feature is enabled, reflecting user preference settings.
+- **CacheType**: Type of caching mechanism used, which impacts performance and efficiency.
+- **BackendOnly**: Boolean indicating whether Langflow is running in backend-only mode, useful for understanding deployment configurations.
+- **Desktop**: Indicates whether Langflow is running in desktop mode (Langflow Desktop), helping to understand usage patterns across different deployment types.
+
+### Email
+
+This telemetry event is sent to track registered email addresses for Langflow Desktop. The event is triggered in two cases:
+
+* Every time a new email address is registered through the POST `/api/v2/registration/` endpoint.
+* Each time you start Langflow Desktop _after_ an email address is registered.
+
+ The first time you start Langflow Desktop and register your email address, the event is reported by the call to the POST `/api/v2/registration/` endpoint.
+
+This telemetry event includes the following information:
+
+- **Email**: The registered email address, which helps track user registrations and facilitate an understanding of the Langflow Desktop user base.
+- **ClientType**: Indicates the client type, which can be "desktop" or "oss".
+
+If telemetry is disabled with the `DO_NOT_TRACK` environment variable in Langflow Desktop, you are still prompted to enter your email address, but the email address is stored in your local Langflow database only.
+
+### Playground
+
+This telemetry event monitors performance and usage patterns in the **Playground** environment.
+
+- **Seconds**: Duration in seconds for **Playground** execution, offering insights into performance during testing or experimental stages.
+- **ComponentCount**: Number of components used in the **Playground**, which helps understand complexity and usage patterns.
+- **Success**: Success status of the **Playground** operation, aiding in identifying the stability of experimental features.
+
+### Component
+
+This telemetry event is sent for each component execution.
+
+- **Name**: Identifies the component, providing data on which components are most utilized or prone to issues.
+- **Seconds**: Time taken by the component to execute, offering performance metrics.
+- **Success**: Whether the component operated successfully, which helps in quality control.
+- **ErrorMessage**: Details of any errors encountered, crucial for debugging and improvement.
+
+### Exception
+
+This telemetry event is sent when an unhandled exception is captured by Langflow's lifecycle or global exception handler.
+
+- **Type**: The exception class name, such as `ValueError`.
+- **Message**: The exception message that was raised.
+- **Context**: Additional contextual information related to where the exception occurred, such as route, component, or operation details, when available.
+- **StackTraceHash**: A hash of the stack trace used to group similar exceptions for easier analysis.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/data-types.mdx b/docs/versioned_docs/version-1.9.0/Develop/data-types.mdx
new file mode 100644
index 0000000000..567428f822
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/data-types.mdx
@@ -0,0 +1,351 @@
+---
+title: Langflow data types
+slug: /data-types
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Icon from "@site/src/components/icon";
+
+Langflow components are designed to accept and produce specific types of inputs and outputs.
+Input and output data types define the structure and flow of information between components.
+Understanding these structures helps you build applications that provide valid input and correctly anticipate the output format.
+
+[Component ports](/concepts-components#component-ports) represent the data types that each component can send and receive.
+Some data types are self-evident from the fields they are attached to; for example, a **System Message** field accepts [message data](#message).
+[Port colors](/concepts-components#port-colors) also indicate the port's data type.
+For example **JSON** ports, represented by , either accept or emit [structured data objects](#json).
+
+When building flows, connect output ports to input ports of the same type (color) to transfer that type of data between two components.
+
+:::tip
+* In the [workspace](/concepts-overview#workspace), hover over a port to see connection details for that port.
+Click a port to **Search** for compatible components.
+
+* If two components have incompatible data types, you can use a processing component like the [**Type Convert** component](/type-convert) to convert the data between components.
+:::
+
+## JSON
+
+:::tip
+In Langflow version 1.9.0, the `Data` type and port were renamed to `JSON`.
+Flows using `Data` are backwards compatible.
+:::
+
+**JSON** ports accept or produce the `JSON` type, which is a structured data object, like a JSON payload that you might send to an API.
+This data type is used to pass key-value pairs between components, such as user profiles, settings, or other structured information.
+
+`JSON` objects include a primary text field, indicated by a `text_key`, and additional metadata.
+
+### Schema and attributes
+
+The schema is defined in [`data.py`](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/schema/data.py).
+
+The following attributes are available:
+
+- `data`: A `JSON` object stores key-value pairs within the `.data` attribute. This is the `JSON` object's core dictionary. Each key is a field name, and the values can be any supported data type.
+- `text_key`: The key in `data` that is considered the primary text value.
+- `default_value`: Fallback if `text_key` is missing. The default `text_key` is `"text"`.
+
+```python
+data_obj = JSON(
+ text_key="text",
+ data={
+ "text": "Hello world",
+ "name": "Charlie",
+ "age": 28
+ },
+ default_value=""
+)
+```
+
+`JSON` objects can be serialized to JSON, created from JSON, or created from other dictionary data.
+However, the resulting `JSON` object is a structured object with validation and methods, not a plain dictionary.
+For example, when serialized into JSON, the previous Python example becomes the following JSON object:
+
+```json
+{
+ "text_key": "text",
+ "data": {
+ "text": "Hello world",
+ "name": "Charlie",
+ "age": 28
+ },
+ "default_value": ""
+}
+```
+
+## Table
+
+:::tip
+In Langflow version 1.9.0, the `DataFrame` type and port were renamed to `Table`.
+Flows using `DataFrame` are backwards compatible.
+:::
+
+**Table** ports accept or produce [pandas DataFrames](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html), which are similar to tabular CSV data.
+
+Use the `Table` type to work with data containing multiple rows or records.
+
+### Schema and attributes
+
+The schema is defined in [`dataframe.py`](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/schema/dataframe.py).
+
+The following attributes are available:
+
+- **Full pandas compatibility**: All pandas DataFrame methods and functionality are supported
+
+- **Langflow integration**: Accepts lists of [`JSON` objects](#json), dictionaries, or existing DataFrames.
+
+- **Convenience methods**:
+ - `to_data_list()`
+ - `add_row()`
+ - `add_rows()`
+ - `to_lc_documents()`
+ - `to_data()`
+ - `to_message()`
+
+- **Text key support**: Maintains `text_key` and `default_value` attributes for [`JSON` object](#json) compatibility.
+
+### Table structure
+
+A Table has a tabular data structure with rows and columns.
+Keys are columns, and each object in the array is a row.
+
+```json
+[
+ {
+ "name": "Charlie Lastname",
+ "age": 28,
+ "email": "charlie.lastname@example.com"
+ },
+ {
+ "name": "Alexandra Example",
+ "age": 34,
+ "email": "alexandra@example.com"
+ }
+]
+```
+
+When represented as tabular data, the preceding Table object is structured as follows:
+
+```text
+| name | age | email |
+|------|-----|-------|
+| Charlie Lastname | 28 | charlie.lastname@example.com |
+| Alexandra Example | 34 | alexandra@example.com |
+```
+
+## Embeddings
+
+**Embeddings** ports emit or ingest vector embeddings to support functions like similarity search.
+
+The `Embeddings` data type is used specifically by components that either produce or consume vector embeddings, such as the [embedding model components](/components-embedding-models) and vector store components.
+
+For example, embedding model components output `Embeddings` data that you can connect to an **Embedding** input port on a vector store component.
+
+For information about the underlying Python classes that produce `Embeddings`, see the [LangChain Embedding models documentation](https://docs.langchain.com/oss/python/integrations/text_embedding).
+
+## LanguageModel
+
+The `LanguageModel` type is a specific data type that can be produced by language model components and accepted by components that use an LLM.
+
+When you change a language model component's output type from **Model Response** to **Language Model**, the component's output port changes from a **Message** port to a **Language Model** port .
+Then, you connect the outgoing **Language Model** port to a **Language Model** input port on a compatible component, such as a **Smart Transform** component.
+
+For more information about using these components in flows and toggling `LanguageModel` output, see [Language model components](/components-models#language-model-output-types).
+
+
+LanguageModel is an instance of LangChain ChatModel
+
+Because Langflow is built on LangChain, `LanguageModel` is actually an instance of a [LangChain chat model](https://docs.langchain.com/oss/python/integrations/chat) that uses the configuration parameters set in the originating component.
+
+Often, components produce an instance of an integrated chat model that is designed to support the specific model provider, such as [`ChatOpenAI`](https://docs.langchain.com/oss/python/integrations/chat/openai) or [`ChatAnthropic`](https://docs.langchain.com/oss/python/integrations/chat/anthropic).
+
+You can inspect the [component code](/concepts-components#component-code) to see the specific `Chat` instance it produces.
+
+
+
+## Memory
+
+**Memory** ports are used to integrate a **Message History** component with external chat memory storage.
+
+For more information, see the [**Message History** component](/message-history).
+
+## Message
+
+**Message** ports accept or produce `Message` data, which extends the [`JSON` type](#json) with additional fields and methods for text input typically used in chat flows.
+
+This data type is used by many components.
+
+:::tip
+Components that accept or produce `Message` data may not include all attributes in the incoming or outgoing `Message` data.
+As long as the data is compatible with the `Message` schema, it can be valid.
+
+When building flows, focus on the fields shown on each component in the workspace, rather than the data types passed between components.
+The details of a particular data type are often only relevant when you are debugging a flow or component that isn't producing the expected output.
+
+For example, a **Chat Input** component only requires the content of the **Input Text** (`input_value`) field.
+The component then constructs a complete `Message` object before passing the data to other components in the flow.
+:::
+
+### Schema, structure, and attributes
+
+The `Message` schema is defined in [`message.py`](https://github.com/langflow-ai/langflow/blob/main/src/lfx/src/lfx/schema/message.py).
+Some `Message` attributes have their own schema definitions, such as [`content_block.py`](https://github.com/langflow-ai/langflow/blob/main/src/backend/base/langflow/schema/content_block.py).
+
+`Message` data is structured as a JSON object.
+For example:
+
+```json
+{
+ "text": "Name: Charlie Lastname, Age: 28, Email: charlie.lastname@example.com",
+ "sender": "User",
+ "sender_name": "Charlie Lastname",
+ "session_id": "some-session-id",
+ "timestamp": "2024-06-01T12:00:00Z",
+ "files": [],
+ "content_blocks": [],
+ "category": "message"
+}
+```
+
+The attributes included in a specific `Message` object depend on the context, including the component type, flow activity, and whether the message is a query or response.
+Some common attributes include the following:
+
+- `text`: The main message content.
+- `sender`: Identifies the originator of a chat message as either `User` or `Language Model`.
+- `sender_name`: The display name for the sender. Defaults to `User` or `Language Model`.
+- `session_id`: The chat [session identifier](/session-id).
+- `flow_id`: The ID of the flow that the message is associated with. `flow_id` and `session_id` are the same if the flow doesn't use custom session IDs.
+- `timestamp`: The UTC timestamp that the message was sent.
+- `files`: A list of file paths or images included with the message
+- `content_blocks`: Container for rich content input, such as text, media, or code. Also contains error message information if the LLM can't process the input.
+- `category`: `"message"`, `"error"`, `"warning"`, or `"info"`.
+
+Not all attributes are required, and some components accept message-compatible input, such as raw text input.
+The strictness depends on the component.
+
+### Message data in Input and Output components
+
+In flows with [**Chat Input and Output** components](/chat-input-and-output), `Message` data provides a consistent structure for chat interactions, and it is ideal for chatbots, conversational analysis, and other use cases based on a dialogue with an LLM or agent.
+In these flows, the **Playground** chat interface prints only the `Message` attributes that are relevant to the conversation, such as `text`, `files`, and error messages from `content_blocks`.
+To see all `Message` attributes, inspect the message logs in the **Playground**.
+
+In flows with [**Text Input and Output** components](/text-input-and-output), `Message` data is used to pass simple text strings without the chat-related metadata.
+These components handle `Message` data as independent text strings, not as part of an ongoing conversation.
+For this reason, a flow with only **Text Input and Output** components isn't compatible with the **Playground**.
+For more information, see [Text input and output components](/text-input-and-output).
+
+When using the Langflow API, the response includes the `Message` object along with other response data from the flow run.
+Langflow API responses can be extremely verbose, so your applications must include code to extract relevant data from the response to return to the user.
+For an example, see the [Langflow quickstart](/get-started-quickstart).
+
+Additionally, input sent to the input port of input/output components does _not_ need to be a complete `Message` object because the component constructs the `Message` object that is then passed to other components in the flow or returned as flow output.
+In fact, some components shouldn't receive a complete `Message` object because some attributes, like `timestamp` should be added by the component for accuracy.
+
+## Tool
+
+**Tool** ports connect tools to an **Agent** component.
+
+Tools can be other components where you enabled **Tool Mode**, they can be the dedicated **MCP Tools** component, or they can be other components that only support **Tool Mode**.
+Multiple tools can be connected to the same **Agent** component at the same port.
+
+Functionally, `Tool` data is a LangChain `StructuredTool` object that can be used in agent flows.
+
+For more information, see [Configure tools for agents](/agents-tools) and [Use Langflow as an MCP client](/mcp-client).
+
+## Unknown or multiple types
+
+If a port can accept or produce multiple data types, it is represented by the gray port icon .
+
+Hover over the port to see the accepted or produced data types.
+
+## View data types in flows
+
+In Langflow, you can use **Inspect output** to view the output of individual components.
+This can help you learn about the different data type and debug problems with invalid or malformed inputs and output.
+
+The following example shows how to inspect the output of a [**Type Convert** component](/type-convert), which can convert data from one type to another:
+
+1. Create a flow, and then connect a **Chat Input** component to a **Type Convert** component.
+
+2. In the **Chat Input** component, enter some text for the type converter to process.
+
+3. On the **Type Convert** component, click **Run component**, and then click **Inspect output**.
+
+ The default output is `Message` data, which is the same as the input coming from the **Chat Input** component.
+ To see the `Message` data converted to `JSON` or `Table`, change the **Output Type** on the **Type Convert** component, and then rerun the component.
+
+
+
+
+ ```text
+ Input text
+ ```
+
+
+
+
+ ```json
+ {
+ "timestamp": "2025-07-15 20:56:20 UTC",
+ "sender": "User",
+ "sender_name": "User",
+ "session_id": "a0c7e888-4fd6-4242-b8c8-e761ad690aeb",
+ "text": "",
+ "files": [],
+ "error": false,
+ "edit": false,
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": false,
+ "source": {
+ "id": null,
+ "display_name": null,
+ "source": null
+ },
+ "icon": "",
+ "allow_markdown": false,
+ "positive_feedback": null,
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": [],
+ "id": "9da72da2-efbb-4ccd-90ad-b32429b0418e",
+ "flow_id": "a0c7e888-4fd6-4242-b8c8-e761ad690aeb",
+ "duration": null
+ }
+ ```
+
+
+
+
+ ```text
+ | Field | Value |
+ |-------|-------|
+ | timestamp | 2025-07-15 20:56:11 UTC |
+ | sender | User |
+ | sender_name | User |
+ | session_id | a0c7e888-4fd6-4242-b8c8-e761ad690aeb |
+ | text | (empty) |
+ | files | [] |
+ | error | False |
+ | edit | False |
+ | properties | text_color: '', background_color: '', edited: False, source: {id: None, display_name: None, source: None}, icon: '', allow_markdown: False, positive_feedback: None, state: 'complete', targets: [] |
+ | category | message |
+ | content_blocks | [] |
+ | id | 341686eb-7a39-4b80-a90a-d8bf267815ef |
+ | flow_id | a0c7e888-4fd6-4242-b8c8-e761ad690aeb |
+ | duration | (empty) |
+ ```
+
+
+
+
+## See also
+
+- [Custom components](/components-custom-components)
+- [Pydantic Models](https://docs.pydantic.dev/latest/api/base_model/)
+- [pandas.DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/enterprise-database-guide.mdx b/docs/versioned_docs/version-1.9.0/Develop/enterprise-database-guide.mdx
new file mode 100644
index 0000000000..071063b945
--- /dev/null
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@@ -0,0 +1,288 @@
+---
+title: Langflow database guide for enterprise DBAs
+slug: /enterprise-database-guide
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+The Langflow database stores data that is essential for more Langflow operations, including startup, flow execution, user interactions, and administrative tasks.
+The database supports both frontend (visual editor) and backend (API) operations, making its availability critical to Langflow's stability and functionality.
+For details about the database schema, see [Memory management options](/memory).
+
+This guide is designed for enterprise database administrators (DBAs) and operators responsible for deploying and managing Langflow in production environments.
+It explains how to configure Langflow to use PostgreSQL, including high availability (HA) and active-active configurations, as well as best practices for monitoring, maintenance, and security.
+
+## Configure Langflow with PostgreSQL
+
+Langflow's default database is SQLite.
+However, PostgreSQL is recommended for production deployments due to its scalability, performance, and robustness.
+
+The following steps explain how to configure Langflow to use PostgreSQL for a standalone or containerized deployment.
+For more information, see [Configure an external PostgreSQL database](/configuration-custom-database).
+
+1. Set up PostgreSQL:
+
+ 1. Deploy a PostgreSQL instance (version 15 or later) using a local server, Docker, or a managed cloud service.
+ 2. Create a database for Langflow.
+ 3. Create a PostgreSQL user with appropriate, minimal permissions to manage and write to the database, such as CREATE, SELECT, INSERT, UPDATE, DELETE on your Langflow tables.
+
+2. Obtain the connection string in the format `postgresql://user:password@host:port/dbname`, such as`postgresql://langflow:securepassword@postgres:5432/langflow`.
+
+ For High Availability, use the virtual IP or proxy hostname instead of the direct database host.
+ For more information, see [High Availability for PostgreSQL](#high-availability-for-postgresql).
+
+3. Configure Langflow with the `.env` or `docker-compose.yml` files.
+
+
+
+
+ 1. Create a `.env` file in the `langflow` directory:
+
+ ```shell
+ touch .env
+ ```
+
+ 2. Add the connection string to the `.env` file:
+
+ ```text
+ LANGFLOW_DATABASE_URL="postgresql://langflow:securepassword@postgres:5432/langflow"
+ ```
+
+ For more environment variables, see the `.env.example` file in the Langflow repository.
+
+
+
+
+ Use the sample `docker-compose.yml` from the Langflow Repository.
+ You can use the default values or customize them as needed.
+
+ ```yaml
+ version: '3'
+ services:
+ langflow:
+ image: langflowai/langflow:latest
+ ports:
+ - "7860:7860"
+ environment:
+ - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow
+ postgres:
+ image: postgres:16
+ ports:
+ - "5432:5432"
+ environment:
+ - POSTGRES_USER=langflow
+ - POSTGRES_PASSWORD=langflow
+ - POSTGRES_DB=langflow
+ volumes:
+ - langflow-postgres:/var/lib/postgresql/data
+ volumes:
+ - langflow-postgres:
+ ```
+
+
+
+
+
+4. Start Langflow with your PostgreSQL connection:
+
+
+
+
+ ```shell
+ uv run langflow run --env-file .env
+ ```
+
+
+
+
+ Navigate to the directory containing `docker-compose.yml`, and then run `docker-compose up`.
+
+
+
+
+5. Optional: Run migrations.
+
+ Langflow uses migrations to manage its database schema.
+ When you first connect to PostgreSQL, Langflow automatically runs migrations to create the necessary tables.
+
+ Direct schema modification can cause conflicts with Langflow's built-in schema management.
+ If you need to update the schema, you can manually run migrations with the Langflow CLI:
+
+ 1. Run `langflow migration` to preview the changes.
+
+ 2. Review the changes to ensure that it's safe to proceed with the migration.
+
+ 3. Run `langflow migration --fix` to run the migration and permanently apply the changes.
+
+ This is a destructive operation that can delete data.
+ For more information, see [`langflow migration`](/configuration-cli#langflow-migration).
+
+6. To verify the configuration, create any flow using the Langflow visual editor or API, and then query your database to confirm the tables and activity are recorded there. The content of the flow doesn't matter; you only need to confirm that the flow is stored in your PostgreSQL database.
+You can query the database in two ways:
+
+ * Query the database container:
+
+ ```
+ docker exec -it psql -U langflow -d langflow
+ ```
+
+ * Use SQL:
+
+ ```
+ SELECT * FROM pg_stat_activity WHERE datname = 'langflow';
+ ```
+
+### High Availability for PostgreSQL {#high-availability-for-postgresql}
+
+To further improve performance, reliability, and scalability, use a High Availability (HA) or Active-Active HA PostgreSQL configuration.
+This is recommended for production deployments to minimize downtime and ensure continuous operations if your database server fails, especially when multiple Langflow instances rely on the same database.
+
+
+
+
+1. Set up streaming replication:
+
+ 1. Configure one primary database for writes.
+ 2. Configure one or more replicas for reads and failover.
+
+ Select either synchronous or asynchronous replication based on your latency and consistency requirements.
+
+2. Implement automatic failover using one of the following options:
+
+ * Use an HA orchestrator, distributed configuration store, and traffic router, such as [Patroni](https://patroni.readthedocs.io/en/latest/), etcd or [Consul](https://developer.hashicorp.com/consul), and [HAProxy](https://www.haproxy.org/).
+ * Use [Pgpool-II](https://www.pgpool.net/docs/46/en/html/index.html) alone or add supporting services for more robust HA support.
+ * Use managed services that provide built-in HA with automatic failover, such as AWS RDS or Google Cloud SQL.
+
+3. Update your PostgreSQL connection string to point to the HA setup.
+If you have a multi-instance deployment, make sure all of your Langflow instances connect to the same HA PostgreSQL database.
+
+ The connection string you use depends on your HA configuration and services.
+
+ * Use a virtual IP or DNS name that resolves to the current primary database, such as `postgresql://langflow:securepassword@db-proxy:5432/langflow?sslmode=require`.
+ * Use the provided endpoint for a managed service, such as `langflow.cluster-xyz.us-east-1.rds.amazonaws.com`.
+
+4. Optional: Implement load balancing for read-heavy workloads:
+
+ 1. Use a connection pooler like [PgBouncer](https://www.pgbouncer.org/) to distribute read queries across replicas.
+ 2. Configure Langflow to use a single connection string pointing to the primary PostgreSQL database or proxy.
+
+
+
+
+To implement Active-Active HA, you must deploy multiple Langflow instances, use load balancing to distribute traffic across the instances, and ensure all instances connect to the same HA PostgreSQL database:
+
+1. Deploy multiple Langflow instances using Kubernetes or Docker Swarm.
+
+ You must configure your instances to use a shared PostgreSQL database.
+ For more information, see [Best practices for Langflow on Kubernetes](/deployment-prod-best-practices).
+
+2. Set up streaming replication:
+
+ 1. Configure one primary database for writes.
+ 2. Configure one or more replicas for reads and failover.
+
+ Select either synchronous or asynchronous replication based on your latency and consistency requirements.
+
+3. Implement automatic failover using one of the following options:
+
+ * Use an HA orchestrator, distributed configuration store, and traffic router, such as [Patroni](https://patroni.readthedocs.io/en/latest/), etcd or [Consul](https://developer.hashicorp.com/consul), and [HAProxy](https://www.haproxy.org/).
+ * Use [Pgpool-II](https://www.pgpool.net/docs/46/en/html/index.html) alone or add supporting services for more robust HA support.
+ * Use managed services that provide built-in HA with automatic failover, such as AWS RDS or Google Cloud SQL.
+
+4. Update your PostgreSQL connection string to point to the HA setup.
+Make sure all of your Langflow instances connect to the same HA PostgreSQL database.
+
+ The connection string you use depends on your HA configuration and services:
+
+ * Use a virtual IP or DNS name that resolves to the current primary database, such as `postgresql://langflow:securepassword@db-proxy:5432/langflow?sslmode=require`.
+ * Use the provided endpoint for a managed service, such as `langflow.cluster-xyz.us-east-1.rds.amazonaws.com`.
+
+5. Use a load balancer to distribute requests across your instances.
+
+The following example configuration is for a production deployment that has three `langflow-runtime` replicas, uses the Kubernetes load balancer service to distribute traffic to healthy pods, and uses the HA PostgreSQL database connection string.
+
+```yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+ name: langflow-runtime
+spec:
+ replicas: 3
+ selector:
+ matchLabels:
+ app: langflow-runtime
+ template:
+ metadata:
+ labels:
+ app: langflow-runtime
+ spec:
+ containers:
+ - name: langflow
+ image: langflowai/langflow:latest
+ ports:
+ - containerPort: 7860
+ env:
+ - name: LANGFLOW_DATABASE_URL
+ value: "postgresql://langflow:securepassword@db-proxy:5432/langflow?sslmode=require"
+---
+apiVersion: v1
+kind: Service
+metadata:
+ name: langflow-runtime
+spec:
+ selector:
+ app: langflow-runtime
+ ports:
+ - port: 80
+ targetPort: 7860
+ type: LoadBalancer
+```
+
+
+
+
+After implementing HA or Active-Active HA, monitor failover events and ensure replicas are in sync.
+Langflow, through [SQLAlchemy](https://docs.sqlalchemy.org/en/20/), supports reconnection attempts if `LANGFLOW_DATABASE_CONNECTION_RETRY=True`, ensures recovery after failover, and reduces disruption once the database is back online.
+
+Although PostgreSQL handles concurrent connections well, you must still monitor for contention, deadlocks, or other performance degradation during high load.
+
+## Impact of database failure
+
+If the PostgreSQL database becomes unavailable, the following Langflow functions will fail:
+
+* **Flow Retrieval**: Cannot load new or existing flows from the database.
+* **Flow Saving**: Unable to save new flows or updates to existing flows.
+* **User Authentication**: Login and user management functions fail.
+* **Project Collection Access**: Cannot access or share community/custom project collections.
+* **Configuration Retrieval**: Unable to load application settings.
+* **Configuration Updates**: Changes to settings cannot be saved.
+* **Execution Log Access**: Cannot retrieve historical flow execution logs.
+* **Log Writing**: New execution or system activity logs cannot be recorded.
+* **Multi-User Collaboration**: Sharing flows or projects across users fails.
+* **API Flow Loading**: API requests to load new flows (non-cached) fail.
+
+Flows already loaded in memory may continue to function with cached configurations.
+However, any operation requiring database access fails until the database is restored.
+For example, a cached flow might run, but it won't record logs or message history to the database.
+
+To minimize the possibility and impact of database failure, use [HA configurations](#high-availability-for-postgresql) and record backups regularly.
+For example, you can use `pg_dump` to create logical backups or set up continuous archiving with write-ahead logs (WAL) for point-in-time recovery.
+Test restoration procedures regularly to ensure your team understands how to execute them in a disaster recovery scenario.
+
+## Database monitoring
+
+Monitor your PostgreSQL database to ensure optimal performance and reliability:
+
+* Use tools like pgAdmin, Prometheus with PostgreSQL exporter, or cloud-based monitoring for PostgreSQL.
+* Track performance metrics such as CPU, memory, and disk I/O usage.
+* Monitor replica health, availability, lag, and synchronization.
+For example, use `pg_stat_activity` to monitor connection counts and contention.
+* Set up alerts and notifications for high latency, failover events, or replication issues.
+* Enable PostgreSQL logging, such as `log_connections` and `log_statements`, to track access and changes.
+
+## See also
+
+* [Configure an external PostgreSQL database](/configuration-custom-database)
+* [Langflow architecture on Kubernetes](/deployment-architecture)
+* [Deploy the Langflow production environment on Kubernetes](/deployment-kubernetes-prod)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/environment-variables.mdx b/docs/versioned_docs/version-1.9.0/Develop/environment-variables.mdx
new file mode 100644
index 0000000000..3acf554748
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/environment-variables.mdx
@@ -0,0 +1,454 @@
+---
+title: Environment variables
+slug: /environment-variables
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Link from '@docusaurus/Link';
+
+In general, environment variables, like `LANGFLOW_PORT` or `LANGFLOW_LOG_LEVEL`, configure how Langflow runs.
+These are broad settings that apply to your entire Langflow deployment.
+
+In contrast, global variables are user-defined values stored in Langflow's database for use in flows, such as `OPENAI_API_KEY`.
+Langflow can also source global variables from environment variables.
+For more information, see [Langflow global variables](/configuration-global-variables).
+
+## Configure environment variables for Langflow OSS
+
+Langflow recognizes [supported environment variables](#supported-variables) from the following sources:
+
+- Environment variables set in your terminal.
+- Environment variables imported from a `.env` file when starting Langflow.
+- Environment variables set with the [Langflow CLI](./configuration-cli), including the `--env-file` option and direct options, such as `--port`.
+
+You can choose to use one or more of these sources.
+
+### Precedence {#precedence}
+
+If the same environment variable is set in multiple places, the following hierarchy applies:
+
+1. Langflow CLI options override all other sources.
+2. The `.env` file overrides system environment variables.
+3. System environment variables are used only if not set elsewhere.
+When running a Langflow Docker image, the `-e` flag can be used to set additional system environment variables.
+
+For example:
+
+* If you set `LANGFLOW_PORT=8080` in your system environment and `LANGFLOW_PORT=7860` in `.env`, Langflow uses `7860` from `.env`.
+* If you use the Langflow CLI to run `langflow run --env-file .env --port 9000`, and you set `LANGFLOW_PORT=7860` in `.env`, then Langflow uses `9000` from the CLI option.
+
+### Set environment variables in your terminal {#configure-variables-terminal}
+
+Run the following commands to set environment variables for your current terminal session:
+
+
+
+
+```bash
+export VARIABLE_NAME='VALUE'
+```
+
+
+
+
+```
+set VARIABLE_NAME='VALUE'
+```
+
+
+
+
+```bash
+docker run -it --rm \
+ -p 7860:7860 \
+ -e VARIABLE_NAME='VALUE' \
+ langflowai/langflow:latest
+```
+
+
+
+
+When you start Langflow, it looks for environment variables that you've set in your terminal.
+If it detects a supported environment variable, then it automatically adopts the specified value, subject to [precedence rules](#precedence).
+
+### Import environment variables from a .env file {#configure-variables-env-file}
+
+1. If Langflow is running, quit Langflow.
+
+2. Create a `.env` file, and then open it in your preferred editor.
+
+3. Define [Langflow environment variables](#supported-variables) in the `.env` file.
+
+
+ Example: .env
+
+ ```text
+ DO_NOT_TRACK=True
+ LANGFLOW_AUTO_LOGIN=False
+ LANGFLOW_AUTO_SAVING=True
+ LANGFLOW_AUTO_SAVING_INTERVAL=1000
+ LANGFLOW_BACKEND_ONLY=False
+ LANGFLOW_BUNDLE_URLS=["https://github.com/user/repo/commit/hash"]
+ LANGFLOW_CACHE_TYPE=async
+ LANGFLOW_COMPONENTS_PATH=/path/to/components/
+ LANGFLOW_COMPONENTS_INDEX_PATH=/path/to/component_index.json
+ LANGFLOW_CONFIG_DIR=/path/to/config/
+ LANGFLOW_DATABASE_URL=postgresql://user:password@localhost:5432/langflow
+ LANGFLOW_DEV=False
+ LANGFLOW_FALLBACK_TO_ENV_VAR=False
+ LANGFLOW_HEALTH_CHECK_MAX_RETRIES=5
+ LANGFLOW_HOST=localhost
+ LANGFLOW_LANGCHAIN_CACHE=InMemoryCache
+ LANGFLOW_MAX_FILE_SIZE_UPLOAD=10000
+ LANGFLOW_MAX_ITEMS_LENGTH=100
+ LANGFLOW_MAX_TEXT_LENGTH=1000
+ LANGFLOW_LOG_LEVEL=error
+ LANGFLOW_OPEN_BROWSER=False
+ LANGFLOW_PORT=7860
+ LANGFLOW_REMOVE_API_KEYS=False
+ LANGFLOW_SAVE_DB_IN_CONFIG_DIR=True
+ LANGFLOW_SECRET_KEY=somesecretkey
+ LANGFLOW_STORE_ENVIRONMENT_VARIABLES=True
+ LANGFLOW_SUPERUSER=adminuser
+ LANGFLOW_SUPERUSER_PASSWORD=adminpass
+ LANGFLOW_WORKER_TIMEOUT=60000
+ LANGFLOW_WORKERS=3
+ ```
+
+ For additional examples, see [`.env.example`](https://github.com/langflow-ai/langflow/blob/main/.env.example) in the Langflow repository.
+
+
+
+4. Save and close `.env`.
+
+5. Start Langflow with your `.env` file:
+
+
+
+
+ ```bash
+ uv run langflow run --env-file .env
+ ```
+
+
+
+
+ ```bash
+ docker run -it --rm \
+ -p 7860:7860 \
+ --env-file .env \
+ langflowai/langflow:latest
+ ```
+
+
+
+
+ If your `.env` file isn't in the same directory, provide the path to your `.env` file.
+
+On startup, Langflow imports the environment variables from your `.env` file, as well as any others that you set in your terminal, and then adopts their specified values.
+
+### Configure environment variables for development
+
+The following examples show how to configure Langflow using environment variables in different development scenarios.
+
+
+
+
+The `.env` file is a text file that contains key-value pairs of environment variables.
+
+Create or edit a `.env` file in the root directory of your application or Langflow environment, and then add your configuration variables to the file:
+
+
+Example: .env
+
+```text title=".env"
+DO_NOT_TRACK=True
+LANGFLOW_AUTO_LOGIN=False
+LANGFLOW_AUTO_SAVING=True
+LANGFLOW_AUTO_SAVING_INTERVAL=1000
+LANGFLOW_BACKEND_ONLY=False
+LANGFLOW_BUNDLE_URLS=["https://github.com/user/repo/commit/hash"]
+LANGFLOW_CACHE_TYPE=async
+LANGFLOW_COMPONENTS_PATH=/path/to/components/
+LANGFLOW_COMPONENTS_INDEX_PATH=/path/to/component_index.json
+LANGFLOW_CONFIG_DIR=/path/to/config/
+LANGFLOW_DATABASE_URL=postgresql://user:password@localhost:5432/langflow
+LANGFLOW_DEV=False
+LANGFLOW_FALLBACK_TO_ENV_VAR=False
+LANGFLOW_HEALTH_CHECK_MAX_RETRIES=5
+LANGFLOW_HOST=localhost
+LANGFLOW_LANGCHAIN_CACHE=InMemoryCache
+LANGFLOW_MAX_FILE_SIZE_UPLOAD=10000
+LANGFLOW_MAX_ITEMS_LENGTH=100
+LANGFLOW_MAX_TEXT_LENGTH=1000
+LANGFLOW_LOG_LEVEL=error
+LANGFLOW_OPEN_BROWSER=False
+LANGFLOW_PORT=7860
+LANGFLOW_REMOVE_API_KEYS=False
+LANGFLOW_SAVE_DB_IN_CONFIG_DIR=True
+LANGFLOW_SECRET_KEY=somesecretkey
+LANGFLOW_STORE_ENVIRONMENT_VARIABLES=True
+LANGFLOW_SUPERUSER=adminuser
+LANGFLOW_SUPERUSER_PASSWORD=adminpass
+LANGFLOW_WORKER_TIMEOUT=60000
+LANGFLOW_WORKERS=3
+```
+
+
+
+
+
+
+A systemd service configuration file configures Linux system services.
+
+To add environment variables, create or edit a service configuration file and add an `override.conf` file. This file allows you to override the default environment variables for the service.
+
+
+Example: override.conf
+
+```ini title="override.conf"
+[Service]
+Environment="DO_NOT_TRACK=true"
+Environment="LANGFLOW_AUTO_LOGIN=false"
+Environment="LANGFLOW_AUTO_SAVING=true"
+Environment="LANGFLOW_AUTO_SAVING_INTERVAL=1000"
+Environment="LANGFLOW_BACKEND_ONLY=false"
+Environment="LANGFLOW_BUNDLE_URLS=[\"https://github.com/user/repo/commit/hash\"]"
+Environment="LANGFLOW_CACHE_TYPE=async"
+Environment="LANGFLOW_COMPONENTS_PATH=/path/to/components/"
+Environment="LANGFLOW_COMPONENTS_INDEX_PATH=/path/to/component_index.json"
+Environment="LANGFLOW_CONFIG_DIR=/path/to/config"
+Environment="LANGFLOW_DATABASE_URL=postgresql://user:password@localhost:5432/langflow"
+Environment="LANGFLOW_DEV=false"
+Environment="LANGFLOW_FALLBACK_TO_ENV_VAR=false"
+Environment="LANGFLOW_HEALTH_CHECK_MAX_RETRIES=5"
+Environment="LANGFLOW_HOST=localhost"
+Environment="LANGFLOW_LANGCHAIN_CACHE=InMemoryCache"
+Environment="LANGFLOW_MAX_FILE_SIZE_UPLOAD=10000"
+Environment="LANGFLOW_MAX_ITEMS_LENGTH=100"
+Environment="LANGFLOW_MAX_TEXT_LENGTH=1000"
+Environment="LANGFLOW_LOG_ENV=container_json"
+Environment="LANGFLOW_LOG_FILE=logs/langflow.log"
+Environment="LANGFLOW_LOG_LEVEL=error"
+Environment="LANGFLOW_OPEN_BROWSER=false"
+Environment="LANGFLOW_PORT=7860"
+Environment="LANGFLOW_REMOVE_API_KEYS=false"
+Environment="LANGFLOW_SAVE_DB_IN_CONFIG_DIR=true"
+Environment="LANGFLOW_SECRET_KEY=somesecretkey"
+Environment="LANGFLOW_STORE_ENVIRONMENT_VARIABLES=true"
+Environment="LANGFLOW_SUPERUSER=adminuser"
+Environment="LANGFLOW_SUPERUSER_PASSWORD=adminpass"
+Environment="LANGFLOW_WORKER_TIMEOUT=60000"
+Environment="LANGFLOW_WORKERS=3"
+```
+
+
+
+For more information on systemd, see the [Red Hat documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/9/html/using_systemd_unit_files_to_customize_and_optimize_your_system/assembly_working-with-systemd-unit-files_working-with-systemd).
+
+
+
+
+The `tasks.json` file located in `.vscode/tasks.json` is a configuration file for development environments using Visual Studio Code.
+
+Create or edit the `.vscode/tasks.json` file in your project root.
+
+
+Example: .vscode/tasks.json
+
+```json title=".vscode/tasks.json"
+{
+ "version": "2.0.0",
+ "options": {
+ "env": {
+ "DO_NOT_TRACK": "true",
+ "LANGFLOW_AUTO_LOGIN": "false",
+ "LANGFLOW_AUTO_SAVING": "true",
+ "LANGFLOW_AUTO_SAVING_INTERVAL": "1000",
+ "LANGFLOW_BACKEND_ONLY": "false",
+ "LANGFLOW_BUNDLE_URLS": "[\"https://github.com/user/repo/commit/hash\"]",
+ "LANGFLOW_CACHE_TYPE": "async",
+ "LANGFLOW_COMPONENTS_PATH": "D:/path/to/components/",
+ "LANGFLOW_COMPONENTS_INDEX_PATH": "D:/path/to/component_index.json",
+ "LANGFLOW_CONFIG_DIR": "D:/path/to/config/",
+ "LANGFLOW_DATABASE_URL": "postgresql://postgres:password@localhost:5432/langflow",
+ "LANGFLOW_DEV": "false",
+ "LANGFLOW_FALLBACK_TO_ENV_VAR": "false",
+ "LANGFLOW_HEALTH_CHECK_MAX_RETRIES": "5",
+ "LANGFLOW_HOST": "localhost",
+ "LANGFLOW_LANGCHAIN_CACHE": "InMemoryCache",
+ "LANGFLOW_MAX_FILE_SIZE_UPLOAD": "10000",
+ "LANGFLOW_MAX_ITEMS_LENGTH": "100",
+ "LANGFLOW_MAX_TEXT_LENGTH": "1000",
+ "LANGFLOW_LOG_ENV": "container_csv",
+ "LANGFLOW_LOG_FILE": "langflow.log",
+ "LANGFLOW_LOG_LEVEL": "error",
+ "LANGFLOW_OPEN_BROWSER": "false",
+ "LANGFLOW_PORT": "7860",
+ "LANGFLOW_REMOVE_API_KEYS": "true",
+ "LANGFLOW_SAVE_DB_IN_CONFIG_DIR": "false",
+ "LANGFLOW_SECRET_KEY": "somesecretkey",
+ "LANGFLOW_STORE_ENVIRONMENT_VARIABLES": "true",
+ "LANGFLOW_SUPERUSER": "adminuser",
+ "LANGFLOW_SUPERUSER_PASSWORD": "adminpass",
+ "LANGFLOW_WORKER_TIMEOUT": "60000",
+ "LANGFLOW_WORKERS": "3"
+ }
+ },
+ "tasks": [
+ {
+ "label": "langflow backend",
+ "type": "shell",
+ "command": ". ./langflownightly/Scripts/activate && langflow run",
+ "isBackground": true,
+ "problemMatcher": []
+ }
+ ]
+}
+```
+
+
+
+To run Langflow using the above VSCode `tasks.json` file, in the VSCode command palette, select **Tasks: Run Task** > **langflow backend**.
+
+
+
+
+## Set environment variables for Langflow Desktop
+
+Environment variables set in your terminal aren't automatically available to GUI-based applications like Langflow Desktop when you launch them from the Windows or macOS GUI.
+
+To modify environment variables for Langflow Desktop, set environment variables in a Desktop `.env` file, and then restart the app.
+
+
+
+
+To modify the macOS `.env` file, do the following:
+
+1. Create or edit `~/.langflow/data/.env`.
+2. Add your Langflow environment variables, for example:
+
+ ```text
+ LANGFLOW_LOG_LEVEL=info
+ LANGFLOW_DOCLING=true
+ ```
+
+3. Save the file.
+4. Restart Langflow Desktop.
+
+
+
+
+To modify the Windows `.env` file, do the following:
+
+1. Create or edit `%APPDATA%\com.LangflowDesktop\data\.env`.
+2. Add your Langflow environment variables, for example:
+
+ ```text
+ LANGFLOW_LOG_LEVEL=info
+ LANGFLOW_DOCLING=true
+ ```
+
+3. Save the file.
+4. Restart Langflow Desktop.
+
+
+
+
+Windows supports two sources for Langflow Desktop environment variables: a Langflow application `.env` file, and Windows user environment variables.
+
+The `.env` file at `%APPDATA%\com.LangflowDesktop\data\.env` is the recommended approach, but
+Windows user variables are useful for single-sourcing API keys between Langflow and other Windows applications.
+If the same variable is defined in both the Langflow application `.env` file and as a Windows user environment variable, the `.env` file takes precedence.
+
+To modify the Windows user environment variables, do the following:
+
+1. Press Win + R, enter `sysdm.cpl`, and then press Enter.
+2. Click the **Advanced** tab, and then click **Environment Variables**.
+3. In **User variables**, click **New**.
+4. Enter the variable name, such as `OPENAI_API_KEY`, and its value.
+5. Click **OK**, and then **restart Langflow Desktop**.
+
+
+
+
+## Supported environment variables {#supported-variables}
+
+The following sections provide information about specific Langflow environment variables.
+
+### Authentication and security
+
+See [API keys and authentication](/api-keys-and-authentication).
+
+### Global variables
+
+For information about the relationship between Langflow global variables and environment variables, as well as environment variables that control handling of global variables, see [Global variables](/configuration-global-variables).
+
+### Logs {#logging}
+
+See [Configure log options](/logging#log-storage).
+
+### MCP servers {#mcp}
+
+See [Use Langflow as an MCP server](/mcp-server).
+
+### Monitoring and metrics
+
+For environment variables for specific monitoring service providers, see the Langflow monitoring integration guides, such as [Langfuse](/integrations-langfuse) and [Best practices for Langflow on Kubernetes](/deployment-prod-best-practices).
+
+### Server
+
+The following environment variables set base Langflow server configuration, such as where the server is hosted, required files for SSL encryption, and the deployment type (frontend and backend, backend-only, development mode).
+
+| Variable | Format | Default | Description |
+|----------|--------|---------|-------------|
+| `LANGFLOW_HOST` | String | `localhost` | The host on which the Langflow server will run. |
+| `LANGFLOW_PORT` | Integer | `7860` | The port on which the Langflow server runs. The server automatically selects a free port if the specified port is in use. |
+| `LANGFLOW_BACKEND_ONLY` | Boolean | `False` | Run only the Langflow backend service (no frontend). |
+| `LANGFLOW_DEV` | Boolean | `False` | Whether to run Langflow in development mode (may contain bugs). |
+| `LANGFLOW_OPEN_BROWSER` | Boolean | `False` | Open the system web browser on startup. |
+| `LANGFLOW_HEALTH_CHECK_MAX_RETRIES` | Integer | `5` | Set the maximum number of retries for Langflow's server status health checks. |
+| `LANGFLOW_WORKERS` | Integer | `1` | Number of worker processes. |
+| `LANGFLOW_WORKER_TIMEOUT` | Integer | `300` | Worker timeout in seconds. |
+| `LANGFLOW_SSL_CERT_FILE` | String | Not set | Path to the SSL certificate file for enabling HTTPS on the Langflow web server. This is separate from [database SSL connections](/configuration-custom-database#connect-langflow-to-a-local-postgresql-database). |
+| `LANGFLOW_SSL_KEY_FILE` | String | Not set | Path to the SSL key file for enabling HTTPS on the Langflow web server. This is separate from [database SSL connections](/configuration-custom-database#connect-langflow-to-a-local-postgresql-database). |
+| `LANGFLOW_DEACTIVATE_TRACING` | Boolean | `False` | Deactivate tracing functionality. |
+| `LANGFLOW_CELERY_ENABLED` | Boolean | `False` | Enable Celery for distributed task processing. |
+| `LANGFLOW_ALEMBIC_LOG_TO_STDOUT` | Boolean | `False` | Whether to log Alembic database migration output to stdout instead of a log file. If `true`, Alembic logs to `stdout` and the default log file is ignored. |
+
+For more information about deploying Langflow servers, see [Langflow deployment overview](/deployment-overview).
+
+### Storage
+
+For file storage environment variables, see [File storage environment variables](/concepts-file-management#file-storage-environment-variables).
+
+For database environment variables, including PostgreSQL configuration, see [Memory management options](/memory#configure-external-memory).
+
+### Telemetry
+
+See [Telemetry](/contributing-telemetry).
+
+### Visual editor and Playground behavior
+
+| Variable | Format | Default | Description |
+|----------|--------|---------|-------------|
+| `LANGFLOW_AUTO_SAVING` | Boolean | `True` | Whether to automatically save flows. |
+| `LANGFLOW_AUTO_SAVING_INTERVAL` | Integer | `1000` | Set the auto-save interval in milliseconds if `LANGFLOW_AUTO_SAVING=True`. |
+| `LANGFLOW_BUNDLE_URLS` | List[String] | `[]` | A list of URLs from which to load custom bundles. Supports GitHub URLs. If `LANGFLOW_AUTO_LOGIN=True`, flows from these bundles are loaded into the database. |
+| `LANGFLOW_COMPONENTS_PATH` | String | Not set | Path to a directory containing custom components. Typically used if you have local custom components or you are building a Docker image with custom components. |
+| `LANGFLOW_COMPONENTS_INDEX_PATH` | String | Not set | File path or URL (`http://` or `https://`) to a prebuilt component index JSON file used to populate built-in components in the visual editor. When not set, Langflow uses the included index. Useful for supplying a curated component index, for example in airgapped deployments. For more information, see [Block custom components](../Deployment/deployment-block-custom-components.mdx). |
+| `LANGFLOW_ALLOW_CUSTOM_COMPONENTS` | Boolean | `True` | If `false`, disables custom components and in-editor editing of component code. This feature is in beta. For more information, see [Block custom components](../Deployment/deployment-block-custom-components.mdx). |
+| `LANGFLOW_LOAD_FLOWS_PATH` | String | Not set | Path to a directory containing flow JSON files to be loaded on startup. Typically used when creating a Docker image with prepackaged flows. Requires `LANGFLOW_AUTO_LOGIN=True`. |
+| `LANGFLOW_CREATE_STARTER_PROJECTS` | Boolean | `True` | Whether to create templates during initialization. If `false`, Langflow doesn't create templates, and `LANGFLOW_UPDATE_STARTER_PROJECTS` is treated as `false`. |
+| `LANGFLOW_UPDATE_STARTER_PROJECTS` | Boolean | `True` | Whether to update templates with the latest component versions when initializing after an upgrade. |
+| `LANGFLOW_LAZY_LOAD_COMPONENTS` | Boolean | `False` | If `true`, Langflow only partially loads components at startup and fully loads them on demand. This significantly reduces startup time but can cause a slight delay when a component is first used. |
+| `LANGFLOW_EVENT_DELIVERY` | String | `streaming` | How to deliver build events to the frontend: `polling`, `streaming` or `direct`. |
+| `LANGFLOW_FRONTEND_PATH` | String | `./frontend` | Path to the frontend directory containing build files. For development purposes only when you need to serve specific frontend code. |
+| `LANGFLOW_MAX_ITEMS_LENGTH` | Integer | `100` | Maximum number of items to store and display in the visual editor. Lists longer than this will be truncated when displayed in the visual editor. Doesn't affect outputs or data passed between components. |
+| `LANGFLOW_MAX_TEXT_LENGTH` | Integer | `1000` | Maximum number of characters to store and display in the visual editor. Responses longer than this will be truncated when displayed in the visual editor. Doesn't truncate outputs or responses passed between components. |
+| `LANGFLOW_MAX_TRANSACTIONS_TO_KEEP` | Integer | `3000` | Maximum number of flow transaction events to keep in the database. |
+| `LANGFLOW_MAX_VERTEX_BUILDS_TO_KEEP` | Integer | `3000` | Maximum number of vertex builds to keep in the database. Relates to [Playground](/concepts-playground) functionality. |
+| `LANGFLOW_MAX_VERTEX_BUILDS_PER_VERTEX` | Integer | `2` | Maximum number of builds to keep per vertex. Older builds are deleted. Relates to [Playground](/concepts-playground) functionality. |
+| `LANGFLOW_PUBLIC_FLOW_CLEANUP_INTERVAL` | Integer | `3600` | The interval in seconds at which data for [shared Playground](/concepts-playground#share-a-flows-playground) flows are cleaned up. Default: 3600 seconds (1 hour). Minimum: 600 seconds (10 minutes). |
+| `LANGFLOW_PUBLIC_FLOW_EXPIRATION` | Integer | `86400` | The time in seconds after which a [shared Playground](/concepts-playground#share-a-flows-playground) flow is considered expired and eligible for cleanup. Default: 86400 seconds (24 hours). Minimum: 600 seconds (10 minutes). |
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/install-custom-dependencies.mdx b/docs/versioned_docs/version-1.9.0/Develop/install-custom-dependencies.mdx
new file mode 100644
index 0000000000..c354a02b11
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/install-custom-dependencies.mdx
@@ -0,0 +1,183 @@
+---
+title: Install custom dependencies
+slug: /install-custom-dependencies
+---
+
+Langflow provides optional dependency groups and support for custom dependencies to extend Langflow functionality. This guide covers how to add dependencies for different Langflow installations, including Langflow Desktop and Langflow OSS.
+
+The Langflow codebase uses three packages, each with its own `pyproject.toml` file:
+
+* The `main` package (`langflow`) is managed by the root level `pyproject.toml`, and it includes end-user features and main application code, such as Langchain and OpenAI. The `main` package depends on the `base` package.
+* The `base` package (`langflow-base`) is managed at `src/backend/base/pyproject.toml`, and it includes core infrastructure, such as the FastAPI web framework. The `base` package depends on the `lfx` package.
+* The `lfx` package is managed at `src/lfx/pyproject.toml`. LFX is a lightweight CLI tool for executing and serving Langflow flows. The `lfx` package does not provide optional dependency groups for end users.
+
+## Install custom dependencies in Langflow Desktop {#langflow-desktop}
+
+To add dependencies to Langflow Desktop, add an entry for the package to the application's `requirements.txt` file:
+
+ * On macOS, the file is located at `/Users/USER/.langflow/data/requirements.txt`.
+ * On Windows, the file is located at `C:\Users\USER\AppData\Roaming\com.Langflow\data\requirements.txt`.
+
+Add each dependency to `requirements.txt` on its own line in the format `DEPENDENCY==VERSION`, such as `matplotlib==3.10.0`.
+
+Restart Langflow Desktop to install the dependencies.
+
+If you need to change or uninstall custom dependencies, edit the `requirements.txt` file, and then restart Langflow Desktop.
+
+## Install custom dependencies in Langflow OSS
+
+To install your own custom dependencies in your Langflow environment, add them with your package manager.
+
+If you're working within a cloned Langflow repository, add dependencies with `uv add` because there is already a `pyproject.toml` file for uv to reference:
+
+```bash
+uv add DEPENDENCY
+```
+
+### Install optional dependency groups for `langflow`
+
+The `langflow` package (main) provides optional dependency groups that extend its functionality.
+
+By default, installing `langflow` without any extras includes all dependencies listed in the `[project.dependencies]` section. Optional dependency groups are not installed by default and must be explicitly requested.
+
+These optional dependencies are listed in the [langflow `pyproject.toml`](https://github.com/langflow-ai/langflow/blob/main/pyproject.toml) file under `[project.optional-dependencies]`.
+
+Install dependency groups using pip's `[extras]` syntax. For example, to install `langflow` with the `postgresql` dependency group, enter the following command:
+
+```bash
+uv pip install "langflow[postgresql]"
+```
+
+To install multiple extras, use commas to separate each dependency group:
+
+```bash
+uv pip install "langflow[postgresql,openai]"
+```
+
+### Install optional dependency groups for `langflow-base`
+
+`langflow-base` is recommended when you want to deploy Langflow with specific dependencies only.
+It contains the same codebase as `langflow`, but `langflow` includes `langflow-base` as a dependency and adds many additional dependencies on top of it.
+
+The `langflow-base` package provides its own optional dependency groups that are separate from those in the `langflow` package. The `langflow-base` package can be installed as a standalone package with these optional dependency groups.
+
+By default, installing `langflow-base` without any extras includes all dependencies listed in the `[project.dependencies]` section. Optional dependency groups are not installed by default and must be explicitly requested.
+These optional dependency groups are listed in the [langflow-base `pyproject.toml`](https://github.com/langflow-ai/langflow/blob/main/src/backend/base/pyproject.toml) file under `[project.optional-dependencies]`.
+
+Install `langflow-base` with optional dependency groups using pip's `[extras]` syntax. For example, to install `langflow-base` with the `postgresql` dependency group:
+
+```bash
+uv pip install "langflow-base[postgresql]"
+```
+
+To install multiple extras, use commas to separate each dependency group:
+
+```bash
+uv pip install "langflow-base[postgresql,openai]"
+```
+
+To install all optional dependencies for `langflow-base`, use the `complete` extra:
+
+```bash
+uv pip install "langflow-base[complete]"
+```
+
+### Use a virtual environment to test custom dependencies
+
+When testing locally, use a virtual environment to isolate your dependencies and prevent conflicts with other Python projects.
+
+For example, if you want to experiment with a custom dependency like `matplotlib` with Langflow:
+
+```bash
+# Create and activate a virtual environment
+uv venv YOUR_LANGFLOW_VENV
+source YOUR_LANGFLOW_VENV/bin/activate
+
+# Install langflow and your additional dependency
+uv pip install langflow matplotlib
+```
+
+You can also install `langflow-base` with specific optional dependency groups in your virtual environment:
+
+```bash
+# Install langflow-base with only the dependencies you need
+uv pip install "langflow-base[postgresql,openai]" matplotlib
+```
+
+If you're working within a cloned Langflow repository, add dependencies with `uv add` to reference the existing `pyproject.toml` files:
+
+```bash
+uv add matplotlib
+```
+
+The `uv add` command automatically updates the `uv.lock` file in the appropriate location.
+
+## Add dependencies to the Langflow codebase
+
+When contributing to the Langflow codebase, you might need to add dependencies to Langflow.
+
+To add a dependency to the `main` package, run `uv add DEPENDENCY` from the project root.
+For example:
+
+```bash
+uv add matplotlib
+```
+
+Dependencies can be added to the `main` package as regular dependencies at `[project.dependencies]` or optional dependencies at `[project.optional-dependencies]`.
+
+To add a dependency to the `base` package, navigate to `src/backend/base` and run:
+```bash
+uv add DEPENDENCY
+```
+
+To add a development dependency for testing, linting, or debugging, navigate to `src/backend/base` and run:
+```bash
+cd src/backend/base && uv add --group dev DEPENDENCY
+```
+
+Dependencies can be added to the `base` package as regular dependencies at `[project.dependencies]`, development dependencies at `[dependency-groups.dev]`, or optional dependencies at `[project.optional-dependencies]`.
+
+You can optionally use `make add` instead of `uv add`:
+
+```bash
+# Equivalent to: uv add matplotlib
+make add main="matplotlib"
+
+# Equivalent to: cd src/backend/base && uv add --group dev matplotlib
+make add devel="matplotlib"
+
+# Equivalent to: cd src/backend/base && uv add matplotlib
+make add base="matplotlib"
+```
+
+Alternatively, you can add these dependencies manually to the appropriate `pyproject.toml` file:
+
+```
+[project]
+dependencies = [
+ "matplotlib>=3.8.0"
+]
+```
+
+Or as an optional dependency in the main package:
+
+```
+[project.optional-dependencies]
+plotting = [
+ "matplotlib>=3.8.0",
+]
+```
+
+Or as a development dependency in the base package:
+
+```
+[dependency-groups]
+dev = [
+ "matplotlib>=3.8.0",
+]
+```
+
+## See also
+
+* [Containerize a Langflow application](/develop-application)
+* [Create custom Python components](/components-custom-components)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/integrations-arize.mdx b/docs/versioned_docs/version-1.9.0/Develop/integrations-arize.mdx
new file mode 100644
index 0000000000..24ec885f67
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/integrations-arize.mdx
@@ -0,0 +1,108 @@
+---
+title: Arize
+slug: /integrations-arize
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Arize is a tool built on [OpenTelemetry](https://opentelemetry.io/) and [OpenInference](https://docs.arize.com/phoenix/reference/open-inference) for monitoring and optimizing LLM applications.
+
+To enable Arize tracing, set the required Arize environment variables in your Langflow deployment.
+Arize begins monitoring and collecting telemetry data from your LLM applications automatically.
+
+:::tip
+Instructions for integrating Langflow and Arize are also available in the Arize documentation:
+
+* [Langflow tracing with Arize Platform](https://arize.com/docs/ax/integrations/frameworks-and-platforms/langflow/langflow-tracing)
+* [Langflow tracing with Arize Phoenix](https://arize.com/docs/phoenix/integrations/langflow/langflow-tracing)
+:::
+
+## Prerequisites
+
+* If you are using the [standard Arize platform](https://docs.arize.com/arize), you need an **Arize Space ID** and **Arize API Key**.
+* If you are using the open-source [Arize Phoenix platform](https://docs.arize.com/phoenix), you need an **Arize Phoenix API key**.
+
+## Connect Arize to Langflow
+
+
+
+
+1. In your [Arize dashboard](https://app.arize.com/), copy your **Space ID** and [**API Key (Ingestion Service Account Key)**](https://arize.com/docs/ax/security-and-settings/api-keys).
+
+2. In the root of your Langflow application, edit your existing Langflow `.env` file or create a new one.
+
+3. Add `ARIZE_SPACE_ID` and `ARIZE_API_KEY` environment variables:
+
+ ```bash
+ ARIZE_SPACE_ID=SPACE_ID
+ ARIZE_API_KEY=API_KEY
+ ```
+
+ Replace `SPACE_ID` and `API_KEY` with the values you copied from the Arize platform.
+
+ You don't need to specify the Arize project name if you're using the standard Arize platform.
+
+4. Start your Langflow application with your `.env` file:
+
+ ```bash
+ uv run langflow run --env-file .env
+ ```
+
+
+
+
+1. In your [Arize Phoenix dashboard](https://app.phoenix.arize.com/), copy your **API Key**.
+
+2. In the root of your Langflow application, edit your existing Langflow `.env` file or create a new one.
+
+3. Add a `PHOENIX_API_KEY` environment variable:
+
+ ```bash
+ PHOENIX_API_KEY=API_KEY
+ ```
+
+ Replace `API_KEY` with the Arize Phoenix API key that you copied from the Arize Phoenix platform.
+
+4. Start your Langflow application with your `.env` file:
+
+ ```bash
+ uv run langflow run --env-file .env
+ ```
+
+
+
+
+## Run a flow and view metrics in Arize
+
+1. In Langflow, run a flow that has an LLM-driven component, such as an **Agent** component or any language model component.
+You must chat with the flow or trigger the LLM to produce traffic for Arize to trace.
+
+ For example, you can create a flow with the **Simple Agent** template, add your OpenAI API key to the **Agent** component, and then click **Playground** to chat with the flow and generate traffic.
+
+2. In Arize, open your project dashboard, and then wait for Arize to process the data.
+This can take a few minutes.
+
+3. To view metrics for your flows, go to the **LLM Tracing** tab.
+
+ Each Langflow execution generates two traces in Arize:
+
+ * The `AgentExecutor` trace is the Arize trace of LangChain's `AgentExecutor`.
+ * The `UUID` trace is the trace of the Langflow components.
+
+4. To view traces, go to the **Traces** tab.
+
+ A _trace_ is the complete journey of a request, made of multiple _spans_.
+
+5. To view spans, go to the **Spans** tab.
+
+ A _span_ is a single operation within a trace.
+ For example, a _span_ could be a single API call to OpenAI or a single function call to a custom tool.
+
+ For information about tracing metrics in Arize, see the [Arize LLM tracing documentation](https://docs.arize.com/arize/llm-tracing/tracing).
+
+6. To add a span to a [dataset](https://docs.arize.com/arize/llm-datasets-and-experiments/datasets-and-experiments), click **Add to Dataset**.
+
+ All metrics on the **LLM Tracing** tab can be added to datasets.
+
+7. To view a dataset, click the **Datasets** tab, and then select your dataset.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/integrations-instana-traceloop.mdx b/docs/versioned_docs/version-1.9.0/Develop/integrations-instana-traceloop.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/integrations-instana-traceloop.mdx
@@ -0,0 +1,106 @@
+---
+title: Traceloop
+slug: /integrations-instana-traceloop
+description: Instrument Langflow with the Traceloop SDK, and export traces and metrics to Instana using OpenTelemetry.
+---
+
+Traceloop SDK is a lightweight instrumentation toolkit designed for LLM applications.
+It enables developers to automatically capture and export traces, metrics, and key observability signals from their LLM-powered workflows.
+
+When combined with Instana, the exported telemetry data from Traceloop provides end-to-end visibility, allowing users to visualize traces, analyze performance bottlenecks, and ensure reliable operation of LLM-driven applications.
+
+This guide demonstrates how to integrate the Instana observability platform with your Langflow application using the Traceloop SDK so you can monitor and analyze LLM performance.
+
+## Prerequisites
+
+- Create a [Traceloop API key](https://app.traceloop.com/settings/api-key)
+- Create an [Instana endpoint and Instana key](https://www.ibm.com/docs/en/instana-observability/1.0.302)
+- [Install Langflow](/get-started-installation)
+
+## Configure environment variables
+
+1. In the root folder of your Langflow application, edit your existing Langflow `.env` file or create a new one.
+
+2. Enter the following environment variables, and then replace the placeholders with the values for your deployment or requirements:
+
+ ```text
+ TRACELOOP_API_KEY=tl_dummy_1234567890abcdef1234567890abcdef
+ TRACELOOP_BASE_URL=https://otlp-magenta-saas.instana.rocks:4318
+ TRACELOOP_HEADERS="x-instana-key=INSTANA_KEY"
+ OTEL_EXPORTER_OTLP_INSECURE=false
+ TRACELOOP_METRICS_ENDPOINT=HOST:8000
+ TRACELOOP_METRICS_ENABLED=true
+ OTEL_METRIC_EXPORT_INTERVAL=10000
+ ```
+
+ Set the necessary values for each environment variable:
+
+ - **`TRACELOOP_API_KEY`**: A Traceloop API key to authenticate your application with Traceloop's monitoring service.
+ You can get this from your Traceloop account dashboard.
+
+ This integration uses the Traceloop SDK for instrumentation, which requires a Traceloop API key to initialize properly.
+ If you don't have a Traceloop API key, you can proceed with the placeholder API key in the preceding example.
+
+ - **`TRACELOOP_BASE_URL`**: The Instana endpoint URL for telemetry data collection, which is your Instana backend endpoint, such as `https://otlp-magenta-saas.instana.rocks:4318`.
+ You can get this from your Instana configuration or by contacting your Instana administrator.
+
+ - **`TRACELOOP_HEADERS`**: Authentication headers for Instana data collection. Set this to `"x-instana-key=INSTANA_KEY"`, replacing `INSTANA_KEY` with the Instana key from your Instana setup.
+
+ - **`OTEL_EXPORTER_OTLP_INSECURE`**: Security setting for OpenTelemetry Protocol connections. Set to `false` for secure HTTPS/TLS connections. This is recommended for production Instana SaaS endpoints. Set to `true` for insecure HTTP connections during local development.
+
+ - **`TRACELOOP_METRICS_ENDPOINT`**: Separate metrics endpoint configuration, in the form of `OTEL_DC_LLM_HOST:8000`. Typically set to `host.docker.internal:8000` for Docker environments. Adjust the host and port based on your deployment setup.
+
+ - **`TRACELOOP_METRICS_ENABLED`**: Boolean to enable metrics collection. Set to `true` to activate metrics gathering.
+
+ - **`OTEL_METRIC_EXPORT_INTERVAL`**: Interval in milliseconds for metrics export. Set to `10000` for 10-second export intervals, or adjust based on your monitoring requirements.
+
+3. Make sure the OpenTelemetry Data Collector (OTel DC) is running and correctly configured.
+Open your Collector's `config.yaml` file, enter the following configuration, and then replace the placeholder values with the values from your data collector setup or requirements:
+
+ ```yaml
+ llm.application: "LLM_DC"
+ instances:
+ - otel.agentless.mode: true
+ # Example endpoint: https://otlp-magenta-saas.instana.rocks:4318
+ otel.backend.url: "INSTANA_ENDPOINT"
+ otel.backend.using.http: false
+ callback.interval: 10
+ otel.service.name: "DC1"
+ otel.service.port: 8000
+ currency: "USD"
+ ```
+
+ This configuration enables the OTel Collector to operate in agentless mode and route telemetry data to your Instana backend with proper service identification and collection intervals for effective monitoring integration.
+
+## Start Langflow with Traceloop environment variables
+
+Launch your Langflow application with your `.env` file:
+
+```bash
+uv run langflow run --env-file .env
+```
+
+Traceloop automatically begins monitoring and collecting telemetry data from your LLM applications.
+
+## Verify the integration
+
+To verify that observability is working correctly:
+
+1. Run a flow in Langflow to generate traffic.
+2. To view traces in Instana, open Instana and click **Applications**.
+3. In **Services**, search for `Langflow`.
+4. Click **Langflow** to view and analyze the associated calls.
+
+ 
+
+5. To view metrics in Instana, open Instana and click **Infrastructure**.
+6. In **Analyze Infrastructure**, click **Otel LLMonitor**.
+7. To view your Metrics dashboard, click `LLM:DC1@your_machine_name.local`.
+
+ 
+
+## See also
+
+* [Traceloop documentation](https://www.traceloop.com/docs/introduction)
+* [Instana setup documentation](https://www.ibm.com/docs/en/instana-observability/1.0.300?topic=started-instana-setup)
+* [Otel DC setup documentation](https://www.ibm.com/docs/en/instana-observability/1.0.300?topic=started-install-otel-data-collector-llm-odcl)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/integrations-langfuse.mdx b/docs/versioned_docs/version-1.9.0/Develop/integrations-langfuse.mdx
new file mode 100644
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--- /dev/null
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@@ -0,0 +1,148 @@
+---
+title: Langfuse
+slug: /integrations-langfuse
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+[Langfuse](https://langfuse.com) is an open-source platform for LLM observability. It provides tracing and monitoring capabilities for AI applications, helping developers debug, analyze, and optimize their AI systems. Langfuse integrates with various tools and frameworks, including workflow builders and runtimes like Langflow.
+
+This guide explains how to configure Langflow to collect [tracing](https://langfuse.com/docs/tracing) data about your flow executions and automatically send the data to Langfuse.
+
+
+
+## Prerequisites
+
+- An account in a [Langfuse Cloud](https://cloud.langfuse.com) or [Langfuse self-hosted](https://langfuse.com/self-hosting) instance
+- A [running Langflow server](/get-started-installation) with a [flow](/concepts-flows) that you want to trace
+
+:::tip
+If you need a flow to test the Langfuse integration, see the [Langflow quickstart](/get-started-quickstart).
+:::
+
+## Set Langfuse credentials as environment variables {#langfuse-credentials}
+
+1. Create a set of [Langfuse API keys](https://langfuse.com/faq/all/where-are-langfuse-api-keys).
+
+2. Copy the following API key information:
+
+ - Secret key
+ - Public key
+ - Base URL
+
+ :::tip
+ Langflow previously used `LANGFUSE_HOST` as the variable for the Langfuse base URL.
+ This is still supported for backward compatibility, but `LANGFUSE_BASE_URL` is now the preferred environment variable and will be used if both values are set.
+ :::
+
+3. Set your Langfuse project credentials as environment variables.
+
+ In the following examples, replace `SECRET_KEY`, `PUBLIC_KEY`, and `LANGFUSE_BASE_URL` with your API key details from Langfuse.
+ Add the following entries to your `.env` file:
+
+ ```bash
+ LANGFUSE_SECRET_KEY=sk-...
+ LANGFUSE_PUBLIC_KEY=pk-...
+ LANGFUSE_BASE_URL=https://us.cloud.langfuse.com
+ ```
+
+4. Start Langflow with the configuration in the `.env` file:
+
+ ```bash
+ uv run langflow run --env-file .env
+ ```
+
+5. Run a flow.
+
+ Langflow automatically collects and sends tracing data about the flow execution to Langfuse.
+
+6. View the collected data in your [Langfuse dashboard](https://langfuse.com/docs/analytics/overview).
+
+ Langfuse also provides a [public live trace example dashboard](https://cloud.langfuse.com/project/cm0nywmaa005c3ol2msoisiho/traces/f016ae6d-4527-43f5-93ba-9d78388cd3d9).
+
+## Disable Langfuse tracing
+
+To disable the Langfuse integration, remove the [Langfuse environment variables](#langfuse-credentials), and then restart Langflow.
+
+## Run Langfuse and Langflow with Docker Compose
+
+As an alternative to the previous setup, particularly for self-hosted Langfuse, you can run both services with Docker Compose.
+
+1. Create a set of [Langfuse API keys](https://langfuse.com/faq/all/where-are-langfuse-api-keys).
+
+2. Copy the following API key information:
+
+ - Secret key
+ - Public key
+ - Base URL
+
+ :::tip
+ Langflow previously used `LANGFUSE_HOST` as the variable for the Langfuse base URL.
+ `LANGFUSE_HOST` is still supported for backward compatibility, but `LANGFUSE_BASE_URL` is the preferred environment variable.
+ If both values are set, then `LANGFLOW_BASE_URL` is used.
+ :::
+
+3. Add your Langflow credentials to your Langflow `docker-compose.yml` file in the `environment` section.
+
+ The following example is based on the [example `docker-compose.yml`](https://github.com/langflow-ai/langflow/blob/main/docker_example/docker-compose.yml).
+
+ ```yml
+ services:
+ langflow:
+ image: langflowai/langflow:latest # or another version tag on https://hub.docker.com/r/langflowai/langflow
+ pull_policy: always # set to 'always' when using 'latest' image
+ ports:
+ - "7860:7860"
+ depends_on:
+ - postgres
+ environment:
+ - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow
+ # This variable defines where the logs, file storage, monitor data and secret keys are stored.
+ - LANGFLOW_CONFIG_DIR=app/langflow
+ - LANGFUSE_SECRET_KEY=sk-...
+ - LANGFUSE_PUBLIC_KEY=pk-...
+ - LANGFUSE_BASE_URL=https://us.cloud.langfuse.com
+ volumes:
+ - langflow-data:/app/langflow
+
+ postgres:
+ image: postgres:16
+ environment:
+ POSTGRES_USER: langflow
+ POSTGRES_PASSWORD: langflow
+ POSTGRES_DB: langflow
+ ports:
+ - "5432:5432"
+ volumes:
+ - langflow-postgres:/var/lib/postgresql/data
+
+ volumes:
+ langflow-postgres:
+ langflow-data:
+ ```
+
+4. Start the Docker container:
+
+ ```text
+ docker-compose up
+ ```
+
+5. To confirm Langfuse is connected to your Langflow container, run the following command:
+
+ ```sh
+ docker compose exec langflow python -c "import requests, os; addr = os.environ.get('LANGFUSE_BASE_URL'); print(addr); res = requests.get(addr, timeout=5); print(res.status_code)"
+ ```
+
+ If there is an error, make sure you have set the `LANGFUSE_BASE_URL` environment variable in your `docker-compose.yml` file.
+
+ Output similar to the following indicates success:
+
+ ```text
+ https://us.cloud.langfuse.com
+ 200
+ ```
+
+## See also
+
+* [Langfuse GitHub repository](https://github.com/langfuse/langfuse)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/integrations-langsmith.mdx b/docs/versioned_docs/version-1.9.0/Develop/integrations-langsmith.mdx
new file mode 100644
index 0000000000..2f2eb1952c
--- /dev/null
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@@ -0,0 +1,38 @@
+---
+title: LangSmith
+slug: /integrations-langsmith
+---
+
+LangSmith is a full-lifecycle DevOps service from LangChain that provides monitoring and observability. To integrate with Langflow, add your LangChain API key and configuration as Langflow environment variables, and then start Langflow.
+
+1. Obtain your LangChain API key from [https://smith.langchain.com](https://smith.langchain.com/)
+2. Set the following environment variables in your Langflow `.env` file, replacing `LANGCHAIN_API_KEY` and `LANGSMITH_PROJECT_NAME` with your own values:
+
+ ```text
+ LANGSMITH_TRACING=True
+ LANGSMITH_ENDPOINT=https://api.smith.langchain.com/
+ LANGSMITH_API_KEY=LANGCHAIN_API_KEY
+ LANGSMITH_PROJECT=LANGSMITH_PROJECT_NAME
+ ```
+
+ Alternatively, you can export the environment variables in your terminal instead of adding them to the `.env` file:
+
+ ```bash
+ export LANGSMITH_TRACING=True && export LANGSMITH_ENDPOINT="https://api.smith.langchain.com/" && export LANGSMITH_API_KEY="LANGCHAIN_API_KEY" && export LANGSMITH_PROJECT="LANGSMITH_PROJECT_NAME"
+ ```
+
+3. Restart Langflow with your modified `.env` file or from the terminal where you set your environment variables:
+
+ ```bash
+ langflow run --env-file .env
+ ```
+
+ If you set the environment variables in your terminal, you can omit `--env-file`.
+ However, Langflow can source environment variables from `.env` _and_ your terminal.
+ For more information, see [Langflow environment variables](/environment-variables).
+
+4. Run a flow in Langflow to generate some activity.
+
+5. View the LangSmith dashboard for monitoring and observability.
+
+ 
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/integrations-langwatch.mdx b/docs/versioned_docs/version-1.9.0/Develop/integrations-langwatch.mdx
new file mode 100644
index 0000000000..cc72be0bb7
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/integrations-langwatch.mdx
@@ -0,0 +1,41 @@
+---
+title: LangWatch
+slug: /integrations-langwatch
+---
+
+[LangWatch](https://app.langwatch.ai/) is an all-in-one LLMOps platform for monitoring, observability, analytics, evaluations and alerting for getting user insights and improve your LLM workflows.
+
+## Integrate LangWatch observability
+
+To integrate with Langflow, add your LangWatch API key as a Langflow environment variable:
+
+1. Get a LangWatch API key from your LangWatch account.
+
+2. Add the key to your Langflow `.env` file:
+
+ ```shell
+ LANGWATCH_API_KEY="API_KEY_STRING"
+ ```
+
+ Alternatively, you can set the environment variable in your terminal session:
+
+ ```shell
+ export LANGWATCH_API_KEY="API_KEY_STRING"
+ ```
+
+3. Restart Langflow with your `.env` file, if you modified the Langflow `.env`:
+
+ ```
+ langflow run --env-file .env
+ ```
+
+4. Run a flow.
+
+5. View the LangWatch dashboard for monitoring and observability.
+
+
+
+## Use the LangWatch Evaluator
+
+In your flows, you can use the **LangWatch Evaluator** component to use LangWatch's evaluation endpoints to assess a model's performance.
+This component is available in the **LangWatch** [bundle](/components-bundle-components).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/integrations-openlayer.mdx b/docs/versioned_docs/version-1.9.0/Develop/integrations-openlayer.mdx
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+---
+title: Openlayer
+slug: /integrations-openlayer
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+[Openlayer](https://www.openlayer.com/) is a testing and evaluation platform for LLM applications. It provides comprehensive observability, testing, and monitoring capabilities to help you ship high-quality AI systems with confidence.
+
+You can configure Langflow to collect tracing data about your flow executions and automatically send the data to Openlayer for analysis, monitoring, and evaluation.
+
+## Prerequisites
+
+- An [Openlayer account](https://www.openlayer.com/)
+- A [running Langflow server](/get-started-installation) with a [flow](/concepts-flows) that you want to trace
+- An Openlayer inference pipeline
+
+:::tip
+If you need a flow to test the Openlayer integration, see the [Langflow quickstart](/get-started-quickstart).
+:::
+
+## Set Openlayer credentials as environment variables
+
+1. Get your [Openlayer API key](https://app.openlayer.com/settings/api-keys) from your Openlayer account.
+
+2. Create an inference pipeline in Openlayer and copy the pipeline ID.
+
+3. Set your Openlayer credentials as environment variables in the same environment where you run Langflow.
+
+ In the following examples, replace `YOUR_API_KEY` and `YOUR_PIPELINE_ID` with your actual Openlayer credentials.
+
+
+
+
+ These commands set the environment variables in a Linux or macOS terminal session:
+
+ ```bash
+ export OPENLAYER_API_KEY="YOUR_API_KEY"
+ export OPENLAYER_INFERENCE_PIPELINE_ID="YOUR_PIPELINE_ID"
+ ```
+
+
+
+
+ These commands set the environment variables in a Windows command prompt session:
+
+ ```cmd
+ set OPENLAYER_API_KEY=YOUR_API_KEY
+ set OPENLAYER_INFERENCE_PIPELINE_ID=YOUR_PIPELINE_ID
+ ```
+
+
+
+
+## Start Langflow and view traces in Openlayer
+
+1. Start Langflow in the same environment where you set the Openlayer environment variables:
+
+ ```bash
+ uv run langflow run
+ ```
+
+2. Run a flow in Langflow.
+
+ Langflow automatically collects and sends tracing data about the flow execution to Openlayer, including:
+ - Component inputs and outputs
+ - Execution timing and latency
+ - LLM calls and nested operations
+ - User and session context
+
+3. View the collected data in your [Openlayer dashboard](https://app.openlayer.com/).
+
+ Each flow execution appears as a trace with a hierarchical view of all components and their nested operations.
+
+## Advanced configuration
+
+### Flow-specific pipelines
+
+You can configure different Openlayer inference pipelines for different flows using flow-specific environment variables:
+
+```bash
+export OPENLAYER_PIPELINE_MY_FLOW_NAME="pipeline-id-1"
+export OPENLAYER_PIPELINE_ANOTHER_FLOW="pipeline-id-2"
+```
+
+The flow name is converted to uppercase and non-alphanumeric characters are replaced with underscores. For example, "My Flow-Name" becomes `OPENLAYER_PIPELINE_MY_FLOW_NAME`.
+
+### JSON mapping
+
+Alternatively, you can use a JSON mapping to configure multiple flows at once:
+
+
+
+
+```bash
+export OPENLAYER_LANGFLOW_MAPPING='{"Flow Name 1":"pipeline-id-1","Flow Name 2":"pipeline-id-2"}'
+```
+
+
+
+
+```cmd
+set OPENLAYER_LANGFLOW_MAPPING={"Flow Name 1":"pipeline-id-1","Flow Name 2":"pipeline-id-2"}
+```
+
+
+
+
+### Configuration priority
+
+Openlayer configuration is resolved in the following order (highest priority first):
+
+1. Flow-specific environment variable: `OPENLAYER_PIPELINE_`
+2. JSON mapping: `OPENLAYER_LANGFLOW_MAPPING`
+3. Default environment variable: `OPENLAYER_INFERENCE_PIPELINE_ID`
+
+This allows you to set a default pipeline for all flows and override it for specific flows as needed.
+
+## Disable Openlayer tracing
+
+To disable the Openlayer integration, remove the `OPENLAYER_API_KEY` environment variable, and then restart Langflow.
+
+## Features
+
+The Openlayer integration automatically captures:
+
+- **Component hierarchy**: All flow components with parent-child relationships
+- **LangChain callbacks**: Nested LLM calls and tool executions appear within their parent components
+- **Timing metrics**: Start time, end time, and latency for each component
+- **Inputs and outputs**: Component inputs and outputs with automatic type conversion
+- **User context**: User ID and session ID propagation for better analytics
+- **Error tracking**: Errors and logs captured in component metadata
+
+## See also
+
+* [Openlayer documentation](https://docs.openlayer.com/)
+* [Openlayer GitHub repository](https://github.com/openlayer-ai/openlayer-python)
+
diff --git a/docs/versioned_docs/version-1.9.0/Develop/integrations-opik.mdx b/docs/versioned_docs/version-1.9.0/Develop/integrations-opik.mdx
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--- /dev/null
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@@ -0,0 +1,51 @@
+---
+title: Opik
+slug: /integrations-opik
+---
+
+[Opik](https://www.comet.com/site/products/opik/) is an open-source platform designed for evaluating, testing, and monitoring large language model (LLM) applications. Developed by Comet, it aims to facilitate more intuitive collaboration, testing, and monitoring of LLM-based applications.
+
+You can configure Langflow to collect [tracing](https://www.comet.com/docs/opik/tracing/log_traces) data about your flow executions and automatically send the data to Opik.
+
+## Prerequisites
+
+- An [Open-Source Opik server or an Opik Cloud account](https://www.comet.com/docs/opik/faq#what-is-the-difference-between-opik-cloud-and-the-open-source-opik-platform-)
+- A [running Langflow server](/get-started-installation) with a [flow](/concepts-flows) that you want to trace
+
+:::tip
+If you need a flow to test the Opik integration, see the [Langflow quickstart](/get-started-quickstart).
+:::
+
+## Integrate Opik with Langflow
+
+1. If you use Opik Cloud, get an [Opik API key](https://www.comet.com/docs/opik/faq#where-can-i-find-my-opik-api-key-).
+
+ An API key isn't required with an Open-Source Opik server.
+
+2. Call the `opik configure` CLI to save your Opik configuration in the same environment where you run Langflow:
+
+ ```bash
+ opik configure
+ ```
+
+ For self-hosted Opik, you can also use the following Opik CLI command:
+
+ ```bash
+ opik configure --use_local
+ ```
+
+ For more information, see the [Opik SDK configuration documentation](https://www.comet.com/docs/opik/tracing/sdk_configuration).
+
+3. Start Langflow in the same terminal or environment where you set the environment variables:
+
+ ```bash
+ uv run langflow run
+ ```
+
+4. In Langflow, run a flow to produce activity for Opik to trace.
+
+5. Navigate to your Opik project dashboard and view the collected tracing data.
+
+## Disable the Opik integration
+
+To disable the Opik integration, remove the environment variables you set with `opik configure`, and then restart Langflow.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/jwt-authentication.mdx b/docs/versioned_docs/version-1.9.0/Develop/jwt-authentication.mdx
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--- /dev/null
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+---
+title: JWT authentication
+slug: /jwt-authentication
+---
+
+import Tabs from "@theme/Tabs";
+import TabItem from "@theme/TabItem";
+
+Langflow supports symmetric or asymmetric JSON Web Tokens (JWT) for user authentication and authorization.
+
+JWT is an [open standard](https://tools.ietf.org/html/rfc7519) for securely transmitting information between parties as a JSON object.
+Use JWT to create credentials that automatically expire, enable stateless authentication without database storage, and work across distributed systems.
+
+JWT authentication with the HS256 algorithm is enabled by default, but can be configured further with the `LANGFLOW_ALGORITHM` environment variable.
+
+
+About the JWT structure and contents
+
+When a user logs in with their username and password at the `/api/v1/login` endpoint, Langflow validates the credentials and creates a JWT token containing the user's identity and expiration time. This token is then used for subsequent API requests instead of sending credentials with each request.
+
+A JWT consists of three parts separated by dots (`.`):
+
+```
+eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIxMjM0NTY3ODkwIiwibmFtZSI6IkpvaG4gRG9lIiwiaWF0IjoxNTE2MjM5MDIyfQ.SflKxwRJSMeKKF2QT4fwpMeJf36POk6yJV_adQssw5c
+```
+
+* The header contains the token type and signing algorithm.
+* The payload contains _claims_, which are token data for user information and expiration time.
+* The signature is a secret key that ensures the token hasn't been tampered with.
+
+Each part of the JWT is Base64URL-encoded.
+You can paste this example JWT to decode the actual JSON data at [jwt.io](https://jwt.io/).
+
+
+
+## Configure JWT environment variables
+
+Configure JWT authentication in Langflow using the following environment variables:
+
+| Variable | Description | Default |
+|----------|-------------|---------|
+| `LANGFLOW_ALGORITHM` | JWT signing algorithm (`HS256`, `RS256`, or `RS512`) | `HS256` |
+| `LANGFLOW_SECRET_KEY` | Secret key for HS256 signing | Auto-generated |
+| `LANGFLOW_PRIVATE_KEY` | RSA private key for RS256/RS512 signing | Auto-generated |
+| `LANGFLOW_PUBLIC_KEY` | RSA public key for RS256/RS512 verification | Derived from private key |
+| `LANGFLOW_ACCESS_TOKEN_EXPIRE_SECONDS` | Access token expiration time | `3600` (1 hour) |
+| `LANGFLOW_REFRESH_TOKEN_EXPIRE_SECONDS` | Refresh token expiration time | `604800` (7 days) |
+
+## Configure signing algorithms
+
+Langflow supports multiple signing algorithms and both symmetric (HS256) and asymmetric (RS256, RS512) JWTs.
+
+Which method you choose depends upon your deployment's requirements.
+
+### HS256 (Default)
+
+HS256 is the default JWT algorithm, with a good security level for single-server deployments.
+Langflow automatically generates and persists a secret key.
+No configuration is necessary, but if you want to explicitly set it in the Langflow `.env`, the default value is `LANGFLOW_ALGORITHM=HS256`.
+
+To generate a custom secure key instead of using the Langflow-generated secret key, do the following:
+
+1. Generate a secure secret key with the Python secrets module or OpenSSL.
+ The key must be at least 32 characters long.
+
+ **Using Python:**
+
+ ```bash
+ python -c "import secrets; print(secrets.token_urlsafe(32))"
+ ```
+
+ **Using OpenSSL:**
+
+ ```bash
+ openssl rand -base64 32
+ ```
+
+2. Set the value for `LANGFLOW_SECRET_KEY` in your `.env` file.
+ ```bash
+ LANGFLOW_ALGORITHM="HS256"
+ LANGFLOW_SECRET_KEY="your-custom-secret-key"
+ ```
+
+### RS256
+
+The RS256 signing algorithm provides better security for production deployments by using a pair of private and public keys.
+The private key signs tokens, and the public verifies them.
+The private key must be kept secret, while the public key can be safely shared.
+
+To automatically generate a private and public key pair and store it in the Langflow [`LANGFLOW_CONFIG_DIR`](/logging), set `LANGFLOW_ALGORITHM="RS256"` in your Langflow `.env`.
+When Langflow starts, it will:
+1. Check if RSA keys exist in the configuration directory.
+2. If not, generate a new 2048-bit RSA key pair.
+3. Save the keys to `private_key.pem` and `public_key.pem`.
+4. Reuse the same keys on subsequent startups.
+
+To use a custom private key instead of the auto-generated keys, set the following in your `.env` file.
+The `LANGFLOW_PUBLIC_KEY` will be automatically derived from the private key.
+
+```bash
+LANGFLOW_ALGORITHM=RS256
+LANGFLOW_PRIVATE_KEY="-----BEGIN PRIVATE KEY-----
+MIIEvgIBADANBgkqhkiG9w0BAQEF...
+-----END PRIVATE KEY-----"
+```
+
+To use a custom key pair, set both keys in your Langflow `.env` file.
+
+```bash
+LANGFLOW_ALGORITHM=RS256
+LANGFLOW_PRIVATE_KEY="-----BEGIN PRIVATE KEY-----
+MIIEvgIBADANBgkqhkiG9w0BAQEF...
+-----END PRIVATE KEY-----"
+LANGFLOW_PUBLIC_KEY="-----BEGIN PUBLIC KEY-----
+MIIBIjANBgkqhkiG9w0BAQEFAAOC...
+-----END PUBLIC KEY-----"
+```
+
+To generate an RSA key pair manually, do the following:
+
+1. Generate a 2048-bit private key:
+ ```bash
+ openssl genrsa -out private_key.pem 2048
+ ```
+
+2. Extract the public key from the private key:
+ ```bash
+ openssl rsa -in private_key.pem -pubout -out public_key.pem
+ ```
+
+3. Verify the keys were created:
+ ```bash
+ cat private_key.pem
+ cat public_key.pem
+ ```
+
+### RS512
+
+RS512 uses the same RSA format of private and public keys as RS256, but uses the SHA-512 hashing algorithm for greater security.
+The private key signs tokens, and the public verifies them.
+The private key must be kept secret, while the public key can be safely shared.
+
+To automatically generate a private and public key pair and store it in the Langflow [`LANGFLOW_CONFIG_DIR`](/logging), set `LANGFLOW_ALGORITHM="RS512"` in your Langflow `.env`.
+When Langflow starts, it does the following:
+1. Check if RSA keys exist in the configuration directory.
+2. If not, generate a new 2048-bit RSA key pair.
+3. Save the keys to `private_key.pem` and `public_key.pem`.
+4. Reuse the same keys on subsequent startups.
+
+To use a custom private key instead of the auto-generated keys, set the following in your `.env` file.
+The `LANGFLOW_PUBLIC_KEY` will be automatically derived from the private key.
+
+```bash
+LANGFLOW_ALGORITHM=RS512
+LANGFLOW_PRIVATE_KEY="-----BEGIN PRIVATE KEY-----
+MIIEvgIBADANBgkqhkiG9w0BAQEF...
+-----END PRIVATE KEY-----"
+```
+
+To use a custom key pair, set both keys in your Langflow `.env` file.
+
+```bash
+LANGFLOW_ALGORITHM=RS512
+LANGFLOW_PRIVATE_KEY="-----BEGIN PRIVATE KEY-----
+MIIEvgIBADANBgkqhkiG9w0BAQEF...
+-----END PRIVATE KEY-----"
+LANGFLOW_PUBLIC_KEY="-----BEGIN PUBLIC KEY-----
+MIIBIjANBgkqhkiG9w0BAQEFAAOC...
+-----END PUBLIC KEY-----"
+```
+
+To generate an RSA key pair manually, do the following:
+
+1. Generate a 2048-bit private key:
+ ```bash
+ openssl genrsa -out private_key.pem 2048
+ ```
+
+2. Extract the public key from the private key:
+ ```bash
+ openssl rsa -in private_key.pem -pubout -out public_key.pem
+ ```
+
+3. Verify the keys were created:
+ ```bash
+ cat private_key.pem
+ cat public_key.pem
+ ```
+
+## Configure Docker and Kubernetes deployments
+
+Use Docker with HS256 (symmetric) for single-server deployments or development environments where simplicity is preferred.
+
+Use Docker or Kubernetes with RS256 (asymmetric) for production deployments requiring enhanced security with private/public key pairs.
+
+### Docker with HS256
+
+1. Add the value for your JWT secret key to the Langflow `.env` file.
+ ```bash
+ JWT_SECRET_KEY=your-secret-key
+ ```
+
+2. Set the signing algorithm and include a variable for the secret key in the Docker Compose file.
+ ```yaml
+ version: "3.8"
+ services:
+ langflow:
+ image: langflowai/langflow:latest
+ environment:
+ - LANGFLOW_ALGORITHM=HS256
+ - LANGFLOW_SECRET_KEY=${JWT_SECRET_KEY} # Set in .env file
+ volumes:
+ - langflow_data:/app/langflow
+
+ volumes:
+ langflow_data:
+ ```
+
+### Docker with RS256
+
+To use Langflow's automatically generated key pair, set the `RS256` signing algorithm in the Docker Compose file.
+
+```yaml
+# docker-compose.yml
+version: "3.8"
+services:
+ langflow:
+ image: langflowai/langflow:latest
+ environment:
+ - LANGFLOW_ALGORITHM=RS256
+ volumes:
+ - langflow_data:/app/langflow # Keys stored here
+
+volumes:
+ langflow_data:
+```
+
+To mount an existing key pair, set the `RS256` signing algorithm and mount the private and public keys as volumes.
+
+```yaml
+# docker-compose.yml
+version: "3.8"
+services:
+ langflow:
+ image: langflowai/langflow:latest
+ environment:
+ - LANGFLOW_ALGORITHM=RS256
+ volumes:
+ - ./keys/private_key.pem:/app/langflow/private_key.pem:ro
+ - ./keys/public_key.pem:/app/langflow/public_key.pem:ro
+ - langflow_data:/app/langflow
+
+volumes:
+ langflow_data:
+```
+
+### Kubernetes with RS256
+
+Store JWT keys as Kubernetes Secrets and reference them in your Langflow deployment configuration.
+
+```yaml
+# jwt-secret.yaml
+apiVersion: v1
+kind: Secret
+metadata:
+ name: langflow-jwt-keys
+type: Opaque
+stringData:
+ algorithm: "RS256"
+ private-key: |
+ -----BEGIN PRIVATE KEY-----
+ MIIEvgIBADANBgkqhkiG9w0BAQEF...
+ -----END PRIVATE KEY-----
+ public-key: |
+ -----BEGIN PUBLIC KEY-----
+ MIIBIjANBgkqhkiG9w0BAQEFAAOC...
+ -----END PUBLIC KEY-----
+---
+# langflow-deployment.yaml
+apiVersion: apps/v1
+kind: Deployment
+metadata:
+ name: langflow
+spec:
+ template:
+ spec:
+ containers:
+ - name: langflow
+ image: langflowai/langflow:latest
+ env:
+ - name: LANGFLOW_ALGORITHM
+ valueFrom:
+ secretKeyRef:
+ name: langflow-jwt-keys
+ key: algorithm
+ - name: LANGFLOW_PRIVATE_KEY
+ valueFrom:
+ secretKeyRef:
+ name: langflow-jwt-keys
+ key: private-key
+ - name: LANGFLOW_PUBLIC_KEY
+ valueFrom:
+ secretKeyRef:
+ name: langflow-jwt-keys
+ key: public-key
+```
+
+## Configure token expiration
+
+To configure access and refresh token expiration times, set the values in the Langflow `.env`.
+
+```bash
+LANGFLOW_ACCESS_TOKEN_EXPIRE_SECONDS=3600 # 1 hour
+LANGFLOW_REFRESH_TOKEN_EXPIRE_SECONDS=604800 # 7 days
+```
+
+Access tokens authenticate API requests and typically expire within 15 minutes to 1 hour to limit security risks.
+
+Refresh tokens obtain new access tokens without requiring the user to log in again.
+Refresh tokens typically expire within 7 to 30 days.
+
+When an access token expires, the client can use the refresh token to get a new access token from the `/api/v1/refresh` endpoint.
+This maintains the user's session without prompting for credentials again.
+
+## See also
+
+- [Langflow API keys and authentication](/api-keys-and-authentication)
+- [JWT.io](https://jwt.io/)
+- [RFC 7519 specification](https://tools.ietf.org/html/rfc7519)
+- [OWASP JWT Security Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/JSON_Web_Token_for_Java_Cheat_Sheet.html)
+- [Langflow Security Best Practices](/security)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/knowledge.mdx b/docs/versioned_docs/version-1.9.0/Develop/knowledge.mdx
new file mode 100644
index 0000000000..fc6a143f14
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Develop/knowledge.mdx
@@ -0,0 +1,131 @@
+---
+title: Manage vector data
+slug: /knowledge
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Icon from "@site/src/components/icon";
+import PartialGlobalModelProviders from '@site/docs/_partial-global-model-providers.mdx';
+
+Vector data is critical to AI applications.
+Langflow provides several components to help you store and retrieve vector data in your flows, including embedding models, vector stores, and knowledge bases.
+
+## Embedding models
+
+Embedding model components generate text embeddings using a specified Large Language Model (LLM).
+
+There are two common use cases for these components:
+
+* **Store vectors**: Generate embeddings for content written to a vector database.
+* **Search vectors**: Generate an embedding from a query to run a similarity search.
+
+In both cases the embedding model component is attached to a vector store component.
+For more information, examples, and available options, see [Embedding model components](/components-embedding-models).
+
+Alternatively, you can use [knowledge bases](#knowledge-bases), which include built-in support for several embedding models.
+
+## Vector stores
+
+Vector store components read and write to vector databases.
+Typically, these components connect to remote databases, but some vector store components support local databases.
+
+import PartialVectorRagBlurb from '@site/docs/_partial-vector-rag-blurb.mdx';
+
+
+
+
+Example: Vector search flow
+
+import PartialVectorRagFlow from '@site/docs/_partial-vector-rag-flow.mdx';
+
+
+
+
+
+## Knowledge bases
+
+import PartialKbSummary from '@site/docs/_partial-kb-summary.mdx';
+
+
+
+### Knowledge base storage locations
+
+Each knowledge base is a [ChromaDB](https://docs.trychroma.com/docs/overview/introduction) vector database.
+Each database is stored in a separate directory that contains the following:
+
+- **Vector embeddings**: Embeddings are stored using the Chroma vector database.
+- **Metadata files**: Configuration and embedding model information.
+- **Source data**: The original data used to create the knowledge base.
+
+Knowledge bases are stored local to your Langflow instance.
+The default storage location depends on your operating system and installation method:
+
+- **Langflow Desktop**:
+ - **macOS**: `/Users//.langflow/knowledge_bases`
+ - **Windows**: `C:\Users\\AppData\Roaming\com.LangflowDesktop\knowledge_bases`
+- **Langflow OSS**:
+ - **macOS/Windows/Linux/WSL with `uv pip install`**: `/lib/python3.12/site-packages/langflow/knowledge_bases` (Python version can vary. Knowledge bases aren't shared between virtual environments.)
+ - **macOS/Windows/Linux/WSL with `git clone`**: `/src/backend/base/langflow/knowledge_bases`
+
+If you set the `LANGFLOW_CONFIG_DIR` environment variable, the `knowledge_bases` subdirectory is created relative to that path.
+
+To change the default `knowledge_bases` directory path, set the `LANGFLOW_KNOWLEDGE_BASES_DIR` environment variable:
+
+```bash
+export LANGFLOW_KNOWLEDGE_BASES_DIR="/path/to/parent/directory"
+```
+
+### Create a knowledge base
+
+In this example, you'll create a knowledge base of chunked customer orders.
+To follow along with this example, download [`customer-orders.csv`](/files/customer_orders.csv) to your local machine, or adapt the steps for your own structured data.
+
+1. On the [**Projects** page](/concepts-flows#projects) page, click **Knowledge** below the list of projects to view and manage your knowledge bases.
+
+2. To create a new knowledge base, click **Add Knowledge**.
+3. In the **Create Knowledge Base** pane, enter a name for your knowledge base, and select an embedding model.
+
+4. To configure sources for your knowledge base, click **Configure Sources**.
+Optionally, to create an empty knowledge base, click **Create**.
+5. In the **Configure Sources** pane, configure the sources for your knowledge base's data, and also how the embedded data will be chunked for vector search retrieval.
+ For this example, click **Add Sources**, and then select the downloaded [`customer-orders.csv`](/files/customer_orders.csv) file from your local machine.
+ The default settings for **Chunk Size**, **Chunk Overlap**, and **Separator** are fine.
+ To continue, click **Next Step**.
+6. The **Review & Build** pane allows you to preview your first chunk before you commit to spending tokens to embedall of the data into the knowledge base.
+ If the chunk isn't what you want to embed, click **Back** to configure your chunking strategy.
+ To embed this data, click **Create**.
+7. Your data is embedded as a **Knowledge**.
+ When it is available to use, the **Status** changes to **Ready**.
+
+To use the new knowledge base in a flow, see [Use the Knowledge Base component in a flow](/knowledge-base).
+
+### Manage knowledge bases
+
+On the [**Projects** page](/concepts-flows#projects) page, click **Knowledge** below the list of projects to view and manage your knowledge bases.
+
+For each knowledge base, you can see the following information:
+
+* Name
+* Embedding model
+* Size on disk
+* Number of words, characters, and chunks
+* The average length and size of chunks
+* The knowledge base's status
+
+Chunking behavior is determined by the embedding model, and the embedding model is set when you create the knowledge base.
+If you need to change the embedding model, you must delete and recreate the knowledge base.
+
+To update a knowledge base with , click **More**, and then select **Update Knowledge Base**.
+
+To view a knowledge base's chunks, click **More**, and then select **View Chunks**.
+
+To delete a knowledge base, click **More**, and then click **Delete**.
+If any flows use the deleted knowledge base, you must update them to use a different knowledge base.
+
+For more information on using knowledge bases in a flow, see the [**Knowledge Base** component](/knowledge-base) documentation.
+
+## See also
+
+* [Use Langflow agents](/agents)
+* [Language model components](/components-models)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/logging.mdx b/docs/versioned_docs/version-1.9.0/Develop/logging.mdx
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+---
+title: Logs
+slug: /logging
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Langflow produces logs for individual flows and the Langflow application itself using the [structlog](https://www.structlog.org) library for logging.
+
+The default, primary logfile is named `langflow.log`.
+
+Log files are stored in JSON format with structured metadata.
+
+## Log storage
+
+Langflow logs are stored in the config directory specified in the `LANGFLOW_CONFIG_DIR` environment variable.
+The default config directory location depends on your operating system and installation method:
+
+- **Langflow Desktop**:
+
+ - **macOS**: `/Users//Library/Logs/com.LangflowDesktop`
+ - **Windows**: `C:\Users\\AppData\Local\com.LangflowDesktop\logs`
+
+- **OSS Langflow**:
+
+ - **macOS with `uv pip install`**: `/Users//Library/Caches/langflow`
+ - **Linux with `uv pip install`**: `/home//.cache/langflow`
+ - **Windows/WSL with `uv pip install`**: `C:\Users\\AppData\Local\langflow\langflow\Cache`
+ - **macOS/Windows/Linux/WSL with `git clone`**: `/src/backend/base/langflow/`
+
+To customize log storage locations and behaviors, set the following [Langflow environment variables](/environment-variables) in your Langflow `.env` file, and then start Langflow with `uv run langflow run --env-file .env`:
+
+| Variable | Format | Default | Description |
+|----------|--------|---------|-------------|
+| `LANGFLOW_CONFIG_DIR` | String | Varies | Set the Langflow configuration directory where files and logs are stored. Default path depends on your installation, as described in the preceding list. |
+| `LANGFLOW_LOG_LEVEL` | String | `ERROR` | Sets the log level as one of `DEBUG`, `ERROR`, `INFO`, `WARNING`, and `CRITICAL`. For example, `LANGFLOW_LOG_LEVEL=DEBUG`. |
+| `LANGFLOW_LOG_FILE` | String | Not set | Sets the log file storage location if you want to use a non-default location. For example, `LANGFLOW_LOG_FILE=path/to/logfile.log`. If not set, logs are written to stdout. |
+| `LANGFLOW_LOG_ENV` | String | `default` | This variable is the primary log format controller. `container`: JSON format for Docker/structured logging. `container_csv`: Key-value format for data analysis. `default` or unset: Uses `LANGFLOW_PRETTY_LOGS` to determine format. |
+| `LANGFLOW_PRETTY_LOGS` | Boolean | `True` | This variable controls log output format when `LANGFLOW_LOG_ENV=default` or unset. When `true`, uses structlog's [ConsoleRenderer](https://www.structlog.org/en/stable/console-output.html). When `false`, outputs logs in JSON format. |
+| `LANGFLOW_LOG_FORMAT` | String | Not set | Switch between key-value format and console format. Set to `key_value` for key-value format or `console` to use structlog's [ConsoleRenderer](https://www.structlog.org/en/stable/console-output.html). This variable only works when `LANGFLOW_LOG_ENV=default` and `LANGFLOW_PRETTY_LOGS=true`. |
+| `LANGFLOW_LOG_ROTATION` | String | `1 day` | Controls when the log file is rotated, either based on time or file size. For time-based rotation, set to `1 day`, `12 hours`, or `1 week`. For size-based rotation, set to `10 MB` or `1 GB`. To disable rotation, set to `None`. If disabled, log files grow without limit. |
+| `LANGFLOW_ENABLE_LOG_RETRIEVAL` | Boolean | `False` | Enables retrieval of logs from your Langflow instance with [Logs endpoints](/api-logs). |
+| `LANGFLOW_LOG_RETRIEVER_BUFFER_SIZE` | Integer | `10000` | Set the buffer size for log retrieval if `LANGFLOW_ENABLE_LOG_RETRIEVAL=True`. Must be greater than `0` for log retrieval to function. |
+| `LANGFLOW_NATIVE_TRACING` | Boolean | `true` | Enables the tracer to record execution traces directly in the Langflow database for use in Trace View. Set to `false` to disable tracing. |
+
+## View logs in real-time
+
+To monitor Langflow logs as they are generated, you can follow the log file:
+
+1. Change to your [Langflow config directory](#log-storage):
+
+
+
+
+ ```bash
+ cd /Users/**USERNAME**/Library/Caches/langflow
+ ```
+
+
+
+
+ ```cmd
+ cd C:\Users\**USERNAME**\AppData\Local\com.LangflowDesktop\logs
+ ```
+
+
+
+
+2. Tail the main log file:
+
+
+
+
+ ```bash
+ tail -f langflow.log
+ ```
+
+
+
+
+ ```cmd
+ Get-Content -Wait -Path langflow.log
+ ```
+
+
+
+
+ If you don't see new log entries, check that Langflow is running, and perform some actions to generate logs events. You can also check the terminal where you started Langflow to see if logs are being printed there.
+
+## Flow and component logs
+
+After you run a flow, you can inspect the logs for the each component and flow run.
+For example, you can inspect `Message` objects ingested and generated by [Input and Output components](/chat-input-and-output).
+
+### View flow logs
+
+In the visual editor, click **Logs** to view logs for the entire flow:
+
+
+
+Then, click the cells in the **inputs** and **outputs** columns to inspect the `Message` objects.
+For example, the following `Message` data could be the output from a **Chat Input** component:
+
+```text
+ "messages": [
+ {
+ "message": "What's the recommended way to install Docker on Mac M1?",
+ "sender": "User",
+ "sender_name": "User",
+ "session_id": "Session Apr 21, 17:37:04",
+ "stream_url": null,
+ "component_id": "ChatInput-4WKag",
+ "files": [],
+ "type": "text"
+ }
+ ],
+```
+
+In the case of Input/Output components, the original input might not be structured as a `Message` object.
+For example, a language model component can pass a raw text response to a **Chat Output** component that is then transformed into a `Message` object.
+
+You can find `.log` files for flows at your Langflow installation's log storage location.
+For filepaths, see [Log storage](#log-storage).
+
+### View chat logs
+
+In the **Playground**, you can inspect the chat history for each chat session.
+For more information, see [View chat history](/concepts-playground#view-chat-history).
+
+### View output from a single component
+
+When debugging issues with the format or content of a flow's output, it can help to inspect each component's output to determine where data is being lost or malformed.
+
+To view the output produced by a single component during the most recent run, click **Inspect output** on the component in the visual editor.
+
+## Access Langflow Desktop logs {#desktop-logs}
+
+If you encounter issues with Langflow Desktop, you might need to access startup logs for debugging.
+Follow the steps for your operating system.
+
+
+
+
+1. Open Terminal and run:
+ ```bash
+ cd ~/Library/Logs/com.LangflowDesktop
+ ```
+
+2. To open the folder and view the log files, run the command:
+ ```bash
+ open .
+ ```
+
+3. Locate the `langflow.log` file.
+
+
+
+
+1. Open the Command Prompt (CMD), and then run the following command:
+
+ ```cmd
+ cd %LOCALAPPDATA%\com.LangflowDesktop\cache
+ ```
+
+2. Open the folder and view the log files:
+
+ ```cmd
+ start .
+ ```
+
+3. Locate the `langflow.log` file.
+
+
+
+
+You can use the log file to investigate the issue on your own, add context to a [GitHub Issue](/contributing-github-issues), or send it to [support](/luna-for-langflow) for debugging assistance.
+
+The log file is only created when Langflow Desktop runs. If you don't see a log file, try starting Langflow Desktop first, then check for the log file.
+
+## See also
+
+* [Logs endpoints](/api-logs)
+* [Memory management options](/memory)
+* [Configure an external PostgreSQL database](/configuration-custom-database)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/memory.mdx b/docs/versioned_docs/version-1.9.0/Develop/memory.mdx
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+---
+title: Memory management options
+slug: /memory
+---
+
+Langflow provides flexible memory management options for storage and retrieval of data relevant to your flows and your Langflow server.
+This includes essential Langflow database tables, file management, and caching, as well as chat memory.
+
+## Storage options and paths
+
+Langflow supports both local memory and external memory options.
+
+Langflow's default storage option is a [SQLite](https://www.sqlite.org/) database.
+The default storage path depends on your operating system and installation method:
+
+- **Langflow Desktop**:
+ - **macOS**: `/Users//.langflow/data/database.db`
+ - **Windows**: `C:\Users\\AppData\Roaming\com.LangflowDesktop\data\database.db`
+- **Langflow OSS**
+ - **macOS/Windows/Linux/WSL with `uv pip install`**: `/lib/python3.12/site-packages/langflow/langflow.db` (Python version can vary. Database isn't shared between virtual environments because it is tied to the venv path.)
+ - **macOS/Windows/Linux/WSL with `git clone`**: `/src/backend/base/langflow/langflow.db`
+
+Langflow offers a few alternatives to the default database path:
+
+* **Config directory**: Set `LANGFLOW_SAVE_DB_IN_CONFIG_DIR=True` to store the database in your Langflow config directory as set in [`LANGFLOW_CONFIG_DIR`](/logging).
+
+* **External PostgreSQL database**: You can use an external PostgreSQL database for all of your Langflow storage.
+For more information, see [Configure external memory](#configure-external-memory)
+
+ External storage can be useful if you want to preserve the data after uninstalling Langflow or to share the same database between multiple virtual environments.
+
+* **Separate chat memory**: You can selectively use external storage for chat memory only, separate from other Langflow storage.
+For more information, see [Store chat memory](#store-chat-memory).
+
+* **No database**: To disable all database operations and run a no-op session, set `LANGFLOW_USE_NOOP_DATABASE=True` in your [Langflow environment variables](/environment-variables).
+This is useful for testing when you don't want to persist any data.
+
+## Langflow database tables
+
+The following tables are stored in `langflow.db`:
+
+• **ApiKey**: Manages Langflow API authentication keys. Component API keys are stored in the **Variables** table. For more information, see [API keys and authentication](/api-keys-and-authentication).
+
+• **File**: Stores metadata for files uploaded to Langflow's file management system, including file names, paths, sizes, and storage providers. For more information, see [Manage files](/concepts-file-management).
+
+• **Flow**: Contains flow definitions, including nodes, edges, and components, stored as JSON or database records. For more information, see [Build flows](/concepts-flows).
+
+ :::tip
+ To automatically remove API keys and tokens from flow data before saving a flow to the database, set `LANGFLOW_REMOVE_API_KEYS=True` in your [Langflow environment variables](/environment-variables).
+ When `true`, any field marked as a password field that _also_ has `api`, `key`, or `token` in its name is set to `null` before the flow is saved.
+ This helps prevent credentials from being stored in the database.
+ :::
+
+• **Folder**: Provides a structure for flow storage, including single-user folders and shared folders accessed by multiple users. For more information, see [Manage flows in projects](/concepts-flows#projects).
+
+• **Message**: Stores chat messages and interactions that occur between components. For more information, see [Message objects](/data-types#message) and [Store chat memory](#store-chat-memory).
+
+• **Trace** and **Span**: Stores traces and spans for flows and components. For more information, see [Traces](/traces).
+
+• **Transactions**: Records execution history and results of flow runs. This information is used for [logging](/logging).
+
+• **User**: Stores user account information including credentials, permissions, profiles, and user management settings. For more information, see [API keys and authentication](/api-keys-and-authentication).
+
+• **Variables**: Stores global encrypted values and credentials. For more information, see [Global variables](/configuration-global-variables) and [Component API keys](/api-keys-and-authentication#component-api-keys).
+
+• **VertexBuild**: Tracks the build status of individual nodes within flows. For more information, see [Test flows in the Playground](/concepts-playground).
+
+For more information, see the database models in the [source code](https://github.com/langflow-ai/langflow/tree/main/src/backend/base/langflow/services/database/models).
+
+## Configure external memory {#configure-external-memory}
+
+To replace the default Langflow SQLite database with another database, set the `LANGFLOW_DATABASE_URL` environment variable to your database URL, and then start Langflow with your `.env` file.
+For more information and examples, see [Configure an external PostgreSQL database](/configuration-custom-database).
+
+```text
+LANGFLOW_DATABASE_URL=postgresql://user:password@localhost:5432/langflow
+```
+
+To fine-tune your database connection pool and timeout settings, you can set the following additional environment variables:
+
+* `LANGFLOW_DATABASE_CONNECTION_RETRY`: Whether to retry lost connections to your Langflow database. If `true`, Langflow tries to connect to the database again if the connection fails. Default: `false`.
+
+* `LANGFLOW_DB_CONNECT_TIMEOUT`: The number of seconds to wait before giving up on a lock to be released or establishing a connection to the database. This may be separate from the `pool_timeout` in `LANGFLOW_DB_CONNECTION_SETTINGS`. Default: 30.
+
+* `LANGFLOW_MIGRATION_LOCK_NAMESPACE`: Optional namespace identifier for PostgreSQL advisory lock during migrations. If not provided, a hash of the database URL will be used. Useful when multiple Langflow instances share the same database and need coordinated migration locking
+
+* `LANGFLOW_DB_CONNECTION_SETTINGS`: A JSON dictionary containing the following database connection pool settings:
+
+ - `pool_size`: The base number of connections to keep open in the connection pool. Default: 20.
+ - `max_overflow`: Maximum number of connections that can be created in excess of `pool_size` if needed. Default: 30.
+ - `pool_timeout`: Number of seconds to wait for a connection from the pool before timing out. Default: 30.
+ - `pool_pre_ping`: If `true`, the pool tests connections for liveness upon each checkout. Default: `true`.
+ - `pool_recycle`: Number of seconds after which a connection is automatically recycled. Default: 1800 (30 minutes).
+ - `echo`: If `true`, SQL queries are logged for debugging purposes. Default: `false`.
+
+ For example:
+
+ ```text
+ LANGFLOW_DB_CONNECTION_SETTINGS='{"pool_size": 20, "max_overflow": 30, "pool_timeout": 30, "pool_pre_ping": true, "pool_recycle": 1800, "echo": false}'
+ ```
+
+ Don't use the deprecated environment variables `LANGFLOW_DB_POOL_SIZE` or `LANGFLOW_DB_MAX_OVERFLOW`.
+ Instead, use `pool_size` and `max_overflow` in `LANGFLOW_DB_CONNECTION_SETTINGS`.
+
+* `LANGFLOW_MIGRATION_LOCK_NAMESPACE`: Optional namespace for PostgreSQL advisory locks used during database migrations. This is useful when running multiple Langflow instances that share the same PostgreSQL database. Each instance should use a unique namespace to avoid conflicts. If not set, Langflow uses a default namespace. This setting only applies when using PostgreSQL as your database backend.
+
+## Configure cache memory
+
+The default Langflow caching behavior is an asynchronous, in-memory cache:
+
+```text
+LANGFLOW_LANGCHAIN_CACHE=InMemoryCache
+LANGFLOW_CACHE_TYPE=async
+```
+
+Langflow officially supports only the default asynchronous, in-memory cache, which is suitable for most use cases.
+Other cache options, such as Redis, are experimental and can change without notice.
+If you want to use a non-default cache setting, you can use the following environment variables:
+
+| Variable | Type | Default | Description |
+|----------|------|---------|-------------|
+| `LANGFLOW_CACHE_TYPE` | String | `async` | Set the cache type for Langflow's internal caching system. Possible values: `async`, `redis`, `memory`, `disk`. If you set the type to `redis`, then you must also set the `LANGFLOW_REDIS_*` environment variables. |
+| `LANGFLOW_LANGCHAIN_CACHE` | String | `InMemoryCache` | Set the cache storage type for the LangChain caching system (a Langflow dependency), either `InMemoryCache` or `SQLiteCache`. |
+| `LANGFLOW_REDIS_HOST` | String | `localhost` | Redis server hostname if `LANGFLOW_CACHE_TYPE=redis`. |
+| `LANGFLOW_REDIS_PORT` | Integer | `6379` | Redis server port if `LANGFLOW_CACHE_TYPE=redis`. |
+| `LANGFLOW_REDIS_DB` | Integer | `0` | Redis database number if `LANGFLOW_CACHE_TYPE=redis`. |
+| `LANGFLOW_REDIS_CACHE_EXPIRE` | Integer | `3600` | Cache expiration time in seconds if `LANGFLOW_CACHE_TYPE=redis`. |
+| `LANGFLOW_REDIS_PASSWORD` | String | Not set | Optional password for Redis authentication if `LANGFLOW_CACHE_TYPE=redis`. |
+
+## Store chat memory
+
+Chat-based flows with a **Chat Input** or **Chat Output** component produce chat history that is stored in the Langflow `messages` table.
+At minimum, this serves as a chat log, but it isn't functionally the same as chat memory that provides historical context to an LLM.
+
+To store and retrieve chat memories in flows, you can use a **Message History** component or the **Agent** component's built-in chat memory.
+
+
+How does chat memory work?
+
+Chat memory is a cache for an LLM or agent to preserve past conversations to retain and reference that context in future interactions.
+For example, if a user has already told the LLM their name, the LLM can retrieve that information from chat memory rather than asking the user to repeat themselves in future conversations or messages.
+
+Chat memory is distinct from vector store memory because it is built specifically for storing and retrieving chat messages from databases.
+
+Components that support chat memory (such as the **Agent** and **Message History** components) provide access to their respective databases _as memory_.
+Retrieval as memory is an important distinction for LLMs and agents because this storage and retrieval mechanism is specifically designed to recall context from past conversations.
+Unlike vector stores, which are designed for semantic search and retrieval of text chunks, chat memory is designed to store and retrieve chat messages in a way that is optimized for conversation history.
+
+
+
+### Session ID and chat memory
+
+Chat history and memories are grouped by [session ID (`session_id`)](/session-id).
+
+The default session ID is the flow ID, which means that all chat messages for a flow are stored under the same session ID as one large chat session.
+
+For better segregation of chat memory, especially in flows used by multiple users, consider using custom session IDs.
+For example, if you use user IDs as session IDs, then each user's chat history is stored separately, isolating the context of their chats from other users' chats.
+
+### Chat memory options
+
+Where and how chat memory is stored depends on the components used in your flow:
+
+* **Agent component**: This component has built-in chat memory that is enabled by default.
+This memory allows the agent to retrieve and reference messages from previous conversations associated with the same session ID.
+All messages are stored in [Langflow storage](#storage-options-and-paths), and the component provides minimal memory configuration options, such as the number of messages to retrieve.
+
+ The **Agent** component's built-in chat memory is sufficient for most use cases.
+
+ If you want to use external chat memory storage, retrieve memories outside the context of a chat, or use chat memory with a language model component (not an agent), you must use the **Message History** component (with or without a third-party chat memory component).
+
+* **Message History component**: By default, this component stores and retrieves memories from Langflow storage, unless you attach a third-party chat memory component. It provides a few more options for sorting and filtering memories, although most of these options are also built-in to the **Agent** component as configurable or fixed parameters.
+
+ You can use the **Message History** component with or without a language model or agent.
+ For example, if you need to retrieve data from memories outside of chat, you can use the **Message History** component to fetch that data directly from your chat memory database without feeding it into a chat.
+
+* **Third-party chat memory components**: Use one of these components only if you need to store or retrieve chat memories from a dedicated external chat memory database.
+Typically, this is necessary only if you have specific storage needs that aren't met by Langflow storage.
+For example, if you want to manage chat memory data by directly working with the database, or if you want to use a different database than the default Langflow storage.
+
+For more information and examples, see [**Message History** component](/message-history) and [Agent memory](/agents#agent-memory).
+
+## See also
+
+* [Langflow file management](/concepts-file-management)
+* [Langflow logs](/logging)
+* [Langflow environment variables](/environment-variables)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/session-id.mdx b/docs/versioned_docs/version-1.9.0/Develop/session-id.mdx
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+---
+title: Use session ID to manage communication between components
+slug: /session-id
+---
+
+Session ID is a unique identifier for client/server connections. A single session equals the duration of a client's connection to a server.
+
+In the Langflow **Playground**, current sessions are listed on the left side of the pane.
+
+Langflow uses session IDs to track different chat interactions within flows. This allows multiple chat sessions to exist in a single flow. Messages are stored in the database with session IDs as a reference.
+
+This differentiation between users per session is helpful in managing client/server connections, but is also important in maintaining separate conversational contexts within a single flow. LLMs rely on past interactions to generate responses to queries, and if these conversations aren't separated, the responses becomes less useful, or even confused.
+
+## Customize session ID
+
+Custom session IDs can be set as part of the payload in API calls, or as advanced settings in individual components. The API session ID value takes precedence. If no session ID is specified, the flow ID is assigned.
+
+If you set a custom session ID in a payload, all downstream components use the upstream component's session ID value.
+Replace `LANGFLOW_SERVER_ADDRESS`, `FLOW_ID`, and `LANGFLOW_API_KEY` with the values from your Langflow deployment.
+```
+curl --request POST \
+ --url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" \
+ --header "Content-Type: application/json" \
+ --header "x-api-key: $LANGFLOW_API_KEY" \
+ --data '{
+ "input_value": "Hello",
+ "output_type": "chat",
+ "input_type": "chat",
+ "session_id": "my_custom_session_value"
+}'
+```
+
+The `my_custom_session_value` value is used in components that accept it, and the stored messages from this flow are stored in `langflow.db` with their respective `session_id` values.
+
+## Retrieval of messages from memory by session ID
+
+To retrieve messages from local Langflow memory, add a [**Message History** component](/message-history) to your flow.
+The component accepts `sessionID` as a filter parameter, and uses the session ID value from upstream automatically to retrieve message history by session ID from storage.
+
+Messages can be retrieved by `session_id` from the Langflow API at `GET /v1/monitor/messages`. For more information, see [Monitor endpoints](https://docs.langflow.org/api-monitor).
+
+For an example of session ID in action, see [Use Session IDs in Langflow](https://www.youtube.com/watch?v=nJiF_eF21MY).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Develop/traces.mdx b/docs/versioned_docs/version-1.9.0/Develop/traces.mdx
new file mode 100644
index 0000000000..9f33733b88
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@@ -0,0 +1,55 @@
+---
+title: Traces
+slug: /traces
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Langflow’s **Traces** feature records detailed execution traces for your flows and components so that you can debug issues, measure latency, and track token usage without relying on external observability services.
+
+Trace data is stored in the Langflow database in the `trace` and `span` tables.
+Trace data is presented in the **Flow Activity** and **Trace Details** pages in the UI, and can be retrieved from the `/monitor/traces` API endpoint.
+
+Traces are enabled by default.
+To disable Langflow tracing and use a different tracing provider, set `LANGFLOW_NATIVE_TRACING` to `false`.
+
+## What traces capture
+
+The tracer records:
+
+- **Flow-level traces**: A trace for each flow run, including total runtime and status.
+- **Component spans**: Spans for each component in the flow, including inputs, outputs, latency, and errors.
+- **LangChain spans**: Deeper spans for chains, tools, retrievers, and LLM calls, including model name and token usage where available.
+
+Each span includes:
+
+- **Name** and **type** (for example, chain, LLM, tool, retriever)
+- **Start and end time** and **latency (ms)**
+- **Inputs and outputs** (serialized)
+- **Error details**, if the span failed
+- **Attributes** such as token counts and model metadata
+
+## View traces in the UI
+
+To view traces in the Langflow UI, do the following:
+1. Run a flow, such as the Simple Agent starter flow in the [Quickstart](/get-started-quickstart).
+2. Click **Traces**.
+ The **Flow Activity** page opens.
+ Each flow run is displayed as a single trace of all of its spans.
+ Flow runs can be sorted further by session ID, status, or time range.
+ Optionally, click **Download** to download a JSON file of that flow's trace to your local machine.
+3. Click a flow run to open the **Trace Details** pane.
+ The **Trace Details** pane displays spans for your flow run, including a flow-level span for the entire run, and a span for each component.
+ Individual component spans include the component's inputs and outputs, timing, and token usage.
+
+## Retrieve traces with the API
+
+To programmatically query traces, use the `/monitor/traces` endpoints.
+For full parameter details and code examples in Python, TypeScript, and curl, see [Monitor endpoints: Get traces](/api-monitor#get-traces).
+
+## See also
+
+- [Logs](/logging)
+- [Monitor endpoints](/api-monitor)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Flows/concepts-flows-import.mdx b/docs/versioned_docs/version-1.9.0/Flows/concepts-flows-import.mdx
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+---
+title: Import and export flows
+slug: /concepts-flows-import
+---
+
+import Icon from "@site/src/components/icon";
+
+You can export flows to transfer them between Langflow instances, share them with others, or create backups.
+
+## Export a flow
+
+There are three ways to export flows:
+
+* **Export from projects**: On the [**Projects** page](/concepts-flows#projects), find the flow you want to export, click **More**, and then select **Export**. To export all flows in a project, click **Options** on the **Projects** list, and then select **Download**.
+
+* **Export by sharing**: When editing a flow, click **Share**, and then click **Export**.
+
+* **Export with the Langflow API**: To export one flow, use the [`/flows/download`](/api-flows#export-flows) endpoint.
+To export all flows in a project, use the [`/projects/download`](/api-projects#export-a-project) endpoint.
+
+Exported flows are downloaded to your local machine as JSON files named `FLOW_NAME.json`.
+If you export an entire project, the JSON files are packaged in a zip archive.
+For more information, see [Langflow JSON file contents](#langflow-json-file-contents).
+
+### Save with my API keys
+
+When exporting from the **Projects** page or **Share** menu, you can select **Save with my API keys** to export the flow _and_ any defined API key variables.
+Non-API key variables are included in the export regardless of the **Save with my API keys** setting.
+
+:::warning
+If you enter the literal key into a component's API key field, then **Save with my API keys** exports the literal key value.
+
+If your key is stored in a Langflow global variable, **Save with my API keys** exports only the variable name.
+:::
+
+When you or another user import the flow to another Langflow instance, that instance must have Langflow global variables with the same names and valid values in order to run the flow successfully.
+If any variables are missing or invalid, those variables must be created or edited after importing the flow.
+
+## Import a flow
+
+You can import Langflow JSON files from your local machine in the following ways:
+
+* **Import to projects**: On the **Projects** page, click **Upload a flow**, and then select the Langflow JSON file to import.
+* **Import anywhere**: Drag and drop Langflow JSON files from your file explorer into your Langflow window to import a flow from any Langflow page.
+* **Import with the Langflow API**: To import one Langflow JSON file, use the [`/flows/upload/`](/api-flows#import-flows) endpoint.
+To import a zip archive of Langflow JSON files, use the [`/projects/upload`](/api-projects#import-a-project) endpoint.
+
+### Run an imported flow
+
+Once imported, your flow is ready to use.
+If the flow contains any global variables, make sure your Langflow instance has global variables with the same names and valid values.
+For more information, see [Save with my API keys](/concepts-flows-import#save-with-my-api-keys).
+
+## Langflow JSON file contents
+
+An exported flow is downloaded to your local machine as a JSON file named `FLOW_NAME.json`.
+
+Langflow JSON files contain the following:
+
+* [Nodes](#nodes) and [edges](#edges) that describe components and connections in the flow.
+* [Additional metadata](#additional-metadata-and-project-information) that describes the flow and the project it belongs to.
+
+For example Langflow JSON files, you can examine any of the https://github.com/langflow-ai/langflow/tree/main/src/backend/base/langflow/initial_setup/starter_projects[templates in the Langflow repository], or you can create a flow from a template in Langflow, export it, and then open the exported JSON file in a text editor.
+
+### Nodes
+
+Nodes represent the components that make up the flow.
+For example, this object represents a **Chat Input** component:
+
+```json
+{
+ "data": {
+ "description": "Get chat inputs from the Playground.",
+ "display_name": "Chat Input",
+ "id": "ChatInput-jFwUm",
+ "node": {
+ "base_classes": ["Message"],
+ "description": "Get chat inputs from the Playground.",
+ "display_name": "Chat Input",
+ "icon": "MessagesSquare",
+ "template": {
+ "input_value": {
+ "display_name": "Text",
+ "info": "Message to be passed as input.",
+ "value": "Hello"
+ },
+ "sender": {
+ "value": "User",
+ "options": ["Machine", "User"]
+ },
+ "sender_name": {
+ "value": "User"
+ },
+ "should_store_message": {
+ "value": true
+ }
+ }
+ },
+ "type": "ChatInput"
+ },
+ "position": {
+ "x": 689.5720422421635,
+ "y": 765.155834131403
+ }
+}
+```
+
+Each node has a unique identifier in the format of `NODE_NAME-UUID`, such as `ChatInput-jFwUm`.
+
+Entrypoint nodes, such as the `ChatInput` node, are the first node executed when running a flow.
+
+### Edges
+
+Edges represent the connections between nodes.
+
+The following example represents the edge (or connection) between the `ChatInput` node and the `OpenAIModel` node:
+
+```json
+{
+ "className": "",
+ "data": {
+ "sourceHandle": {
+ "dataType": "ChatInput",
+ "id": "ChatInput-jFwUm",
+ "name": "message",
+ "output_types": ["Message"]
+ },
+ "targetHandle": {
+ "fieldName": "input_value",
+ "id": "OpenAIModel-OcXkl",
+ "inputTypes": ["Message"],
+ "type": "str"
+ }
+ },
+ "id": "reactflow__edge-ChatInput-jFwUm{œdataTypeœ:œChatInputœ,œidœ:œChatInput-jFwUmœ,œnameœ:œmessageœ,œoutput_typesœ:[œMessageœ]}-OpenAIModel-OcXkl{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-OcXklœ,œinputTypesœ:[œMessageœ],œtypeœ:œstrœ}",
+ "source": "ChatInput-jFwUm",
+ "sourceHandle": "{œdataTypeœ: œChatInputœ, œidœ: œChatInput-jFwUmœ, œnameœ: œmessageœ, œoutput_typesœ: [œMessageœ]}",
+ "target": "OpenAIModel-OcXkl",
+ "targetHandle": "{œfieldNameœ: œinput_valueœ, œidœ: œOpenAIModel-OcXklœ, œinputTypesœ: [œMessageœ], œtypeœ: œstrœ}"
+}
+```
+
+This edge shows that the `ChatInput` component outputs a `Message` type to the `target` node, which is the `OpenAIModel` node.
+The `OpenAIModel` component accepts the `Message` type at the `input_value` field.
+
+### Additional metadata and project information
+
+Additional information about the flow is stored in the root `data` object:
+
+* Metadata and project information including the name, description, and `last_tested_version` of the flow.
+For example:
+
+ ```json
+ {
+ "name": "Basic Prompting",
+ "description": "Perform basic prompting with an OpenAI model.",
+ "tags": ["chatbots"],
+ "id": "1511c230-d446-43a7-bfc3-539e69ce05b8",
+ "last_tested_version": "1.0.19.post2",
+ "gradient": "2",
+ "icon": "Braces"
+ }
+ ```
+
+* Visual information about the flow defining the position of the viewport when you open the flow in the workspace:
+
+ ```json
+ "viewport": {
+ "x": -37.61270157375441,
+ "y": -155.91266341888854,
+ "zoom": 0.7575251406952855
+ }
+ ```
+
+* **Notes** are comments that help explain the flow's purpose, configuration details, and any other information relevant to users who might be editing the flow.
+They can contain text, links, code snippets, and other information.
+They are encoded in Markdown format and stored as `node` objects.
+
+ ```json
+ {
+ "id": "undefined-kVLkG",
+ "node": {
+ "description": "## 📖 README\nPerform basic prompting with an OpenAI model.\n\n#### Quick Start\n- Add your **OpenAI API key** to the **OpenAI Model**\n- Open the **Playground** to chat with your bot.\n..."
+ }
+ }
+ ```
+
+## See also
+
+* [Build flows](/concepts-flows)
+* [Share and embed flows](/concepts-publish)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Flows/concepts-flows.mdx b/docs/versioned_docs/version-1.9.0/Flows/concepts-flows.mdx
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+---
+title: Build flows
+slug: /concepts-flows
+---
+
+import Icon from "@site/src/components/icon";
+
+A _flow_ is a functional representation of an application workflow.
+Flows receive input, process it, and produce output.
+
+Flows consist of _components_ that represent individual steps in your application's workflow.
+
+
+
+Langflow flows are fully serializable and can be saved and loaded from the file system where Langflow is installed.
+
+:::tip
+To try building and running a flow in a few minutes, see the [Langflow quickstart](/get-started-quickstart).
+:::
+
+## Create a flow
+
+From the [**Projects** page](#projects), there are four ways to create a flow:
+
+* **Create a blank flow**: Select a project, click **New Flow**, and then click **Blank Flow**.
+
+* **Create a flow from a template**: Select a project, click **New Flow**, and then click the template that you want to use.
+
+
+ What are templates?
+
+ Templates are pre-built flows that you can use as a starting point for your own flow.
+ They range from basic flows with a few components to complex flows with many components and sub-flows.
+
+ For example, the **Basic Prompting** template demonstrates a small flow that passes both chat input and pre-defined instructions (as a prompt) to an LLM.
+ In contrast, the **Vector Store RAG** template consists of two sub-flows that demonstrate how to create a Retrieval Augmented Generation (RAG) chatbot. One sub-flow populates the vector store with contextually relevant data and embeddings, and the other sub-flow queries the vector store for similar data to answer user questions.
+
+ You can also [contribute templates](/contributing-templates) to the Langflow codebase.
+
+
+
+* **Duplicate an existing flow**: Locate the flow you want to copy, click **More**, and then select **Duplicate**.
+
+* **Import a flow**: See [Import and export flows](/concepts-flows-import).
+
+You can also create a flow with the [Langflow API](/api-flows), but the Langflow team recommends using the [visual editor](/concepts-overview) until you are familiar with flow creation.
+
+### Add components
+
+Flows consist of [components](/concepts-components), which are nodes that you configure and connect in the [workspace](/concepts-overview#workspace).
+Each component performs a specific task, like serving an AI model or connecting a data source.
+
+Drag and drop components from the **Core components** and **Bundles** menus to add them to your flow.
+Then, configure the component settings and connect the components together.
+
+
+
+Each component has configuration settings and options. Some of these are common to all components, and some are unique to specific components.
+
+To form a cohesive flow, you connect components by _edges_ or _ports_, which have a specific data type they receive or send.
+For example, message ports send text strings between components.
+
+For more information about component configuration, including port types and underlying component code, see [Components overview](/concepts-components).
+
+### Run a flow
+
+After you build a prototype flow, you can test it in the [**Playground**](/concepts-playground).
+When you're ready to use Langflow for application development, learn how to [trigger flows with the Langflow API](/concepts-publish), explore more advanced configuration options like [custom dependencies](/install-custom-dependencies), and, eventually, [containerize your Langflow application](/develop-application).
+
+When you're ready to go to production or deploy a Langflow MCP server for access over the public internet, see [Langflow deployment overview](/deployment-overview).
+
+#### Flow graphs
+
+When a flow runs, Langflow builds a Directed Acyclic Graph (DAG) object from the nodes (components) and edges (connections), and the nodes are sorted to determine the order of execution.
+
+The graph build calls each component's `def_build` function to validate and prepare the nodes.
+This graph is then processed in dependency order.
+Each node is built and executed sequentially, with results from each built node being passed to nodes that are dependent on that node's results.
+
+## Manage flows in projects {#projects}
+
+The **Projects** page is where you arrive when you launch Langflow.
+From here, you can manage flows and your projects' [MCP servers](/mcp-server).
+
+Langflow projects are like folders that you can use to organize related flows.
+The default project is **Starter Project**, and your flows are stored here unless you create another project.
+To create a project, click **Create new project**.
+
+
+
+:::tip
+To get back to the **Projects** page after editing a flow, click the project name or Langflow icon in the Langflow header.
+:::
+
+### Edit flow details
+
+1. On the **Projects** page, locate the flow you want to edit.
+2. Click **More**, and then select **Edit details**.
+3. Edit the **Name** and **Description**, and then click **Save**.
+
+### Save and restore flow versions
+
+You can save versions of a flow from the **Version History** menu in the flow editor.
+
+1. Open the flow that you want to version.
+2. In the flow editor sidebar, click **Version History**.
+3. In **Current**, click **Save** to capture the current state of the flow as a saved version.
+4. Select a previously saved version to preview the flow in read-only mode.
+5. To replace the current draft with the selected version, click **Restore**.
+
+When restoring a version, you can keep **Save current draft before restoring** enabled to create a backup of your current work before the selected version replaces it.
+
+From a saved version's menu, you can also **Export** or **Delete** that version.
+
+Saved flow versions are stored in the database configured for your Langflow deployment.
+
+### Lock a flow
+
+To prevent changes to a flow, you can lock it:
+
+1. On the **Projects** page, locate the flow you want to lock.
+2. Click **More**, and then select **Edit details**.
+3. Enable **Lock Flow**, and then click **Save**.
+
+Repeat these steps to unlock the flow by disabling **Lock Flow**.
+
+When editing a flow, the **Lock Status** indicates whether the flow is **Locked** or **Unlocked**.
+You cannot change the lock status while editing the flow.
+
+### Move a flow
+
+To move a flow from one project to another, do the following:
+
+1. On the **Projects** page, locate the flow you want to move.
+2. Click and drag the flow from the list of flows to the target project name in the list of projects.
+
+### Delete a flow
+
+1. On the **Projects** page, locate the flow you want to delete.
+2. Click **More**, and then select **Delete**.
+
+## Flow storage and logs
+
+By default, flows and flow execution data are stored in the Langflow database, and flow logs are stored with other Langflow logs in the Langflow config directory.
+For more information, see [Memory management options](/memory) and [Logging](/logging).
+
+## See also
+
+* [Share and embed flows](/concepts-publish)
+* [Import and export flows](/concepts-flows-import)
+* [Langflow environment variables](/environment-variables)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Flows/concepts-overview.mdx b/docs/versioned_docs/version-1.9.0/Flows/concepts-overview.mdx
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+---
+title: Use the visual editor
+slug: /concepts-overview
+---
+
+import Icon from "@site/src/components/icon";
+
+You use Langflow's visual editor to create, test, and share flows, which are functional representations of application workflows.
+Flows consist of components that represent individual steps in your application's workflow.
+
+Langflow's drag-and-drop interface allows you to create complex AI workflows without writing extensive code.
+You can connect different resources, including prompts, large language models (LLMs), data sources, agents, MCP servers, and other tools and integrations.
+
+:::tip
+To try building and running a flow in a few minutes, see the [Langflow quickstart](/get-started-quickstart).
+:::
+
+## Workspace
+
+When building a [flow](/concepts-flows), you primarily interact with the workspace.
+This is where you add [components](/concepts-components), configure them, and attach them together.
+
+
+
+From the workspace, you can also access the [**Playground**](#playground), [**Share** menu](#share-menu), and [**Logs**](/logging).
+
+### Workspace gestures and interactions
+
+Use these shortcuts, gestures, and functionality to navigate the workspace:
+
+- **Pan horizontally and vertically**: Click and drag an empty area of the workspace.
+
+- **Rearrange components**: Click and drag the components anywhere on the workspace.
+
+ To change the programmatic relationship between components, you must manipulate the component _edges_ or _ports_. For more information, see [Components overview](/concepts-components).
+
+ To enable guide lines, click **Help**, and then toggle **Enable smart guides**.
+
+ If you can't edit any components, make sure the flow is [unlocked](/concepts-flows#lock-a-flow).
+
+- **Zoom**: Scroll on the mouse or trackpad, or click **Canvas controls** next to the zoom percentage more zoom options: **Zoom In**, **Zoom Out**, **Zoom To 100%**, and **Zoom To Fit**.
+
+- **Add notes and comments**: Click **Add Note**.
+
+- **Keyboard shortcuts**: To view available shortcuts, click **Help**, and then select **Shortcuts**.
+
+## Playground
+
+If your flow has a **Chat Input** component, you can use the **Playground** to run your flow, chat with your flow, view inputs and outputs, and modify the LLM's memories to tune the flow's responses in real time.
+
+To try this for yourself, create a flow based on the **Simple Agent** template, and then click **Playground** when editing the flow in the workspace.
+
+
+
+If you have an **Agent** component in your flow, the **Playground** displays its tool calls and outputs so you can monitor the agent's tool use and understand the reasoning behind its responses.
+
+
+
+For more information, see [Test flows in the Playground](/concepts-playground).
+
+## Share {#share-menu}
+
+The **Share** menu provides the following options for integrating your flow into external applications:
+
+* [**API access**](/concepts-publish#api-access): Integrate your flow into your applications with automatically-generated Python, JavaScript, and curl code snippets.
+* [**Export**](/concepts-flows-import#export-a-flow): Export your flow to your local machine as a JSON file.
+* [**MCP Server**](/mcp-server): Expose your flow as a tool for MCP-compatible clients.
+* [**Embed into site**](/concepts-publish#embedded-chat-widget): Embed your flow in HTML, React, or Angular applications.
+* [**Shareable Playground**](/concepts-playground#share-a-flows-playground): Share your **Playground** interface with another user.
+
+ This is specifically for sharing the **Playground** experience; it isn't for running a flow in a production application.
+
+ The **Sharable Playground** isn't available for Langflow Desktop.
+
+## See also
+
+* [Manage files in Langflow](/concepts-file-management)
+* [Global variables](/configuration-global-variables)
+* [API keys and authentication](/api-keys-and-authentication)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Flows/concepts-playground.mdx b/docs/versioned_docs/version-1.9.0/Flows/concepts-playground.mdx
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+---
+title: Test flows in the Playground
+slug: /concepts-playground
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Icon from "@site/src/components/icon";
+
+Langflow's **Playground** is a dynamic interface you can use to test your LLM-based flows in real-time.
+
+You can test how a flow responds to different inputs, review and modify memories, and monitor flow output and logic.
+For example, you can make sure agent flows use the appropriate tools to respond to different inputs.
+
+The **Playground** allows you to quickly iterate over your flow's logic and behavior, making it easier to prototype and refine your applications.
+
+## Run a flow in the Playground
+
+To run a flow in the **Playground**, open the flow, and then click **Playground**.
+Then, if your flow has a [**Chat Input** component](/chat-input-and-output), enter a prompt or [use voice mode](/concepts-voice-mode) to trigger the flow and start a chat session.
+
+To expand the **Playground** view, click **Enter fullscreen** within the **Playground** panel.
+
+
+
+:::tip
+If there is no message input field in the **Playground**, make sure your flow has a **Chat Input** component that is connected, directly or indirectly, to the **Input** port of a **Language Model** or **Agent** component.
+
+Because the **Playground** is designed for flows that use an LLM in a query-and-response format, such as chatbots and agents, a flow must have **Chat Input**, **Language Model**/**Agent**, and **Chat Output** components to be fully supported by the **Playground** chat interface
+
+For flows that require another type of input, such as a webhook event, file upload, or text input, you can [use the Langflow API to trigger the flow](/api-flows-run), and then open the **Playground** to review the LLM activity for the flow run, if applicable.
+:::
+
+
+
+For technical details about how the **Playground** works, see [Monitor endpoints](/api-monitor).
+
+### Review agent logic
+
+If your flow has an **Agent** component, the **Playground** prints the tools used by the agent and the output from each tool.
+This helps you monitor the agent's tool use and understand the logic behind the agent's responses.
+For example, the following agent used a connected `fetch_content` tool to perform a web search:
+
+
+
+### View chat history {#view-chat-history}
+
+In the **Playground**, you can view message logs for each of your flow's chat sessions, including timestamps, content, and senders.
+
+In the **Playground** sidebar, find the chat session you want to review, click **Options**, and then select **Message Logs**.
+
+
+
+Message logs break apart the [`Message` data](/data-types#message) for each chat message.
+Click any cell in the message logs to view the full contents of that cell.
+
+### Modify memories in the Playground
+
+To help debug and test your flows, you can edit or delete individual messages in [message logs](#view-chat-history).
+For example, you might want to delete messages that you sent while testing a component that is no longer part of your flow.
+
+You can also delete entire chat sessions from the sidebar: click **Options**, and then select **Delete**.
+
+Modifying memories influences the behavior of the chatbot responses if you continue the chat session or if you preserve memories over multiple chat sessions.
+
+**Editing message logs edits Langflow's internal `messages` table, which is the default chat memory storage.**
+For more information about managing sessions and chat memory in Langflow, see [Use custom session IDs](#session-ids) and [Memory management options](/memory).
+
+## Set custom session IDs {#session-ids}
+
+Chat sessions are identified by session ID (`session_id`), which is a unique identifier for a flow run.
+
+The default session ID is the flow ID, which means that all chat messages for a flow are stored under the same session ID as one enormous chat session.
+
+If you need to preserve chat context over multiple flow runs or differentiate chat sessions when debugging flows, you can set a custom `session_id`.
+
+Custom session IDs are helpful for multiple reasons:
+
+* Separate chat sessions in situations where one flow has multiple chat sessions, such as a chatbot that can have multiple simultaneous user interactions.
+* Preserve memory when continuing a chat session across multiple flow runs or when passing context from one flow to another.
+* Differentiate activity from multiple users within the same flow.
+* Identify your own chat sessions when debugging and testing flows.
+
+You can set custom session IDs in the visual editor and programmatically.
+
+
+
+
+In your [input and output components](/chat-input-and-output), use the **Session ID** field:
+
+1. Click the component where you want to set a custom session ID.
+2. In the [component inspection panel](/concepts-components#component-menus), enable **Session ID**.
+3. Click **Close**.
+4. Enter a custom session ID.
+If the field is empty, the flow uses the default session ID.
+5. Open the **Playground** to start a chat under your custom session ID.
+
+Make sure to change the **Session ID** when you want to start a new chat session or continue an earlier chat session with a different session ID.
+
+
+
+
+When you trigger a flow with the Langflow API, include the `session_id` parameter in the request payload.
+For example:
+
+```bash
+curl -X POST "http://$LANGFLOW_SERVER_ADDRESS/api/v1/run/$FLOW_ID" \
+-H "Content-Type: application/json" \
+-H "x-api-key: $LANGFLOW_API_KEY" \
+-d '{
+ "session_id": "CUSTOM_SESSION_ID",
+ "input_value": "message",
+ "input_type": "chat",
+ "output_type": "chat"
+}'
+```
+
+This command starts a new chat sessions with the specified `session_id` or it retrieves an existing session with that ID, if one exists.
+
+
+
+
+:::tip
+In a production environment, consider using a variable for the session ID rather than a hardcoded value.
+
+For example, if you want to preserve context for authenticated users, user ID could be a useful variable for the session ID.
+Alternatively, if you want every chat to be unique, you might want to automatically generate a UUID for each session ID.
+:::
+
+For more information, see [Use session ID to manage communication between components](/session-id).
+
+## Share a flow's Playground
+
+:::warning
+The **Shareable Playground** is for testing purposes only.
+The **Playground** isn't meant for embedding flows in applications. For information about running flows in applications or websites, see [Trigger flows with the Langflow API](/concepts-publish).
+
+The **Shareable Playground** isn't available in Langflow Desktop.
+:::
+
+The **Shareable Playground** option exposes the **Playground** for a single flow at the `/public_flow/$FLOW_ID` endpoint.
+
+After you [deploy a public Langflow server](/deployment-overview), you can share this public URL with another user to allow them to access the specified flow's **Playground** only.
+The user can interact with the flow's chat input and output and view the results without installing Langflow or generating a Langflow API key.
+
+To share a flow's **Playground** with another user, do the following:
+
+1. In Langflow, open the flow you want share.
+2. In the [workspace](/concepts-overview#workspace), click **Share**, and then enable **Shareable Playground**.
+3. Click **Shareable Playground** again to open the **Playground** window.
+This window's URL is the flow's **Shareable Playground** address, such as `https://3f7c-73-64-93-151.ngrok-free.app/playground/d764c4b8-5cec-4c0f-9de0-4b419b11901a`.
+4. Send the URL to another user to give them access to the flow's **Playground**.
+
+## See also
+
+- [Upload images](/concepts-file-management#upload-images)
+- [Use voice mode](/concepts-voice-mode)
+- [Trigger flows with the Langflow API](/concepts-publish)
+- [Session ID](/session-id)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Flows/concepts-publish.mdx b/docs/versioned_docs/version-1.9.0/Flows/concepts-publish.mdx
new file mode 100644
index 0000000000..f898a45dc2
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Flows/concepts-publish.mdx
@@ -0,0 +1,541 @@
+---
+title: Trigger flows with the Langflow API
+slug: /concepts-publish
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+After you build a flow, you probably want to run it within an application, such as a chatbot within a mobile app or website.
+
+Langflow provides several ways to run flows from external applications:
+
+* [Trigger flows with the Langflow API](#api-access)
+* [Add an embedded chat widget to a website](#embedded-chat-widget)
+* [Serve flows through a Langflow MCP server](#serve-flows-through-a-langflow-mcp-server)
+* [Run flows with the OpenAI Responses compatible endpoint](#openai-responses-api)
+
+Although you can use these options with an isolated, local Langflow instance, they are typically more valuable when you have [deployed a Langflow server](/deployment-overview) or [packaged Langflow as a dependency of an application](/develop-application).
+
+## Use the Langflow API to run flows {#api-access}
+
+The Langflow API is the primary way to access your flows and Langflow servers programmatically.
+
+:::tip Try it
+For an example of a script that calls the Langflow API, see the [Quickstart](/get-started-quickstart).
+:::
+
+### Generate API code snippets
+
+To help you embed Langflow API requests in your scripts, Langflow automatically generates Python, JavaScript, and curl code snippets for your flows.
+To get these code snippets, do the following:
+
+1. In Langflow, open the flow that you want to embed in your application.
+2. Click **Share**, and then select **API access**.
+
+ These code snippets call the `/v1/run/$FLOW_ID` endpoint, and they automatically populate minimum values, like the Langflow server URL, flow ID, headers, and request parameters.
+
+ :::tip Windows
+ The paths generated by the API access pane assume a *nix environment.
+ If you use Microsoft Windows or WSL, you might need to adjust the filepaths given in the code snippets.
+ :::
+
+ 
+
+3. Optional: Click [**Input Schema**](#input-schema) to modify component parameters in the code snippets without changing the flow itself.
+
+4. Copy the snippet for the language that you want to use.
+
+5. Run the snippet as is, or use the snippet in the context of a larger script.
+
+For more information and examples of other Langflow API endpoints, see [Get started with the Langflow API](/api-reference-api-examples).
+
+### Langflow API authentication
+
+In Langflow versions 1.5 and later, most API endpoints require authentication with a Langflow API key.
+
+Code snippets generated in the **API access** pane include a script that checks for a `LANGFLOW_API_KEY` environment variable set in the local terminal session.
+This script doesn't check for Langflow API keys set anywhere besides the local terminal session.
+
+For this script to work, you must set a `LANGFLOW_API_KEY` variable in the terminal session where you intend to run the code snippet, such as `export LANGFLOW_API_KEY="sk..."`.
+
+Alternatively, you can edit the code snippet to include an `x-api-key` header and ensure that the request can authenticate to the Langflow API.
+
+For more information, see [API keys and authentication](/api-keys-and-authentication) and [Get started with the Langflow API](/api-reference-api-examples).
+
+### Input Schema (tweaks) {#input-schema}
+
+Tweaks are one-time overrides that modify component parameters at runtime, rather than permanently modifying the flow itself.
+For an example of tweaks in a script, see the [Quickstart](/get-started-quickstart).
+
+:::tip
+Tweaks make your flows more dynamic and reusable.
+
+You can create one flow and use it for multiple applications by passing application-specific tweaks in each application's Langflow API requests.
+:::
+
+In the **API access** pane, click **Input Schema** to add `tweaks` to the request payload in a flow's code snippets.
+
+Changes to a flow's **Input Schema** are saved exclusively as tweaks for that flow's **API access** code snippets.
+These tweaks don't change the flow parameters set in the [workspace](/concepts-overview#workspace), and they don't apply to other flows.
+
+Adding tweaks through the **Input Schema** can help you troubleshoot formatting issues with tweaks that you manually added to Langflow API requests.
+
+For example, the following curl command includes a tweak that disables the **Store Messages** setting in a flow's **Chat Input** component:
+
+```bash
+curl --request POST \
+ --url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" \
+ --header "Content-Type: application/json" \
+ --header "x-api-key: LANGFLOW_API_KEY" \
+ --data '{
+ "input_value": "Text to input to the flow",
+ "output_type": "chat",
+ "input_type": "chat",
+ "tweaks": {
+ "ChatInput-4WKag": {
+ "should_store_message": false
+ }
+ }
+}'
+```
+
+### Use a flow ID alias
+
+If you want your requests to use an alias instead of the actual flow ID, you can rename the flow's `/v1/run/$FLOW_ID` endpoint:
+
+1. In Langflow, open the flow, click **Share**, and then select **API access**.
+2. Click **Input Schema**.
+3. In the **Endpoint Name** field, enter an alias for your flow's ID, such as a memorable, human-readable name.
+
+ The name can contain only letters, numbers, hyphens, and underscores, such as `flow-customer-database-agent`.
+
+4. To save the change, close the **Input Schema** pane.
+
+The automatically generated code snippets now use your new endpoint name instead of the original flow ID, such as `url = "http://localhost:7868/api/v1/run/flow-customer-database-agent"`.
+
+## Embed a flow into a website {#embedded-chat-widget}
+
+For each flow, Langflow provides a code snippet that you can insert into the `` of your website's HTML to interact with your flow through an embedded chat widget.
+
+:::warning Required components
+The chat widget only supports flows that have **Chat Input** and **Chat Output** components, which are required for the chat experience.
+**Text Input** and **Text Output** components can send and receive messages, but they don't include ongoing LLM chat context.
+
+Attempting to chat with a flow that doesn't have a [**Chat Input** component](/chat-input-and-output) will trigger the flow, but the response only indicates that the input was empty.
+:::
+
+### Get a langflow-chat snippet
+
+To get a flow's embedded chat widget code snippet, do the following:
+
+1. In Langflow, open the flow you want to embed.
+2. Click **Share**, and then select **Embed into site**.
+3. Copy the code snippet and use it in the `` of your website's HTML.
+For more information, see [Embed the chat widget with React, Angular, or HTML](#embed-the-chat-widget).
+4. Add the `api_key` prop to ensure the widget has permission to run the flow, as explained in [Configure the langflow-chat web component](#configure-the-langflow-chat-web-component).
+
+The chat widget is implemented as a web component called `langflow-chat` that is loaded from a CDN. For more information, see the [langflow-embedded-chat repository](https://github.com/langflow-ai/langflow-embedded-chat).
+
+For example, the following HTML embeds a chat widget for a **Basic Prompting** template flow hosted on a Langflow server deployed on ngrok:
+
+```html
+
+
+
+
+
+
+
+
+```
+
+When this code is deployed to a live site, it renders as a responsive chatbot.
+If a user interacts with the chatbot, the input triggers the specified flow, and then the chatbot returns the output from the flow run.
+
+
+
+:::tip Try it
+Use the [Langflow embedded chat CodeSandbox](https://codesandbox.io/p/sandbox/langflow-embedded-chat-example-dv9zpx) for an interactive live demo of the embedded chat widget that uses your own flow.
+For more information, see the [langflow-embedded-chat README](https://github.com/langflow-ai/langflow-embedded-chat?tab=readme-ov-file#live-example).
+:::
+
+### Embed the chat widget with React, Angular, or HTML {#embed-the-chat-widget}
+
+The following examples show how to use embedded chat widget in React, Angular, and plain HTML.
+
+
+
+
+To use the chat widget in your React application, create a component that loads the widget script and renders the chat interface:
+
+1. Declare your web component, and then encapsulate it in a React component:
+
+ ```javascript
+ //Declaration of langflow-chat web component
+ declare global {
+ namespace JSX {
+ interface IntrinsicElements {
+ "langflow-chat": any;
+ }
+ }
+ }
+
+ //Definition for langflow-chat React component
+ export default function ChatWidget({ className }) {
+ return (
+
+
+
+ );
+ }
+ ```
+
+2. Place the component anywhere in your code to render the chat widget.
+
+ In the following example, the React widget component is located at `docs/src/components/ChatWidget/index.tsx`, and `index.tsx` includes a script to load the chat widget code from CDN, along with the declaration and definition from the previous step:
+
+ ```javascript
+ import React, { useEffect } from 'react';
+
+ // Component to load the chat widget script
+ const ChatScriptLoader = () => {
+ useEffect(() => {
+ if (!document.querySelector('script[src*="langflow-embedded-chat"]')) {
+ const script = document.createElement('script');
+ script.src = 'https://cdn.jsdelivr.net/gh/langflow-ai/langflow-embedded-chat@main/dist/build/static/js/bundle.min.js';
+ script.async = true;
+ document.body.appendChild(script);
+ }
+ }, []);
+
+ return null;
+ };
+
+ //Declaration of langflow-chat web component
+ declare global {
+ namespace JSX {
+ interface IntrinsicElements {
+ "langflow-chat": any;
+ }
+ }
+ }
+
+ //Definition for langflow-chat React component
+ export default function ChatWidget({ className }) {
+ return (
+
+
+
+
+ );
+ }
+ ```
+
+3. Import the `langflow-chat` React component to make it available for use on a page.
+Modify the following import statement with your React component's name and path:
+
+ ```jsx
+ import ChatWidget from '@site/src/components/ChatWidget';
+ ```
+
+4. To display the widget, call your `langflow-chat` component in the desired location on the page.
+Modify the following reference for your React component's name and the desired `className`:
+
+ ```
+
+ ```
+
+
+
+
+To use the chat widget in your Angular application, create a component that loads the widget script and renders the chat interface.
+
+In an Angular application, `langflow-chat` is a custom web component that you must explicitly allow in your site's `.components.ts`.
+Therefore, to use the embedded chat widget, you must add `CUSTOM_ELEMENTS_SCHEMA` to your module's configuration, and then integrate the `` element.
+
+Angular requires you to explicitly allow custom web components, like `langflow-chat`, in your site's `components`.
+Therefore, you must add the `` element to your Angular template and configure Angular to recognize it.
+You must add `CUSTOM_ELEMENTS_SCHEMA` to your module's configuration to enable this.
+
+1. In your Angular application, edit the `.module.ts` file where you want to add the `langflow-chat` web component.
+
+2. At the top of `.module.ts`, import `CUSTOM_ELEMENTS_SCHEMA`:
+
+ ```
+ import { NgModule, CUSTOM_ELEMENTS_SCHEMA } from '@angular/core';
+ ```
+
+3. In the `@NgModule` decorator, add `CUSTOM_ELEMENTS_SCHEMA` to the `schemas` array:
+
+ ```javascript
+ import { NgModule, CUSTOM_ELEMENTS_SCHEMA } from '@angular/core';
+ import { BrowserModule } from '@angular/platform-browser';
+ import { AppComponent } from './app.component';
+
+ @NgModule({
+ declarations: [
+ AppComponent
+ ],
+ imports: [
+ BrowserModule
+ ],
+ schemas: [CUSTOM_ELEMENTS_SCHEMA],
+ providers: [],
+ bootstrap: [AppComponent]
+ })
+ export class AppModule { }
+ ```
+
+4. Edit the `.component.ts` file where you want to use the embedded chat widget.
+
+5. In the `@Component` decorator, add the `` element to the `template` key:
+
+ ```javascript
+ import { Component } from '@angular/core';
+
+ @Component({
+ selector: 'app-root',
+ template: `
+
+
Langflow Chat Test
+
+
+ `,
+ styles: [`
+ .container {
+ padding: 20px;
+ text-align: center;
+ }
+ `]
+ })
+ export class AppComponent {
+ title = 'Langflow Chat Test';
+ }
+ ```
+
+
+
+
+```html
+
+
+
+
+
+
+
+
+```
+
+
+
+
+### Configure the langflow-chat web component {#configure-the-langflow-chat-web-component}
+
+To use the embedded chat widget in your HTML, the `langflow-chat` web component must include the following minimum inputs (also known as _props_ in React):
+
+* `host_url`: Your Langflow server URL. Must be `HTTPS`. Don't include a trailing slash (`/`).
+* `flow_id`: The ID of the flow you want to embed.
+* `api_key`: A [Langflow API key](/api-keys-and-authentication).
+This prop is recommended to ensure the widget has permission to run the flow.
+
+The minimum inputs are automatically populated in the [**Embed into site** code snippet](#get-a-langflow-chat-snippet) that is generated by Langflow.
+
+You can use additional inputs (props) to modify the embedded chat widget.
+For a list of all props, types, and descriptions, see the [langflow-embedded-chat README](https://github.com/langflow-ai/langflow-embedded-chat?tab=readme-ov-file#configuration).
+
+
+Example: Langflow API key prop
+
+The `api_key` prop stores a Langflow API key that the chat widget can use to authenticate the underlying Langflow API request.
+
+The Langflow team recommends following industry best practices for handling sensitive credentials.
+For example, securely store your API key, and then retrieve with an environment variable:
+
+```html
+
+```
+
+
+
+
+Example: Style props
+
+There are many props you can use to customize the style and positioning of the embedded chat widget.
+Many of these props are of type JSON, and they require specific formatting, depending on where you embed the `langflow-chat` web component.
+
+In React and plain HTML, JSON props are expressed as JSON objects or stringified JSON, such as `\{"key":"value"\}`:
+
+```html
+
+```
+
+For Angular applications, use [property binding syntax](https://angular.dev/guide/templates/binding#binding-dynamic-properties-and-attributes) to pass JSON props as JavaScript objects.
+For example:
+
+```javascript
+import { Component } from '@angular/core';
+
+@Component({
+ selector: 'app-root',
+ template: `
+
+
Langflow Chat Test
+
+
+ `,
+ styles: [`
+ .container {
+ padding: 20px;
+ text-align: center;
+ }
+ `]
+})
+export class AppComponent {
+ title = 'Langflow Chat Test';
+}
+```
+
+
+
+
+Example: Session ID prop
+
+The following example adds a custom [session ID](/session-id) to help identify flow runs started by the embedded chat widget:
+
+```html
+
+```
+
+
+
+
+Example: Tweaks prop
+
+Use the `tweaks` prop to modify flow parameters at runtime.
+The available keys for the `tweaks` object depend on the flow you are serving through the embedded chat widget.
+
+In React and plain HTML, `tweaks` are declared as a JSON object, similar to how you would pass them to a Langflow API endpoint like [`/v1/run/$FLOW_ID`](/api-flows-run#run-flow).
+For example:
+
+```html
+
+```
+
+For Angular applications, use [property binding syntax](https://angular.dev/guide/templates/binding#binding-dynamic-properties-and-attributes) to pass JSON props as JavaScript objects.
+For example:
+
+```javascript
+import { Component } from '@angular/core';
+
+@Component({
+ selector: 'app-root',
+ template: `
+
+
Langflow Chat Test
+
+
+ `,
+ styles: [`
+ .container {
+ padding: 20px;
+ text-align: center;
+ }
+ `]
+})
+export class AppComponent {
+ title = 'Langflow Chat Test';
+}
+```
+
+
+
+## Serve flows through a Langflow MCP server
+
+Each [Langflow project](/concepts-flows#projects) has an MCP server that exposes the project's flows as [tools](https://modelcontextprotocol.io/docs/concepts/tools) that [MCP clients](https://modelcontextprotocol.io/clients) can use to generate responses.
+
+In addition to serving flows through Langflow MCP servers, you can use Langflow as an MCP client to access any MCP server, including your Langflow MCP servers.
+
+Interactions with Langflow MCP servers happen through the Langflow API's `/mcp` endpoints.
+
+For more information, see [Use Langflow as an MCP server](/mcp-server) and [Use Langflow as an MCP client](/mcp-client).
+
+## Run flows with the OpenAI Responses compatible endpoint {#openai-responses-api}
+
+Langflow includes an OpenAI Responses API-compatible endpoint at `/api/v1/responses` that allows you to use existing OpenAI client libraries and code with minimal modifications.
+
+For more information, see [OpenAI Responses API](/api-openai-responses).
+
+## See also
+
+* [Import and export flows](/concepts-flows-import)
+* [Files endpoints](/api-files)
+* [Test flows in the Playground](/concepts-playground)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Flows/langflow-assistant.mdx b/docs/versioned_docs/version-1.9.0/Flows/langflow-assistant.mdx
new file mode 100644
index 0000000000..f4b4f796d1
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Flows/langflow-assistant.mdx
@@ -0,0 +1,226 @@
+---
+title: Build components with Langflow Assistant
+slug: /langflow-assistant
+---
+
+import Icon from "@site/src/components/icon";
+
+**Langflow Assistant** is an in-app virtual assistant pane within the **Playground**.
+
+It can answer questions about the application and help you get more out of Langflow.
+
+**Langflow Assistant** understands the structure of the Langflow graph, so it can create components from natural language prompts.
+
+Behind the scenes, **Langflow Assistant** runs a built-in Langflow flow on your Langflow server each time you send a message.
+This flow is distinct from the flow that is open in the canvas, and has its own language model.
+The language model in **Langflow Assistant** only has the currently opened flow in your workspace for context.
+To give **Langflow Assistant** context for a different flow, switch to that flow in your workspace, and open **Langflow Assistant**.
+
+## Prerequisites
+
+* Connect an LLM provider in the [Global model providers page](/components-models) for **Langflow Assistant** to use
+
+## Create a custom component with Langflow Assistant
+
+In this example, you'll prompt **Langflow Assistant** to create a custom component that validates and normalizes a list of URLs.
+
+You'll then iterate on the code based on the results in the **Playground**.
+
+1. In the **Playground**, click the **Langflow** icon.
+ The **Langflow Assistant** pane opens.
+2. Optionally, ask `What can you help me with?` for a list of **Langflow Assistant's** capabilities.
+3. Prompt **Langflow Assistant** to generate a custom component. For example:
+ ```
+ Create a custom component URLTitleExtractor with:
+ input: text
+ output: list of {url, title, status}
+ timeout handling + per-URL error handling
+ clean docstring and typed methods.”
+ ```
+
+4. **Langflow Assistant** generates component code from your prompt. Because generation is model-driven, your code may differ from the example below.
+
+ Python component code
+
+ ```
+ import html
+ import re
+ from typing import Any
+ from urllib.error import HTTPError, URLError
+ from urllib.request import Request, urlopen
+
+ from lfx.custom import Component
+ from lfx.io import FloatInput, MessageTextInput, Output
+ from lfx.schema import DataFrame
+
+
+ class URLTitleExtractor(Component):
+ """Extract URLs from text, fetch each page, and return URL/title/status rows."""
+
+ display_name = "URLTitleExtractor"
+ description = "Extracts URLs from input text and fetches page titles with per-URL status handling."
+ icon = "Link"
+
+ inputs = [
+ MessageTextInput(
+ name="text",
+ display_name="Text",
+ info="Text that may contain one or more URLs.",
+ required=True,
+ ),
+ FloatInput(
+ name="timeout",
+ display_name="Timeout",
+ value=5.0,
+ info="HTTP request timeout in seconds for each URL.",
+ range_spec={"min": 0.1, "max": 60.0, "step": 0.1, "step_type": "float"},
+ ),
+ ]
+
+ outputs = [
+ Output(
+ name="dataframe",
+ display_name="DataFrame",
+ method="build_dataframe",
+ ),
+ ]
+
+ URL_PATTERN = re.compile(r"https?://[^\s<>\]\"'`{}|\\^]+", re.IGNORECASE)
+ TITLE_PATTERN = re.compile(r"]*>(.*?)", re.IGNORECASE | re.DOTALL)
+
+ def build_dataframe(self) -> DataFrame:
+ """Build a DataFrame containing URL, extracted title, and request status."""
+ urls = self._extract_urls(self.text)
+ rows = [self._process_url(url, self.timeout) for url in urls]
+ return DataFrame(rows)
+
+ def _extract_urls(self, text: str) -> list[str]:
+ """Extract unique URLs from text using a conservative regex."""
+ if not text:
+ return []
+
+ matches = self.URL_PATTERN.findall(text)
+ cleaned_urls: list[str] = []
+ seen: set[str] = set()
+
+ for url in matches:
+ cleaned = url.rstrip(".,);:!?]}")
+ if cleaned and cleaned not in seen:
+ seen.add(cleaned)
+ cleaned_urls.append(cleaned)
+
+ return cleaned_urls
+
+ def _process_url(self, url: str, timeout: float) -> dict[str, Any]:
+ """Fetch a URL and return a row with url, title, and status."""
+ request = Request(
+ url,
+ headers={
+ "User-Agent": "Mozilla/5.0 (compatible; Langflow URLTitleExtractor/1.0)"
+ },
+ )
+
+ try:
+ with urlopen(request, timeout=float(timeout)) as response:
+ status_code = getattr(response, "status", 200)
+ content_bytes = response.read()
+ content_type = response.headers.get_content_charset() or "utf-8"
+ html_text = content_bytes.decode(content_type, errors="replace")
+ title = self._extract_title(html_text)
+ return {"url": url, "title": title, "status": str(status_code)}
+
+ except HTTPError as exc:
+ title = ""
+ try:
+ body = exc.read()
+ charset = exc.headers.get_content_charset() if exc.headers else None
+ if body:
+ html_text = body.decode(charset or "utf-8", errors="replace")
+ title = self._extract_title(html_text)
+ except Exception:
+ title = ""
+ return {"url": url, "title": title, "status": str(exc.code)}
+
+ except TimeoutError:
+ return {"url": url, "title": "", "status": "timeout"}
+
+ except URLError as exc:
+ reason = getattr(exc, "reason", None)
+ if isinstance(reason, TimeoutError):
+ status = "timeout"
+ else:
+ status = "request_error"
+ return {"url": url, "title": "", "status": status}
+
+ except Exception:
+ return {"url": url, "title": "", "status": "request_error"}
+
+ def _extract_title(self, html_text: str) -> str:
+ """Extract and normalize the HTML title from a document string."""
+ if not html_text:
+ return ""
+
+ match = self.TITLE_PATTERN.search(html_text)
+ if not match:
+ return ""
+
+ title = html.unescape(match.group(1))
+ title = re.sub(r"\s+", " ", title).strip()
+ return title
+ ```
+
+
+
+ To inspect the code, click **View Code**.
+ To add the component to the canvas, click **Add to Canvas**.
+
+5. Connect the component to **Chat Input** and **Chat Output** components.
+ At this point your flow has three connected components:
+
+ * **Chat Input** sends the text that contains URLs into the **URLTitleExtractor** so the flow runs when you chat in the **Playground** or send a prompt from an app.
+ * **URLTitleExtractor** reads that text, finds URLs, fetches each page, and outputs a [Table](/data-types#table) with `url`, `title`, and `status` columns.
+ * **Chat Output** receives **URLTitleExtractor**'s result and displays the [Table](/data-types#table) back to the user or calling application.
+
+ 
+
+6. Open the **Playground**, and tell Langflow to check a list of URLs.
+ For example:
+ ```
+ Check these links: https://langflow.org
+ https://github.com/langflow-ai/langflow
+ https://python.org
+ https://this-domain-should-not-resolve-12345.invalid
+ ```
+7. Run the flow.
+ The output will be similar to the following, with a [Table](/data-types#table) with one row per URL, including page titles and status codes.
+
+ | url | title | status |
+ | --- | --- | --- |
+ | https://langflow.org | Langflow \| Low-code AI builder for agentic and RAG applications | 200 |
+ | https://github.com/langflow-ai/langflow | GitHub - langflow-ai/langflow: Langflow is a powerful tool for building and deploying AI-powered agents and workflows. - GitHub | 200 |
+ | https://python.org | Welcome to Python.org | 200 |
+ | https://this-domain-should-not-resolve-12345.invalid | | |
+
+8. To iterate further, tell **Langflow Assistant** what you want.
+ For example, prompt it to `Update URLTitleExtractor to add max_urls (default 5) and skip duplicates.`.
+
+ **Langflow Assistant** generates an updated **URLTitleExtractor** component (again, the exact code may differ from a previous run).
+9. Replace the old component with the new component.
+ Set **max_urls** to `3`.
+ In the **Playground**, enter a list that includes a duplicate URL and the invalid URL from before:
+ ```
+ Check these links: https://langflow.org
+ https://github.com/langflow-ai/langflow
+ https://python.org
+ https://langflow.org
+ https://this-domain-should-not-resolve-12345.invalid
+ ```
+
+10. Run the flow.
+ The output will be similar to the following, with the duplicate `https://langflow.org` removed and only 3 valid URLs displayed.
+
+ | url | title | status |
+ | --- | --- | --- |
+ | https://langflow.org | Langflow \| Low-code AI builder for agentic and RAG applications | 200 |
+ | https://github.com/langflow-ai/langflow | GitHub - langflow-ai/langflow: Langflow is a powerful tool for building and deploying AI-powered agents and workflows. - GitHub | 200 |
+ | https://python.org | Welcome to Python.org | 200 |
diff --git a/docs/versioned_docs/version-1.9.0/Flows/webhook.mdx b/docs/versioned_docs/version-1.9.0/Flows/webhook.mdx
new file mode 100644
index 0000000000..b4a38deb6f
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Flows/webhook.mdx
@@ -0,0 +1,149 @@
+---
+title: Trigger flows with webhooks
+slug: /webhook
+---
+
+import Icon from "@site/src/components/icon";
+
+You can use the **Webhook** component to start a flow run in response to an external event.
+
+With the **Webhook** component, a flow can receive data directly from external sources. Then, the flow can parse the data and pass it to other components in the flow to initiate other actions, such as calling APIs, writing to databases, and chatting with LLMs.
+If the input isn't valid JSON, the **Webhook** component wraps it in a `payload` object so that it can be accepted as input to trigger the flow.
+
+The **Webhook** component provides a versatile entrypoint that can make your flows more event-driven and integrated with your entire stack of applications and services.
+For example:
+
+* Use an LLM to analyze the sentiment and content of customer feedback or survey responses.
+* Receive notifications from a monitoring system, and then trigger automated responses based on alert type and severity.
+* Integrate with e-commerce platforms to process orders and update inventory.
+
+## Configure the Webhook component
+
+To use the **Webhook** component in a flow, do the following:
+
+1. In Langflow, open the flow where you want to use the **Webhook** component.
+
+2. Add a [**Webhook** component](/webhook) and a [**Parser** component](/parser) to your flow.
+
+ These two components are commonly paired together because the **Parser** component extracts relevant data from the raw payload received by the **Webhook** component.
+
+3. Connect the **Webhook** component's **Data** output to the **Parser** component's **Data** input.
+
+4. In the **Parser** component's **Template** field, enter a template to parse the raw payload into structured text.
+
+ In the template, use variables for payload keys in the same way you would define variables in a [**Prompt Template** component](/components-prompts).
+
+ For example, assume that you expect your **Webhook** component to receive the following JSON data:
+
+ ```json
+ {
+ "id": "",
+ "name": "",
+ "email": ""
+ }
+ ```
+
+ Then, you can [use curly braces](/components-prompts#define-variables-in-prompts) to reference the JSON keys anywhere in your parser template:
+
+ ```text
+ ID: {id} - Name: {name} - Email: {email}
+ ```
+
+5. Connect the **Parser** component's **Parsed Text** output to the next logical component in your flow, such as a **Chat Input** component.
+
+ If you want to test only the **Webhook** and **Parser** components, you can connect the **Parsed Text** output directly to a **Chat Output** component's **Text** input. Then, you can see the parsed data in the **Playground** after you run the flow.
+
+6. From the **Webhook** component's **Endpoint** field, copy the API endpoint that you will use to send data to the **Webhook** component and trigger the flow.
+
+ Alternatively, to get a complete `POST /v1/webhook/$FLOW_ID` code snippet, open the flow's [**API access** pane](/concepts-publish#api-access), and then click the **Webhook curl** tab.
+ You can also modify the default curl command in the **Webhook** component's **curl** field.
+ If this field isn't visible by default, click the **Webhook** component to expose the [component inspection panel](/concepts-components#component-menus).
+
+7. Send a POST request with `data` to the flow's `webhook` endpoint to trigger the flow.
+
+ The following example sends a payload containing `id`, `name`, and `email` strings:
+
+ ```bash
+ curl -X POST "http://localhost:7860/api/v1/webhook/FLOW_ID" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: LANGFLOW_API_KEY" \
+ -d '{"id": "12345", "name": "alex", "email": "alex@email.com"}'
+ ```
+
+ A successful response indicates that Langflow started the flow.
+ The response doesn't include the output for the entire flow, only an indication that the flow started.
+
+ ```json
+ {
+ "message": "Task started in the background",
+ "status": "in progress"
+ }
+ ```
+
+8. To view the flow's most recent parsed payload, click the **Parser** component, and then click **Inspect output**.
+For the preceding example, the parsed payload would be a string like `ID: 12345 - Name: alex - Email: alex@email.com`.
+
+## Trigger flows with Composio webhooks
+
+Typically, you won't manually trigger the **Webhook** component.
+To learn about triggering flows with payloads from external applications, see the video tutorial [How to Use Webhooks in Langflow](https://www.youtube.com/watch?v=IC1CAtzFRE0).
+
+## Require authentication for webhooks {#require-authentication-for-webhooks}
+
+By default, webhooks run as the flow owner without authentication (`LANGFLOW_WEBHOOK_AUTH_ENABLE=False`).
+
+To require API key authentication for webhooks, in your Langflow `.env` file, set `LANGFLOW_WEBHOOK_AUTH_ENABLE=True`.
+
+When webhook authentication is enabled, you must provide a Langflow API key with each webhook request.
+
+To include the API key with a request as an HTTP header:
+```bash
+curl -X POST "http://LANGFLOW_SERVER_ADDRESS/api/v1/webhook/FLOW_ID" \
+ -H "Content-Type: application/json" \
+ -H "x-api-key: LANGFLOW_API_KEY" \
+ -d '{"id": "12345", "name": "alex", "email": "alex@email.com"}'
+```
+
+To include the API key with a request as a query parameter:
+```bash
+curl -X POST "http://LANGFLOW_SERVER_ADDRESS/api/v1/webhook/FLOW_ID?x-api-key=LANGFLOW_API_KEY" \
+ -H "Content-Type: application/json" \
+ -d '{"id": "12345", "name": "alex", "email": "alex@email.com"}'
+```
+
+Replace `LANGFLOW_SERVER_ADDRESS`, `FLOW_ID`, and `LANGFLOW_API_KEY` with values from your deployment.
+
+For more information, see [API keys and authentication](/api-keys-and-authentication).
+
+## Troubleshoot flows with Webhook components
+
+Use the following information to help address common issues that can occur with the **Webhook** component.
+
+### Validate data received by the Webhook component
+
+To troubleshoot a flow with a **Webhook** component and verify that the component is receiving data, you can create a small flow that outputs only the parsed payload:
+
+1. Create a flow with **Webhook**, **Parser**, and **Chat Output** components.
+2. Connect the **Webhook** component's **Data** output to the **Parser** component's **Data** input.
+3. Connect the **Parser** component's **Parsed Text** output to the **Chat Output** component's **Text** input.
+4. Edit the **Parser** component to set **Mode** to **Stringify**.
+
+ This mode passes the data received by the **Webhook** component as a string that is printed by the **Chat Output** component.
+
+5. Click **Share**, select **API access**, and then copy the **Webhook curl** code snippet.
+6. Optional: Edit the `data` in the code snippet if you want to pass a different payload.
+7. Send the POST request to trigger the flow.
+8. Click **Playground** to verify that the **Chat Output** component printed the JSON data from your POST request.
+
+### Parser component build failure
+
+The **Parser** component can fail to build if it doesn't receive data from the **Webhook** component or if there is a problem with the incoming data.
+
+If this occurs, try changing the **Parser** component's **Mode** to **Stringify** so that the component outputs the parsed payload as a single string.
+Then, you can examine the string output and troubleshoot your parsing template, or work with the parsed data in string form.
+
+## See also
+
+- [Get started with the Langflow API](/api-reference-api-examples)
+- [**Webhook** component](/webhook)
+- [Flow trigger endpoints](/api-flows-run)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Get-Started/about-langflow.mdx b/docs/versioned_docs/version-1.9.0/Get-Started/about-langflow.mdx
new file mode 100644
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Get-Started/about-langflow.mdx
@@ -0,0 +1,117 @@
+---
+title: What is Langflow?
+slug: /
+---
+
+import Cards from "@site/src/components/Cards";
+import Card from "@site/src/components/Card";
+import Icon from "@site/src/components/icon";
+
+Langflow is an open-source, Python-based, customizable framework for building AI applications.
+It supports important AI functionality like agents and the Model Context Protocol (MCP), and it doesn't require you to use specific large language models (LLMs) or vector stores.
+
+The visual editor simplifies prototyping of application workflows, enabling developers to quickly turn their ideas into powerful, real-world solutions.
+
+:::tip Try it
+Build and run your first flow in minutes: [Install Langflow](/get-started-installation), and then try the [Quickstart](/get-started-quickstart).
+:::
+
+
+ } title="Quickstart" href="/get-started-quickstart">
+ Build and run your first flow in minutes.
+
+ } title="Components" href="/concepts-components">
+ Explore all available components and integrations.
+
+ } title="Deploy" href="/deployment-overview">
+ Deploy Langflow to production environments.
+
+
+
+## Application development and prototyping
+
+Langflow can help you develop a wide variety of AI applications, such as chatbots, document analysis systems, content generators, and agentic applications.
+
+Langflow includes several pre-built templates that are ready to use or customize to your needs.
+
+### Create flows in minutes
+
+The primary purpose of Langflow is to create and serve flows, which are functional representations of application workflows.
+
+To [build a flow](/concepts-flows), you connect and configure component nodes. Each component is a single step in the workflow.
+
+With Langflow's [visual editor](/concepts-overview), you can drag and drop components to quickly build and test a functional AI application workflow.
+For example, you could build a chatbot flow for an e-commerce store that uses an LLM and a product data store to allow customers to ask questions about the store's products.
+
+
+
+### Test flows in real-time
+
+You can use the [**Playground**](/concepts-playground) to test flows without having to build your entire application stack.
+You can interact with your flows and get real-time feedback about flow logic and response generation.
+
+You can also run individual components to test dependencies in isolation.
+
+### Run and serve flows
+
+You can use your flows as prototypes for more formal application development, or you can use the Langflow API to embed your flows into your application code.
+
+For more extensive development, you can build Langflow as a dependency or deploy a Langflow server to serve flows over the public internet.
+
+
+ } title="Trigger flows with the API" href="/concepts-publish">
+ Use the Langflow API to run flows from your application code.
+
+ } title="Containerize your app" href="/develop-application">
+ Build and deploy Langflow as a containerized application.
+
+
+
+## Endless modifications and integrations
+
+Langflow provides [components](/concepts-components) that support many services, tools, and functionality that are required for AI applications.
+
+Some components are generalized, such as inputs, outputs, and data stores.
+Others are specialized, such as agents, language models, and embedding providers.
+
+All components offer parameters that you can set to fixed or variable values. You can also use tweaks to temporarily override flow settings at runtime.
+
+### Agent and MCP support
+
+
+ } title="Langflow Agents" href="/agents">
+ Build and configure AI agents with Langflow.
+
+ } title="Agent tools" href="/agents-tools">
+ Use components and flows as agent tools.
+
+ } title="MCP server" href="/mcp-server">
+ Expose Langflow as an MCP server.
+
+ } title="MCP client" href="/mcp-client">
+ Connect Langflow to external MCP servers.
+
+
+
+### Extensibility
+
+In addition to the core components, Langflow supports custom components.
+
+You can use custom components developed by others, and you can develop your own custom components for personal use or to share with other Langflow users.
+
+
+ } title="Contribute" href="/contributing-how-to-contribute">
+ Help build Langflow.
+
+ } title="Custom components" href="/components-custom-components">
+ Create your own Python components.
+
+ } title="Get help" href="/contributing-github-issues">
+ Request enhancements and report issues.
+
+
+
+## Next steps
+
+* [Install Langflow](/get-started-installation)
+* [Quickstart](/get-started-quickstart)
diff --git a/docs/versioned_docs/version-1.9.0/Get-Started/get-started-installation.mdx b/docs/versioned_docs/version-1.9.0/Get-Started/get-started-installation.mdx
new file mode 100644
index 0000000000..9f313c0bae
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Get-Started/get-started-installation.mdx
@@ -0,0 +1,215 @@
+---
+title: Install Langflow
+slug: /get-started-installation
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Cards from "@site/src/components/Cards";
+import Card from "@site/src/components/Card";
+import Icon from "@site/src/components/icon";
+
+Langflow can be installed in multiple ways. Choose an option below to jump to the steps.
+
+
+ } title="Langflow Desktop (recommended)" href="#install-and-run-langflow-desktop">
+ Standalone app with dependency management and simple upgrades.
+
+ } title="Docker" href="#install-and-run-langflow-docker">
+ Run Langflow in a container in isolation.
+
+ } title="Python package" href="#install-and-run-the-langflow-oss-python-package">
+ Full control over environment, dependencies, and versioning.
+
+ } title="Install from source" href="/contributing-how-to-contribute#install-langflow-from-source">
+ Contribute to Langflow or work from a local clone.
+
+
+
+## Install and run Langflow Desktop {#install-and-run-langflow-desktop}
+
+Langflow Desktop is a desktop version of Langflow that simplifies dependency management and upgrades.
+However, some features aren't available for Langflow Desktop, such as the **Shareable Playground** and **Voice Mode**.
+
+
+
+
+Langflow Desktop requires macOS 13 or later.
+
+1. Navigate to [Langflow Desktop](https://www.langflow.org/desktop).
+2. Click **Download Langflow**, enter your contact information, and then click **Download**.
+3. Mount and install the Langflow application.
+4. When the installation completes, open the Langflow application, and then create your first flow with the [Quickstart](/get-started-quickstart).
+
+
+
+
+ :::warning
+ If you are upgrading Langflow Desktop on Windows, **don't** use the in-app update feature to upgrade to Langflow version 1.6.0.
+ For more information, see [Known issues for 1.6.0](/release-notes#windows-desktop-update-issue).
+ :::
+
+1. Navigate to [Langflow Desktop](https://www.langflow.org/desktop).
+2. Click **Download Langflow**, enter your contact information, and then click **Download**.
+3. Open the **File Explorer**, and then navigate to **Downloads**.
+4. Double-click the downloaded `.msi` file, and then use the install wizard to install Langflow Desktop.
+
+ :::tip
+ Windows installations of Langflow Desktop require a C++ compiler that may not be present on your system. If you receive a `C++ Build Tools Required!` error, follow the on-screen prompt to install Microsoft C++ Build Tools, or [install Microsoft Visual Studio](https://visualstudio.microsoft.com/downloads/).
+ :::
+
+5. When the installation completes, open the Langflow application, and then create your first flow with the [Quickstart](/get-started-quickstart).
+
+
+
+
+For upgrade information, see the [Release notes](/release-notes).
+
+To manage dependencies in Langflow Desktop, see [Install custom dependencies in Langflow Desktop](/install-custom-dependencies#langflow-desktop).
+
+## Install and run Langflow with Docker {#install-and-run-langflow-docker}
+
+You can use the Langflow Docker image to start a Langflow container.
+For more information, see [Deploy Langflow on Docker](/deployment-docker).
+
+1. Install and start [Docker](https://docs.docker.com/).
+
+2. Pull the latest [Langflow Docker image](https://hub.docker.com/r/langflowai/langflow) and start it:
+
+ ```bash
+ docker run -p 7860:7860 langflowai/langflow:latest
+ ```
+
+3. To access Langflow, navigate to `http://localhost:7860/`.
+
+4. Create your first flow with the [Quickstart](/get-started-quickstart).
+
+## Install and run the Langflow OSS Python package {#install-and-run-the-langflow-oss-python-package}
+
+1. Make sure you have the required dependencies and infrastructure:
+
+ - [Python](https://www.python.org/downloads/release/python-3100/)
+ - macOS and Linux: Version 3.10 to 3.13
+ - Windows: Version 3.10 to 3.12
+ - [uv](https://docs.astral.sh/uv/getting-started/installation/)
+ - Sufficient infrastructure:
+ - Minimum: Dual-core CPU and 2 GB RAM
+ - Recommended: Multi-core CPU and at least 4 GB RAM
+ - Browser:
+ - Google Chrome is recommended but not required
+
+2. Create a virtual environment with [uv](https://docs.astral.sh/uv/pip/environments).
+
+
+ Need help with virtual environments?
+
+ Virtual environments ensure Langflow is installed in an isolated, fresh environment.
+ To create a new virtual environment, do the following.
+
+
+
+
+ 1. Navigate to where you want your virtual environment to be created, and then create it with `uv`:
+
+ ```shell
+ uv venv VENV_NAME
+ ```
+
+ Replace `VENV_NAME` with a name for your virtual environment.
+
+ 2. Start the virtual environment:
+
+ ```shell
+ source VENV_NAME/bin/activate
+ ```
+
+ Your shell's prompt changes to display that you're currently working in a virtual environment:
+
+ ```text
+ (VENV_NAME) ➜ langflow git:(main) ✗
+ ```
+
+ 3. To deactivate the virtual environment and return to your regular shell, type `deactivate`.
+
+ When activated, the virtual environment temporarily modifies your `PATH` variable to prioritize packages installed within the virtual environment.
+ To avoid conflicts with other projects, it's a good idea to deactivate your virtual environment when you're done working in it.
+
+ To delete the virtual environment, type `rm -rf VENV_NAME`.
+ This completely removes the virtual environment directory and its contents.
+
+
+
+
+ 1. Navigate to where you want your virtual environment to be created, and create it with `uv`.
+
+ ```shell
+ uv venv VENV_NAME
+ ```
+
+ Replace `VENV_NAME` with a name for your virtual environment.
+
+ 2. Start the virtual environment:
+
+ ```shell
+ VENV_NAME\Scripts\activate
+ ```
+
+ Your shell's prompt changes to display that you're currently working in a virtual environment:
+
+ ```text
+ (VENV_NAME) PS C:/users/username/langflow-dir>
+ ```
+
+ 3. To deactivate the virtual environment and return to your regular shell, type `deactivate`.
+
+ When activated, the virtual environment temporarily modifies your `PATH` variable to prioritize packages installed within the virtual environment.
+ To avoid conflicts with other projects, it's a good idea to deactivate your virtual environment when you're done working in it.
+
+ To delete the virtual environment, type `Remove-Item VENV_NAME`.
+ This completely removes the virtual environment directory and its contents.
+
+
+
+
+
+
+3. In your virtual environment, install Langflow:
+
+ ```bash
+ uv pip install langflow
+ ```
+
+ To install a specific version of the Langflow package, add the required version to the command, such as `uv pip install langflow==1.4.22`.
+
+
+ Reinstall or upgrade Langflow
+
+ To reinstall Langflow and all of its dependencies, run `uv pip install langflow --force-reinstall`.
+
+ To upgrade Langflow to the latest version, run `uv pip install langflow -U`.
+ However, the Langflow team recommends taking steps to backup your existing installation before you upgrade Langflow.
+ For more information, see [Prepare to upgrade](/release-notes#prepare-to-upgrade).
+
+
+
+4. Start Langflow:
+
+ ```bash
+ uv run langflow run
+ ```
+
+ It can take a few minutes for Langflow to start.
+
+5. To confirm that a local Langflow instance is running, navigate to the default Langflow URL `http://127.0.0.1:7860`.
+
+6. Create your first flow with the [Quickstart](/get-started-quickstart).
+
+For upgrade information, see the [Release notes](/release-notes).
+
+For information about optional dependency groups and support for custom dependencies to extend Langflow OSS functionality, see [Install custom dependencies](/install-custom-dependencies).
+
+## Next steps
+
+* [Quickstart](/get-started-quickstart): Build and run your first flow in minutes.
+* [Build flows](/concepts-flows): Learn about building flows.
+* [Troubleshoot Langflow](/troubleshoot): Get help with common Langflow install and startup issues.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Get-Started/get-started-quickstart.mdx b/docs/versioned_docs/version-1.9.0/Get-Started/get-started-quickstart.mdx
new file mode 100644
index 0000000000..99ca86cb2b
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Get-Started/get-started-quickstart.mdx
@@ -0,0 +1,572 @@
+---
+title: Quickstart
+slug: /get-started-quickstart
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import PartialGlobalModelProviders from '@site/docs/_partial-global-model-providers.mdx';
+
+Get started with Langflow by loading a template flow, running it, and then serving it at the `/run` API endpoint.
+
+## Prerequisites
+
+- [Install and start Langflow](/get-started-installation)
+- Create an [OpenAI API key](https://platform.openai.com/api-keys)
+- Create a [Langflow API key](/api-keys-and-authentication)
+
+
+ Create a Langflow API key
+
+ A Langflow API key is a user-specific token you can use with Langflow.
+
+ To create a Langflow API key, do the following:
+
+ 1. In Langflow, click your user icon, and then select **Settings**.
+ 2. Click **Langflow API Keys**, and then click **Add New**.
+ 3. Name your key, and then click **Create API Key**.
+ 4. Copy the API key and store it securely.
+ 5. To use your Langflow API key in a request, set a `LANGFLOW_API_KEY` environment variable in your terminal, and then include an `x-api-key` header or query parameter with your request.
+ For example:
+
+ ```bash
+ # Set variable
+ export LANGFLOW_API_KEY="sk..."
+
+ # Send request
+ curl --request POST \
+ --url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" \
+ --header "Content-Type: application/json" \
+ --header "x-api-key: $LANGFLOW_API_KEY" \
+ --data '{
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": "Hello"
+ }'
+ ```
+
+
+
+:::tip
+If you encounter the error "An API key must be passed as query or header" when attempting to sign up, see [Troubleshooting](/troubleshoot#an-api-key-must-be-passed-as-query-or-header).
+:::
+
+## Run the Simple Agent template flow
+
+1. In Langflow, click **New Flow**, and then select the **Simple Agent** template.
+
+
+
+The **Simple Agent** template consists of an [**Agent** component](/agents) connected to [**Chat Input** and **Chat Output** components](/chat-input-and-output), a [**Calculator** component](/calculator), and a [**URL** component](/url). When you run this flow, you submit a query to the agent through the **Chat Input** component, the agent uses the **Calculator** and **URL** tools to generate a response, and then returns the response through the **Chat Output** component.
+
+Many components can be tools for agents, including [Model Context Protocol (MCP) servers](/mcp-server). The agent decides which tools to call based on the context of a given query.
+
+2. In the **Agent** component, click **Setup Provider** to select your language model provider.
+
+3. In the **Agent** component, select your configured model from the **Language Model** dropdown.
+
+
+ Access more models and providers
+
+ There are two ways to access more models and providers:
+
+ * Edit Langflow's global **Models** configuration. These providers and models are part of Langflow's core functionality. Use the **Ollama** provider to connect to any model hosted on a local or remote Ollama instance.
+ * Connect any [additional language model component](/components-models#additional-language-models) to the **Agent** component's **Language Model** port.
+
+
+
+4. To run the flow, click **Playground**.
+
+5. To test the **Calculator** tool, ask the agent a simple math question, such as `I want to add 4 and 4.`
+To help you test and evaluate your flows, the **Playground** shows the agent's reasoning process as it analyzes the prompt, selects a tool, and then uses the tool to generate a response.
+In this case, a math question causes the agent to select the **Calculator** tool and use an action like `evaluate_expression`.
+
+
+
+6. To test the **URL** tool, ask the agent about current events.
+For this request, the agent selects the **URL** tool's `fetch_content` action, and then returns a summary of current news headlines.
+
+7. When you are done testing the flow, click **Close**.
+
+:::tip Next steps
+Now that you've run your first flow, try these next steps:
+
+- Edit your **Simple Agent** flow by attaching different tools or adding more [components](/concepts-components) to the flow.
+- [Build your own flows](/concepts-flows) from scratch or by modifying other template flows.
+- Integrate flows into your applications, as explained in [Run your flows from external applications](#run-your-flows-from-external-applications).
+:::
+
+## Run your flows from external applications
+
+Langflow is an IDE, but it's also a runtime you can call through the [Langflow API](/api-reference-api-examples) with Python, JavaScript, or HTTP.
+
+When you start Langflow locally, you can send requests to the local Langflow server.
+For production applications, you need to [deploy a stable Langflow instance](/deployment-overview) to handle API calls.
+
+For example, you can use the `/run` endpoint to run a flow and get the result.
+
+Langflow provides code snippets to help you get started with the Langflow API.
+
+1. When editing a flow, click **Share**, and then click **API access**.
+
+ The default code in the API access pane constructs a request with the Langflow server `url`, `headers`, and a `payload` of request data.
+ The code snippets automatically include the `LANGFLOW_SERVER_ADDRESS` and `FLOW_ID` values for the flow, and a script to include your `LANGFLOW_API_KEY` if you've set it as an environment variable in your terminal session.
+ Replace these values if you're using the code for a different server or flow.
+ The default Langflow server address is `http://localhost:7860`.
+
+
+
+
+ ```python
+ import requests
+
+ url = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" # The complete API endpoint URL for this flow
+
+ # Request payload configuration
+ payload = {
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": "hello world!"
+ }
+
+ # Request headers
+ headers = {
+ "Content-Type": "application/json",
+ "x-api-key": "$LANGFLOW_API_KEY"
+ }
+
+ try:
+ # Send API request
+ response = requests.request("POST", url, json=payload, headers=headers)
+ response.raise_for_status() # Raise exception for bad status codes
+
+ # Print response
+ print(response.text)
+
+ except requests.exceptions.RequestException as e:
+ print(f"Error making API request: {e}")
+ except ValueError as e:
+ print(f"Error parsing response: {e}")
+ ```
+
+
+
+
+ ```js
+ const payload = {
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": "hello world!",
+ "session_id": "user_1"
+ };
+
+ const options = {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json',
+ 'x-api-key': 'LANGFLOW_API_KEY'
+ },
+ body: JSON.stringify(payload)
+ };
+
+ fetch('http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID', options)
+ .then(response => response.json())
+ .then(response => console.log(response))
+ .catch(err => console.error(err));
+ ```
+
+
+
+
+ ```bash
+ curl --request POST \
+ --url 'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false' \
+ --header 'Content-Type: application/json' \
+ --header "x-api-key: LANGFLOW_API_KEY" \
+ --data '{
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": "hello world!"
+ }'
+
+ # A 200 response confirms the call succeeded.
+ ```
+
+
+
+
+2. Copy the snippet, paste it in a script file, and then run the script to send the request.
+If you are using the curl snippet, you can run the command directly in your terminal.
+
+If the request is successful, the response includes many details about the flow run, including the session ID, inputs, outputs, components, durations, and more.
+The following is an example of a response from running the **Simple Agent** template flow:
+
+
+Result
+
+```json
+{
+ "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
+ "outputs": [
+ {
+ "inputs": {
+ "input_value": "hello world!"
+ },
+ "outputs": [
+ {
+ "results": {
+ "message": {
+ "text_key": "text",
+ "data": {
+ "timestamp": "2025-06-16 19:58:23 UTC",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
+ "text": "Hello world! 🌍 How can I assist you today?",
+ "files": [],
+ "error": false,
+ "edit": false,
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": false,
+ "source": {
+ "id": "Agent-ZOknz",
+ "display_name": "Agent",
+ "source": "gpt-4o-mini"
+ },
+ "icon": "bot",
+ "allow_markdown": false,
+ "positive_feedback": null,
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": [
+ {
+ "title": "Agent Steps",
+ "contents": [
+ {
+ "type": "text",
+ "duration": 2,
+ "header": {
+ "title": "Input",
+ "icon": "MessageSquare"
+ },
+ "text": "**Input**: hello world!"
+ },
+ {
+ "type": "text",
+ "duration": 226,
+ "header": {
+ "title": "Output",
+ "icon": "MessageSquare"
+ },
+ "text": "Hello world! 🌍 How can I assist you today?"
+ }
+ ],
+ "allow_markdown": true,
+ "media_url": null
+ }
+ ],
+ "id": "f3d85d9a-261c-4325-b004-95a1bf5de7ca",
+ "flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
+ "duration": null
+ },
+ "default_value": "",
+ "text": "Hello world! 🌍 How can I assist you today?",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "files": [],
+ "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
+ "timestamp": "2025-06-16T19:58:23+00:00",
+ "flow_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
+ "error": false,
+ "edit": false,
+ "properties": {
+ "text_color": "",
+ "background_color": "",
+ "edited": false,
+ "source": {
+ "id": "Agent-ZOknz",
+ "display_name": "Agent",
+ "source": "gpt-4o-mini"
+ },
+ "icon": "bot",
+ "allow_markdown": false,
+ "positive_feedback": null,
+ "state": "complete",
+ "targets": []
+ },
+ "category": "message",
+ "content_blocks": [
+ {
+ "title": "Agent Steps",
+ "contents": [
+ {
+ "type": "text",
+ "duration": 2,
+ "header": {
+ "title": "Input",
+ "icon": "MessageSquare"
+ },
+ "text": "**Input**: hello world!"
+ },
+ {
+ "type": "text",
+ "duration": 226,
+ "header": {
+ "title": "Output",
+ "icon": "MessageSquare"
+ },
+ "text": "Hello world! 🌍 How can I assist you today?"
+ }
+ ],
+ "allow_markdown": true,
+ "media_url": null
+ }
+ ],
+ "duration": null
+ }
+ },
+ "artifacts": {
+ "message": "Hello world! 🌍 How can I assist you today?",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "files": [],
+ "type": "object"
+ },
+ "outputs": {
+ "message": {
+ "message": "Hello world! 🌍 How can I assist you today?",
+ "type": "text"
+ }
+ },
+ "logs": {
+ "message": []
+ },
+ "messages": [
+ {
+ "message": "Hello world! 🌍 How can I assist you today?",
+ "sender": "Machine",
+ "sender_name": "AI",
+ "session_id": "29deb764-af3f-4d7d-94a0-47491ed241d6",
+ "stream_url": null,
+ "component_id": "ChatOutput-aF5lw",
+ "files": [],
+ "type": "text"
+ }
+ ],
+ "timedelta": null,
+ "duration": null,
+ "component_display_name": "Chat Output",
+ "component_id": "ChatOutput-aF5lw",
+ "used_frozen_result": false
+ }
+ ]
+ }
+ ]
+}
+```
+
+
+
+In a production application, you probably want to select parts of this response to return to the user, store in logs, and so on. The next steps demonstrate how you can extract data from a Langflow API response to use in your application.
+
+### Extract data from the response
+
+The following example builds on the API pane's example code to create a question-and-answer chat in your terminal that stores the agent's previous answer.
+
+1. Incorporate your **Simple Agent** flow's `/run` snippet into the following script.
+This script runs a question-and-answer chat in your terminal and stores the agent's previous answer so you can compare them.
+
+
+
+
+ ```python
+ import requests
+ import json
+
+ url = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID"
+
+ def ask_agent(question):
+ payload = {
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": question,
+ }
+
+ headers = {
+ "Content-Type": "application/json",
+ "x-api-key": "LANGFLOW_API_KEY"
+ }
+
+ try:
+ response = requests.post(url, json=payload, headers=headers)
+ response.raise_for_status()
+
+ # Get the response message
+ data = response.json()
+ message = data["outputs"][0]["outputs"][0]["outputs"]["message"]["message"]
+ return message
+
+ except Exception as e:
+ return f"Error: {str(e)}"
+
+ def extract_message(data):
+ try:
+ return data["outputs"][0]["outputs"][0]["outputs"]["message"]["message"]
+ except (KeyError, IndexError):
+ return None
+
+ # Store the previous answer from ask_agent response
+ previous_answer = None
+
+ # the terminal chat
+ while True:
+ # Get user input
+ print("\nAsk the agent anything, such as 'What is 15 * 7?' or 'What is the capital of France?')")
+ print("Type 'quit' to exit or 'compare' to see the previous answer")
+ user_question = input("Your question: ")
+
+ if user_question.lower() == 'quit':
+ break
+ elif user_question.lower() == 'compare':
+ if previous_answer:
+ print(f"\nPrevious answer was: {previous_answer}")
+ else:
+ print("\nNo previous answer to compare with!")
+ continue
+
+ # Get and display the answer
+ result = ask_agent(user_question)
+ print(f"\nAgent's answer: {result}")
+ # Store the answer for comparison
+ previous_answer = result
+ ```
+
+
+
+
+ ```js
+ const readline = require('readline');
+
+ const rl = readline.createInterface({
+ input: process.stdin,
+ output: process.stdout
+ });
+
+ const url = 'http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID';
+
+ // Store the previous answer from askAgent response
+ let previousAnswer = null;
+
+ // the agent flow, with question as input_value
+ async function askAgent(question) {
+ const payload = {
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": question
+ };
+
+ const options = {
+ method: 'POST',
+ headers: {
+ 'Content-Type': 'application/json',
+ 'x-api-key': 'LANGFLOW_API_KEY'
+ },
+ body: JSON.stringify(payload)
+ };
+
+ try {
+ const response = await fetch(url, options);
+ const data = await response.json();
+
+ // Extract the message from the nested response
+ const message = data.outputs[0].outputs[0].outputs.message.message;
+ return message;
+ } catch (error) {
+ return `Error: ${error.message}`;
+ }
+ }
+
+ // the terminal chat
+ async function startChat() {
+ console.log("\nAsk the agent anything, such as 'What is 15 * 7?' or 'What is the capital of France?'");
+ console.log("Type 'quit' to exit or 'compare' to see the previous answer");
+
+ const askQuestion = () => {
+ rl.question('\nYour question: ', async (userQuestion) => {
+ if (userQuestion.toLowerCase() === 'quit') {
+ rl.close();
+ return;
+ }
+
+ if (userQuestion.toLowerCase() === 'compare') {
+ if (previousAnswer) {
+ console.log(`\nPrevious answer was: ${previousAnswer}`);
+ } else {
+ console.log("\nNo previous answer to compare with!");
+ }
+ askQuestion();
+ return;
+ }
+
+ const result = await askAgent(userQuestion);
+ console.log(`\nAgent's answer: ${result}`);
+ previousAnswer = result;
+ askQuestion();
+ });
+ };
+
+ askQuestion();
+ }
+
+ startChat();
+ ```
+
+
+
+
+2. To view the agent's previous answer, type `compare`. To close the terminal chat, type `exit`.
+
+### Use tweaks to apply temporary overrides to a flow run
+
+You can include tweaks with your requests to temporarily modify flow parameters.
+Tweaks are added to the API request, and temporarily change component parameters within your flow.
+Tweaks override the flow's components' settings for a single run only.
+They don't modify the underlying flow configuration or persist between runs.
+
+Tweaks are added to the `/run` endpoint's `payload`.
+To assist with formatting, you can define tweaks in Langflow's **Input Schema** pane before copying the code snippet.
+
+1. To open the **Input Schema** pane, from the **API access** pane, click **Input Schema**.
+2. In the **Input Schema** pane, select the parameter you want to modify in your next request.
+Enabling parameters in the **Input Schema** pane doesn't permanently change the listed parameters. It only adds them to the sample code snippets.
+3. For example, to change the agent's LLM model from OpenAI to Anthropic and include your Anthropic API key with the request, select the **Agent** component in the **Input Schema** pane and enable the **Language Model** field.
+
+Langflow updates the `tweaks` object in the code snippets based on your input parameters, and includes default values to guide you.
+Use the updated code snippets in your script to run your flow with your overrides.
+
+```json
+payload = {
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": "hello world!",
+ "tweaks": {
+ "Agent-ZOknz": {
+ "agent_llm": "Anthropic",
+ "api_key": "ANTHROPIC_API_KEY",
+ "model_name": "claude-opus-4-5-20251101"
+ }
+ }
+}
+```
+
+## Next steps
+
+* [Trigger flows with the Langflow API](/concepts-publish)
+* [Use Langflow as a Model Context Protocol (MCP) server](/mcp-server)
+* [Containerize a Langflow application](/develop-application)
+* [File management](/concepts-file-management)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Support/contributing-github-issues.mdx b/docs/versioned_docs/version-1.9.0/Support/contributing-github-issues.mdx
new file mode 100644
index 0000000000..91ad17025e
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Support/contributing-github-issues.mdx
@@ -0,0 +1,41 @@
+---
+title: Get help and request enhancements
+slug: /contributing-github-issues
+---
+
+The Langflow GitHub repository is an integral part of the [Langflow community](/contributing-community).
+
+In addition to general assistance with Langflow, the repository is the best place to report bugs and request enhancements to ensure that they are tracked by Langflow maintainers.
+
+## GitHub issues
+
+The [Issues page in the Langflow repository](https://github.com/langflow-ai/langflow/issues) is actively updated with bugs and feature requests.
+
+:::tip
+The best way to promote a request or bug is to comment on an existing issue.
+Highly active issues are more likely to receive attention from contributors.
+
+Before you report a bug or submit a feature request, search for existing similar issues.
+Use the [Langflow repository's labels](https://github.com/langflow-ai/langflow/labels) to help filter your search.
+:::
+
+## GitHub discussions
+
+If you need help with your code or Langflow in general, you can visit the [Langflow GitHub Discussions page](https://github.com/langflow-ai/langflow/discussions) or reach out through other [Langflow community](/contributing-community) channels.
+
+The Langflow team doesn't provide individual support over email, and the team believes that public discussions help more users by virtue of their discoverability.
+
+## Community guidelines and tips
+
+Because the Issues and Discussion pages are public, the Langflow team asks that you follow these guidelines when submitting questions and issues:
+
+* **Provide as many details as possible**: Simply stating that a feature doesn't work isn't helpful. The Langflow team needs details in order to recreate and find the issue.
+* **Explain what exactly went wrong**: Including error messages and descriptions of _how_ your code failed, not just the fact that it failed.
+* **Retrace your steps**: Explain what happened before the error, what you expected to happen instead of the error, and any recent changes you made, such as upgrading Langflow or a dependency.
+* **Describe your environment**: Include your operating system, Langflow version, Python version, and any other environment-related details that could have contributed to the issue.
+* **Include snippets of the code that failed**: Be sure to omit any sensitive values, and only provide parts relevant to the failure, rather than the entire script. Providing code snippets makes it much easier to reproduce errors, troubleshoot, and provide specific advice.
+ * If your submission includes long sections of code, logs, or tracebacks, wrap them in [details tags](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) (` PASTE CODE HERE `) to collapse the content and make it easier to read your submission.
+* **Omit sensitive information**: Other than the information available on your public GitHub profile, don't include sensitive or personally identifying data, such as security keys, full names, personal identification numbers, addresses, and phone numbers.
+* **Be kind**: Although bugs can be frustrating with any software, remember that your messages are read by real people who want to help. While you don't have to be saccharine, there's no need to be rude to get support.
+ * Your issues and discussions are attached to your GitHub account, and they can be read by anyone on the internet, including current and potential employers and colleagues.
+ * The Langlow repository is a public GitHub repository and, therefore, subject to the [GitHub Code of Conduct](https://docs.github.com/en/site-policy/github-terms/github-community-code-of-conduct).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Support/luna-for-langflow.mdx b/docs/versioned_docs/version-1.9.0/Support/luna-for-langflow.mdx
new file mode 100644
index 0000000000..0b7274e2fc
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Support/luna-for-langflow.mdx
@@ -0,0 +1,14 @@
+---
+title: IBM Elite Support for Langflow
+slug: /luna-for-langflow
+---
+
+IBM Elite Support offers enterprise support for Langflow to help you develop and deploy Langflow applications with confidence.
+
+IBM Elite Support for Langflow is a subscription to the Langflow expertise at IBM.
+It's meant for Langflow users who want all the benefits of running their own open-source deployments, as well as the peace of mind that comes with having direct access to the team that has authored the majority of the Langflow code.
+
+IBM Elite Support subscribers can get help with general-purpose and technical questions for their open-source Langflow deployments.
+If an issue is encountered, IBM is there to help.
+
+To subscribe or learn more, see [IBM Elite Support for Langflow](https://ibm.com/docs/esfl?topic=elite-support-langflow-specification).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Support/release-notes.mdx b/docs/versioned_docs/version-1.9.0/Support/release-notes.mdx
new file mode 100644
index 0000000000..5226531d27
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Support/release-notes.mdx
@@ -0,0 +1,579 @@
+---
+title: Langflow release notes
+slug: /release-notes
+---
+
+import Icon from "@site/src/components/icon";
+import McpIcon from '@site/static/logos/mcp-icon.svg';
+
+This page summarizes significant changes to Langflow in each release.
+For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases/latest).
+
+Due to strict SemVer requirements, Langflow Desktop can have different patch versions than the core Langflow OSS Python package, but the major and minor versions are aligned.
+
+## Prepare to upgrade
+
+:::warning
+Whenever possible, the Langflow team recommends installing new Langflow versions in a new virtual environment or VM before upgrading your primary installation.
+This allows you to [import flows](/concepts-flows-import#import-a-flow) from your existing installation and test them in the new version without disrupting your existing installation.
+In the event of breaking changes or bugs, your existing installation is preserved in a stable state.
+If you are upgrading Windows Desktop to 1.6.0, don't auto-upgrade with the in-app **Update** button. Instead, follow the instructions in [Known issue: Don't auto-upgrade Windows Desktop](#windows-desktop-update-issue).
+:::
+
+To avoid the impact of potential breaking changes and test new versions, the Langflow team recommends the following upgrade process:
+
+1. Recommended: [Export your projects](/api-projects#export-a-project) to create backups of your flows:
+
+ ```bash
+ curl -X GET \
+ "$LANGFLOW_SERVER_URL/api/v1/projects/download/$PROJECT_ID" \
+ -H "accept: application/json" \
+ -H "x-api-key: $LANGFLOW_API_KEY"
+ ```
+
+ To export flows from the visual editor, see [Import and export flows](/concepts-flows-import).
+
+2. Install the new version:
+
+ * **Langflow OSS Python package**: Install the new version in a new virtual environment. For instructions, see [Install and run the Langflow OSS Python package](/get-started-installation#install-and-run-the-langflow-oss-python-package).
+ * **Langflow Docker image**: Run the new image in a separate container.
+ * **Langflow Desktop**: To upgrade in place, open Langflow Desktop, and then click **Upgrade Available** in the Langflow header. If you want to isolate the new version, you must install Langflow Desktop on a separate physical or virtual machine, and then [import your flows](/concepts-flows-import) to the new installation.
+
+3. [Import your flows](/concepts-flows-import) to test them in the new version, [upgrading components](/concepts-components#component-versions) as needed.
+
+ When upgrading components, you can use the **Create backup flow before updating** option if you didn't previously export your flows.
+
+4. If you installed the new version in isolation, upgrade your primary installation after testing the new version.
+
+ If you made changes to your flows in the isolated installation, you might want to export and import those flows back to your upgraded primary installation so you don't have to repeat the component upgrade process.
+
+## 1.9.x
+
+Highlights of this release include the following changes.
+For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases).
+
+### New features and enhancements
+
+- Langflow MCP client for Bob (IBM) and Claude Code
+
+ The **Settings** page now includes **Langflow MCP Client**, with instructions to connect **Bob (IBM)** and **Claude Code** to your Langflow instance.
+ For more information, see [Langflow MCP client](../Agents/langflow-mcp-client.mdx).
+
+- MCP Tools: register servers first, and then add from the MCP sidebar
+
+ **MCP Tools** is no longer added from the **Agents** component list. Register each MCP server under **Settings** > **MCP Servers*, or from the flow editor **MCP** sidebar.
+ Each saved server appears as a unique **MCP Tools** component that can be dragged to the canvas.
+ For more information, see [Use Langflow as an MCP client](/mcp-client#use-the-mcp-tools-component).
+
+- Flow DevOps Toolkit SDK
+
+ Use the Flow DevOps Toolkit SDK to manage local and remote flows with Git-style terminal commands.
+ For more information, see the [Langflow Flow DevOps Toolkit SDK](../API-Reference/flow-devops-sdk.mdx).
+
+- Renamed `Data` and `DataFrame` types
+
+ The `Data` object is now named `JSON`, and the `DataFrame` object is now named `Table`.
+ Flows using `Data` or `DataFrame` are backwards compatible.
+ For more information, see [Langflow data types](/data-types).
+
+- Renamed **Data Operations** and **DataFrame Operations** components
+
+ The **Data Operations** component is now the [**JSON Operations** component](/data-operations), and the **DataFrame Operations** component is now the [**Table Operations** component](/dataframe-operations).
+
+- Flow version history in the visual editor
+
+ You can now save point-in-time versions of a flow from **Version History**, preview saved versions in read-only mode, and restore an earlier version to your working draft.
+ For more information, see [Build flows](../Flows/concepts-flows.mdx#save-and-restore-flow-versions).
+
+- Enabled tool calling for Gemini 3 preview models
+
+ Tool calling is enabled for the following Google Gemini 3 preview models:
+ `gemini-3.1-pro-preview`, `gemini-3-pro-preview`, `gemini-3-flash-preview`, and `gemini-3-pro-image-preview`.
+
+- Build components with **Langflow Assistant**
+
+ **Langflow Assistant** is a graph-aware co-pilot that can create components from natural language prompts, directly within the Langflow workspace.
+ For more information, see [Langflow Assistant](../Flows/langflow-assistant.mdx).
+
+- Vulnerability reporting now uses HackerOne
+
+ Security reports should now be filed through [IBM's HackerOne program](https://hackerone.com/ibm).
+ Please do not report security vulnerabilities through public GitHub issues or GitHub security advisories.
+ For the full policy and what to include in a report, see the [Langflow Security Policy](https://github.com/langflow-ai/langflow/blob/main/SECURITY.md).
+
+- Disable custom components
+
+ Set `LANGFLOW_ALLOW_CUSTOM_COMPONENTS=false` to disable custom components and in-editor editing of component code.
+ For more information, see [Block custom components](../Deployment/deployment-block-custom-components.mdx).
+
+- **Policies** component (beta)
+
+ The **Policies** component uses [ToolGuard](https://github.com/AgentToolkit/toolguard) to generate guard code from natural-language business policies and apply it to agent tools.
+ For more information, see [Policies (Beta)](../Components/policies.mdx).
+
+## 1.8.x
+
+Highlights of this release include the following changes.
+For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases).
+
+### Breaking changes
+
+- `langflow-base` dependency structure refactored
+
+ The `langflow-base` package now uses granular optional dependency groups. As a result, many dependencies that were previously included in the `langflow-base` installation were moved to optional extras.
+
+ If you installed Langflow with `uv pip install langflow`, this isn't a breaking change. Installing `langflow` automatically installs `langflow-base[complete]`, which includes all optional dependencies and maintains the same functionality as before.
+
+ However, if you installed Langflow with `uv pip install langflow-base` without specifying extra dependencies, this _is_ a breaking change.
+ Some dependencies that were previously included by default are now available only through optional extras.
+ Therefore, installing `langflow-base` directly only installs the [core base dependencies](https://github.com/langflow-ai/langflow/blob/main/src/backend/base/pyproject.toml).
+
+ If you installed `langflow-base`, there are two ways to resolve dependency errors that result from this breaking change:
+
+ * If you need the full set of dependencies, you must install `langflow-base` with the `complete` extra:
+
+ ```bash
+ uv pip install "langflow-base[complete]"
+ ```
+
+ * If you need specific dependencies, you must install `langflow-base` with those optional dependency groups. For example:
+
+ ```bash
+ uv pip install "langflow-base[postgresql,openai,chroma]"
+ ```
+
+ For more information about available optional dependency groups, see [Install optional dependency groups for `langflow-base`](/install-custom-dependencies#install-optional-dependency-groups-for-langflow-base).
+
+- Docker image does not include `uv` or `uvx`
+
+ :::tip
+ Starting with Langflow 1.8.1, the official Docker images include `uv` and `uvx` again.
+ If you're using Langflow 1.8.0, follow the steps in this release note to add `uv` and `uvx` in a derived image.
+ :::
+
+ In Langflow 1.8.0, the Langflow Docker image does not include `uv` or `uvx` in the runtime image.
+ This means that MCP server configurations, including the default Langflow MCP server, that rely on commands like `uvx mcp-proxy` will fail inside the container with a `command not found` error.
+
+ If you use MCP from within a Langflow 1.8.0 Docker image, you must install `uv` in an image derived from the official `langflowai/langflow` image.
+
+ To install `uv` and `uvx` in a derived image based on the official `langflowai/langflow` image, do the following:
+
+ 1. Create a `Dockerfile` in your project.
+ 2. Use the official Langflow image as the base and install `uv` into the existing virtual environment:
+ ```dockerfile
+ FROM langflowai/langflow:1.8.0
+
+ USER root
+ RUN /app/.venv/bin/pip install uv
+ USER user
+ ```
+ 3. Build the image.
+
+ ```bash
+ docker build -t YOUR_IMAGE_NAME .
+ ```
+
+ Replace `YOUR_IMAGE_NAME` with your Docker image name.
+
+ 4. Run the new image:
+
+ ```bash
+ docker run -p 7860:7860 YOUR_IMAGE_NAME
+ ```
+
+ For more information, see [Upgrade the Langflow Docker image](/deployment-docker#upgrade-the-langflow-docker-image).
+
+- Disabled tool calling for Gemini 3 preview models
+
+ Tool calling is temporarily disabled for the following Google Gemini 3 preview models:
+ `gemini-3.1-pro-preview`, `gemini-3-pro-preview`, `gemini-3-flash-preview`, and `gemini-3-pro-image-preview`.
+
+- Docling installation behavior differs between Desktop and OSS
+
+ In Langflow 1.8.0, Docling behavior depends on how Langflow is installed.
+
+ For Langflow Desktop installations, you need to set `LANGFLOW_DOCLING=True` in your `.env` file to enable Docling dependency installation. For more information, see [Set environment variables for Langflow Desktop](/environment-variables#set-environment-variables-for-langflow-desktop).
+
+ For Langflow OSS installations with `uv pip install langflow`, Docling is included with the Langflow package.
+
+ For Langflow OSS installations with `uv pip install langflow-base`, install Docling explicitly as an extra with `uv pip install "langflow-base[docling]"`.
+
+### New features and enhancements
+
+- Global model provider configuration
+
+ Model providers for language models, embedding models, and agents are now configured globally in the **Model providers** pane, instead of within individual components.
+ For more information, see the [Language Model component](/components-models).
+
+- Component inspection panel
+
+ The component inspection panel replaces the component header menu for managing component parameters and settings.
+ For more information, see [Component inspection panel](/concepts-components#component-inspection-panel).
+
+- Developer API: `/workflow` synchronous endpoints (Beta)
+
+ The Developer API is part of a larger effort to improve Langflow's APIs with enhanced capabilities and better developer experience.
+ The Developer API now includes `/v2/workflow` endpoints for executing flows with enhanced error handling, timeout protection, and structured responses.
+ The synchronous execution endpoint is available at `POST /api/v2/workflows`.
+ For more information, see [Workflow API (Beta)](/workflow-api).
+
+- Traces and trace view
+
+ Langflow now records execution traces for flows and components.
+ View your traces in the **Trace Details** pane, and inspect span trees, latencies, and errors.
+ For more information, see [Traces](/traces).
+
+- Knowledge bases
+
+ Knowledge bases let you organize documents and other reference data into reusable vector databases that can be attached to multiple flows.
+ This makes it easier to centralize domain knowledge and reuse the same data across agents and retrieval workflows.
+ For more information, see [Manage vector data](/knowledge).
+
+- Mustache templating support for Prompt Template component
+
+ The **Prompt Template** component now supports Mustache templating syntax.
+ Mustache templating eliminates the need to escape curly braces when including JSON structures in your prompts. For more information, see [Prompt Template](/components-prompts#use-mustache-templating-in-prompt-templates).
+
+- More configuration options for JWT-based session authentication
+
+ Langflow 1.8 offers additional configuration options for JWT algorithms, including support for RS256/RS512 algorithms, configurable keys, and token lifetimes. For more information, see [JWT authentication](/jwt-authentication).
+
+- Global variables in MCP server headers
+
+ You can now use [global variables](/configuration-global-variables) in MCP server header values to securely store and reference sensitive values. For more information, see [Use global variables in MCP server headers](/mcp-client#use-global-variables-in-mcp-server-headers).
+
+- Pass environment variables to flows in API headers and CLI
+
+ The ability to pass environment variables in HTTP headers (previously available for the [`/responses` endpoint](/api-openai-responses#global-var)) is now also available for the [`/run` endpoint](/api-flows-run#pass-global-variables-in-headers).
+
+- Guardrails component
+
+ The **Guardrails** component validates input text against security and safety guardrails by using a connected language model to check for content such as PII, tokens/passwords, or offensive content. For more information, see [Guardrails](/guardrails).
+
+- Token usage tracking for OpenAI Responses API
+
+ The OpenAI Responses API endpoint now tracks and returns token usage statistics when your flow uses language model APIs that provide token usage information.
+ For more information, see [Token usage tracking](/api-openai-responses#token-usage-tracking).
+
+- Docker AMD vs ARM image sizes
+
+ Langflow 1.8.0 addresses the AMD vs ARM Docker image size gap.
+ We reconfigured our Python dependencies to use CPU-only PyTorch wheels through `uv` sources, which removes large CUDA-related dependencies from the AMD64 images.
+ With this change, both AMD64 and ARM64 images are now smaller than 2 GB.
+
+- New [**Agentics** bundle](/bundles-agentics)
+
+ Uses LLMs to transform tabular data, including mapping, reducing, and generating DataFrame rows based on a defined schema.
+
+- New [**LiteLLM** bundle](/bundles-lite-llm)
+
+ Connects to models through a LiteLLM proxy so you can route requests to multiple LLM providers and switch providers without changing flow credentials.
+
+- New [**Openlayer** observability integration](/integrations-openlayer)
+
+ Configures Langflow to send tracing data to Openlayer for analysis, monitoring, and evaluation of your flow executions.
+
+## 1.7.x
+
+:::warning Version yanked
+Version 1.7.0 was yanked due to a critical bug. Version 1.7.0 has been replaced with version 1.7.1, which includes a fix for this issue.
+:::
+
+Highlights of this release include the following changes.
+For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases).
+
+### Known issue: Data not accessible when upgrading to version 1.7.0 {#v170-data-path}
+
+A critical issue was identified during the upgrade process to version 1.7.0.
+
+Flows, projects, and global variables are **not** deleted or corrupted. The data still exists, but version 1.7.0 cannot find it due to a path change in how flows are located.
+All Langflow versions upgrading to 1.7.0 are affected.
+
+Don't upgrade to Langflow version 1.7.0.
+Instead, upgrade directly to version 1.7.1, which includes a fix for this bug.
+
+If you installed version 1.7.0 before the fix was released, follow these steps to recover your flows:
+
+1. Revert Langflow to version 1.6.9:
+
+ ```bash
+ uv pip install langflow==1.6.9
+ ```
+
+2. Verify that your flows, projects, and global variables are accessible.
+
+3. Upgrade directly to version 1.7.1, which includes the fix for this issue:
+
+ ```bash
+ uv pip install langflow==1.7.1
+ ```
+
+### New features and enhancements
+
+- Support for streamable HTTP transport for MCP clients and servers
+
+ Langflow now supports [streamable HTTP transport](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http) for both MCP clients and servers.
+ When using Langflow as an [MCP client](/mcp-client), you can connect to MCP servers with streamable HTTP transport.
+ When using Langflow as an [MCP server](/mcp-server), clients can connect using streamable HTTP transport.
+ SSE transport is still supported as a fallback for backwards compatibility.
+
+- Webhook authentication
+
+ Added the `LANGFLOW_WEBHOOK_AUTH_ENABLE` environment variable for authenticating requests to the [`/webhook` endpoint](/api-flows-run#webhook-run-flow). When `LANGFLOW_WEBHOOK_AUTH_ENABLE=TRUE`, webhook endpoints require API key authentication and validate that the authenticated user owns the flow being executed. When `FALSE`, no Langflow API key is required and all requests to the webhook endpoint are treated as being sent by the flow owner. For more information, see [Trigger flows with webhooks](/webhook).
+
+- Configurable API key validation
+
+ Added the `LANGFLOW_API_KEY_SOURCE` environment variable to control how Langflow validates API keys. When set to `db`, Langflow validates API keys against keys stored in the database. When set to `env`, Langflow validates API keys against the `LANGFLOW_API_KEY` environment variable. For more information, see [API keys and authentication](/api-keys-and-authentication#langflow-api-key-source).
+
+- SSRF protection
+
+ Added SSRF (Server-Side Request Forgery) protection to the [**API Request** component](/api-request). HTTP redirects are disabled by default to prevent SSRF bypass attacks. To enable SSRF protection, set `LANGFLOW_SSRF_PROTECTION_ENABLED=TRUE`. Configure allowed hosts with `LANGFLOW_SSRF_ALLOWED_HOSTS`. Flows that relied on automatic redirects will need to enable it manually.
+
+- Email registration in Langflow Desktop
+
+ Langflow Desktop now includes an email registration screen at startup. The registered email address helps track user registrations and understand the user base. For more information, see [Telemetry](/contributing-telemetry#email).
+
+- Changes to read/write file components
+
+ The **Save File** component was renamed to **Write File**, and it can now save to S3 and Google Drive.
+ The **File** component was renamed to **Read File**, and it can now read from AWS S3 and Google Drive.
+ Both components support **Tool Mode**.
+
+- New integrations, bundles, and components:
+
+ New filter operator for **DataFrame Operations** component
+
+ The [**DataFrame Operations** component](/dataframe-operations) now includes a `not contains` filter operator.
+ Use it to clean data by extracting only records that _don't_ contain specific values.
+ For example, you can filter out invalid email addresses that don't contain `@`.
+
+ New JSON operations for **Data Operations** component
+
+ The [**Data Operations** component](/data-operations) now includes two operations for advanced JSON data manipulation.
+ The **Path Selection** operation extracts values from nested JSON structures, and the **JQ Expression** operation uses the [`jq`](https://jqlang.org/) query language to perform advanced JSON filtering, projections, and transformations.
+
+ New [**Smart Router** component](/smart-router)
+
+ New [**Mock Data** component](/mock-data)
+
+ New [**Dynamic Create Data** component](/dynamic-create-data)
+
+ New [**ALTK** bundle](/bundles-altk)
+
+ New [**CometAPI** bundle](/bundles-cometapi)
+
+ New [**CUGA** bundle](/bundles-cuga)
+
+ The **LLM Router** component is now called the [**LLM Selector** component](/llm-selector).
+
+ The [**Web Search** component](/web-search) now consolidates Web Search, News Search, and RSS Reader into a single component with tabs for different search modes. You can search the web using DuckDuckGo, search Google News, or read RSS feeds—all from one component. The separate **News Search** and **RSS Reader** components have been removed.
+
+## 1.6.0
+
+Highlights of this release include the following changes.
+For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases).
+
+### Known issue, potential security vulnerability: .env file not loaded in versions 1.6.0 through 1.6.3 {#env-file-bug}
+
+Langflow versions 1.6.0 through 1.6.3 have a critical bug where environment variables from `.env` files aren't read.
+This affects all deployments using environment variables for configuration, including security settings.
+
+:::warning Potential security vulnerability
+If your `.env` file includes `AUTO_LOGIN=false`, upgrading to the impacted versions causes Langflow to fall back to default settings, potentially giving all users superuser access immediately upon upgrade.
+Additionally, database credentials, API keys, and other sensitive configurations can't be loaded from `.env` files.
+
+_Don't_ upgrade to any Langflow version from 1.6.0 through 1.6.3 if you use `.env` files for configuration.
+Instead, upgrade to 1.6.4, which includes a fix for this bug.
+:::
+
+### Known issue: Don't auto-upgrade Windows Desktop {#windows-desktop-update-issue}
+
+:::warning
+Windows users of Langflow Desktop should **not** use the in-app update feature to upgrade to Langflow version 1.6.0.
+Only Windows Desktop users upgrading to Langflow version 1.6.0 are affected.
+:::
+
+The **Update** button in Langflow Desktop will not work for Windows users, and may result in data loss.
+
+Instead, download a fresh installation from the [Langflow website](https://langflow.org) when a new version is available.
+
+Follow the instructions below to minimize the risk of losing flows.
+
+These instructions assume the default path for the Langflow database of `C:\Users\YOUR_USERNAME\AppData\Roaming\com.langflow\data\database.db` or `C:\Users\%YOUR_USERNAME%\AppData\Roaming\com.langflow\data\database.db`. Replace `YOUR_USERNAME` with your Windows username.
+
+1. Ensure you have Administrator privileges.
+2. Ensure you have enough disk space for a second installation of Langflow.
+3. Close Langflow, and ensure no Langflow process is running in Task Manager.
+4. The Langflow 1.6.0 installer automatically performs a database backup in a later step, but an additional manual backup provides additional redundancy and is recommended.
+To manually back up your Langflow database file, do the following:
+ 1. In Windows Explorer, navigate to `C:\Users\YOUR_USERNAME\AppData\Roaming\com.langflow\data\`. Replace `YOUR_USERNAME` with your Windows username.
+ 2. Copy the `database.db` at this location, and paste it to a safe location.
+5. Download the Windows installer from the [Langflow website](https://langflow.org).
+6. Run the Windows installer as an Administrator. To run the installer as an Administrator, right-click the executable and select **Run as administrator**.
+7. Follow the Windows installer's guided steps.
+The Langflow 1.6.0 installer automatically performs a database backup.
+These steps install Langflow from scratch, and result in two Langflow installations: the previously installed version, and version 1.6.0.
+This is expected behavior.
+8. Start version 1.6.0 of Langflow, and confirm your flows behave as expected.
+9. **If flows are missing**, restore your flows from your manual backup by doing the following:
+ 1. Close Langflow.
+ 2. Navigate to your backup location, and copy the `database.db` file.
+ 3. Replace the database file in the new installation's `data` directory.
+ 4. Start Langflow, and confirm your flows behave as expected.
+ 5. After confirmation, uninstall the previous version of Langflow, and keep version 1.6.0.
+
+### Breaking changes
+
+- Authentication enforced for Langflow API requests by default
+
+ In Langflow version 1.6, `LANGFLOW_AUTO_LOGIN=True` and `LANGFLOW_SKIP_AUTH_AUTO_LOGIN=False` by default.
+ This enforces authentication for Langflow API requests while still automatically authenticating all users as superusers in the visual editor.
+
+ **This is a breaking change from 1.5 where both of these environment variables were `true` by default, bypassing all authentication.**
+
+ For temporary backwards compatibility, you can revert to the earlier unauthenticated behavior by setting both variables to `true`.
+ However, a future release will set `LANGFLOW_AUTO_LOGIN=False` and remove `LANGFLOW_SKIP_AUTH_AUTO_LOGIN`.
+ At that point, Langflow will strictly enforce API key authentication for API requests, and you can manually disable authentication for some features, like the visual editor, by setting `LANGFLOW_AUTO_LOGIN=True`.
+
+ For more information, see the documentation for [`LANGFLOW_AUTO_LOGIN`](/api-keys-and-authentication#langflow-auto-login).
+
+### New features and enhancements
+
+- OpenAI Responses API compatibility
+
+ Langflow now includes an endpoint that is compatible with the [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses) at `POST /api/v1/responses`.
+ This allows you to use existing OpenAI client libraries with minimal code changes by replacing the `model` name with your `flow_id`.
+ The endpoint supports streaming responses, conversation continuity, tool call results, and global variable passing through headers.
+ For more information, see [OpenAI Responses API](/api-openai-responses).
+
+- Advanced document parsing with built-in Docling support
+
+ The **Read File** component supports advanced parsing with the Docling library.
+
+ To make it easier to use the [**Docling** components](/bundles-docling) and the **Read File** component's new advanced parsing feature, the Docling dependency is now included with Langflow for all operating systems except macOS Intel (x86_64).
+
+ For more information, see [Advanced parsing](/read-file#advanced-parsing).
+
+- Reorganized component menus and visual editor controls
+
+ - The [workspace](/concepts-overview#workspace) sidebar is divided into separate sections for **Search**, **Core components**, [**MCP servers**](/mcp-server), [**Bundles**](/components-bundle-components), and **Add Note**.
+ - Lock/unlock controls moved to flow details in [**Projects**](/concepts-flows#projects).
+ - Zoom and help controls moved to the lower-right corner of the workspace.
+ - Vector store components moved to provider-specific [**Bundles**](/components-bundle-components)
+ - **Serper Google Search API** component moved to the **Serper** bundle
+
+- Increased the default maximum file upload size from `100 MB` to `1024 MB`.
+
+- New integrations and bundles:
+
+ - [Traceloop](/integrations-instana-traceloop)
+ - [Cohere Rerank](/bundles-cohere)
+ - [S3 Bucket Uploader](/bundles-amazon)
+ - [Composio single-service components](/bundles-composio)
+
+### Deprecations
+
+- The **Local DB** component is now in legacy status.
+Replace this component with the [**Chroma DB** component](/bundles-chroma).
+
+## 1.5.0
+
+Highlights of this release include the following changes.
+For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases).
+
+### New features and enhancements
+
+- Langflow API requests can require authentication
+
+ To enhance security and ensure proper authentication for automatic login features, Langflow API endpoints now require authentication with a Langflow API key, even when `LANGFLOW_AUTO_LOGIN=True`.
+ This change will be enforced in a future release.
+ For temporary backwards compatibility, this release adds the `LANGFLOW_SKIP_AUTH_AUTO_LOGIN` environment variable.
+ The default value is `true`, which disables API authentication enforcement.
+ To enforce API authentication, set `LANGFLOW_SKIP_AUTH_AUTO_LOGIN=False`.
+ For more information, see the documentation for [`LANGFLOW_AUTO_LOGIN`](/api-keys-and-authentication#langflow-auto-login).
+
+- Centralized **Language Model** and **Embedding Model** components
+
+ The [**Language Model** component](/components-models) and [**Embedding Model** component](/components-embedding-models) are now core components for your LLM and embeddings flows. They support multiple models and model providers, and allow you to experiment with different models without swapping out single-provider components.
+ Find them in the visual editor in the **Models** category.
+
+ The single-provider components moved to the [**Bundles**](/components-bundle-components) section.
+ You can use them to replace the **Language Model** and **Embedding Model** core components, or connect them to the **Agent** component with the **Connect other models** provider option.
+
+- MCP server one-click installation
+
+ On your Langflow project's **MCP server** page, click **Auto install** to install your Langflow MCP server to MCP clients with just one click.
+ The option to install with a JSON configuration file is available for macOS, Windows, and WSL.
+ For more information, see [Use Langflow as an MCP server](/mcp-server).
+
+- MCP server management
+
+ You can now add, remove, and edit your MCP servers in the **MCP Tools** components and through your Langflow **Settings** page.
+ For more information, see [Use Langflow as an MCP client](/mcp-client).
+
+- Input schema replaces temporary overrides
+
+ The **Input schema** pane replaces the need to manage tweak values in the **API access** pane. When you enable a parameter in the **Input schema** pane, the parameter is automatically added to your flow's code snippets, providing ready-to-use templates for making requests in your preferred programming language.
+
+- Tools components are redistributed
+
+ All components in the **Tools** category were moved to other component categories, such as **Helpers** and [**Bundles**](/components-bundle-components), or marked as legacy.
+
+ The [**MCP Tools** component](/mcp-client) is now under the **Agents** category.
+
+ Tools that performed the same function were combined into single components that support multiple providers, such as the [**Web Search** component](/web-search) and the **News Search** component.
+
+
+- Stability improvements
+
+ General stability improvements and bug fixes for enhanced reliability.
+ See an issue? [Raise it on GitHub](https://github.com/langflow-ai/langflow/issues).
+
+- New integrations and bundles
+
+ - [**Cleanlab** bundle](/bundles-cleanlab)
+
+## 1.4.2
+
+Highlights of this release include the following changes.
+For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases).
+
+### New features and enhancements
+
+- Enhanced file and flow management system with improved bulk capabilities.
+- Added the **BigQuery** component
+- Added the **Twelve Labs** bundle
+- Added the **NVIDIA System-Assist** component
+
+### Deprecations
+
+- Deprecated the **Combine Text** component.
+
+## 1.4.1
+
+Highlights of this release include the following changes.
+For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases).
+
+### New features and enhancements
+
+- Added an enhanced **Breaking Changes** feature to help update components without breaking flows after updating Langflow.
+
+## 1.4.0
+
+Highlights of this release include the following changes.
+For all changes, see the [Changelog](https://github.com/langflow-ai/langflow/releases).
+
+### New features and enhancements
+
+- Introduced MCP server functionality to serve Langflow tools to MCP-compatible clients.
+- Renamed **Folders** to **Projects** in the visual editor.
+- The `/folders` endpoints now redirect to `/projects`.
+
+### Deprecations
+
+- Deprecated the **Gmail**, **Google Drive**, and **Google Search** components.
+For alternatives, see the [**Google** bundle](/bundles-google).
+
+## Earlier releases
+
+See the [Changelog](https://github.com/langflow-ai/langflow/releases).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Support/troubleshooting.mdx b/docs/versioned_docs/version-1.9.0/Support/troubleshooting.mdx
new file mode 100644
index 0000000000..620ddda9db
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Support/troubleshooting.mdx
@@ -0,0 +1,435 @@
+---
+title: Troubleshoot Langflow
+slug: /troubleshoot
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import Icon from "@site/src/components/icon";
+
+This page provides troubleshooting advice for issues you might encounter when using Langflow or contributing to Langflow.
+
+## Missing components
+
+As Langflow development continues, components are often recategorized or deprecated for better alignment or to prepare for new components.
+
+If a component appears to be missing from the **Core components** and menus, try the following:
+
+* **Search** for the component.
+* Check other component categories and [**Bundles**](/components-bundle-components).
+* Check [legacy components](/concepts-components#legacy-components), which are hidden by default.
+* Check the [Changelog](https://github.com/langflow-ai/langflow/releases/latest) for component changes in recent releases.
+* Make sure the component isn't already present in your flow if it is a single-use component.
+
+If you still cannot locate the component, see [Langflow GitHub Issues and Discussions](/contributing-github-issues).
+
+## No input in the Playground
+
+If there is no message input field in the **Playground**, make sure your flow has a [**Chat Input** component](/chat-input-and-output) that is connected, directly or indirectly, to the **Input** port of a **Language Model** or **Agent** component.
+
+Because the **Playground** is designed for flows that use an LLM in a query-and-response format, such as chatbots and agents, a flow must have **Chat Input**, **Language Model**/**Agent**, and **Chat Output** components to be fully supported by the **Playground** chat interface.
+
+For more information, see [Test flows in the Playground](/concepts-playground).
+
+## Missing key, no key found, or invalid API key
+
+If you get an API key error when running a flow, try the following:
+
+* For all components that require credentials, make sure those components have a valid credential in the component's settings, such as the **API Key** field.
+* If you store your credentials in [Langflow global variables](/configuration-global-variables), make sure you selected the correct global variable and that the variable contains a valid credential.
+* Make sure the provided credentials are active, have the required permissions, and, if applicable, have sufficient funds in the account to execute the required action. For example, model providers require credits to use their LLMs.
+
+## Langflow installation issues
+
+The following issues can occur when installing Langflow.
+
+### Langflow installation freezes at pip dependency resolution
+
+Installing Langflow OSS with `pip install langflow` slowly fails with this error message:
+
+```text
+pip is looking at multiple versions of <> to determine which version is compatible with other requirements. This could take a while.
+```
+
+To work around this issue, install Langflow with `uv` instead of `pip`, as explained in [Install and run the Langflow OSS Python package](/get-started-installation#install-and-run-the-langflow-oss-python-package).
+
+### Linux installation fails to build required package
+
+When you try to install Langflow OSS on Linux, installation fails because of outdated or missing packages:
+
+```bash
+Resolved 455 packages in 18.92s
+ × Failed to build `webrtcvad==2.0.10`
+ ├─▶ The build backend returned an error
+ ╰─▶ Call to `setuptools.build_meta:__legacy__.build_wheel` failed (exit status: 1)
+```
+
+To resolve this error, install the required build dependencies, and then retry the Langflow installation:
+
+```bash
+sudo apt-get update
+sudo apt-get install build-essential python3-dev
+```
+
+If upgrading your packages doesn't fix the issue, install `gcc` separately, and then retry the Langflow installation:
+
+```bash
+sudo apt-get install gcc
+```
+
+### Installation failure from `webrtcvad` package
+
+If you experience an error from the `webrtcvad` package, run `uv pip install webrtcvad-wheels` in your virtual environment, and then retry the Langflow installation.
+
+### Protocol buffers (protoc) required for Intel-based Macs
+
+If you're installing Langflow on an Intel-based Mac, you may encounter installation errors if Protocol Buffers Compiler (`protoc`) is not installed.
+
+This requirement doesn't apply to Apple Silicon Macs on ARM64.
+
+To resolve this issue, install `protoc` using `brew install protobuf`.
+
+For more information, including alternative installation methods, see the [Protocol buffers installation documentation](https://protobuf.dev/installation/).
+
+### C++ build tools required for Langflow Desktop on Windows
+
+Microsoft Windows installations of Langflow Desktop require a C++ compiler that may not be present on your system. If you receive a `C++ Build Tools Required!` error, follow the on-screen prompt to install Microsoft C++ Build Tools, or [install Microsoft Visual Studio](https://visualstudio.microsoft.com/downloads/).
+
+## Langflow startup issues
+
+The following issues can occur when attempting to start Langflow.
+
+### No `langflow.__main__` module
+
+When you try to run Langflow with the command `langflow run`, you encounter the following error:
+
+```bash
+> No module named 'langflow.__main__'
+```
+
+To resolve this issue, try the following:
+
+1. Run `uv run langflow run` instead of `langflow run`.
+2. If that doesn't work, reinstall the latest Langflow version with `uv pip install langflow -U`.
+3. If that doesn't work, reinstall Langflow and its dependencies with `uv pip install langflow --pre -U --force-reinstall`.
+
+### Langflow runTraceback
+
+When you try to run Langflow using the command `langflow run`, you encounter the following error:
+
+```bash
+> langflow runTraceback (most recent call last): File ".../langflow", line 5, in from langflow.__main__ import mainModuleNotFoundError: No module named 'langflow.__main__'
+```
+
+There are two possible reasons for this error:
+
+* **Multiple Langflow installations**: You installed Langflow using `pip install langflow` but you already had a previous version of Langflow installed in your system. In this case, you might be running the wrong executable.
+
+ To solve this issue, run the correct executable by running `python -m langflow run` instead of `langflow run`.
+
+ If that doesn't work, try uninstalling and reinstalling Langflow with `uv pip install langflow --pre -U`.
+
+* **Version conflict during installation**: Some version conflicts might have occurred during the installation process. To resolve this issue, reinstall Langflow and its dependencies by running `python -m pip install langflow --pre -U --force-reinstall`.
+
+### Environment variables not available from terminal
+
+Environment variables set in your terminal aren't automatically available to GUI-based applications like Langflow Desktop when launched through the Finder or the Start Menu.
+To set environment variables for Langflow Desktop, see [Set environment variables for Langflow Desktop](/environment-variables#set-environment-variables-for-langflow-desktop).
+
+### Access Langflow Desktop startup logs
+
+If you encounter issues with Langflow Desktop, you might need to [access Langflow Desktop startup logs](/logging#desktop-logs) for debugging.
+
+### User not found or inactive when running multiple flows
+
+When running multiple local Langflow OSS instances on different ports, such as `localhost:7860` and `localhost:7861`, you might see authentication errors in the logs.
+For example:
+
+```text
+[07/22/25 10:57:07] INFO 2025-07-22 10:57:07 - INFO - utils - User not found or inactive.
+```
+
+To resolve this error, use separate browser instances or browser profiles to access each Langflow instance.
+
+### Package is not installed
+
+In Langflow OSS, you can follow the error message's instructions to install the missing dependency.
+
+To manage dependencies in Langflow Desktop, see [Install custom dependencies in Langflow Desktop](/install-custom-dependencies#langflow-desktop).
+
+### PostgreSQL asyncpg driver compatibility issue
+
+The following error can occur when initializing Langflow with a PostgreSQL database:
+
+```text
+sqlalchemy.exc.DBAPIError: (sqlalchemy.dialects.postgresql.asyncpg.Error) : invalid input for query argument $7: datetime.datetime(2025, 10, 31, 14, 8, 5... (can't subtract offset-naive and offset-aware datetimes)
+```
+
+This error occurs because `asyncpg` has stricter timezone handling than `psycopg2`, and is not fully compatible with Langflow's current database schema.
+
+To resolve this issue, remove `asyncpg` from your installation and use `psycopg2` instead.
+
+### PostgreSQL version mismatch: `UNIQUE NULLS DISTINCT` syntax error
+
+The following error can occur when initializing Langflow with a PostgreSQL database:
+
+```text
+(psycopg.errors.SyntaxError) syntax error at or near "NULLS"
+LINE 13: CONSTRAINT file_name_user_id_key UNIQUE NULLS DISTINCT (nam...
+```
+
+This occurs when you're using a PostgreSQL version prior to version 15.
+These versions don't support the `UNIQUE NULLS DISTINCT` syntax used by Langflow's database schema.
+Langflow requires PostgreSQL 15 or higher when configured to use PostgreSQL.
+
+To resolve this issue, upgrade your PostgreSQL instance to version 15 or higher, and then restart Langflow.
+
+### An API key must be passed as query or header
+
+The following error can occur when attempting to sign up on the Langflow sign in page: `An API key must be passed as query or header`.
+
+This means that `LANGFLOW_AUTO_LOGIN` is set to `false`, and only superusers can create and activate non-superuser accounts.
+Therefore, you are locked out of this Langflow instance.
+
+If you aren't an administrator, then a superuser must create and activate a non-superuser account before you can sign in.
+
+If you are the administrator, sign in with a superuser account, or restart Langflow with `LANGFLOW_AUTO_LOGIN=true`.
+
+For more information, see [Start Langflow Server with authentication enabled](/api-keys-and-authentication#start-a-langflow-server-with-authentication-enabled).
+
+## Langflow upgrade issues
+
+The following issues can occur when upgrading your Langflow version.
+
+For information about managing Langflow versions, see [Install Langflow](/get-started-installation).
+
+### Something went wrong running migrations
+
+The following error can occur during Langflow upgrades when the new version can't override `langflow-pre.db` in the Langflow cache folder:
+
+```bash
+> Something went wrong running migrations. Please, run 'langflow migration --fix'
+```
+
+To resolve this error, clear the cache by deleting the contents of your Langflow cache folder.
+The filepath depends on your operating system, installation type, and configuration options.
+For more information and default filepaths, see [Memory management options](/memory).
+
+:::danger
+Clearing the cache erases your settings.
+If you want to retain your settings files, create a backup of those files before clearing the cache folder.
+:::
+
+### Langflow Desktop says it is running the latest version, but it is actually behind
+
+If you are running Langflow Desktop version 1.4.2 or earlier, the UI might incorrectly report that you are on the latest version when a newer version is available.
+
+This happens because the automatic update feature in the UI was introduced in version 1.4.2.
+Earlier versions can't automatically detect or apply updates.
+
+To resolve this issue, uninstall Langflow Desktop, and then [download and install the latest version of Langflow Desktop](https://langflow.org/desktop).
+
+### 422 error when freezing components after upgrading Pydantic/FastAPI dependencies
+
+If you locally upgrade your Pydantic and FastAPI dependencies, you may encounter a 422 error when trying to freeze components.
+
+This error occurs due to changes in how request bodies are handled in newer versions of these dependencies.
+
+If you're experiencing this issue, update your Langflow installation to version 1.6.5 or later, which includes a fix for this issue.
+
+```bash
+uv pip install langflow -U
+```
+
+## Langflow uninstall issues
+
+The following issues can occur when uninstalling Langflow.
+
+### Dot directory isn't deleted when uninstalling Langflow Desktop on macOS
+
+On macOS, uninstalling Langflow Desktop deletes the `.app` file but doesn't delete files in `~/.langflow`, which includes files generated during usage like cache and settings.
+
+If you reinstall Langflow Desktop, it starts with the existing data from the previous installation.
+
+To fully remove a Langflow Desktop macOS installation, you must also delete `~/.langflow`:
+
+ ```bash
+ rm -rf .langflow
+ ```
+
+## Langflow MCP issues {#mcp}
+
+The following issues can occur when using Langflow as an MCP server or client.
+
+### Default project MCP server only works when authentication is None
+
+If the default project MCP server works without authentication, but fails after adding an API key to the server configuration, the API key might have been added to the wrong section of the configuration.
+
+The default MCP server uses streamable HTTP transport.
+The API key must be added to the `args` array that is passed to `mcp-proxy`, not the `env` object.
+
+Your `args` array must include `"--headers"`, `"x-api-key"`, and your key value. For example:
+
+```json
+{
+ "mcpServers": {
+ "PROJECT_NAME": {
+ "command": "uvx",
+ "args": [
+ "mcp-proxy",
+ "--transport",
+ "streamablehttp",
+ "--headers",
+ "x-api-key",
+ "YOUR_API_KEY",
+ "http://LANGFLOW_SERVER_ADDRESS/api/v1/mcp/project/PROJECT_ID/streamable"
+ ]
+ }
+ }
+}
+```
+
+For more information, see [Connect clients to your Langflow MCP server](/mcp-server#connect-clients-to-use-the-servers-actions).
+
+### Claude for Desktop doesn't use MCP server tools correctly
+
+If Claude for Desktop doesn't use your server's tools correctly, try explicitly defining the path to your local `uvx` or `npx` executable file in the `claude_desktop_config.json` configuration file:
+
+1. To find your `uvx` path, run `which uvx`.
+
+ To find your `npx` path, run `which npx`.
+
+2. In your `claude_desktop_config.json` file, add the path to your Langflow MCP server configuration, as shown in the following examples.
+
+
+
+
+ ```json
+ {
+ "mcpServers": {
+ "PROJECT_NAME": {
+ "command": "PATH_TO_UVX",
+ "args": [
+ "mcp-proxy",
+ "http://LANGFLOW_SERVER_ADDRESS/api/v1/mcp/project/PROJECT_ID/streamable"
+ ]
+ }
+ }
+ }
+ ```
+
+
+
+
+ ```json
+ {
+ "mcpServers": {
+ "PROJECT_NAME": {
+ "command": "PATH_TO_NPX",
+ "args": [
+ "-y",
+ "supergateway",
+ "--sse",
+ "http://LANGFLOW_SERVER_ADDRESS/api/v1/mcp/project/PROJECT_ID/streamable"
+ ]
+ }
+ }
+ }
+ ```
+
+
+
+
+### MCP browser-based flows don't open a browser on Windows
+
+This is a known issue when using MCP Tools with browser navigation actions, such as Playwright, on Windows: The agent can execute the tool successfully but the browser tab or window doesn't open.
+
+This issue occurs because the MCP server runs from a Python process, which prevents it from opening browser windows in WSL or Windows.
+
+To work around this issue, use the standalone MCP server approach documented in the [Playwright MCP repository](https://github.com/microsoft/playwright-mcp?tab=readme-ov-file#standalone-mcp-server).
+After the server is up and running, you can add it as an HTTP/SSE server in Langflow.
+
+For other browser navigation tools, see the provider's documentation for specific troubleshooting guidance.
+
+### "No tools or prompts connected" on MCP servers in mixed Windows/WSL environments
+
+If you encounter "No tools or prompts connected" errors or connection failures when using Langflow Desktop as an MCP server with clients running in different environments, such as Langflow on a Windows host and an MCP client in WSL, this is due to network isolation between Windows and WSL environments.
+
+WSL cannot directly access Windows localhost services, and Langflow running on a Windows host is not accessible from WSL clients at `localhost:7860`.
+
+To work around this issue, run the server and host in the same operating environment.
+
+Alternatively, configure Langflow Desktop to accept connections from WSL at the default Windows IP address of `10.255.255.254:7860` instead of `localhost`.
+
+### MCP Tools component loses Tool Mode option after upgrading flows {#mcp-tools-component-loses-tool-mode-option-after-upgrading-flows}
+
+If you upgrade an existing flow that uses the [**MCP Tools** component](/mcp-tools) in **Tool Mode**, the component might lose its **Tool Mode** setting after upgrading the flow.
+This can break flows that rely on the component's **Tool Mode** to expose MCP tools to agents.
+
+If you experience this when upgrading a flow that you created in Langflow version 1.7.1 or earlier, do the following:
+
+1. Select the **MCP Tools** component in your flow.
+2. Click **Code** to open the component's code editor.
+3. In the component's template structure, locate the `tool_mode` field under `inputs`.
+ The field can be either missing or set to `false`.
+4. Add the `tool_mode` field if it's missing, and set it to `true`:
+
+ ```python
+ tool_mode = True
+ ```
+
+5. Click **Check & Save** to apply the code changes.
+
+## Token length limit errors in Embedding Model components
+
+Token length errors can happen if your chunking strategy doesn't align with your embedding model's tokenization limits.
+For more information, see [Tokenization errors due to chunk size](/split-text#chunk-size).
+
+## Document processing errors in Docker containers
+
+If you're running Langflow in a Docker container, you might encounter errors when using components that handle files or images, such as the [**Read File** component](/read-file) or [**Docling** components](/bundles-docling). To resolve these errors, you might need to install additional system dependencies.
+
+When running Langflow in a Linux-based Docker container, Docling requires system libraries that aren't included in the base Docker image. If you see errors related to document or image processing, add the following to your Dockerfile:
+
+```dockerfile
+RUN apt-get update && apt-get install -y libgl1 libglib2.0-0
+```
+
+These dependencies are required by Docling for document processing operations.
+
+For more information about customizing Docker images, see [Customize the Langflow Docker image with your own code](/deployment-docker#customize-the-langflow-docker-image).
+
+## Custom components and integrations issues
+
+For troubleshooting advice for a third-party integration, see the information about that integration in the Langflow documentation and the provider's documentation.
+
+If you are building a custom component, see [Error handling and logging for custom Python components](/components-custom-components#error-handling-and-logging).
+
+### Custom components not appearing in the visual editor
+
+If your custom components are not appearing in the Langflow visual editor, try the following troubleshooting steps:
+
+1. Ensure your components follow the [required directory structure](https://docs.langflow.org/components-custom-components#directory-structure-requirements).
+ ```
+ /your/custom/components/path/ # Base directory set by LANGFLOW_COMPONENTS_PATH
+ └── category_name/ # Required category subfolder that determines menu name
+ ├── __init__.py # Required
+ └── custom_component.py # Component file
+ ```
+
+2. Verify each category directory includes an `__init__.py` file.
+This is required for Python to recognize the directory as a module.
+
+3. Use the command line argument instead of the environment variable for `LANGFLOW_COMPONENTS_PATH`.
+If you're using the `LANGFLOW_COMPONENTS_PATH` environment variable and components aren't loading, try the `--components-path` command line argument instead:
+
+ ```bash
+ uv run langflow run --components-path /path/to/your/custom/components
+ ```
+
+If you continue to experience issues, please [report them on GitHub](https://github.com/langflow-ai/langflow/issues) with details about your directory structure and component setup.
+
+## See also
+
+- [Langflow GitHub Issues and Discussions](/contributing-github-issues)
+- [Langflow telemetry](/contributing-telemetry)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Tutorials/agent.mdx b/docs/versioned_docs/version-1.9.0/Tutorials/agent.mdx
new file mode 100644
index 0000000000..72170d8d8c
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Tutorials/agent.mdx
@@ -0,0 +1,178 @@
+---
+title: Connect applications to agents
+slug: /agent-tutorial
+---
+
+import Icon from "@site/src/components/icon";
+
+This tutorial shows you how to connect a JavaScript application to a [Langflow agent](/agents).
+
+With an agent, your application can use any connected tools to retrieve more contextual and timely data without changing any application code. The tools are selected by the agent's internal LLM to solve problems and answer questions.
+
+## Prerequisites
+
+* [Install and start Langflow](/get-started-installation)
+* Create a [Langflow API key](/api-keys-and-authentication)
+* Install the [Langflow JavaScript client](/typescript-client)
+* Create an [OpenAI API key](https://platform.openai.com/api-keys)
+
+This tutorial uses an OpenAI LLM. If you want to use a different provider, you need a valid credential for that provider.
+
+## Create an agent flow
+
+The following steps modify the **Simple Agent** template to connect a [**Directory** component](/directory) and a [**Web Search** component](/web-search) as tools for an **Agent** component.
+The **Directory** component loads all files of a given type from a target directory on your local machine, and the **Web Search** component performs a DuckDuckGo search.
+When connected to an **Agent** component as tools, the agent has the option to use these components when handling requests.
+
+1. In Langflow, click **New Flow**, and then select the **Simple Agent** template.
+2. Remove the **URL** and **Calculator** tools, and then add **Directory** and **Web Search** components to your flow.
+3. In the **Directory** component's **Path** field, enter the directory path and file types that you want to make available to the **Agent** component.
+
+ In this tutorial, the agent needs access to a record of customer purchases, so the directory name is `customer_orders` and the file type is `.csv`. Later in this tutorial, the agent will be prompted to find `email` values in the customer data.
+
+ You can adapt the tutorial to suit your data, or, to follow along with the tutorial, you can download [`customer-orders.csv`](/files/customer_orders.csv) and save it in a `customer_orders` folder on your local machine.
+
+4. In the **Directory** and **Web Search** [components' header menus](/concepts-components#component-menus), enable **Tool Mode** so you can use the components with an agent.
+
+5. Connect the **Directory** and **Web Search** components' **Toolset** ports to the **Agent** component's **Tools** port.
+6. In the **Agent** component, enter your OpenAI API key.
+
+ If you want to use a different provider or model, edit the **Model Provider**, **Model Name**, and **API Key** fields accordingly.
+
+7. To test the flow, click **Playground**, and then ask the LLM a question, such as `Recommend 3 used items for carol.davis@example.com, based on previous orders.`
+
+ Given the example prompt, the LLM would respond with recommendations and web links for items based on previous orders in `customer_orders.csv`.
+
+ The **Playground** prints the agent's chain of thought as it selects tools to use and interacts with functionality provided by those tools.
+ For example, the agent can use the **Directory** component's `as_dataframe` tool to retrieve a [DataFrame](/data-types#table), and the **Web Search** component's `perform_search` tool to find links to related items.
+
+## Add a Prompt Template component to the flow
+
+In this example, the application sends a customer's email address to the Langflow agent. The agent compares the customer's previous orders within the **Directory** component, searches the web for used versions of those items, and returns three results.
+
+1. To include the email address as a value in your flow, add a [**Prompt Template** component](/components-prompts) to your flow between the **Chat Input** and **Agent** components.
+2. In the **Prompt Template** component's **Template** field, enter `Recommend 3 used items for {email}, based on previous orders.`
+Adding the `{email}` value in curly braces creates a new input in the **Prompt Template** component, and the component connected to the `{email}` port is supplying the value for that variable.
+This creates a point for the user's email to enter the flow from your request.
+If you aren't using the `customer_orders.csv` example file, modify the input to search for a value in your dataset.
+
+ At this point your flow has six components. The **Chat Input** component is connected to the **Prompt Template** component's **email** input port. Then, the **Prompt Template** component's output is connected to the **Agent** component's **System Message** input port. The **Directory** and **Web Search** components are connected to the **Agent** component's **Tools** port. Finally, the **Agent** component's output is connected to the **Chat Output** component, which returns the final response to the application.
+
+ 
+
+## Send requests to your flow from a JavaScript application
+
+With your flow operational, connect it to a JavaScript application to use the agent's responses.
+
+1. To construct a JavaScript application to connect to your flow, gather the following information:
+
+ * `LANGFLOW_SERVER_ADDRESS`: Your Langflow server's domain. The default value is `127.0.0.1:7860`. You can get this value from the code snippets on your flow's [**API access** pane](/concepts-publish#api-access).
+ * `FLOW_ID`: Your flow's UUID or custom endpoint name. You can get this value from the code snippets on your flow's [**API access** pane](/concepts-publish#api-access).
+ * `LANGFLOW_API_KEY`: A valid [Langflow API key](/api-keys-and-authentication).
+
+2. Copy the following script into a JavaScript file, and then replace the placeholders with the information you gathered in the previous step.
+If you're using the `customer_orders.csv` example file, you can run this example as-is with the example email address in the code sample.
+If not, modify the `const email = "isabella.rodriguez@example.com"` to search for a value in your dataset.
+
+ ```js
+ import { LangflowClient } from "@datastax/langflow-client";
+
+ const LANGFLOW_SERVER_ADDRESS = 'LANGFLOW_SERVER_ADDRESS';
+ const FLOW_ID = 'FLOW_ID';
+ const LANGFLOW_API_KEY = 'LANGFLOW_API_KEY';
+ const email = "isabella.rodriguez@example.com";
+
+ async function runAgentFlow(): Promise {
+ try {
+ // Initialize the Langflow client
+ const client = new LangflowClient({
+ baseUrl: LANGFLOW_SERVER_ADDRESS,
+ apiKey: LANGFLOW_API_KEY
+ });
+
+ console.log(`Connecting to Langflow server at: ${LANGFLOW_SERVER_ADDRESS} `);
+ console.log(`Flow ID: ${FLOW_ID}`);
+ console.log(`Email: ${email}`);
+
+ // Get the flow instance
+ const flow = client.flow(FLOW_ID);
+
+ // Run the flow with the email as input
+ console.log('\nSending request to agent...');
+ const response = await flow.run(email, {
+ session_id: email // Use email as session ID for context
+ });
+
+ console.log('\n=== Response from Langflow ===');
+ console.log('Session ID:', response.sessionId);
+
+ // Extract URLs from the chat message
+ const chatMessage = response.chatOutputText();
+ console.log('\n=== URLs from Chat Message ===');
+ const messageUrls = chatMessage.match(/https?:\/\/[^\s"')\]]+/g) || [];
+ const cleanMessageUrls = [...new Set(messageUrls)].map(url => url.trim());
+ console.log('URLs from message:');
+ cleanMessageUrls.slice(0, 3).forEach(url => console.log(url));
+
+ } catch (error) {
+ console.error('Error running flow:', error);
+
+ // Provide error messages
+ if (error instanceof Error) {
+ if (error.message.includes('fetch')) {
+ console.error('\nMake sure your Langflow server is running and accessible at:', LANGFLOW_SERVER_ADDRESS);
+ }
+ if (error.message.includes('401') || error.message.includes('403')) {
+ console.error('\nCheck your API key configuration');
+ }
+ if (error.message.includes('404')) {
+ console.error('\nCheck your Flow ID - make sure it exists and is correct');
+ }
+ }
+ }
+ }
+
+ // Run the function
+ console.log('Starting Langflow Agent...\n');
+ runAgentFlow().catch(console.error);
+ ```
+
+3. Save and run the script to send the request and test the flow.
+
+ Your application receives three URLs for recommended used items based on a customer's previous orders in your local CSV, all without changing any code.
+
+
+ Result
+
+ The following is an example response from this tutorial's flow. Due to the nature of LLMs and variations in your inputs, your response might be different.
+
+ ```
+ Starting Langflow Agent...
+
+ Connecting to Langflow server at: http://localhost:7860
+ Flow ID: local-db-search
+ Email: isabella.rodriguez@example.com
+
+ Sending request to agent...
+
+ === Response from Langflow ===
+ Session ID: isabella.rodriguez@example.com
+
+ URLs found:
+ https://www.facebook.com/marketplace/258164108225783/electronics/
+ https://www.facebook.com/marketplace/458332108944152/furniture/
+ https://www.facebook.com/marketplace/613732137493719/kitchen-cabinets/
+ ```
+
+
+
+4. To quickly check traffic to your flow, open the **Playground**.
+ New sessions are named after the user's email address.
+ Keeping sessions distinct helps the agent maintain context. For more on session IDs, see [Session ID](/session-id).
+
+## Next steps
+
+For more information on building or extending this tutorial, see the following:
+
+* [Model Context Protocol (MCP) servers](/mcp-server)
+* [Langflow deployment overview](/deployment-overview)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Tutorials/chat-with-files.mdx b/docs/versioned_docs/version-1.9.0/Tutorials/chat-with-files.mdx
new file mode 100644
index 0000000000..6bc9d2100a
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Tutorials/chat-with-files.mdx
@@ -0,0 +1,228 @@
+---
+title: Create a chatbot that can ingest files
+slug: /chat-with-files
+---
+
+import Icon from "@site/src/components/icon";
+
+This tutorial shows you how to build a chatbot that can read and answer questions about files you upload, such as meeting notes or job applications.
+
+For example, you could upload a contract and ask, “What are the termination clauses in this agreement?” Or upload a resume and ask, “Does this candidate have experience with marketing analytics?”
+
+The main focus of this tutorial is to show you how to provide files as input to a Langflow flow, so your chatbot can use the content of those files in its responses.
+
+## Prerequisites
+
+* [Install and start Langflow](/get-started-installation)
+* Create a [Langflow API key](/api-keys-and-authentication)
+* Create an [OpenAI API key](https://platform.openai.com/api-keys)
+
+This tutorial uses an OpenAI LLM. If you want to use a different provider, you need a valid credential for that provider.
+
+## Create a flow that accepts file input
+
+To ingest files, your flow must have a **Read File** component attached to a component that receives input, such as a **Prompt Template** or **Agent** component.
+
+The following steps modify the **Basic Prompting** template to accept file input:
+
+1. In Langflow, click **New Flow**, and then select the **Basic Prompting** template.
+2. In the **Language Model** component, enter your OpenAI API key.
+
+ If you want to use a different provider or model, edit the **Model Provider**, **Model Name**, and **API Key** fields accordingly.
+
+3. To verify that your API key is valid, click **Playground**, and then ask the LLM a question.
+The LLM should respond according to the specifications in the **Prompt Template** component's **Template** field.
+
+4. Exit the **Playground**, and then modify the **Prompt Template** component to accept file input in addition to chat input.
+To do this, edit the **Template** field, and then replace the default prompt with the following text:
+
+ ```text
+ ChatInput:
+ {chat-input}
+ File:
+ {file}
+ ```
+
+ :::tip
+ You can use any string to name your template variables.
+ These strings become the names of the fields (input ports) on the **Prompt Template** component.
+
+ For this tutorial, the variables are named after the components that connect to them: **chat-input** for the **Chat Input** component and **file** for the **Read File** component.
+ :::
+
+5. Add a [**Read File** component](/read-file) to the flow, and then connect the **Raw Content** output port to the **Prompt Template** component's **file** input port.
+To connect ports, click and drag from one port to the other.
+
+ You can add files directly to the **Read File** component to pre-load input before running the flow, or you can load files at runtime. The next section of this tutorial covers runtime file uploads.
+
+ At this point your flow has five components. The **Chat Input** and **Read File** components are connected to the **Prompt Template** component's input ports. Then, the **Prompt Template** component's output port is connected to the **Language Model** component's input port. Finally, the **Language Model** component's output port is connected to the **Chat Output** component, which returns the final response to the user.
+
+ 
+
+## Send requests to your flow from a Python application
+
+This section of the tutorial demonstrates how you can send file input to a flow from an application.
+
+To do this, your application must send a `POST /run` request to your Langflow server with the file you want to upload and a text prompt.
+The result includes the outcome of the flow run and the LLM's response.
+
+This example uses a local Langflow instance, and it asks the LLM to evaluate a sample resume.
+If you don't have a resume on hand, you can download [fake-resume.txt](/files/fake-resume.txt).
+
+:::tip
+For an example of constructing file upload requests in JavaScript, see the [Create a vector RAG chatbot tutorial](/chat-with-rag#load-data-and-generate-embeddings).
+:::
+
+1. To construct the request, gather the following information:
+
+ * `LANGFLOW_SERVER_ADDRESS`: Your Langflow server's domain. The default value is `127.0.0.1:7860`. You can get this value from the code snippets on your flow's [**API access** pane](/concepts-publish#api-access).
+ * `FLOW_ID`: Your flow's UUID or custom endpoint name. You can get this value from the code snippets on your flow's [**API access** pane](/concepts-publish#api-access).
+ * `FILE_COMPONENT_ID`: The UUID of the **Read File** component in your flow, such as `File-KZP68`. To find the component ID, open your flow in Langflow, click the **Read File** component, and then click **Controls**. The component ID is at the top of the **Controls** pane.
+ * `CHAT_INPUT`: The message you want to send to the Chat Input of your flow, such as `Evaluate this resume for a job opening in my Marketing department.`
+ * `FILE_NAME` and `FILE_PATH`: The name and path to the local file that you want to send to your flow.
+ * `LANGFLOW_API_KEY`: A valid [Langflow API key](/api-keys-and-authentication).
+
+2. Copy the following script into a Python file, and then replace the placeholders with the information you gathered in the previous step:
+
+ ```python
+ # Python example using requests
+ import requests
+ import json
+
+ # 1. Set the upload URL
+ url = "http://LANGFLOW_SERVER_ADDRESS/api/v2/files/"
+
+ # 2. Prepare the file and payload
+ payload = {}
+ files = [
+ ('file', ('FILE_PATH', open('FILE_NAME', 'rb'), 'application/octet-stream'))
+ ]
+ headers = {
+ 'Accept': 'application/json',
+ 'x-api-key': 'LANGFLOW_API_KEY'
+ }
+
+ # 3. Upload the file to Langflow
+ response = requests.request("POST", url, headers=headers, data=payload, files=files)
+ print(response.text)
+
+ # 4. Get the uploaded file path from the response
+ uploaded_data = response.json()
+ uploaded_path = uploaded_data.get('path')
+
+ # 5. Call the Langflow run endpoint with the uploaded file path
+ run_url = "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID"
+ run_payload = {
+ "input_value": "CHAT_INPUT",
+ "output_type": "chat",
+ "input_type": "chat",
+ "tweaks": {
+ "FILE_COMPONENT_ID": {
+ "path": uploaded_path
+ }
+ }
+ }
+ run_headers = {
+ 'Content-Type': 'application/json',
+ 'Accept': 'application/json',
+ 'x-api-key': 'LANGFLOW_API_KEY'
+ }
+ run_response = requests.post(run_url, headers=run_headers, data=json.dumps(run_payload))
+ langflow_data = run_response.json()
+ # Output only the message
+ message = None
+ try:
+ message = langflow_data['outputs'][0]['outputs'][0]['results']['message']['data']['text']
+ except (KeyError, IndexError, TypeError):
+ pass
+ print(message)
+
+ ```
+
+ This script contains two requests.
+
+ The first request uploads a file, such as `fake-resume.txt`, to your Langflow server at the `/v2/files` endpoint. This request returns a file path that can be referenced in subsequent Langflow requests, such as `02791d46-812f-4988-ab1c-7c430214f8d5/fake-resume.txt`
+
+ The second request sends a chat message to the Langflow flow at the `/v1/run/` endpoint.
+ The `tweaks` parameter includes the path to the uploaded file as the variable `uploaded_path`, and sends this file directly to the **Read File** component.
+
+3. Save and run the script to send the requests and test the flow.
+
+ The initial output contains the JSON response object from the file upload endpoint, including the internal path where Langflow stores the file.
+ Then, the LLM retrieves the file and evaluates its content, in this case the suitability of the resume for a job position.
+
+
+ Result
+
+ The following is an example response from this tutorial's flow. Due to the nature of LLMs and variations in your inputs, your response might be different.
+
+ ```
+ {"id":"793ba3d8-5e7a-4499-8b89-d9a7b6325fee","name":"fake-resume (1)","path":"02791d46-812f-4988-ab1c-7c430214f8d5/fake-resume.txt","size":1779,"provider":null}
+ The resume for Emily J. Wilson presents a strong candidate for a position in your Marketing department. Here are some key points to consider:
+
+ ### Strengths:
+ 1. **Experience**: With over 8 years in marketing, Emily has held progressively responsible positions, culminating in her current role as Marketing Director. This indicates a solid foundation in the field.
+
+ 2. **Quantifiable Achievements**: The resume highlights specific accomplishments, such as a 25% increase in brand recognition and a 30% sales increase after launching new product lines. These metrics demonstrate her ability to drive results.
+
+ 3. **Diverse Skill Set**: Emily's skills encompass various aspects of marketing, including strategy development, team management, social media marketing, event planning, and data analysis. This versatility can be beneficial in a dynamic marketing environment.
+
+ 4. **Educational Background**: Her MBA and a Bachelor's degree in Marketing provide a strong academic foundation, which is often valued in marketing roles.
+
+ 5. **Certifications**: The Certified Marketing Professional (CMP) and Google Analytics Certification indicate a commitment to professional development and staying current with industry standards.
+
+ ### Areas for Improvement:
+ 1. **Specificity in Skills**: While the skills listed are relevant, providing examples of how she has applied these skills in her previous roles could strengthen her resume further.
+
+ 2. **References**: While stating that references are available upon request is standard, including a couple of testimonials or notable endorsements could enhance credibility.
+
+ 3. **Formatting**: Ensure that the resume is visually appealing and easy to read. Clear headings and bullet points help in quickly identifying key information.
+
+ ### Conclusion:
+ Overall, Emily J. Wilson's resume reflects a well-rounded marketing professional with a proven track record of success. If her experience aligns with the specific needs of your Marketing department, she could be a valuable addition to your team. Consider inviting her for an interview to further assess her fit for the role.
+ ```
+
+
+
+## Next steps
+
+To continue building on this tutorial, try these next steps.
+
+### Process multiple files loaded at runtime
+
+To process multiple files in a single flow run, add a separate **Read File** component for each file you want to ingest. Then, modify your script to upload each file, retrieve each returned file path, and then pass a unique file path to each **Read File** component ID.
+
+For example, you can modify `tweaks` to accept multiple **Read File** components.
+The following code is just an example; it isn't working code:
+
+```python
+## set multiple file paths
+file_paths = {
+ FILE_COMPONENT_1: uploaded_path_1,
+ FILE_COMPONENT_2: uploaded_path_2
+}
+
+def chat_with_flow(input_message, file_paths):
+ """Compare the contents of these two files."""
+ run_url = f"{LANGFLOW_SERVER_ADDRESS}/api/v1/run/{FLOW_ID}"
+ # Prepare tweaks with both file paths
+ tweaks = {}
+ for component_id, file_path in file_paths.items():
+ tweaks[component_id] = {"path": file_path}
+```
+
+You can also use a [**Directory** component](/directory) to load all files in a directory or pass an archive file to the **Read File** component.
+
+### Upload external files at runtime
+
+To upload files from another machine that isn't your local environment, your Langflow server must first be accessible over the internet. Then, authenticated users can upload files to your public Langflow server's `/v2/files/` endpoint, as shown in the tutorial. For more information, see [Langflow deployment overview](/deployment-overview).
+
+### Preload files outside the chat session
+
+You can use the **Read File** component to load files anywhere in a flow, not just in a chat session.
+
+In the visual editor, you can preload files to the **Read File** component by selecting them from your local machine or [Langflow file management](/concepts-file-management).
+
+For example, you can preload an instructions file for a prompt template, or you can preload a vector store with documents that you want to query in a Retrieval Augmented Generation (RAG) flow.
+
+For more information about the **Read File** component and other data loading components, see the [**Read File** component](/read-file).
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Tutorials/chat-with-rag.mdx b/docs/versioned_docs/version-1.9.0/Tutorials/chat-with-rag.mdx
new file mode 100644
index 0000000000..89bef3a829
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Tutorials/chat-with-rag.mdx
@@ -0,0 +1,227 @@
+---
+title: Create a vector RAG chatbot
+slug: /chat-with-rag
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+This tutorial demonstrates how you can use Langflow to create a chatbot application that uses Retrieval Augmented Generation (RAG) to embed your data as vectors in a vector database, and then chat with the data.
+
+## Prerequisites
+
+* [Install and start Langflow](/get-started-installation)
+* Create a [Langflow API key](/api-keys-and-authentication)
+* Create an [OpenAI API key](https://platform.openai.com/api-keys)
+* Install the [Langflow JavaScript client](/typescript-client)
+* Be familiar with vector search concepts and applications, such as vector databases and RAG
+
+## Create a vector RAG flow
+
+1. In Langflow, click **New Flow**, and then select the **Vector Store RAG** template.
+
+
+ About the Vector Store RAG template
+
+ This template has two flows.
+
+ The **Load Data Flow** populates a vector store with data from a file.
+ This data is used to respond to queries submitted to the **Retriever Flow**.
+
+ Specifically, the **Load Data Flow** ingests data from a local file, splits the data into chunks, loads and indexes the data in your vector database, and then computes embeddings for the chunks. The embeddings are also stored with the loaded data. This flow only needs to run when you need to load data into your vector database.
+
+ The **Retriever Flow** receives chat input, generates an embedding for the input, and then uses several components to reconstruct chunks into text and generate a response by comparing the new embedding to the stored embeddings to find similar data.
+
+
+
+2. Add your **OpenAI** API key to both **OpenAI Embeddings** components.
+
+3. Optional: Replace both **Astra DB** vector store components with a **Chroma DB** or another vector store component of your choice.
+This tutorial uses Chroma DB.
+
+ The **Load Data Flow** should have **Read File**, **Split Text**, **Embedding Model**, vector store (such as **Chroma DB**), and **Chat Output** components:
+
+ 
+
+ The **Retriever Flow** should have **Chat Input**, **Embedding Model**, vector store, **Parser**, **Prompt**, **Language Model**, and **Chat Output** components:
+
+ 
+
+ The flows are ready to use.
+ Continue the tutorial to learn how to use the loading flow to load data into your vector store, and then call the chat flow in a chatbot application.
+
+## Load data and generate embeddings
+
+To load data and generate embeddings, you can use the visual editor or the `/v2/files` endpoint.
+
+The visual editor option is simpler, but it is only recommended for scenarios where the user who created the flow is the same user who loads data into the database.
+
+In situations where many users load data or you need to load data programmatically, use the Langflow API option.
+
+
+
+
+1. In your RAG chatbot flow, click the **Read File** component, and then click **File**.
+2. Select the local file you want to upload, and then click **Open**.
+ The file is loaded to your Langflow server.
+3. To load the data into your vector database, click the vector store component, and then click **Run component** to run the selected component and all prior dependent components.
+
+
+
+
+To load data programmatically, use the `/v2/files/` and `/v1/run/$FLOW_ID` endpoints. The first endpoint loads a file to your Langflow server, and then returns an uploaded file path. The second endpoint runs the **Load Data Flow**, referencing the uploaded file path, to chunk, embed, and load the data into the vector store.
+
+:::tip
+For an example of constructing file upload requests in Python, see the [Create a chatbot that can ingest files tutorial](/chat-with-files#send-requests-to-your-flow-from-a-python-application).
+:::
+
+The following script demonstrates this process.
+
+```js
+// Node 18+ example using global fetch, FormData, and Blob
+import fs from 'fs/promises';
+
+// 1. Prepare the form data with the file to upload
+const fileBuffer = await fs.readFile('FILE_NAME');
+const data = new FormData();
+data.append('file', new Blob([fileBuffer]), 'FILE_NAME');
+const headers = { 'x-api-key': 'LANGFLOW_API_KEY' };
+
+// 2. Upload the file to Langflow
+const uploadRes = await fetch('LANGFLOW_SERVER_ADDRESS/api/v2/files/', {
+ method: 'POST',
+ headers,
+ body: data
+});
+const uploadData = await uploadRes.json();
+const uploadedPath = uploadData.path;
+
+// 3. Call the Langflow run endpoint with the uploaded file path
+const payload = {
+ input_value: "Analyze this file",
+ output_type: "chat",
+ input_type: "text",
+ tweaks: {
+ 'FILE_COMPONENT_NAME': {
+ path: uploadedPath
+ }
+ }
+};
+const runRes = await fetch('LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID', {
+ method: 'POST',
+ headers: { 'Content-Type': 'application/json', 'x-api-key': 'LANGFLOW_API_KEY' },
+ body: JSON.stringify(payload)
+});
+const langflowData = await runRes.json();
+// Output only the message
+console.log(langflowData.outputs?.[0]?.outputs?.[0]?.results?.message?.data?.text);
+```
+
+
+
+
+When the flow runs, the flow ingests the selected file, chunks the data, loads the data into the vector store database, and then generates embeddings for the chunks, which are also stored in the vector store.
+
+Your database now contains data with vector embeddings that an LLM can use as context to respond to queries, as demonstrated in the next section of the tutorial.
+
+## Chat with your flow from a JavaScript application
+
+To chat with the data in your vector database, create a chatbot application that runs the **Retriever Flow** programmatically.
+
+This tutorial uses JavaScript for demonstration purposes.
+
+1. To construct the chatbot, gather the following information:
+
+ * `LANGFLOW_SERVER_ADDRESS`: Your Langflow server's domain. The default value is `127.0.0.1:7860`. You can get this value from the code snippets on your flow's [**API access** pane](/concepts-publish#api-access).
+ * `FLOW_ID`: Your flow's UUID or custom endpoint name. You can get this value from the code snippets on your flow's [**API access** pane](/concepts-publish#api-access).
+ * `LANGFLOW_API_KEY`: A valid [Langflow API key](/api-keys-and-authentication).
+
+2. Copy the following script into a JavaScript file, and then replace the placeholders with the information you gathered in the previous step:
+
+ ```js
+ const readline = require('readline');
+ const { LangflowClient } = require('@datastax/langflow-client');
+
+ # pragma: allowlist nextline secret
+ const API_KEY = 'LANGFLOW_API_KEY';
+ const SERVER = 'LANGFLOW_SERVER_ADDRESS';
+ const FLOW_ID = 'FLOW_ID';
+
+ const rl = readline.createInterface({ input: process.stdin, output: process.stdout });
+
+ // Initialize the Langflow client
+ const client = new LangflowClient({
+ baseUrl: SERVER,
+ apiKey: API_KEY
+ });
+
+ async function sendMessage(message) {
+ try {
+ const response = await client.flow(FLOW_ID).run(message, {
+ session_id: 'user_1'
+ });
+
+ // Use the convenience method to get the chat output text
+ return response.chatOutputText() || 'No response';
+ } catch (error) {
+ return `Error: ${error.message}`;
+ }
+ }
+
+ function chat() {
+ console.log('🤖 Langflow RAG Chatbot (type "quit" to exit)\n');
+
+ const ask = () => {
+ rl.question('👤 You: ', async (input) => {
+ if (['quit', 'exit', 'bye'].includes(input.trim().toLowerCase())) {
+ console.log('👋 Goodbye!');
+ rl.close();
+ return;
+ }
+
+ const response = await sendMessage(input.trim());
+ console.log(`🤖 Assistant: ${response}\n`);
+ ask();
+ });
+ };
+
+ ask();
+ }
+
+ chat();
+ ```
+
+ The script creates a Node application that chats with the content in your vector database, using the `chat` input and output types to communicate with your flow.
+ Chat maintains ongoing conversation context across multiple messages. If you used `text` type inputs and outputs, each request is a standalone text string.
+
+ :::tip
+ The [Langflow TypeScript client](/typescript-client) has a `chatOutputText()` convenience method that simplifies working with Langflow's complex JSON response structure.
+ Instead of manually navigating through multiple levels of nested objects with `data.outputs[0].outputs[0].results.message.data.text`, the client automatically extracts the message text and handles potentially undefined values gracefully.
+ :::
+
+3. Save and run the script to send the requests and test the flow.
+
+
+ Result
+
+ The following is an example response from this tutorial's flow. Due to the nature of LLMs and variations in your inputs, your response might be different.
+
+ ```
+ 👤 You: Do you have any documents about engines?
+ 🤖 Assistant: Yes, the provided text contains several warnings and guidelines related to engine installation, maintenance, and selection. It emphasizes the importance of using the correct engine for specific applications, ensuring all components are in good condition, and following safety precautions to prevent fire or explosion. If you need more specific information or details, please let me know!
+
+ 👤 You: It should be about a Briggs and Stratton engine.
+ 🤖 Assistant: The text provides important safety and installation guidelines for Briggs & Stratton engines. It emphasizes that these engines should not be used on 3-wheel All-Terrain Vehicles (ATVs), motor bikes, aircraft products, or vehicles intended for competitive events, as such uses are not approved by Briggs & Stratton.
+
+ If you have any specific questions about Briggs & Stratton engines or need further information, feel free to ask!
+ ```
+
+
+
+## Next steps
+
+For more information on building or extending this tutorial, see the following:
+
+* [Model Context Protocol (MCP) servers](/mcp-server)
+* [Langflow deployment overview](/deployment-overview)
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/Tutorials/mcp-tutorial.mdx b/docs/versioned_docs/version-1.9.0/Tutorials/mcp-tutorial.mdx
new file mode 100644
index 0000000000..a9bf233768
--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/Tutorials/mcp-tutorial.mdx
@@ -0,0 +1,290 @@
+---
+title: Connect to MCP servers from your application
+slug: /mcp-tutorial
+---
+
+import Icon from "@site/src/components/icon";
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+import McpIcon from '@site/static/logos/mcp-icon.svg';
+
+This tutorial shows you how to connect MCP servers to your applications using Langflow's [**MCP Tools** component](/mcp-client).
+
+The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) helps agents integrate with LLMs through _MCP clients_ and _MCP servers_.
+Specifically, MCP servers host tools that agents (MCP clients) use to complete specialized tasks.
+MCP servers are connected to MCP clients like Cursor.
+Then, you interact with the client, and the client uses tools from the connected servers as needed to complete your requests.
+
+You can run Langflow as an MCP client and an MCP server:
+
+* [Use Langflow as an MCP client](/mcp-client): When run as an MCP client, an **Agent** component in a Langflow flow can use connected components as tools to handle requests.
+You can use existing components as tools, and you can connect any MCP server to your flow to make that server's tools available to the agent.
+
+* [Use Langflow as an MCP server](/mcp-server): When run as an MCP server, your flows become tools that can be used by an MCP client, which could be an external client or another Langflow flow.
+
+In this tutorial, you will use the Langflow **MCP Tools** component to connect multiple MCP servers to your flow, and then you'll use a Python application to run your flow and chat with the agent programmatically.
+
+## Prerequisites
+
+* [Install and start Langflow](/get-started-installation)
+* Create a [Langflow API key](/api-keys-and-authentication)
+* Create an [OpenAI API key](https://platform.openai.com/api-keys)
+
+This tutorial uses an OpenAI LLM. If you want to use a different provider, you need a valid credential for that provider.
+
+## Create an agent flow
+
+1. In Langflow, click **New Flow**, and then select the **Simple Agent** template.
+
+2. In the **Agent** component, enter your OpenAI API key.
+
+ If you want to use a different provider or model, edit the **Model Provider**, **Model Name**, and **API Key** fields accordingly.
+
+3. To test the flow, click **Playground**, and then ask the LLM `Is it safe to go hiking in the Adirondacks today?`
+
+ This query demonstrates how an LLM, by itself, might not have access to information or functions designed to address specialized queries. In this example, the default OpenAI model provides a vague response, although the agent does know the current date by using its internal `get_current_date` function.
+
+ ```text
+ Today is July 11, 2025.
+ To determine if it's safe to go hiking in the Adirondacks today, you should check the current weather conditions, trail advisories, and any local alerts (such as bear activity or flooding).
+ Would you like a detailed weather forecast or information on trail conditions for the Adirondacks today?
+ ```
+
+ To improve the response, you can connect MCP servers to your flow that provide specialized tools for the agent to use when generating responses. In the next part of this tutorial, you'll connect an MCP server that provides the agent with real-time weather information so that it can generate a more specific response.
+
+## Add an MCP Tools component
+
+There are many MCP servers available online that offer different tools for different tasks.
+To use an MCP server with an MCP client, you have to make the server available to the client.
+With all MCP clients, there are several ways to do this:
+
+* Install the server locally.
+* Use `uvx` or `npx` to fetch and run a server package.
+* Call a server running remotely, like those available on Smithery.
+
+This tutorial demonstrates how to install a weather server locally with `uv pip install`, and how to use `npx` to run the geolocation server package.
+Your particular MCP server's requirements may vary.
+
+In Langflow, you use the **MCP Tools** component to connect a specific MCP server to a flow.
+You need one **MCP Tools** component for each MCP server that you want your flow to use.
+
+1. For this tutorial, install a [weather MCP server](https://github.com/isdaniel/mcp_weather_server) on your local machine with uv and Python:
+
+ ```shell
+ uv pip install mcp_weather_server
+ ```
+
+ Make sure you install the server in the same Python environment where Langflow is running:
+
+ * Langflow in a virtual environment: Activate the environment before installing the server.
+ * Langflow Docker image: Install the server inside the Docker container.
+ * Langflow Desktop or system-wide Langflow OSS: Install the server globally or in the same user environment where you run Langflow.
+
+2. In your **Simple Agent** flow, remove the **URL** and **Calculator** tools.
+
+3. Open the **MCP** section in the left sidebar, click **Add MCP Server**, and register the weather MCP server.
+
+4. In the **Add MCP Server** pane, provide the server startup command and arguments to connect the weather MCP server to your flow. For this tutorial, use either the **JSON** or **STDIO** option.
+
+ Langflow runs the command to launch the server when the agent determines that it needs to use a tool provided by that server.
+
+ Notice that both configurations provide the same information but in different formats.
+ This means that if your MCP server repository only provides a JSON file for the server, you can still use those values with the STDIO option.
+
+
+
+
+ To provide the MCP server configuration as a JSON object, select **JSON**, and then paste the server configuration into the **JSON** field:
+
+ ```json
+ {
+ "mcpServers": {
+ "weather": {
+ "command": "python",
+ "args": [
+ "-m",
+ "mcp_weather_server"
+ ],
+ "disabled": false,
+ "autoApprove": []
+ }
+ }
+ }
+ ```
+
+
+
+
+ To provide the MCP server configuration in a GUI format, select **STDIO**, and then enter the MCP server configuration values into the given fields:
+
+ - **Name**: `weather`
+ - **Command**: `python`
+ - **Arguments**:
+ - `-m`
+ - `mcp_weather_server`
+
+
+
+
+5. Click **Add Server**, and then wait for the **Actions** list to populate. This means that the MCP server successfully connected.
+
+6. Drag the weather MCP server from the **MCP** sidebar onto the canvas to add the [**MCP Tools**](/mcp-client) component for that server.
+
+ With this weather server, the **MCP Tools** component also adds an optional **City** field.
+ For this tutorial, don't enter anything in this field.
+ Instead, you will add a geolocation MCP server in the next step, which the agent will use to detect your location.
+
+7. Click the **MCP Tools** component, enable **Tool Mode** in the [component's header menu](/concepts-components#component-menus), and then connect the component's **Toolset** port to the **Agent** component's **Tools** port.
+
+ At this point your flow has four connected components:
+
+ * The **Chat Input** component is connected to the **Agent** component's **Input** port. This allows to flow to be triggered by an incoming prompt from a user or application.
+ * The **MCP Tools** component with the weather MCP server is connected to the **Agent** component's **Tools** port. The agent may not use this server for every request; the agent only uses this connection if it decides the server can help respond to the prompt.
+ * The **Agent** component's **Output** port is connected to the **Chat Output** component, which returns the final response to the user or application.
+
+ 
+
+8. To test the weather MCP server, click **Playground**, and then ask the LLM `Is it safe to go hiking in the Adirondacks today?`
+
+ The **Playground** shows you the agent's logic as it analyzes the request and select tools to use.
+
+ Ideally, the agent's response will be more specific than the previous response because of the additional context provided by the weather MCP server.
+ For example:
+
+ ```text
+ The current weather in Lake Placid, a central location in the Adirondacks,
+ is foggy with a temperature of 17.2°C (about 63°F).
+ If you plan to go hiking today, be cautious as fog can reduce visibility
+ on trails and make navigation more difficult.
+ ```
+
+ This is a better response, but what makes this MCP server more valuable than just calling a weather API?
+
+ MCP servers are often customized for specific tasks, such as highly specialized actions or chained tools for complex, multi-step problem solving.
+ Typically, you would have to write a custom script for a specific task, possibly including multiple API calls in a single script, and then you would have to either execute this script outside the context of the agent or provide it to your agent in some way.
+
+ Instead, the MCP ensures that all MCP servers are added to agents in the same way, without having to know each server's specific endpoint structures or write custom integrations.
+ The MCP is a standardized way to integrate many diverse tools into agentic applications.
+ You don't have to learn a new API or write custom code every time you want to use a new MCP server.
+
+ Additionally, you can attach many MCP servers to one agent, depending on the problems you want your application to solve.
+ The more servers you add, the more specialized context the agent can use in its responses.
+ In this tutorial, adding the weather MCP server already improved the quality of the LLM's response.
+ In the next section of the tutorial, you will add a `ip_geolocation` MCP server so the agent can detect the user's location if they don't specify a location in their prompt.
+
+## Add a geolocation server
+
+The [Toolkit MCP server](https://github.com/cyanheads/toolkit-mcp-server) includes multiple MCP tools for network monitoring, including IP geolocation. It isn't extremely precise, but it doesn't require an API key.
+
+Note that this tool returns the IP geolocation of your Langflow server, so if your server is deployed remotely, consider alternative approaches for getting user-specific location data, such as browser geolocation APIs.
+
+This MCP server can be started with one [npx](https://docs.npmjs.com/cli/v8/commands/npx) command, which downloads and runs the [Toolkit MCP server Node registry package](https://www.npmjs.com/package/@cyanheads/toolkit-mcp-server) without installing the package locally.
+
+To add the Toolkit MCP server to your flow, do the following:
+
+1. Open the **MCP** sidebar, click **Add MCP Server**, and register a second server.
+
+2. Select **STDIO**.
+
+3. For **Name**, enter `ip_geolocation`.
+
+ :::tip
+ The tool name and description help the agent select tools.
+ If your agent is struggling to select tools, make sure the names and descriptions are clear and human-readable.
+ :::
+
+4. For **Command**, enter `npx @cyanheads/toolkit-mcp-server`.
+
+5. Click **Add Server**, and then wait for the **Actions** list to populate. This means that the MCP server successfully connected.
+
+6. Drag the **ip_geolocation** MCP server from the **MCP** sidebar to the canvas to add the second **MCP Tools** component.
+
+7. Click the new **MCP Tools** component, enable **Tool Mode** in the [component's header menu](/concepts-components#component-menus), and then connect the component's **Toolset** port to the **Agent** component's **Tools** port.
+
+ Your flow now has an additional **MCP Tools** component for a total of five components.
+
+ 
+
+## Create a Python application that connects to Langflow
+
+At this point, you can open the **Playground** and ask about the weather in your current location to test the IP geolocation tool.
+However, geolocation tools are most useful in applications where you or your users want to ask about the weather from different places around the world.
+
+In the last part of this tutorial, you'll learn how to use the Langflow API to run a flow in a script.
+This could be part of a larger application, such as a mobile app where users want to know if the weather is good for a particular sport.
+
+When you use the Langflow API to run a flow, you can change some aspects of the flow without changing the code.
+For example, you can add more MCP servers to your flow in Langflow, and then use the same script to run the flow.
+You can use the same input or a new input that prompts the agent to use other tools.
+
+1. For this tutorial's Python script, gather the following information:
+
+ * `LANGFLOW_SERVER_ADDRESS`: Your Langflow server's domain. The default value is `127.0.0.1:7860`. You can get this value from the code snippets on your flow's [**API access** pane](/concepts-publish#api-access).
+ * `FLOW_ID`: Your flow's UUID or custom endpoint name. You can get this value from the code snippets on your flow's [**API access** pane](/concepts-publish#api-access).
+ * `LANGFLOW_API_KEY`: A valid [Langflow API key](/api-keys-and-authentication).
+
+2. Copy the following script into a Python file, and then replace the placeholders with the information you gathered in the previous step:
+
+ ```python
+ import requests
+ import os
+
+ url = "LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID" # The complete API endpoint URL for this flow
+
+ # Request payload configuration
+ payload = {
+ "output_type": "chat",
+ "input_type": "chat",
+ "input_value": "What's the weather like where I am right now?"
+ }
+
+ # Request headers
+ headers = {
+ "Content-Type": "application/json",
+ "x-api-key": "LANGFLOW_API_KEY"
+ }
+
+ try:
+ # Send API request
+ response = requests.request("POST", url, json=payload, headers=headers)
+ response.raise_for_status() # Raise exception for bad status codes
+
+ # Parse and print only the message text
+ data = response.json()
+ message = data["outputs"][0]["outputs"][0]["results"]["message"]["text"]
+ print(message)
+
+ except requests.exceptions.RequestException as e:
+ print(f"Error making API request: {e}")
+ except ValueError as e:
+ print(f"Error parsing response: {e}")
+ except (KeyError, IndexError) as e:
+ print(f"Error extracting message from response: {e}")
+ ```
+
+ Notice that this script uses a different prompt than the previous **Playground** examples.
+ In this script, the `input_value` asks about the weather in the user's current location without providing any hints about the user's location, such as a particular city.
+
+ Additionally, this script includes parsing code to extract the LLM's reply from the entire Langflow API response.
+ You will want to use similar extraction in your own applications because the Langflow API response includes metadata and other information that isn't relevant to the reply passed to the user.
+
+3. Save and run the script to send the request and test the flow.
+
+ The agent uses the `ip_geolocation` tool to detect the requester's location, and then it uses the `weather` tool to retrieve weather information for that location.
+ For example:
+
+ ```text
+ The weather in Waynesboro, Pennsylvania, is currently overcast with a temperature of 23.0°C (about 73.4°F).
+ If you need more details or have any other questions, feel free to ask!
+ ```
+
+ Remember, the `ip_geolocation` tool used in this tutorial uses your Langflow server's location, which can be different from your actual location.
+
+## Next steps
+
+To continue building on the concepts introduced in this tutorial, see the following:
+
+* [Use Langflow as an MCP client](/mcp-client)
+* [Use Langflow Agents](/agents)
+* [Use Langflow as an MCP server](/mcp-server)
+* [Langflow deployment overview](/deployment-overview)
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-agents-work.mdx b/docs/versioned_docs/version-1.9.0/_partial-agents-work.mdx
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+
+How do agents work?
+
+Agents extend Large Language Models (LLMs) by integrating _tools_, which are functions that provide additional context and enable autonomous task execution.
+These integrations make agents more specialized and powerful than standalone LLMs.
+
+Whereas an LLM might generate acceptable, inert responses to general queries and tasks, an agent can leverage the integrated context and tools to provide more relevant responses and even take action.
+For example, you might create an agent that can access your company's documentation, repositories, and other resources to help your team with tasks that require knowledge of your specific products, customers, and code.
+
+Agents use LLMs as a reasoning engine to process input, determine which actions to take to address the query, and then generate a response.
+The response could be a typical text-based LLM response, or it could involve an action, like editing a file, running a script, or calling an external API.
+
+In an agentic context, tools are functions that the agent can run to perform tasks or access external resources.
+A function is wrapped as a `Tool` object with a common interface that the agent understands.
+Agents become aware of tools through tool registration, which is when the agent is provided a list of available tools typically at agent initialization.
+The `Tool` object's description tells the agent what the tool can do so that it can decide whether the tool is appropriate for a given request.
+
+
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-api-setup.mdx b/docs/versioned_docs/version-1.9.0/_partial-api-setup.mdx
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+## Prerequisites
+
+Before using the API, you need:
+
+* [Install and start Langflow](/get-started-installation) with the developer API enabled
+
+ The Workflows API endpoints require the `developer_api_enabled` setting to be enabled. If this setting is disabled, these endpoints will return a 404 Not Found error.
+
+ To enable the developer API endpoint, do the following:
+ 1. In the Langflow `.env` file, set the environment variable to `true`:
+ ```
+ LANGFLOW_DEVELOPER_API_ENABLED=true
+ ```
+ 2. Start your Langflow server with the `.env` file enabled:
+ ```
+ uv run langflow run --env-file .env
+ ```
+
+ For more information about configuring environment variables, see [Environment variables](/environment-variables).
+* [Create a Langflow API key](/api-keys-and-authentication)
+* [Create a flow](/concepts-flows) that you want to execute
+* [Get the flow ID](/concepts-publish#api-access) or endpoint name of the flow you want to execute
+
+### Set environment variables
+
+All code examples in this documentation assume you have set the following environment variables:
+
+**Python:**
+```python
+import os
+
+LANGFLOW_SERVER_URL = os.getenv("LANGFLOW_SERVER_URL")
+LANGFLOW_API_KEY = os.getenv("LANGFLOW_API_KEY")
+```
+
+**TypeScript/JavaScript:**
+```typescript
+const LANGFLOW_SERVER_URL = process.env.LANGFLOW_SERVER_URL;
+const LANGFLOW_API_KEY = process.env.LANGFLOW_API_KEY;
+```
+
+Set these environment variables before running the examples, or replace the variable references in the code examples with your actual Langflow server URL and API key.
+
+The default `LANGFLOW_SERVER_URL` for a local Langflow deployment is `http://localhost:7860`.
+For remote deployments, the domain is set by your hosting service, such as `https://UUID.ngrok.app`.
+
+### Authentication and headers
+
+All Workflows API requests require authentication using a Langflow API key. The API key is passed in the `x-api-key` header.
+
+For more information, see [Create a Langflow API key](/api-keys-and-authentication).
+
+| Header | Description | Example |
+|--------|-------------|---------|
+| `Content-Type` | Specifies the JSON format. | `application/json` |
+| `x-api-key` | Your Langflow API key. | `sk-...` |
+| `accept` | Optional. Specifies the response format. | `application/json` |
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-basic-component-structure.mdx b/docs/versioned_docs/version-1.9.0/_partial-basic-component-structure.mdx
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+1. Create a Python file for your component, such as `dataframe_processor.py`.
+
+2. Write your component as an object of the [`Component`](https://github.com/langflow-ai/langflow/blob/main/src/backend/base/langflow/custom/custom_component/component.py) class. Create a new class that inherits from `Component` and override the base class's methods.
+
+ :::tip Backwards compatibility
+ The `lfx` import path replaced the `import from langflow.custom import Component` in Langflow 1.7, but the original input is still compatible and works the same way.
+ :::
+
+ ```python
+ from typing import Any, Dict, Optional
+ import pandas as pd
+ from lfx.custom.custom_component.component import Component
+
+ class DataFrameProcessor(Component):
+ """A component that processes pandas DataFrames with various operations."""
+ ```
+
+3. Define class attributes to provide information about your custom component:
+
+ ```python
+ from typing import Any, Dict, Optional
+ import pandas as pd
+ from lfx.custom.custom_component.component import Component
+
+ class DataFrameProcessor(Component):
+ """A component that processes pandas DataFrames with various operations."""
+
+ display_name: str = "DataFrame Processor"
+ description: str = "Process and transform pandas DataFrames with various operations like filtering, sorting, and aggregation."
+ documentation: str = "https://docs.langflow.org/components-dataframe-processor"
+ icon: str = "DataframeIcon"
+ priority: int = 100
+ name: str = "dataframe_processor"
+ ```
+
+ * `display_name`: A user-friendly name shown in the visual editor.
+ * `description`: A brief description of what your component does.
+ * `documentation`: A link to detailed documentation.
+ * `icon`: An emoji or icon identifier for visual representation.
+ Langflow uses [Lucide](https://lucide.dev/icons) for icons. To assign an icon to your component, set the icon attribute to the name of a Lucide icon as a string, such as `icon = "file-text"`. Langflow renders icons from the Lucide library automatically.
+ For more information, see [Contributing bundles](/contributing-bundles#add-the-bundle-to-the-frontend-folder).
+ * `priority`: An optional integer to control display order. Lower numbers appear first.
+ * `name`: An optional internal identifier that defaults to class name.
+4. Define the component's interface by specifying its inputs, outputs, and the method that will process them. The method name must match the `method` field in your outputs list, as this is how Langflow knows which method to call to generate each output.
+
+ This example creates a minimal custom component skeleton.
+
+ ```python
+ from typing import Any, Dict, Optional
+ import pandas as pd
+ from lfx.custom.custom_component.component import Component
+
+ class DataFrameProcessor(Component):
+ """A component that processes pandas DataFrames with various operations."""
+
+ display_name: str = "DataFrame Processor"
+ description: str = "Process and transform pandas DataFrames with various operations like filtering, sorting, and aggregation."
+ documentation: str = "https://docs.langflow.org/components-dataframe-processor"
+ icon: str = "DataframeIcon"
+ priority: int = 100
+ name: str = "dataframe_processor"
+
+ # input and output lists
+ inputs = []
+ outputs = []
+
+ # method
+ def some_output_method(self):
+ return ...
+ ```
diff --git a/docs/versioned_docs/version-1.9.0/_partial-conditional-params.mdx b/docs/versioned_docs/version-1.9.0/_partial-conditional-params.mdx
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+Some parameters are conditional, and they are only available after you set other parameters or select specific options for other parameters.
+Conditional parameters may not be visible on the **Controls** pane until you set the required dependencies.
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-dev-mode-windows.mdx b/docs/versioned_docs/version-1.9.0/_partial-dev-mode-windows.mdx
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+If you are running Langflow Desktop on Windows, you must [enable Developer Mode](https://learn.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development#activate-developer-mode) to use the Docling components.
+The location of this setting depends on your Windows OS version.
+Find **For developers** in your Windows Settings, or search for "Developer" in the Windows search bar, and then enable **Developer mode**.
+You might need to restart your computer or Langflow to apply the change.
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-docker-docling-deps.mdx b/docs/versioned_docs/version-1.9.0/_partial-docker-docling-deps.mdx
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+* **Docker/Linux system dependencies**: If running Langflow in a Docker container on Linux, you might need to install additional system packages for document processing. For more information, see [Document processing errors in Docker containers](/troubleshoot#document-processing-errors-in-docker-containers).
+
diff --git a/docs/versioned_docs/version-1.9.0/_partial-escape-curly-braces.mdx b/docs/versioned_docs/version-1.9.0/_partial-escape-curly-braces.mdx
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+If your template includes literal text and variables, you can use double curly braces to escape literal curly braces in the template and prevent interpretation of that text as a variable.
+For example: `This is a template with {{literal text in curly braces}} and a {variable}`.
+
+If your template contains many literal curly braces, such as JSON structures, consider using Mustache templating instead.
+For more information, see [Use Mustache templating in prompt templates](/components-prompts#use-mustache-templating-in-prompt-templates).
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-global-model-providers.mdx b/docs/versioned_docs/version-1.9.0/_partial-global-model-providers.mdx
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+import Icon from "@site/src/components/icon";
+
+To edit Langflow's global model provider configuration, do the following:
+
+1. To open the **Model Providers** pane, click your profile icon, select **Settings**, and then click **Model Providers**.
+2. In the **Model Providers** pane, select a provider.
+3. In the **API Key** field, add your provider's API key.
+
+ The key must have permission to call the models you want to use in your flow, and your account must have sufficient credits for the actions you want to perform.
+
+ You can only add one key for each provider. Make sure the key has access to _all_ models that you want to use in Langflow.
+4. Enable the specific models that you want to use in Langflow.
+The available models depend on the provider and your API key's permissions.
+Models that generate text are listed under **Language Models**.
+Models that generate embeddings are listed under **Embedding Models**.
+
+After you enable a model in Langflow's global model configuration, you can use that model in any model-driven component in your flows.
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-hidden-params.mdx b/docs/versioned_docs/version-1.9.0/_partial-hidden-params.mdx
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+Some parameters are hidden by default in the visual editor.
+You can modify all component parameters through the [component inspection panel](/concepts-components#component-inspection-panel) that appears when you select a component.
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-kb-summary.mdx b/docs/versioned_docs/version-1.9.0/_partial-kb-summary.mdx
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+A Langflow knowledge base is a local vector database that is stored in Langflow storage.
+
+Because knowledge bases are local, the data isn't remotely requested and re-ingested with every flow run.
+This can be more efficient than using a remote vector database, and it is a good choice for flows that use custom, domain-specific datasets, like slices of customer and product data.
+
+You can use knowledge base components in much the same way that you use vector store components.
+However, there are several key differences:
+
+* **Local storage**: Langflow knowledge bases are exclusively local.
+In contrast, only some vector store components support local databases.
+* **Built-in embedding models**: Langflow knowledge bases include built-in support for several embedding models.
+Other models aren't supported for use with knowledge bases.
+To use a different provider or model, you must use a vector store component along with your preferred embedding model component.
+* **Basic similarity search**: When querying Langflow knowledge bases, only standard similarity search is supported.
+For more advanced searches, you must use a vector store component for a vector database provider that supports your desired functionality.
+* **Structured data**: Langflow knowledge bases only support structured data.
+For unstructured data, you must use a compatible vector store component.
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-legacy.mdx b/docs/versioned_docs/version-1.9.0/_partial-legacy.mdx
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+++ b/docs/versioned_docs/version-1.9.0/_partial-legacy.mdx
@@ -0,0 +1,17 @@
+import Icon from "@site/src/components/icon";
+
+Legacy components are longer supported and can be removed in a future release.
+You can continue to use them in existing flows, but it is recommended that you replace them with supported components as soon as possible.
+Suggested replacements are included in the **Legacy** banner on components in your flows.
+They are also given in release notes and Langflow documentation whenever possible.
+
+If you aren't sure how to replace a legacy component, **Search** for components by provider, service, or component name.
+The component may have been deprecated in favor of a completely new component, a similar component, or a new version of the same component in a different category.
+
+If there is no obvious replacement, consider whether another component can be adapted to your use case.
+For example, many **Core components** provide generic functionality that can support multiple providers and use cases, such as the [**API Request** component](/api-request).
+
+If neither of these options are viable, you could use the legacy component's code to create your own custom component, or [start a discussion](/contributing-github-issues) about the legacy component.
+
+To discourage use of legacy components in new flows, these components are hidden by default.
+In the visual editor, you can click **Component settings** to toggle the **Legacy** filter.
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-mcp-node-tip.mdx b/docs/versioned_docs/version-1.9.0/_partial-mcp-node-tip.mdx
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@@ -0,0 +1,8 @@
+:::tip
+`uvx` is included with `uv` in the Langflow package.
+
+To use `npx` server commands, you must first install an LTS release of [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm). If you run Langflow in Docker, install Node.js inside the container image and rebuild so that `npx`-based MCP servers are available at runtime. For more information, see [Package management](/develop-application#package-management).
+
+For an example of an `npx` MCP server in Langflow, see [Connect an Astra DB MCP server to Langflow](/mcp-component-astra).
+:::
+
diff --git a/docs/versioned_docs/version-1.9.0/_partial-mcp-tools-add-steps.mdx b/docs/versioned_docs/version-1.9.0/_partial-mcp-tools-add-steps.mdx
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@@ -0,0 +1,16 @@
+import Icon from "@site/src/components/icon";
+import McpIcon from '@site/static/logos/mcp-icon.svg';
+
+As of Langflow 1.9.x, the **MCP Tools** component is not listed as a draggable component under the **Agents** category. To add an MCP tool to a flow, do the following:
+
+1. Register the MCP server.
+ Open **Settings** → **MCP Servers**, and then click **Add MCP Server**, or click **MCP** in the flow editor’s left sidebar, and then click **Add MCP Server**.
+
+2. Configure and save the server.
+ Enter the connection details, and then click **Save**.
+ For more information, see [Use Langflow as an MCP client](/mcp-client).
+
+3. In the **MCP** sidebar, drag the new server to the canvas.
+ The **MCP Tools** component for that server is added to the canvas.
+
+For more information, see [Use Langflow as an MCP client](/mcp-client) and [Use Langflow as an MCP server](/mcp-server).
diff --git a/docs/versioned_docs/version-1.9.0/_partial-podman-alt.mdx b/docs/versioned_docs/version-1.9.0/_partial-podman-alt.mdx
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+++ b/docs/versioned_docs/version-1.9.0/_partial-podman-alt.mdx
@@ -0,0 +1,3 @@
+:::tip
+Podman can be used instead of Docker for all commands shown here. For more information, see the [Podman documentation](https://podman.io/docs).
+:::
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-vector-rag-blurb.mdx b/docs/versioned_docs/version-1.9.0/_partial-vector-rag-blurb.mdx
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@@ -0,0 +1,11 @@
+import Icon from "@site/src/components/icon";
+
+By design, vector data is essential for LLM applications, such as chatbots and agents.
+
+While you can use an LLM alone for generic chat interactions and common tasks, you can take your application to the next level with context sensitivity (such as RAG) and custom datasets (such as internal business data).
+This often requires integrating vector databases and vector searches that provide the additional context and define meaningful queries.
+
+Langflow includes vector store components that can read and write vector data, including embedding storage, similarity search, Graph RAG traversals, and dedicated search instances like OpenSearch.
+Because of their interdependent functionality, it is common to use vector store, language model, and embedding model components in the same flow or in a series of dependent flows.
+
+To find available vector store components, browse [**Bundles**](/components-bundle-components) or **Search** for your preferred vector database provider.
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diff --git a/docs/versioned_docs/version-1.9.0/_partial-vector-rag-flow.mdx b/docs/versioned_docs/version-1.9.0/_partial-vector-rag-flow.mdx
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--- /dev/null
+++ b/docs/versioned_docs/version-1.9.0/_partial-vector-rag-flow.mdx
@@ -0,0 +1,75 @@
+import Icon from "@site/src/components/icon";
+
+:::tip
+For a tutorial that uses vector data in a flow, see [Create a vector RAG chatbot](/chat-with-rag).
+:::
+
+The following example demonstrates how to use vector store components in flows alongside related components like embedding model and language model components.
+These steps walk through important configuration details, functionality, and best practices for using these components effectively.
+This is only one example; it isn't a prescriptive guide to all possible use cases or configurations.
+
+1. Create a flow with the **Vector Store RAG** template.
+
+ This template has two subflows.
+ The **Load Data** subflow loads embeddings and content into a vector database, and the **Retriever** subflow runs a vector search to retrieve relevant context based on a user's query.
+
+2. Configure the database connection for both [**Astra DB** components](/bundles-datastax#astra-db), or replace them with another pair of vector store components of your choice.
+Make sure the components connect to the same vector store, and that the component in the **Retriever** subflow is able to run a similarity search.
+
+ The parameters you set in each vector store component depend on the component's role in your flow.
+ In this example, the **Load Data** subflow _writes_ to the vector store, whereas the **Retriever** subflow _reads_ from the vector store.
+ Therefore, search-related parameters are only relevant to the **Vector Search** component in the **Retriever** subflow.
+
+ For information about specific parameters, see the documentation for your chosen vector store component.
+
+3. To configure the embedding model, do one of the following:
+
+ * **Use an OpenAI model**: In both **OpenAI Embeddings** components, enter your OpenAI API key.
+ You can use the default model or select a different OpenAI embedding model.
+
+ * **Use another provider**: Replace the **OpenAI Embeddings** components with another pair of [embedding model components](/components-embedding-models) of your choice, and then configure the parameters and credentials accordingly.
+
+ * **Use Astra DB vectorize**: If you are using an Astra DB vector store that has a vectorize integration, you can remove both **OpenAI Embeddings** components.
+ If you do this, the vectorize integration automatically generates embeddings from the **Ingest Data** (in the **Load Data** subflow) and **Search Query** (in the **Retriever** subflow).
+
+
+ :::tip
+ If your vector store already contains embeddings, make sure your embedding model components use the same model as your previous embeddings.
+ Mixing embedding models in the same vector store can produce inaccurate search results.
+ :::
+
+4. Recommended: In the [**Split Text** component](/split-text), optimize the chunking settings for your embedding model.
+For example, if your embedding model has a token limit of 512, then the **Chunk Size** parameter must not exceed that limit.
+
+ Additionally, because the **Retriever** subflow passes the chat input directly to the vector store component for vector search, make sure that your chat input string doesn't exceed your embedding model's limits.
+ For this example, you can enter a query that is within the limits; however, in a production environment, you might need to implement additional checks or preprocessing steps to ensure compliance.
+ For example, use additional components to prepare the chat input before running the vector search, or enforce chat input limits in your application code.
+
+5. In the **Language Model** component, enter your OpenAI API key, or select a different provider and model to use for the chat portion of the flow.
+
+6. Run the **Load Data** subflow to populate your vector store.
+In the **Read File** component, select one or more files, and then click **Run component** on the vector store component in the **Load Data** subflow.
+
+ The **Load Data** subflow loads files from your local machine, chunks them, generates embeddings for the chunks, and then stores the chunks and their embeddings in the vector database.
+
+ 
+
+ The **Load Data** subflow is separate from the **Retriever** subflow because you probably won't run it every time you use the chat.
+ You can run the **Load Data** subflow as needed to preload or update the data in your vector store.
+ Then, your chat interactions only use the components that are necessary for chat.
+
+ If your vector store already contains data that you want to use for vector search, then you don't need to run the **Load Data** subflow.
+
+7. Open the **Playground** and start chatting to run the **Retriever** subflow.
+
+ The **Retriever** subflow generates an embedding from chat input, runs a vector search to retrieve similar content from your vector store, parses the search results into supplemental context for the LLM, and then uses the LLM to generate a natural language response to your query.
+ The LLM uses the vector search results along with its internal training data and tools, such as basic web search and datetime information, to produce the response.
+
+ 
+
+ To avoid passing the entire block of raw search results to the LLM, the **Parser** component extracts `text` strings from the search results `JSON` object, and then passes them to the **Prompt Template** component in `Message` format.
+ From there, the strings and other template content are compiled into natural language instructions for the LLM.
+
+ You can use other components for this transformation, such as the **JSON Operations** component, depending on how you want to use the search results.
+
+ To view the raw search results, click **Inspect output** on the vector store component after running the **Retriever** subflow.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/_partial-vector-search-results.mdx b/docs/versioned_docs/version-1.9.0/_partial-vector-search-results.mdx
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--- /dev/null
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@@ -0,0 +1,2 @@
+If you use a vector store component to query your vector database, it produces search results that you can pass to downstream components in your flow as a list of [`JSON`](/data-types#json) objects or a tabular [`Table`](/data-types#table).
+If both types are supported, you can set the format near the vector store component's output port in the visual editor.
\ No newline at end of file
diff --git a/docs/versioned_docs/version-1.9.0/_partial-vector-store-instance.mdx b/docs/versioned_docs/version-1.9.0/_partial-vector-store-instance.mdx
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+++ b/docs/versioned_docs/version-1.9.0/_partial-vector-store-instance.mdx
@@ -0,0 +1,8 @@
+Because Langflow is based on LangChain, vector store components use an instance of [LangChain vector store](https://docs.langchain.com/oss/python/integrations/vectorstores) to drive the underlying read and write functions.
+These instances are provider-specific and configured according to the component's parameters, such as the connection string, index name, and schema.
+
+In component code, this is often instantiated as `vector_store`, but some vector store components use a different name, such as the provider name.
+
+Some LangChain classes don't expose all possible options as component parameters.
+Depending on the provider, these options might use default values or allow modification through environment variables, if they are supported in Langflow.
+For information about specific options, see the LangChain API reference and vector store provider's documentation.
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diff --git a/docs/versioned_sidebars/version-1.9.0-sidebars.json b/docs/versioned_sidebars/version-1.9.0-sidebars.json
new file mode 100644
index 0000000000..d567e52513
--- /dev/null
+++ b/docs/versioned_sidebars/version-1.9.0-sidebars.json
@@ -0,0 +1,572 @@
+{
+ "docs": [
+ {
+ "type": "html",
+ "value": "