Flows
Flows
Flows in Langflow are fully serializable and can be saved and loaded from the file system. In this guide, we'll explore how to import and export flows.
Import Flow
If you've already got a Langflow JSON file, import it into Langflow by clicking on the project name and choosing Import Flow.
diff --git a/Deployment/deployment-docker.html b/Deployment/deployment-docker.html deleted file mode 100644 index d546061c5e..0000000000 --- a/Deployment/deployment-docker.html +++ /dev/null @@ -1,81 +0,0 @@ - - - - - -Docker
This guide will help you get LangFlow up and running using Docker and Docker Compose.
-Prerequisites
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-
-
- Docker -
- Docker Compose -
Docker
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Clone repo and build Docker container
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-
-
Clone the LangFlow repository:
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-git clone https://github.com/langflow-ai/langflow.git
- -
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Navigate to the
-docker_exampledirectory:
-cd langflow/docker_example
- -
-
Run the Docker Compose file:
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-docker compose up
-
LangFlow will now be accessible at http://localhost:7860/.
Docker Compose configuration
-The Docker Compose configuration spins up two services: langflow and postgres.
LangFlow service
-The langflow service uses the langflowai/langflow:latest Docker image and exposes port 7860. It depends on the postgres service.
Environment variables:
--
-
LANGFLOW_DATABASE_URL: The connection string for the PostgreSQL database.
-LANGFLOW_CONFIG_DIR: The directory where LangFlow stores logs, file storage, monitor data, and secret keys.
-
Volumes:
--
-
langflow-data: This volume is mapped to/app/langflowin the container.
-
PostgreSQL service
-The postgres service uses the postgres:16 Docker image and exposes port 5432.
Environment variables:
--
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POSTGRES_USER: The username for the PostgreSQL database.
-POSTGRES_PASSWORD: The password for the PostgreSQL database.
-POSTGRES_DB: The name of the PostgreSQL database.
-
Volumes:
--
-
langflow-postgres: This volume is mapped to/var/lib/postgresql/datain the container.
-
Switch to a specific LangFlow version
-If you want to use a specific version of LangFlow, you can modify the image field under the langflow service in the Docker Compose file. For example, to use version 1.0-alpha, change langflowai/langflow:latest to langflowai/langflow:1.0-alpha.
Agents overview
Agents are AI systems that use LLMs as a brain to analyze problems and select external tools.
+Agents overview
Agents are AI systems that use LLMs as a brain to analyze problems and select external tools.
Instead of developers having to create logical statements to direct every possible path of a program, an agent can operate with autonomy. An agent can leverage external tools and APIs to gather information and take action, demonstrate chain-of-thought reasoning, and generate tailored text for specific purposes.
To simplify the development of agents, Langflow created a custom Tool calling agent component that simplifies configuration and lets developers focus on solving problems with agents.

Create a problem-solving agent
Create a problem-solving agent
Developing agents in Langchain is complex.
The AgentComponent is a component for easily creating an AI agent capable of analyzing tasks using tools you provide.
The component contains all of the elements you'll need for creating an agent. Instead of managing LLM models and providers, pick your model and enter your API key. Instead of connecting a Prompt component, enter instructions in the component's Agent Instruction fields.
diff --git a/api-reference-api-examples.html b/api-reference-api-examples.html index fe50880ff6..380f2b7609 100644 --- a/api-reference-api-examples.html +++ b/api-reference-api-examples.html @@ -16,8 +16,8 @@ - - + + @@ -25,7 +25,7 @@ -API examples
API examples
This page provides examples and practices for managing Langflow using the Langflow API.
The Langflow API's OpenAPI spec can be viewed and tested at your Langflow deployment's docs endpoint.
diff --git a/api.html b/api.html
index be4b20fe27..0e744ba2a5 100644
--- a/api.html
+++ b/api.html
@@ -16,8 +16,8 @@
-
-
+
+
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