diff --git a/404.html b/404.html index 57e56efcf8..011a0f4ad4 100644 --- a/404.html +++ b/404.html @@ -23,8 +23,8 @@ - - + + diff --git a/about-langflow.html b/about-langflow.html index 6497cd3498..cad12805ba 100644 --- a/about-langflow.html +++ b/about-langflow.html @@ -23,8 +23,8 @@ - - + + @@ -52,7 +52,8 @@ It supports important AI functionality like agents and the Model Context Protoco

The visual editor simplifies prototyping of application workflows, enabling developers to quickly turn their ideas into powerful, real-world solutions.

Try it

Build and run your first flow in minutes: Install Langflow, and then try the Quickstart.

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 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, you connect and configure component nodes. Each component is a single step in the workflow.

diff --git a/agent-tutorial.html b/agent-tutorial.html index 50d72def30..07ea4a6c7f 100644 --- a/agent-tutorial.html +++ b/agent-tutorial.html @@ -23,8 +23,8 @@ - - + + @@ -60,12 +60,12 @@

This tutorial uses an OpenAI LLM. If you want to use a different provider, you need a valid credential for that provider.

Create an agentic flow​

-

The following steps modify the Simple agent template to connect Directory and Web search components as tools for an Agent component. +

The following steps modify the Simple Agent template to connect Directory and Web search components 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.

    +

    In Langflow, click New Flow, and then select the Simple Agent template.

  2. Remove the URL and Calculator tools, and then drag the Directory and Web search components into your workspace.

    diff --git a/agents-tools.html b/agents-tools.html index 83cf060f90..f898130a5c 100644 --- a/agents-tools.html +++ b/agents-tools.html @@ -23,8 +23,8 @@ - - + + @@ -58,23 +58,32 @@ These fields help connected agents understand how to use the action, without hav

    A Langflow Agent has a clear idea of each tool's capabilities based on the name and description metadata. The enabled boolean controls the tool's availability to the agent. If you think an agent is using a tool incorrectly, edit a tool's description metadata to help the agent better understand the tool.

    Tool names and descriptions can be edited, but the default tool identifiers cannot be changed. If you want to change the tool identifier, create a custom component.

    Use an agent as a tool​

    -

    The agent component itself also supports Tool Mode for creating multi-agent flows.

    -

    Add an agent to your flow that uses a different OpenAI model for a larger context window.

    +

    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 the Simple agent starter flow.
      2. -
    2. Add a second agent component to the flow.
      3. -
    3. Add your OpenAI API Key to the Agent component.
      4. -
    4. In the Model Name field, select gpt-4.1.
      5. -
    5. Click Tool Mode to use this new agent as a tool.
      6. -
    6. Connect the new agent's Toolset port to the previously created agent's Tools port. -The new agent will use gpt-4.1 for the larger tasks of scraping and searching information that require large context windows. -The previously created agent will now use this agent as a tool, with its unique LLM and toolset.
      7. -
    +
  3. +

    Create a flow based on the Simple Agent template.

    +
    4. +
  4. +

    Add a second Agent component to the flow.

    +
    5. +
  5. +

    Add your OpenAI API Key to both Agent components.

    +
    6. +
  6. +

    In the second Agent component, change the model to gpt-4.1, and then enable Tool Mode.

    +
    7. +
  7. +

    Click Edit Tools to set tool names and descriptions that help the primary agent understand how to use those tools.

    +

    For this example, change the tool name 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.

    +
    8. +
  8. +

    Connect the new agent's Toolset port to the existing agent's Tools port.

    Agent as a tool

    -
      -
    1. The new agent's actions can be edited to help the agent understand how to use it. -Click Edit Tools to modify its name, description, or enabled metadata. -For example, the default tool name is Agent. Edit the name to Agent-gpt-41, and edit the description to Use the gpt-4.1 model for complex problem solving. The connected agent will understand that this is the gpt-4.1 agent, and will use it for tasks requiring a larger context window.
      2. +

    Add custom components as tools​

    An agent can use custom components as tools.

    diff --git a/agents.html b/agents.html index 806eaa3f2a..ad7e67ec1a 100644 --- a/agents.html +++ b/agents.html @@ -23,8 +23,8 @@ - - + + @@ -52,7 +52,7 @@

    Langflow's Agent component is critical for building agentic 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.

    -
    How agents work

    Agents extend LLMs by integrating tools, which are functions that provide additional context and enable autonomous task execution. +

    How do agents work?

    Agents extend 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 knowledge base, 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. @@ -61,7 +61,7 @@ Agents become aware of tools through tool registration, which is when the agent 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.

    Use the Agent component in a flow​

    The following steps explain how to create an agentic flow in Langflow from a blank flow. -For a prebuilt example, use the Simple Agent template or try the Langflow quickstart.

    +For a prebuilt example, use the Simple Agent template or try the Langflow quickstart.

    1. Click New Flow, and then click Blank Flow.

      diff --git a/api-build.html b/api-build.html index 6f2ca6d7c3..569ff8b646 100644 --- a/api-build.html +++ b/api-build.html @@ -23,8 +23,8 @@ - - + + diff --git a/api-files.html b/api-files.html index 488cb04b25..a69a08e9fc 100644 --- a/api-files.html +++ b/api-files.html @@ -23,8 +23,8 @@ - - + + @@ -86,7 +86,7 @@ For example:

    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 a Basic Prompting flow, passing the image file and the query describe this image as input for the Chat Input component. +

      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.


      _14
      curl -X POST \
      _14
      "$LANGFLOW_URL/api/v1/run/a430cc57-06bb-4c11-be39-d3d4de68d2c4?stream=false" \
      _14
      -H "Content-Type: application/json" \
      _14
      -H "x-api-key: $LANGFLOW_API_KEY" \
      _14
      -d '{
      _14
      "output_type": "chat",
      _14
      "input_type": "chat",
      _14
      "tweaks": {
      _14
      "ChatInput-b67sL": {
      _14
      "files": "a430cc57-06bb-4c11-be39-d3d4de68d2c4/2024-11-27_14-47-50_image-file.png",
      _14
      "input_value": "describe this image"
      _14
      }
      _14
      }
      _14
      }'

      tip

      For help with tweaks, use the Input Schema in a flow's API access pane. diff --git a/api-flows-run.html b/api-flows-run.html index 294c4b7a89..c41c1d607d 100644 --- a/api-flows-run.html +++ b/api-flows-run.html @@ -23,8 +23,8 @@ - - + + @@ -56,7 +56,7 @@ For more information, see 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 or in a flow's URL.

      -

      The following example runs a Basic Prompting flow with flow parameters passed in the request body. +

      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.


      _12
      curl -X POST \
      _12
      "$LANGFLOW_SERVER_URL/api/v1/run/$FLOW_ID" \
      _12
      -H "Content-Type: application/json" \
      _12
      -H "x-api-key: $LANGFLOW_API_KEY" \
      _12
      -d '{
      _12
      "input_value": "Tell me about something interesting!",
      _12
      "session_id": "chat-123",
      _12
      "input_type": "chat",
      _12
      "output_type": "chat",
      _12
      "output_component": "",
      _12
      "tweaks": null
      _12
      }'

      The response from /v1/run/$FLOW_ID includes metadata, inputs, and outputs for the run.

      diff --git a/api-flows.html b/api-flows.html index 8c7524997c..082f9c411b 100644 --- a/api-flows.html +++ b/api-flows.html @@ -23,8 +23,8 @@ - - + + diff --git a/api-keys-and-authentication.html b/api-keys-and-authentication.html index cb1b8a8652..96b715942a 100644 --- a/api-keys-and-authentication.html +++ b/api-keys-and-authentication.html @@ -23,8 +23,8 @@ - - + + @@ -61,36 +61,34 @@ For more information, see Langflow API keys​

      You can use Langflow API keys to interact with Langflow programmatically.

      -

      A Langflow API key has the same permissions and access as the user who created it. -This means your API key can only access your own flows, components, and database. -You cannot access other users' resources with your own Langflow API keys.

      -

      If you create a key as a superuser, then that key has superuser privileges within your Langflow server.

      -

      In Langflow version 1.5 and later, most API endpoints require a Langflow API key, even when AUTO_LOGIN is True. +

      In Langflow version 1.5 and later, most API endpoints require a Langflow API key, even when LANGFLOW_AUTO_LOGIN is True. For more information, see LANGFLOW_AUTO_LOGIN.

      +

      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. +You must start your Langflow server with authentication enabled to allow user management and creation of non-superuser accounts.

      Create a Langflow API key​

      You can generate a Langflow API key with the UI or the CLI.

      -

      The UI-generated key is appropriate for most cases. The CLI-generated key is needed when your Langflow server is running in --backend-only mode.

      +

      The UI-generated key is appropriate for most development use cases. The CLI-generated key is needed when your Langflow server is running in --backend-only mode.

      1. In the Langflow UI 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.
        5. -

      Use a Langflow API key​

      @@ -177,6 +175,10 @@ The superuser can also deactivate a user's account as needed.

      When LANGFLOW_NEW_USER_IS_ACTIVE=True, new user accounts are automatically activated.


      _10
      LANGFLOW_NEW_USER_IS_ACTIVE=False

      For more information, see Start a Langflow server with authentication enabled.

      +

      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.

      Start a Langflow server with authentication enabled​

      This section shows you how to use the 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.

      @@ -187,7 +189,7 @@ Additionally, you must sign in as a superuser to manage users and

      _10
      LANGFLOW_AUTO_LOGIN=False
      _10
      LANGFLOW_SUPERUSER=
      _10
      LANGFLOW_SUPERUSER_PASSWORD=
      _10
      LANGFLOW_SECRET_KEY=
      _10
      LANGFLOW_NEW_USER_IS_ACTIVE=False

    +

    _10
    LANGFLOW_AUTO_LOGIN=False
    _10
    LANGFLOW_SUPERUSER=
    _10
    LANGFLOW_SUPERUSER_PASSWORD=
    _10
    LANGFLOW_SECRET_KEY=
    _10
    LANGFLOW_NEW_USER_IS_ACTIVE=False
    _10
    LANGFLOW_ENABLE_SUPERUSER_CLI=False

    Your .env file can have other environment variables. This example focuses on authentication variables.

    9. @@ -203,11 +205,13 @@ Strong, securely-stored credentials are recommended for true development and pro

  9. Save your .env file with the populated variables. For example:

    -

    _10
    LANGFLOW_AUTO_LOGIN=False
    _10
    LANGFLOW_SUPERUSER=administrator
    _10
    LANGFLOW_SUPERUSER_PASSWORD=securepassword
    _10
    LANGFLOW_SECRET_KEY=dBuu...2kM2_fb
    _10
    LANGFLOW_NEW_USER_IS_ACTIVE=False

    +

    _10
    LANGFLOW_AUTO_LOGIN=False
    _10
    LANGFLOW_SUPERUSER=administrator
    _10
    LANGFLOW_SUPERUSER_PASSWORD=securepassword
    _10
    LANGFLOW_SECRET_KEY=dBuu...2kM2_fb
    _10
    LANGFLOW_NEW_USER_IS_ACTIVE=False
    _10
    LANGFLOW_ENABLE_SUPERUSER_CLI=False

  10. Start Langflow with the configuration from your .env file:


    _10
    uv run langflow run --env-file .env

    +

    Starting Langflow with an .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.

  11. Verify the server is running. The default location is http://localhost:7860.

    @@ -241,7 +245,7 @@ Strong, securely-stored credentials are recommended for true development and pro

    Try to access the /admin page. You are redirected to the /flows page if the new user isn't a superuser.

    12. -