LFX MCP Server

lfx-mcp is an MCP (Model Context Protocol) server that gives any MCP-compatible client full programmatic control over a Langflow instance to build and run flows.

The server is implemented in src/lfx/src/lfx/mcp/ using FastMCP. It connects to Langflow's REST API. Flow data is never cached server-side, so every mutating tool does a GET → modify → PATCH cycle. The component registry is cached on first access per session.

Prerequisites

• A running Langflow instance
• A Langflow API key
• lfx installed (uv pip install lfx), or uv installed if you want to run via uvx without a permanent install

Connect a client

lfx-mcp runs over stdio: your MCP client spawns it as a subprocess and communicates over stdin and stdout. There is no HTTP port to connect to.

Any client that supports stdio MCP servers can connect to the lfx-mcp server. Set the command to lfx-mcp (or uvx --from lfx lfx-mcp) and pass the following environment variables:

For example, to connect to Claude Desktop, add the following to the Claude Desktop configuration file at ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "langflow": {
      "command": "lfx-mcp",
      "env": {
        "LANGFLOW_SERVER_URL": "http://localhost:7860",
        "LANGFLOW_API_KEY": "<your-api-key>"
      }
    }
  }
}

If lfx is not installed globally, use it through uvx instead.

{
  "mcpServers": {
    "langflow": {
      "command": "uvx",
      "args": ["--from", "lfx", "lfx-mcp"],
      "env": {
        "LANGFLOW_SERVER_URL": "http://localhost:7860",
        "LANGFLOW_API_KEY": "<your-api-key>"
      }
    }
  }
}

Server tools

The server exposes the following tool groups to the connected MCP client.

Auth

Flows

Starter projects

Components

Connections

Execution

Utility

How to use the server

The server's instructions describe the intended usage pattern:

1. Authenticate — call login, or set LANGFLOW_API_KEY before starting
2. Discover components — use search_component_types or describe_component_type
3. Build a flow — use create_flow_from_spec for a complete flow in one call, or step-by-step with create_flow → add_component → configure_component → connect_components
4. Run the flow — call run_flow

The batch tool lets you send multiple actions in a single call, with $N.field references to chain results:

[
  {"tool": "create_flow", "args": {"name": "My Chatbot"}},
  {"tool": "add_component", "args": {"flow_id": "$0.id", "component_type": "ChatInput"}},
  {"tool": "add_component", "args": {"flow_id": "$0.id", "component_type": "OpenAIModel"}},
  {"tool": "add_component", "args": {"flow_id": "$0.id", "component_type": "ChatOutput"}},
  {"tool": "connect_components", "args": {
    "flow_id": "$0.id", "source_id": "$1.id", "source_output": "message",
    "target_id": "$2.id", "target_input": "input_value"
  }},
  {"tool": "connect_components", "args": {
    "flow_id": "$0.id", "source_id": "$2.id", "source_output": "text_output",
    "target_id": "$3.id", "target_input": "input_value"
  }}
]

Quickstart: build and run a flow with Claude Code

This example shows how to connect Claude Code to a running Langflow instance using lfx-mcp, then build, validate, and run a chatbot flow from your terminal.

Prerequisites

• A Langflow server running at http://localhost:7860
• A Langflow API key. Create one in the Langflow UI under Settings → Langflow API → Create new API key.
• An OpenAI API key. This example uses Langflow's Agent component with OpenAI. Add your OpenAI API key as a Global Variable in Langflow under Settings → Global Variables so all flows can use it automatically, or pass it explicitly when prompted. If you prefer a different provider, adjust the prompt accordingly.
• uv installed. The uvx command used to run lfx-mcp requires uv. For more information, see the uv docs.
• Claude Code installed. For more information, see the Claude Code docs.

1. Add lfx-mcp to Claude Code.

Run the following command in your terminal. Replacing the placeholder values with your actual keys:

claude mcp add langflow \
  -e LANGFLOW_SERVER_URL=http://localhost:7860 \
  -e LANGFLOW_API_KEY=<YOUR_LANGFLOW_API_KEY> \
  -- uvx --from lfx lfx-mcp

2. Verify lfx-mcp was added to Claude Code:

claude mcp list

The output should include:

langflow: uvx --from lfx lfx-mcp

This confirms that Claude Code knows to spawn an lfx-mcp process when it needs to talk to Langflow.

3. Start Claude Code in your terminal:

claude

4. Give Claude Code instructions. For example:

Create a simple agent chatbot flow in Langflow using OpenAI, validate the flow, and then run it with the message "What is Langflow?"

Given this instruction, Claude Code will typically do the following:

1. Discover the available components using `search_component_types` or `describe_component_type`.
2. Create the flow with all nodes and connections in one request using `create_flow_from_spec`.
3. Validate that every component is correctly connected using `validate_flow`.
4. Run the flow using `run_flow` and return the response.

The flow appears in your Langflow UI at http://localhost:7860 because lfx-mcp creates it through the Langflow API. The answer is printed in your terminal:

Langflow is a visual workflow builder for AI-powered agents. It lets you
connect LLMs, tools, and data sources in a drag-and-drop UI, then expose
the result as an API endpoint or run it from the command line.

Troubleshooting

• lfx-mcp not found when adding the server Use uvx --from lfx lfx-mcp, not uvx lfx-mcp. The lfx-mcp binary ships inside the lfx package, and there is no standalone lfx-mcp package on PyPI.

• 403 Forbidden when Claude Code tries to use tools The API key is invalid or expired. Create a new API key in Langflow under Settings → Langflow API, and then remove and re-add the MCP server:

claude mcp remove langflow
claude mcp add langflow \
  -e LANGFLOW_SERVER_URL=http://localhost:7860 \
  -e LANGFLOW_API_KEY=<YOUR_LANGFLOW_API_KEY> \
  -- uvx --from lfx lfx-mcp

• Flow validation fails with an LLM provider error The API key for your LLM provider is not configured. Add it as a Global Variable in Langflow (Settings → Global Variables → Add), and then ask Claude Code to validate the flow again.