Description

# LangChain MCP Adapters This library provides a lightweight wrapper that makes [Anthropic Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) tools compatible with [LangChain](https://github.com/langchain-ai/langchain) and [LangGraph](https://github.com/langchain-ai/langgraph).  > [!note] > A JavaScript/TypeScript version of this library is also available at [langchainjs](https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-mcp-adapters/). ## Features - 🛠️ Convert MCP tools into [LangChain tools](https://python.langchain.com/docs/concepts/tools/) that can be used with [LangGraph](https://github.com/langchain-ai/langgraph) agents - 📦 A client implementation that allows you to connect to multiple MCP servers and load tools from them ## Installation ```bash pip install langchain-mcp-adapters ``` ## Quickstart Here is a simple example of using the MCP tools with a LangGraph agent. ```bash pip install langchain-mcp-adapters langgraph "langchain[openai]" export OPENAI_API_KEY=<your_api_key> ``` ### Server First, let's create an MCP server that can add and multiply numbers. ```python # math_server.py from mcp.server.fastmcp import FastMCP mcp = FastMCP("Math") @mcp.tool() def add(a: int, b: int) -> int: """Add two numbers""" return a + b @mcp.tool() def multiply(a: int, b: int) -> int: """Multiply two numbers""" return a * b if __name__ == "__main__": mcp.run(transport="stdio") ``` ### Client ```python # Create server parameters for stdio connection from mcp import ClientSession, StdioServerParameters from mcp.client.stdio import stdio_client from langchain_mcp_adapters.tools import load_mcp_tools from langchain.agents import create_agent server_params = StdioServerParameters( command="python", # Make sure to update to the full absolute path to your math_server.py file args=["/path/to/math_server.py"], ) async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: # Initialize the connection await session.initialize() # Get tools tools = await load_mcp_tools(session) # Create and run the agent agent = create_agent("openai:gpt-4.1", tools) agent_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"}) ``` ## Multiple MCP Servers The library also allows you to connect to multiple MCP servers and load tools from them: ### Server ```python # math_server.py ... # weather_server.py from typing import List from mcp.server.fastmcp import FastMCP mcp = FastMCP("Weather") @mcp.tool() async def get_weather(location: str) -> str: """Get weather for location.""" return "It's always sunny in New York" if __name__ == "__main__": mcp.run(transport="http") ``` ```bash python weather_server.py ``` ### Client ```python from langchain_mcp_adapters.client import MultiServerMCPClient from langchain.agents import create_agent client = MultiServerMCPClient( { "math": { "command": "python", # Make sure to update to the full absolute path to your math_server.py file "args": ["/path/to/math_server.py"], "transport": "stdio", }, "weather": { # Make sure you start your weather server on port 8000 "url": "http://localhost:8000/mcp", "transport": "http", } } ) tools = await client.get_tools() agent = create_agent("openai:gpt-4.1", tools) math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"}) weather_response = await agent.ainvoke({"messages": "what is the weather in nyc?"}) ``` > [!note] > Example above will start a new MCP `ClientSession` for each tool invocation. If you would like to explicitly start a session for a given server, you can do: > > ```python > from langchain_mcp_adapters.tools import load_mcp_tools > > client = MultiServerMCPClient({...}) > async with client.session("math") as session: > tools = await load_mcp_tools(session) > ``` ## Streamable HTTP MCP now supports [streamable HTTP](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http) transport. To start an [example](examples/servers/streamable-http-stateless/) streamable HTTP server, run the following: ```bash cd examples/servers/streamable-http-stateless/ uv run mcp-simple-streamablehttp-stateless --port 3000 ``` Alternatively, you can use FastMCP directly (as in the examples above). To use it with Python MCP SDK `streamablehttp_client`: ```python # Use server from examples/servers/streamable-http-stateless/ from mcp import ClientSession from mcp.client.streamable_http import streamablehttp_client from langchain.agents import create_agent from langchain_mcp_adapters.tools import load_mcp_tools async with streamablehttp_client("http://localhost:3000/mcp") as (read, write, _): async with ClientSession(read, write) as s