LangChain MCP Adapters

This library provides a lightweight wrapper that makes Anthropic Model Context Protocol (MCP) tools compatible with LangChain and LangGraph.

Features

• 🛠️ Convert MCP tools into LangChain tools that can be used with LangGraph agents
• 📦 A client implementation that allows you to connect to multiple MCP servers and load tools from them

Installation

pip install langchain-mcp-adapters

Quickstart

Here is a simple example of using the MCP tools with a LangGraph agent.

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.

# 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

# 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 langgraph.prebuilt import create_react_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_react_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

# 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="streamable-http")

python weather_server.py

Client

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_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": "streamable_http",
        }
    }
)
tools = await client.get_tools()
agent = create_react_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:

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 transport.

To start an example streamable HTTP server, run the following:

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:

# Use server from examples/servers/streamable-http-stateless/

from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

from langgraph.prebuilt import create_react_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 session:
        # Initialize the connection
        await session.initialize()

        # Get tools
        tools = await load_mcp_tools(session)
        agent = create_react_agent("openai:gpt-4.1", tools)
        math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})

Use it with MultiServerMCPClient:

# Use server from examples/servers/streamable-http-stateless/
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

client = MultiServerMCPClient(
    {
        "math": {
            "transport": "streamable_http",
            "url": "http://localhost:3000/mcp"
        },
    }
)
tools = await client.get_tools()
agent = create_react_agent("openai:gpt-4.1", tools)
math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})

Using with LangGraph StateGraph

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.graph import StateGraph, MessagesState, START
from langgraph.prebuilt import ToolNode, tools_condition

from langchain.chat_models import init_chat_model
model = init_chat_model("openai:gpt-4.1")

client = MultiServerMCPClient(
    {
        "math": {
            "command": "python",
            # Make sure to update to the full absolute path to your math_server.py file
            "args": ["./examples/math_server.py"],
            "transport": "stdio",
        },
        "weather": {
            # make sure you start your weather server on port 8000
            "url": "http://localhost:8000/mcp",
            "transport": "streamable_http",
        }
    }
)
tools = await client.get_tools()

def call_model(state: MessagesState):
    response = model.bind_tools(tools).invoke(state["messages"])
    return {"messages": response}

builder = StateGraph(MessagesState)
builder.add_node(call_model)
builder.add_node(ToolNode(tools))
builder.add_edge(START, "call_model")
builder.add_conditional_edges(
    "call_model",
    tools_condition,
)
builder.add_edge("tools", "call_model")
graph = builder.compile()
math_response = await graph.ainvoke({"messages": "what's (3 + 5) x 12?"})
weather_response = await graph.ainvoke({"messages": "what is the weather in nyc?"})

Using with LangGraph API Server

Tip

Check out this guide on getting started with LangGraph API server.

If you want to run a LangGraph agent that uses MCP tools in a LangGraph API server, you can use the following setup:

# graph.py
from contextlib import asynccontextmanager
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

async def make_graph():
    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": "streamable_http",
            }
        }
    )
    tools = await client.get_tools()
    agent = create_react_agent("openai:gpt-4.1", tools)
    return agent

In your langgraph.json make sure to specify make_graph as your graph entrypoint:

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./graph.py:make_graph"
  }
}

Add LangChain tools to a FastMCP server

Use to_fastmcp to convert LangChain tools to FastMCP, and then add them to the FastMCP server via the initializer:

Note

tools argument is only available in FastMCP as of mcp >= 1.9.1

from langchain_core.tools import tool
from langchain_mcp_adapters.tools import to_fastmcp
from mcp.server.fastmcp import FastMCP


@tool
def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b


fastmcp_tool = to_fastmcp(add)

mcp = FastMCP("Math", tools=[fastmcp_tool])
mcp.run(transport="stdio")