This library provides a lightweight wrapper that makes Anthropic Model Context Protocol (MCP) tools compatible with LangChain and LangGraph.
Note
A JavaScript/TypeScript version of this library is also available at langchainjs.
- 🛠️ 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
pip install langchain-mcp-adaptersHere 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>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")# 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?"})The library also allows you to connect to multiple MCP servers and load tools from them:
# 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")python weather_server.pyfrom 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:
from langchain_mcp_adapters.tools import load_mcp_tools client = MultiServerMCPClient({...}) async with client.session("math") as session: tools = await load_mcp_tools(session)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 3000Alternatively, 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 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 session: # Initialize the connection await session.initialize() # Get tools tools = await load_mcp_tools(session) agent = create_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 langchain.agents import create_agent client = MultiServerMCPClient( { "math": { "transport": "http", "url": "http://localhost:3000/mcp" }, } ) 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?"})When connecting to MCP servers, you can include custom headers (e.g., for authentication or tracing) using the headers field in the connection configuration. This is supported for the following transports:
ssehttp(orstreamable_http)
from langchain_mcp_adapters.client import MultiServerMCPClient from langchain.agents import create_agent client = MultiServerMCPClient( { "weather": { "transport": "http", "url": "http://localhost:8000/mcp", "headers": { "Authorization": "Bearer YOUR_TOKEN", "X-Custom-Header": "custom-value" }, } } ) tools = await client.get_tools() agent = create_agent("openai:gpt-4.1", tools) response = await agent.ainvoke({"messages": "what is the weather in nyc?"})Only
sseandhttptransports support runtime headers. These headers are passed with every HTTP request to the MCP server.
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": "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?"})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 langchain.agents import create_agent async def make_graph(): client = MultiServerMCPClient( { "weather": { # make sure you start your weather server on port 8000 "url": "http://localhost:8000/mcp", "transport": "http", }, # ATTENTION: MCP's stdio transport was designed primarily to support applications running on a user's machine. # Before using stdio in a web server context, evaluate whether there's a more appropriate solution. # For example, do you actually need MCP? or can you get away with a simple `@tool`? "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", }, } ) tools = await client.get_tools() agent = create_agent("openai:gpt-4.1", tools) return agentIn your langgraph.json make sure to specify make_graph as your graph entrypoint:
{ "dependencies": ["."], "graphs": { "agent": "./graph.py:make_graph" } }
