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# 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).
## 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 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
```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="streamable-http")
```
```bash
python weather_server.py
```
### Client
```python
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:
>
> ```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 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`:
```python
# 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?"})
```
## Passing runtime headers
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:
* `sse`
* `streamable_http`
### Example: passing headers with `MultiServerMCPClient`
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
client = MultiServerMCPClient(
{
"weather": {
"transport": "streamable_http",
"url": "http://localhost:8000/mcp",
"headers": {
"Authorization": "Bearer YOUR_TOKEN",
"X-Custom-Header": "custom-value"
},
}
}
)
tools = await client.get_tools()
agent = create_react_agent("openai:gpt-4.1", tools)
response = await agent.ainvoke({"messages": "what is the weather in nyc?"})
```
> Only `sse` and `streamable_http` transports support runtime headers. These headers are passed with every HTTP request to the MCP server.
## Using with LangGraph StateGraph
```python
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](https://langchain-ai.github.io/langgraph/tutorials/langgraph-platform/local-server/) 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:
```python
# 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`](https://langchain-ai.github.io/langgraph/cloud/reference/cli/#configuration-file) make sure to specify `make_graph` as your graph entrypoint:
```json
{
"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`
```python
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")
```
