nostr-langchain-mcp 0.0.4
Make Anthropic Model Context Protocol (MCP) tools compatible with LangChain and LangGraph agents.
Requires Python: >=3.11

# 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).

![MCP](static/img/mcp.png)

## 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="sse")
```

```bash
python weather_server.py
```

### Client

```python
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

async with 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/sse",
            "transport": "sse",
        }
    }
) as client:
    agent = create_react_agent("openai:gpt-4.1", client.get_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?"})
```

## 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/) stremable HTTP server:

```bash
cd examples/servers/streamable-http-stateless/
uv run mcp-simple-streamablehttp-stateless --port 3000
```

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

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

## 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")

async with 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/sse",
            "transport": "sse",
        }
    }
) as client:
    tools = 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
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(model="claude-3-5-sonnet-latest")

@asynccontextmanager
async def make_graph():
    async with 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/sse",
                "transport": "sse",
            }
        }
    ) as client:
        agent = create_react_agent(model, client.get_tools())
        yield 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"
  }
}
```