langchain-mcp-adapters

Name: langchain-mcp-adapters
Author: langchain-ai

bylangchain-ai· 1 MCP server

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langchain-ai/langchain-mcp-adapters

$git clone https://github.com/langchain-ai/langchain-mcp-adapters

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Install langchain-mcp-adapters by running `git clone https://github.com/langchain-ai/langchain-mcp-adapters`, then use it for the current task and follow its documentation at https://github.com/langchain-ai/langchain-mcp-adapters.

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README.md

1	# LangChain MCP Adapters
2
3	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).
4
5	![MCP](static/img/mcp.png)
6
7	> [!note]
8	> A JavaScript/TypeScript version of this library is also available at [langchainjs](https://github.com/langchain-ai/langchainjs/tree/main/libs/langchain-mcp-adapters/).
9
10	## Features
11
12	- 🛠️ 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
13	- 📦 A client implementation that allows you to connect to multiple MCP servers and load tools from them
14
15	## Installation
16
17	```bash
18	pip install langchain-mcp-adapters
19	```
20
21	## Quickstart
22
23	Here is a simple example of using the MCP tools with a LangGraph agent.
24
25	```bash
26	pip install langchain-mcp-adapters langgraph "langchain[openai]"
27
28	export OPENAI_API_KEY=<your_api_key>
29	```
30
31	### Server
32
33	First, let's create an MCP server that can add and multiply numbers.
34
35	```python
36	# math_server.py
37	from mcp.server.fastmcp import FastMCP
38
39	mcp = FastMCP("Math")
40
41	@mcp.tool()
42	def add(a: int, b: int) -> int:
43	"""Add two numbers"""
44	return a + b
45
46	@mcp.tool()
47	def multiply(a: int, b: int) -> int:
48	"""Multiply two numbers"""
49	return a * b
50
51	if __name__ == "__main__":
52	mcp.run(transport="stdio")
53	```
54
55	### Client
56
57	```python
58	# Create server parameters for stdio connection
59	from mcp import ClientSession, StdioServerParameters
60	from mcp.client.stdio import stdio_client
61
62	from langchain_mcp_adapters.tools import load_mcp_tools
63	from langchain.agents import create_agent
64
65	server_params = StdioServerParameters(
66	command="python",
67	# Make sure to update to the full absolute path to your math_server.py file
68	args=["/path/to/math_server.py"],
69	)
70
71	async with stdio_client(server_params) as (read, write):
72	async with ClientSession(read, write) as session:
73	# Initialize the connection
74	await session.initialize()
75
76	# Get tools
77	tools = await load_mcp_tools(session)
78
79	# Create and run the agent
80	agent = create_agent("openai:gpt-4.1", tools)
81	agent_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
82	```
83
84	## Multiple MCP Servers
85
86	The library also allows you to connect to multiple MCP servers and load tools from them:
87
88	### Server
89
90	```python
91	# math_server.py
92	...
93
94	# weather_server.py
95	from typing import List
96	from mcp.server.fastmcp import FastMCP
97
98	mcp = FastMCP("Weather")
99
100	@mcp.tool()
101	async def get_weather(location: str) -> str:
102	"""Get weather for location."""
103	return "It's always sunny in New York"
104
105	if __name__ == "__main__":
106	mcp.run(transport="http")
107	```
108
109	```bash
110	python weather_server.py
111	```
112
113	### Client
114
115	```python
116	from langchain_mcp_adapters.client import MultiServerMCPClient
117	from langchain.agents import create_agent
118
119	client = MultiServerMCPClient(
120	{
121	"math": {
122	"command": "python",
123	# Make sure to update to the full absolute path to your math_server.py file
124	"args": ["/path/to/math_server.py"],
125	"transport": "stdio",
126	},
127	"weather": {
128	# Make sure you start your weather server on port 8000
129	"url": "http://localhost:8000/mcp",
130	"transport": "http",
131	}
132	}
133	)
134	tools = await client.get_tools()
135	agent = create_agent("openai:gpt-4.1", tools)
136	math_response = await agent.ainvoke({"messages": "what's (3 + 5) x 12?"})
137	weather_response = await agent.ainvoke({"messages": "what is the weather in nyc?"})
138	```
139
140	> [!note]
141	> 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:
142	>
143	> ```python
144	> from langchain_mcp_adapters.tools import load_mcp_tools
145	>
146	> client = MultiServerMCPClient({...})
147	> asy

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Install langchain-mcp-adapters by running `git clone https://github.com/langchain-ai/langchain-mcp-adapters`, then use it for the current task and follow its documentation at https://github.com/langchain-ai/langchain-mcp-adapters.

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