Content
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<h1 align="center">Open Source MCP CLient Library </h1>
[](https://pypi.org/project/mcp_use/)
[](https://pypi.org/project/mcp_use/)
[](https://pypi.org/project/mcp_use/)
[](https://pypi.org/project/mcp_use/)
[](https://docs.mcp-use.io)
[](https://github.com/pietrozullo/mcp-use/blob/main/LICENSE)
[](https://github.com/astral-sh/ruff)
[](https://github.com/pietrozullo/mcp-use/stargazers)
🌐 MCP-Use is the open source way to connect any LLM to MCP tools and build custom agents that have tool access, without using closed source or application clients.
💡 Let developers easily connect any LLM to tools like web browsing, file operations, and more.
# Quick start
With pip:
```bash
pip install mcp-use
```
Or install from source:
```bash
git clone https://github.com/pietrozullo/mcp-use.git
cd mcp-use
pip install -e .
```
### Installing LangChain Providers
mcp_use works with various LLM providers through LangChain. You'll need to install the appropriate LangChain provider package for your chosen LLM. For example:
```bash
# For OpenAI
pip install langchain-openai
# For Anthropic
pip install langchain-anthropic
# For other providers, check the [LangChain chat models documentation](https://python.langchain.com/docs/integrations/chat/)
```
and add your API keys for the provider you want to use to your `.env` file.
```bash
OPENAI_API_KEY=
ANTHROPIC_API_KEY=
```
> **Important**: Only models with tool calling capabilities can be used with mcp_use. Make sure your chosen model supports function calling or tool use.
### Spin up your agent:
```python
import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient
async def main():
# Load environment variables
load_dotenv()
# Create configuration dictionary
config = {
"mcpServers": {
"playwright": {
"command": "npx",
"args": ["@playwright/mcp@latest"],
"env": {
"DISPLAY": ":1"
}
}
}
}
# Create MCPClient from configuration dictionary
client = MCPClient.from_dict(config)
# Create LLM
llm = ChatOpenAI(model="gpt-4o")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
# Run the query
result = await agent.run(
"Find the best restaurant in San Francisco",
)
print(f"\nResult: {result}")
if __name__ == "__main__":
asyncio.run(main())
```
You can also add the servers configuration from a config file like this:
```python
client = MCPClient.from_config_file(
os.path.join("browser_mcp.json")
)
```
Example configuration file (`browser_mcp.json`):
```json
{
"mcpServers": {
"playwright": {
"command": "npx",
"args": ["@playwright/mcp@latest"],
"env": {
"DISPLAY": ":1"
}
}
}
}
```
For other settings, models, and more, check out the documentation.
# Example Use Cases
## Web Browsing with Playwright
```python
import asyncio
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient
async def main():
# Load environment variables
load_dotenv()
# Create MCPClient from config file
client = MCPClient.from_config_file(
os.path.join(os.path.dirname(__file__), "browser_mcp.json")
)
# Create LLM
llm = ChatOpenAI(model="gpt-4o")
# Alternative models:
# llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# llm = ChatGroq(model="llama3-8b-8192")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
# Run the query
result = await agent.run(
"Find the best restaurant in San Francisco USING GOOGLE SEARCH",
max_steps=30,
)
print(f"\nResult: {result}")
if __name__ == "__main__":
asyncio.run(main())
```
## Airbnb Search
```python
import asyncio
import os
from dotenv import load_dotenv
from langchain_anthropic import ChatAnthropic
from mcp_use import MCPAgent, MCPClient
async def run_airbnb_example():
# Load environment variables
load_dotenv()
# Create MCPClient with Airbnb configuration
client = MCPClient.from_config_file(
os.path.join(os.path.dirname(__file__), "airbnb_mcp.json")
)
# Create LLM - you can choose between different models
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
try:
# Run a query to search for accommodations
result = await agent.run(
"Find me a nice place to stay in Barcelona for 2 adults "
"for a week in August. I prefer places with a pool and "
"good reviews. Show me the top 3 options.",
max_steps=30,
)
print(f"\nResult: {result}")
finally:
# Ensure we clean up resources properly
if client.sessions:
await client.close_all_sessions()
if __name__ == "__main__":
asyncio.run(run_airbnb_example())
```
Example configuration file (`airbnb_mcp.json`):
```json
{
"mcpServers": {
"airbnb": {
"command": "npx",
"args": ["-y", "@openbnb/mcp-server-airbnb"]
}
}
}
```
## Blender 3D Creation
```python
import asyncio
from dotenv import load_dotenv
from langchain_anthropic import ChatAnthropic
from mcp_use import MCPAgent, MCPClient
async def run_blender_example():
# Load environment variables
load_dotenv()
# Create MCPClient with Blender MCP configuration
config = {"mcpServers": {"blender": {"command": "uvx", "args": ["blender-mcp"]}}}
client = MCPClient.from_dict(config)
# Create LLM
llm = ChatAnthropic(model="claude-3-5-sonnet-20240620")
# Create agent with the client
agent = MCPAgent(llm=llm, client=client, max_steps=30)
try:
# Run the query
result = await agent.run(
"Create an inflatable cube with soft material and a plane as ground.",
max_steps=30,
)
print(f"\nResult: {result}")
finally:
# Ensure we clean up resources properly
if client.sessions:
await client.close_all_sessions()
if __name__ == "__main__":
asyncio.run(run_blender_example())
```
# Configuration File Support
MCP-Use supports initialization from configuration files, making it easy to manage and switch between different MCP server setups:
```python
import asyncio
from mcp_use import create_session_from_config
async def main():
# Create an MCP session from a config file
session = create_session_from_config("mcp-config.json")
# Initialize the session
await session.initialize()
# Use the session...
# Disconnect when done
await session.disconnect()
if __name__ == "__main__":
asyncio.run(main())
```
# Multi-Server Support
MCP-Use supports working with multiple MCP servers simultaneously, allowing you to combine tools from different servers in a single agent. This is useful for complex tasks that require multiple capabilities, such as web browsing combined with file operations or 3D modeling.
## Configuration
You can configure multiple servers in your configuration file:
```json
{
"mcpServers": {
"airbnb": {
"command": "npx",
"args": ["-y", "@openbnb/mcp-server-airbnb", "--ignore-robots-txt"]
},
"playwright": {
"command": "npx",
"args": ["@playwright/mcp@latest"],
"env": {
"DISPLAY": ":1"
}
}
}
}
```
## Usage
The `MCPClient` class provides several methods for managing multiple servers:
```python
import asyncio
from mcp_use import MCPClient, MCPAgent
from langchain_anthropic import ChatAnthropic
async def main():
# Create client with multiple servers
client = MCPClient.from_config_file("multi_server_config.json")
# Create agent with the client
agent = MCPAgent(
llm=ChatAnthropic(model="claude-3-5-sonnet-20240620"),
client=client
)
try:
# Run a query that uses tools from multiple servers
result = await agent.run(
"Search for a nice place to stay in Barcelona on Airbnb, "
"then use Google to find nearby restaurants and attractions."
)
print(result)
finally:
# Clean up all sessions
await client.close_all_sessions()
if __name__ == "__main__":
asyncio.run(main())
```
# Tool Access Control
MCP-Use allows you to restrict which tools are available to the agent, providing better security and control over agent capabilities:
```python
import asyncio
from mcp_use import MCPAgent, MCPClient
from langchain_openai import ChatOpenAI
async def main():
# Create client
client = MCPClient.from_config_file("config.json")
# Create agent with restricted tools
agent = MCPAgent(
llm=ChatOpenAI(model="gpt-4"),
client=client,
disallowed_tools=["file_system", "network"] # Restrict potentially dangerous tools
)
# Run a query with restricted tool access
result = await agent.run(
"Find the best restaurant in San Francisco"
)
print(result)
# Clean up
await client.close_all_sessions()
if __name__ == "__main__":
asyncio.run(main())
```
# Roadmap
<ul>
<li>[x] Multiple Servers at once </li>
<li>[x] Test remote connectors (http, ws)</li>
<li>[ ] ... </li>
</ul>
# Contributing
We love contributions! Feel free to open issues for bugs or feature requests.
# Requirements
- Python 3.11+
- MCP implementation (like Playwright MCP)
- LangChain and appropriate model libraries (OpenAI, Anthropic, etc.)
# Citation
If you use MCP-Use in your research or project, please cite:
```bibtex
@software{mcp_use2025,
author = {Zullo, Pietro},
title = {MCP-Use: MCP Library for Python},
year = {2025},
publisher = {GitHub},
url = {https://github.com/pietrozullo/mcp-use}
}
```
# License
MIT
