mcp-use

<picture> <img alt="" src="./static/image.jpg" width="full"> </picture> <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 . ``` 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 MCPClient from config file client = MCPClient.from_config_file("browser_mcp.json") # 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 USING GOOGLE SEARCH", ) print(f"\nResult: {result}") if __name__ == "__main__": asyncio.run(main()) ``` Example configuration file (`browser_mcp.json`): ```json { "mcpServers": { "playwright": { "command": "npx", "args": ["@playwright/mcp@latest"], "env": { "DISPLAY": ":1" } } } } ``` Add your API keys for the provider you want to use to your `.env` file. ```bash OPENAI_API_KEY= ANTHROPIC_API_KEY= ``` 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()) ``` ## Roadmap <ul> <li>[x] Multiple Servers at once </li> <li>[ ] 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