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mcp-use is a unified MCP framework to build MCP servers, MCP clients and MCP Agents.

MCP Agent

mcp-use has a complete MCP Agent implementation for Python. MCP Agents are AI-powered agents that can use tools from MCP servers to accomplish complex tasks. They reason across multiple steps, selecting and executing tools as needed. Building such agents is easy with mcp-use, all you need is an LLM and the MCP Client.
The agent requires a LangChain LLM provider package. The examples use langchain-openai - swap for langchain-anthropic, langchain-google-genai, or langchain-groq as needed.
Here’s a simple example to get you started for an agent with browser tools support.
For multi-server setups, tool restrictions, and advanced configuration options, see the Agent Configuration and Client Configuration guides.
For a complete overview of the mcp-use MCP Agent, see the MCP Agent documentation.

Available MCP Servers

mcp-use supports any MCP server. Check out the Awesome MCP Servers list for available options. Or deploy your own following the instruction below.

MCP Client

mcp-use has a complete MCP Client implementation for Python with full async support. It supports stdio and HTTP with SSE transports for connecting to any MCP server.
Then create a new MCP Client:
To learn more about the MCP Client, see the MCP Client documentation.

MCP Server

mcp-use has a complete MCP server framework implementation for Python. It supports all official MCP features including tools, resources, prompts, and middleware.

Scaffold a new project

Create an MCP server manually:

To learn more about the MCP Server, see the MCP Server documentation.

Next Steps

Agent Configuration

Configure your Agent and connected MCP servers

LLM Integration

Explore supported LLM providers and their setup

Examples

Browse real-world examples and use cases
Need Help? Join our Discord or Github communities.