mcp-use is an open-source framework designed to seamlessly connect any Language Learning Model (LLM) to any Model Context Protocol (MCP) server, enabling developers to build, deploy, and manage AI agents efficiently. By eliminating vendor lock-in and promoting open standards, mcp-use empowers developers to integrate models with real-world capabilities, such as data access and workflow automation, on their own terms. Key Features: - Agent Framework: Facilitates the creation of tool-using AI agents with support for various LLM providers, including OpenAI, Anthropic, Google, and Groq. Agents can dynamically select and execute appropriate tools, manage conversation history, and provide structured outputs with schema validation. - Client Library: Offers a fully compliant MCP client that supports all protocol primitives, such as sampling, tools, resources, prompts, elicitation, logging, and notifications. This ensures seamless communication between clients and MCP servers. - Server Framework: Provides a comprehensive MCP server framework for TypeScript, enhancing the official MCP SDK with support for Edge Runtime, ChatGPT Apps SDK, and MCP-UI. It includes built-in tools like the MCP Inspector for debugging and testing, and supports the creation of UI widgets compatible with various chat clients. - Inspector Tool: A web-based debugging and inspection tool that allows developers to test tools, explore resources, manage prompts, and monitor server connections directly from the browser. It supports multi-server management and interactive tool execution with real-time results. Primary Value and Problem Solved: mcp-use addresses the challenges of integrating LLMs with diverse MCP servers by providing a unified, open-source framework that simplifies development and deployment processes. It eliminates the need for proprietary clients, reduces operational complexity, and enhances security through centralized configuration management and access control. By supporting a wide range of MCP servers and LLM providers, mcp-use enables developers to build scalable, compliant, and efficient AI applications without being constrained by vendor-specific limitations.