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<!-- ~ Copyright (c) 2023-2024 Datalayer, Inc. ~ ~ BSD 3-Clause License --> [](https://datalayer.io) [](https://github.com/sponsors/datalayer) # 🦜 🔗 LangChain MCP Client [](https://github.com/datalayer/langchain-mcp-client/actions/workflows/build.yml) [](https://pypi.org/project/langchain-mcp-client) This simple [Model Context Protocol (MCP)](https://modelcontextprotocol.io) client demonstrates the use of MCP server tools by LangChain ReAct Agent. - 🌐 Seamlessly connect to any MCP servers. - 🤖 Use any LangChain-compatible LLM for flexible model selection. - 💬 Interact via CLI, enabling dynamic conversations. ## Conversion to LangChain Tools It leverages a utility function `convert_mcp_to_langchain_tools()`. This function handles parallel initialization of specified multiple MCP servers and converts their available tools into a list of LangChain-compatible tools ([List[BaseTool]](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#langchain_core.tools.base.BaseTool)). ## Installation The python version should be 3.11 or higher. ```bash pip install langchain_mcp_client ``` ## Configuration Create a `.env` file containing all the necessary `API_KEYS` to access your LLM. Configure the LLM, MCP servers, and prompt example in the `llm_mcp_config.json5` file: 1. **LLM Configuration**: Set up your LLM parameters. 2. **MCP Servers**: Specify the MCP servers to connect to. 3. **Example Queries**: Define example queries that invoke MCP server tools. Press Enter to use these example queries when prompted. ## Usage Below an example with a [Jupyter MCP …
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