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Langchain MCP Client
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Langchain MCP Client

The LangChain MCP client is a project that demonstrates how to use MCP server tools through the LangChain ReAct agent. It supports flexible selection of LLM models and enables dynamic interaction via the CLI.
Developer toolsArtificial intelligence chatbots#LangChain#MCP Protocol#AI Tools.JavaScript
2.5 points
7.9K
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Content Details

Alternatives

Installation

Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.

๐Ÿš€ ๐Ÿฆœ ๐Ÿ”— LangChain MCP Client

This simple Model Context Protocol (MCP) client demonstrates the use of LangChain ReAct agents with MCP server tools.

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๐Ÿš€ Quick Start

The LangChain MCP Client is designed to seamlessly integrate with MCP servers and leverage LangChain ReAct agents. It offers several key features:

  • ๐ŸŒ Smoothly connect to any MCP server.
  • ๐Ÿค– Flexibly select any LangChain-compatible LLM for model usage.
  • ๐Ÿ’ฌ Interact via CLI, supporting dynamic conversations.

โœจ Features

Convert to LangChain Tools

It utilizes a utility function convert_mcp_to_langchain_tools(). This function handles the simultaneous initialization of multiple specified MCP servers and converts their available tools into a list of LangChain-compatible tools (List[BaseTool]).

๐Ÿ“ฆ Installation

The Python version should be 3.11 or higher.

pip install langchain_mcp_client

๐Ÿ“š Documentation

Configuration

Create a .env file containing all the necessary API_KEYS to access your LLM. Configure the LangChain LLM, MCP servers, and example prompts in the llm_mcp_config.json5 file:

  1. LLM Configuration: Set the LangChain LLM parameters.
  2. MCP Servers: Specify the MCP servers to connect to.
  3. Example Queries: Define example queries for invoking MCP server tools. After pressing Enter, these example queries will be used for prompting.

๐Ÿ’ป Usage Examples

Basic Usage

Here is an example with the Jupyter MCP Server: Check the llm_mcp_config.json5 configuration (the command depends on whether you are running on Linux, macOS, or Windows).

# Start jupyterlab.
make jupyterlab

# Launch the CLI.
make cli

Here is an example prompt.

Create many variants of a matplotlib example

๐Ÿ“„ License

The initial code for this project is from hideya/mcp-client-langchain-py (MIT License) and from langchain_mcp_tools (MIT License).

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