LangChain MCP Chat Platform

Name: LangChain MCP Chat Platform
Author: BilalAltundag

@BilalAltundag

关于 LangChain MCP Chat Platform

A versatile chat platform that integrates LangChain, custom MCP servers, and various AI models for enhanced chat capabilities.

基本信息

分类

AI 与智能体

运行时

python

传输方式

stdio

发布者

BilalAltundag

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "langchain-mcp-chat-platform": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "venv"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is LangChain MCP Chat Platform?

LangChain MCP Chat Platform is a chat platform that integrates LangChain, custom MCP (Model Control Protocol) servers, and Google's Gemini AI model for enhanced conversational capabilities. It is designed for developers who want to build a multi-service conversational application with web search, email, and accounting integrations.

How to use LangChain MCP Chat Platform?

Clone the repository, create a Python virtual environment, install dependencies from requirements.txt, set up a .env file with your GOOGLE_API_KEY and SMITHERY_KEY, then run the application from the web_js directory with python main.py. Optionally, enable the Gmail API in Google Cloud Console for email features.

Key features of LangChain MCP Chat Platform

LangChain integration for advanced conversation management and tool usage
Powered by Google Gemini 2.0 Flash model
Custom MCP servers: Tavily web search, Gmail, and accounting (muhasebe)
Conversation history tracking for contextual responses
Responsive web UI built with FastAPI and WebSockets
Extensible architecture to add new tools and capabilities

Use cases of LangChain MCP Chat Platform

Build a personal assistant that can search the web via Tavily and extract content
Automate email operations such as sending, reading, or managing Gmail messages
Manage financial records through the integrated accounting system (muhasebe)
Create a conversational interface that maintains context across multiple interactions
Prototype and test new MCP server integrations with a ready-made web UI

FAQ from LangChain MCP Chat Platform

What API keys are required?

You need GOOGLE_API_KEY for Gemini and SMITHERY_KEY to run Gmail and Tavily MCP servers. The SMITHERY_KEY alone is sufficient; you do not need separate TAVILY_API_KEY or GMAIL_API_KEY in this application.

What runtime dependencies are needed?

The application requires Python 3 with packages listed in requirements.txt, Node.js and NPM (for Tavily/Gmail via Smithery CLI), and a Google Cloud project with the Gmail API enabled if using email features.

How do I run only the custom accounting MCP server?

Modify web_js/main.py to include only the custom_mcp service in MultiServerMCPClient and comment out Tavily and Gmail entries. Start the application with python main.py.

What should I do if the application hangs during startup?

Try starting only with the custom MCP server, install Node.js globally, run npm install -g @smithery/cli, or run services manually in separate terminals to identify the cause.

Where does conversation data live?

The README does not specify a data storage backend beyond describing database operations in the app/database/ folder, but no details on persistence location or format are provided.

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