MCP_Assistant

# MCP based AI Assistant This is an MCP based AI Assistant built using [Groq](https://groq.com/), [LangChain](https://www.langchain.com/), and [MCP](https://github.com/langchain-ai/mcp), with a Streamlit frontend and FastAPI backend. It enables chat interactions powered by large language models, enhanced with optional tools like search agents and browser automation. --- ## Features - Groq LLM with LangChain and memory enabled conversation - MCP server such as Google Search and Playwright integration - FastAPI backend to handle chat API requests - Streamlit frontend for real-time chat - Utilized Docker for containerized deployment --- ## Project Structure ``` MCP_Assistant/ ├── app.py ├── main.py # FastAPI server with chat endpoint ├── streamlit_app.py # streamlit frontend UI ├── browser_mcp.json # MCP servers ├── .env # Environment variables (API keys) ├── requirements.txt # required dependencies └── README.md # You are here! ``` --- ## Setup Instructions ### 1. Clone and Install Dependencies ```bash git clone https://github.com/itsabhishekm/MCP_Assistant.git cd MCP_Assistant python -m venv .venv .venv\Scripts\activate pip install -r requirements.txt ``` ### 2. Set Environment Variables In the .env file in the root past your groq API KEY: ```GROQ_API_KEY=your_groq_api_key``` ### 3. Configure MCP Tools (Not Required but optional) If you want to add any other MCP server customize browser_mcp.json: ```{ "mcpServers": { "google-search": { "command": "npx", "args": ["-y", "@mcp-server/google-search-mcp@latest"] }, "playwright": { "command": "npx", "args": ["@playwright/mcp@latest"] } } } ``` Or if you don't want any MCP server, just leave it empty: ``` { "mcpServers": {} } ``` ## Running the app ### 1. Start FastAPI Backend ``` uvicorn main:app --reload --port 8000 ``` ### 2. Launch Streamlit Frontend ```streamlit run streamlit_app.py``` ```Visit: http://localhost:8501 ```