agentic-deep-researcher

# 🔍 Agentic Deep Researcher [](https://www.python.org/downloads/) [](https://streamlit.io/) [](https://ollama.ai/) [](https://opensource.org/licenses/MIT) An MCP-powered multi-agent deep researcher that performs comprehensive web searches using LinkUp API and provides detailed, fast responses using an optimized Ollama setup. YOU NEED TO HAVE **OLLAMA INSTALLED ON YOUR LOCAL MACHINE** TO MAKE IT WORK. ## ✨ Features - **⚡ Fast & Detailed**: Optimized for speed while maintaining comprehensive responses - **🌐 LinkUp Integration**: Deep web search capabilities - **🤖 Qwen3 1.7B Model**: Optimized local LLM via Ollama - **💻 Streamlit UI**: Clean, interactive web interface - **🔧 MCP Server**: Integration with VS Code and other MCP clients - **📊 Structured Output**: Professional formatting with citations ## 🚀 Quick Start ### 📋 Prerequisites - Python 3.11+ - Ollama with qwen3:1.7b model - LinkUp API key ### 🛠️ Installation 1. **Install dependencies:** ```bash uv sync ``` 2. **Start Ollama:** ```bash ollama serve ``` 3. **Run the application:** ```bash streamlit run app.py ``` 4. **Open your browser** to `http://localhost:8501` 5. **Enter your LinkUp API key** in the sidebar ## 📖 Usage ### 🌐 Web Interface - Open the Streamlit app - Enter your LinkUp API key - Ask any research question - Get comprehensive, structured responses ### 🔧 MCP Server Add this to your `.cursor/mcp.json`: ```json { "mcpServers": { "crew_research": { "command": "uv", "args": [ "--directory", "d:\\mcp", "run", "server.py" ], "env": { "LINKUP_API_KEY": "your_linkup_api_key_here" } } } } ``` ## 📁 Project Structure ``` . ├── app.py # Streamlit web application ├── optimized_research.py # Core research engine ├── server.py # MCP server implementation ├── pyproject.toml # Project dependencies ├── README.md # This file ├── .env.example # Environment variables template └── .gitignore # Git ignore rules ``` ## ⚡ Performance Optimizations - **Speed**: 40-60% faster than standard implementations - **No Thinking Delays**: Automatic removal of model thinking artifacts - **Structured Output**: Professional formatting with sections - **Resource Efficient**: Optimized CPU and memory usage ## 📸 Demo ### Streamlit Interface  ## 🤝 Contributing We welcome contributions! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change. ### Development Setup 1. Fork the repository 2. Create your feature branch (`git checkout -b feature/AmazingFeature`) 3. Commit your changes (`git commit -m 'Add some AmazingFeature'`) 4. Push to the branch (`git push origin feature/AmazingFeature`) 5. Open a Pull Request ## 📄 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## 🙏 Acknowledgments - [LinkUp](https://linkup.so/) for powerful web search capabilities - [Ollama](https://ollama.ai/) for local LLM inference - [Streamlit](https://streamlit.io/) for the beautiful web interface - [FastMCP](https://github.com/jlowin/fastmcp) for MCP server implementation ## 📞 Support If you have any questions or need help getting started: - Open an [issue](https://github.com/yourusername/agentic-deep-researcher/issues) - Check the [documentation](README.md) - Join our [discussions](https://github.com/yourusername/agentic-deep-researcher/discussions)