RefNet is a comprehensive tool for searching research papers and visualizing their citation networks. It combines a powerful search interface with an interactive graph visualization and AI-powered analysis to help researchers explore academic literature and understand citation relationships.

• Cedar OS + Mastra Backend: Intelligent chat interface for paper analysis
• Smart Context: AI understands your selected papers and graph relationships
• Research Insights: Compare papers, identify patterns, and discover research gaps
• Real-time Analysis: Ask questions about your selected papers and get instant insights

• Search research papers by title, authors, topics, and keywords
• Filter results by publication date, citation count, and relevance
• Sort by most cited, relevance score, or publication date
• Paginated results with customizable page sizes
• Multiselect functionality - Select multiple papers to build combined citation networks

• Build citation networks from any research paper or multiple papers
• Interactive D3.js-powered graph with zoom, pan, and drag functionality
• Node selection and highlighting
• Timeline-based color coding
• Light grey/white lines for clean, academic appearance
• Export selected papers and graph data

• Responsive design that works on desktop and mobile
• Fast, modern React frontend
• Real-time API integration
• Intuitive user experience

• Python 3.8+ (for search API)
• Node.js 18+ (for Cedar OS + Mastra)
• npm or yarn
• OpenAI API key

1. Set your OpenAI API key:

   export OPENAI_API_KEY='your-openai-api-key-here'

2. Start all services:

   ./start_cedar_mastra.sh

   This will start:

   • Flask search API on http://localhost:8000
   • Mastra AI backend on http://localhost:4111
   • React frontend on http://localhost:3000

If you prefer to start services manually:

1. Start Flask search API:

   python app.py

2. Start Mastra AI backend:

   cd mastra-backend
   npm start

3. Start React frontend:

   cd refnet/frontend
   npm start

To build the frontend for production:

cd refnet/frontend
npm run build

The built files will be in refnet/frontend/build/ and will be automatically served by the Flask backend.

1. Search Papers: Use the landing page to search for research papers by entering keywords, author names, or topics.

2. Select Papers:

   • Use checkboxes to select multiple papers from search results
   • Click "Build Graph" to create a combined citation network from all selected papers
   • Or click "View Graph" on individual papers for single-paper networks

3. Explore Network:

   • Click and drag nodes to rearrange the graph
   • Click nodes to select/deselect them
   • Use the controls to adjust graph parameters (iterations, limits)
   • Export selected papers as JSON

4. AI Analysis:

   • Click the chat button to open the AI research assistant
   • Ask questions about your selected papers
   • Get intelligent insights, comparisons, and research recommendations
   • The AI understands your paper context and graph relationships

5. Navigate: Use the back button to return to search results or start a new search.

• GET /api/search?q=query&page=1&per_page=25&sort=cited_by_count - Search papers

• GET /api/paper/{paper_id} - Get paper details
• GET /api/paper/{paper_id}/citations - Get paper citations
• GET /api/paper/{paper_id}/references - Get paper references

• GET /api/graph/{paper_id}?iterations=3&cited_limit=5&ref_limit=5 - Build citation graph from single paper
• POST /api/graph/multiple - Build graph from multiple papers (multiselect)
• GET /api/graph/data - Get current graph data
• POST /api/graph/clear - Clear current graph

RefNet/
├── app.py                    # Flask search API entry point
├── config.py                # Configuration settings
├── requirements.txt         # Python dependencies
├── mastra-backend/          # Mastra AI backend
│   ├── server.js           # AI agent server
│   └── package.json        # Backend dependencies
├── refnet/
│   ├── api/                # API route blueprints
│   ├── models/             # Data models
│   ├── services/           # Business logic services
│   ├── utils/              # Utility functions
│   └── frontend/           # Cedar OS React frontend
│       ├── src/
│       │   ├── components/
│       │   │   └── FloatingCedarChat.js  # AI chat interface
│       │   ├── cedar/       # Cedar OS configuration
│       │   └── ...
│       ├── public/
│       └── package.json
├── start_cedar_mastra.sh   # Startup script
└── README.md

• Flask: Search API framework
• Mastra: AI agent orchestration
• OpenAI GPT-4o: Research analysis AI
• OpenAlex API: Research paper data source
• NetworkX: Graph analysis
• Flask-CORS: Cross-origin resource sharing

• Cedar OS: AI-powered chat interface
• React 18: UI framework
• React Router: Client-side routing
• D3.js: Graph visualization
• Axios: HTTP client
• Tailwind CSS: Styling

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

This project is licensed under the MIT License - see the LICENSE file for details.

• OpenAlex for providing research paper data
• D3.js for graph visualization capabilities
• The academic research community for inspiration and use cases