The Research Intelligence Platform is a powerful, AI-driven research assistant that transforms complex research queries into comprehensive, data-driven reports. Built with a sophisticated LangGraph architecture, this platform automates the entire research workflow from planning and data collection to statistical analysis and visualization.
- Advanced Research Automation: Transform natural language queries into comprehensive research reports
- Multi-stage LangGraph Architecture: Sophisticated agent workflow with specialized nodes for each research phase
- Data Validation & Quality Assessment: Automatic validation of extracted data with quality scoring
- Citation Management: Automatic extraction and formatting of citations in multiple academic styles
- Comparative Analysis: Segment data across multiple dimensions for insightful comparisons
- Interactive Visualizations: Generate charts, graphs, and tables to represent research findings
- Modern React UI: Professional, responsive interface with dashboard analytics
- Project History: Track and revisit previous research projects
The platform uses a multi-stage LangGraph architecture to process research queries:
- Research Planning: Analyzes the query and creates a structured research plan
- Web Search & Content Scraping: Gathers relevant information from authoritative sources
- Citation Management: Extracts and formats citations from all sources
- Content Synthesis: Processes and synthesizes information into coherent insights
- Quantitative Extraction: Identifies and extracts numerical data from sources
- Data Validation: Validates extracted data for quality and consistency
- Statistical Analysis: Performs statistical analysis on validated data
- Comparative Analysis: Compares data across different segments and categories
- Visualization: Generates charts, graphs, and tables to represent findings
- Report Compilation: Creates a comprehensive, professional research report
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Backend:
- Python 3.9+
- LangGraph for agent orchestration
- FastAPI for API endpoints
- Pandas & NumPy for data processing
- Matplotlib for chart generation
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Frontend:
- React 18+
- Material-UI for component library
- React Router for navigation
- Chart.js for interactive visualizations
- Python 3.9+
- Node.js 16+
- API key for LLM service (Google Gemini, OpenAI, etc.)
# Clone the repository
git clone https://github.com/yourusername/personal-research-agent.git
cd personal-research-agent
# Create and activate virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Set up environment variables
cp .env.example .env
# Edit .env with your API keys
# Start the backend server
cd backend
uvicorn main:app --reload# Navigate to frontend directory
cd frontend
# Install dependencies
npm install
# Start the development server
npm startVisit http://localhost:3000 to access the application.
- Enter a research query like "Market size and growth projections for AI in healthcare diagnostics in North America for 2024-2026"
- The system will process the query through its LangGraph workflow
- Monitor progress in real-time as the agent works through each research phase
- Receive a comprehensive report with:
- Executive summary
- Detailed analysis
- Statistical findings
- Comparative insights
- Data visualizations
- Citations and references
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
For freelance inquiries or collaboration opportunities:
- Email: your.email@example.com
- LinkedIn: Your Name
- Portfolio: yourportfolio.com
Built with ❤️ by Your Name