metadata

title: 🤖 ResearchCopilot
emoji: 🔬
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: true
license: mit
short_description: Multi-agent AI research system
tags:
  - agent-demo-track
  - multi-agent
  - research
  - perplexity
  - claude
  - openai
video_overview: https://youtu.be/SuBUxtBKUvQ
collection: https://huggingface.co/collections/Agents-MCP-Hackathon

🤖 ResearchCopilot - Multi-Agent Research System

Track 3: Agentic Demo Showcase - Gradio MCP Hackathon 2025

A sophisticated multi-agent AI system that demonstrates the power of collaborative AI agents working together to conduct comprehensive research. ResearchCopilot breaks down complex research queries into structured tasks and employs specialized agents to gather, analyze, and synthesize information from multiple sources.

🎯 Demo Video

[Link to video demonstration will be added here]

🚀 Features

Multi-Agent Architecture

🎯 Planner Agent: Intelligently breaks down research queries into structured, prioritized tasks
🔍 Retriever Agent: Searches multiple sources (Perplexity API, Google Search, Academic databases)
📝 Summarizer Agent: Analyzes and synthesizes information using Claude/GPT models
📚 Citation Agent: Generates proper academic citations in multiple formats (APA, MLA, Chicago, IEEE, Harvard)

Key Capabilities

Real-time collaborative agent orchestration
Adaptive research planning based on query complexity
Cross-agent learning and decision making
Parallel task execution for efficient research
Professional citation generation
Comprehensive research documentation

Technical Highlights

Built with Gradio for intuitive user experience
Deployed on Modal for scalable serverless execution
Asynchronous agent communication
Real API integrations (Perplexity, Google, Anthropic)
Comprehensive error handling and fallbacks

🏗️ System Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   User Query    │───▶│   Orchestrator  │───▶│   Results UI    │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                               │
                ┌──────────────┼──────────────┐
                │              │              │
        ┌───────▼───────┐ ┌────▼──────┐ ┌────▼──────┐
        │ Planner Agent │ │ Retriever │ │Summarizer │
        └───────────────┘ │   Agent   │ │   Agent   │
                          └───────────┘ └───────────┘
                                 │             │
                          ┌──────▼──────┐ ┌───▼────────┐
                          │   APIs      │ │ Citation   │
                          │ Perplexity  │ │   Agent    │
                          │   Google    │ └────────────┘
                          │  Academic   │
                          └─────────────┘

🛠️ Installation & Setup

Local Development

Clone and Install Dependencies

git clone <repository-url>
cd research-copilot
pip install -r requirements.txt

Environment Configuration

cp .env.example .env
# Edit .env with your API keys

Run Locally

python research_copilot.py

Modal Deployment

Install Modal

pip install modal
modal setup

Configure Secrets

modal secret create research-copilot-secrets \
  PERPLEXITY_API_KEY=your_key \
  GOOGLE_API_KEY=your_key \
  GOOGLE_SEARCH_ENGINE_ID=your_id \
  ANTHROPIC_API_KEY=your_key

Deploy to Modal

modal deploy modal_app.py

🔧 API Keys Required

Required for Full Functionality

Perplexity API: Real-time search capabilities
Google Custom Search API: Web search functionality
Anthropic Claude API: Advanced summarization

Optional

OpenAI API: Alternative summarization
Additional APIs: ArXiv, CrossRef for academic sources

Note: The system includes comprehensive mock data for demonstration without API keys

💡 Usage Examples

Basic Research Query

"Latest developments in quantum computing for drug discovery"

Comparative Analysis

"Compare renewable energy adoption in Europe vs Asia 2024"

Academic Research

"Recent peer-reviewed studies on AI bias in healthcare diagnostics"

Technical Analysis

"How does blockchain technology improve supply chain transparency"

🎨 User Interface

The Gradio interface provides:

Interactive Research Input: Natural language query processing with example prompts
Real-time Agent Activity: Live visualization of agent collaboration and decision-making
Tabbed Results Display:
- 📊 Summary: Comprehensive research synthesis with key findings
- 📚 Sources: Detailed source analysis with relevance scoring
- 📖 Citations: Multi-format academic citations (APA, MLA, Chicago, IEEE, Harvard)
- 🔍 Process Log: Complete agent activity timeline and reasoning
Progress Tracking: Real-time progress indicators for each research phase
Responsive Design: Works seamlessly across desktop and mobile devices

🏆 Hackathon Submission - Track 3

Innovation Highlights

Multi-Agent Orchestration: Demonstrates sophisticated AI agent collaboration
Adaptive Intelligence: Agents learn from each other and adjust strategies dynamically
Real-world Integration: Production-ready with actual API integrations
Scalable Architecture: Built for real-world deployment and usage

Demo Scenarios

Academic Research: "Climate change impact on Arctic biodiversity"
Technology Analysis: "Comparison of LLM architectures for code generation"
Market Research: "Sustainable packaging trends in food industry 2025"
Policy Analysis: "AI regulation frameworks across major economies"

📁 Project Structure

research-copilot/
├── research_copilot.py      # Main app with full UI and agent system
├── modal_app.py             # Modal deployment configuration  
├── enhanced_agents.py       # Production agents with API integrations
├── requirements.txt         # All dependencies
├── .env.example            # API key template
├── deploy.sh               # One-command deployment
├── README.md               # Comprehensive documentation
└── Project_Structure.md    # This summary

🧪 Testing

# Run agent tests
python -m pytest tests/test_agents.py -v

# Run integration tests
python -m pytest tests/test_integration.py -v

# Run UI tests
python -m pytest tests/test_ui.py -v

🔮 Future Enhancements

Planned Features

Voice Interface: Natural language voice queries and responses
Research Templates: Pre-configured workflows for different research types
Collaborative Research: Multi-user research sessions with shared workspaces
Export Options: PDF reports, Word documents, presentation slides
Advanced Analytics: Research quality metrics and bias detection
Custom Agent Training: User-specific agent customization and learning

API Integrations Roadmap

ArXiv: Academic paper search and analysis
PubMed: Medical and life sciences research
CrossRef: DOI resolution and metadata
Semantic Scholar: AI-powered academic search
News APIs: Real-time news aggregation
Social Media: Trend analysis and public sentiment

🤝 Contributing

We welcome contributions! Please see our contributing guidelines:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

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

🙏 Acknowledgments

Gradio Team: For the amazing interface framework
Modal: For serverless deployment platform
Anthropic: For Claude API integration
Perplexity: For real-time search capabilities
Hackathon Organizers: For the opportunity to showcase multi-agent AI

📞 Contact

Team: ResearchCopilot Development Team
Email: 24f1001493@ds.study.iitm.ac.in
Demo: [Link to live demo]
Video: [Link to demonstration video]

Built for the Gradio Agents & MCP Hackathon 2025 - Track 3: Agentic Demo Showcase

Demonstrating the future of AI-powered research through intelligent agent collaboration