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Multi-Agent-Deep-Researcher

architecture diagram

A sophisticated multi-type research and analysis system that combines web content extraction, PDF processing, and specialized domain research to provide comprehensive insights across various research domains.

🚀 Features

Multi-Domain Research Capabilities

  • Developer Tools Research: Analyze development tools, APIs, and technologies
  • Product Research: Compare products, features, and alternatives
  • Educational Research: Evaluate institutions, programs and courses
  • Financial Research: Analyze stocks, companies, and market trends
  • Technical Documentation: Review APIs, SDKs, and technical specifications
  • Industry Research: Study market trends, competitors, and industry analysis
  • General Research: Fallback for uncategorized queries.

Intelligent Content Processing

  • Web Content Extraction: Advanced web scraping and content analysis
  • PDF Document Processing: Intelligent PDF analysis with relevance scoring
  • Entity Extraction: Automated identification of companies, products, and technologies
  • Context-Aware Analysis: Domain-specific insights and recommendations

Flexible PDF Integration

  • PDF Selection Options:
    • Auto-select relevant PDFs
    • Manual PDF selection
    • Relevance-based PDF ranking
  • S3 Integration: Secure PDF storage and retrieval
  • Relevance Scoring: Intelligent PDF filtering based on query context

Specialized Output Formatting

Each research type provides tailored output with domain-specific metrics and insights.

🏗️ Architecture

The system uses a multi-agent architecture with specialized nodes for different research types:

  • Intent Detection Agent: Classifies research queries
  • Content Extraction Agent: Processes web content
  • PDF Processing Agent: Analyzes PDF documents
  • Specialized Research Agents: Domain-specific analysis
  • Analysis & Synthesis Agent: Combines insights and generates recommendations

📋 Prerequisites

  • Python 3.8+
  • AWS S3 access (for PDF storage)
  • Required API keys (configured via environment variables)

🛠️ Installation

  1. Clone the repository

    git clone <repository-url>
cd Advanced-Research-Agent

  2. Install dependencies

    cd advanced-agent
pip install -r requirements.txt

  3. Configure environment variables

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

  4. Run the application

    python main.py

🔧 Configuration

Create a .env file with the following variables:

# API Keys
ANTHROPIC_API_KEY=your_anthropic_key
FIRECRAWL_API_KEY=your_firecrawl_key

# AWS Configuration
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_REGION=us-east-2
S3_BUCKET_NAME=your_bucket_name

# Application Settings
MAX_ENTITIES=10
MAX_PDFS=5

🎯 Usage

Basic Research

python main.py

Research Options

  1. Regular Research: Auto-selects relevant PDFs
  2. Select Specific PDFs: Choose specific PDFs for analysis
  3. Find Relevant PDFs: Rank PDFs by relevance to query

Example Queries

  • "Best tools to build AI agents"
  • "Compare Python web frameworks"
  • "Top universities for computer science"
  • "Stock analysis for tech companies"
  • "API documentation for payment processing"

📊 Output Examples

Developer Tools Research

1. 🏢 LangChain
   🌐 Website: https://langchain.com
   💰 Pricing: Freemium
   📖 Open Source: Yes
   🛠️ Tech Stack: Python, JavaScript, TypeScript
   💻 Language Support: Python, JavaScript, TypeScript
   🔌 API: ✅ Available
   🔗 Integrations: OpenAI, Anthropic, Pinecone

Product Research

1. 📱 ChatGPT
   🏷️ Category: AI Assistant
   🌐 Website: https://chat.openai.com
   💰 Price: Free + Premium
   ⭐ Rating: 4.8/5
   ✨ Features: Natural language processing, Code generation
   🎯 Target: Developers, Content creators

🔒 Security

  • Environment variables for sensitive configuration
  • AWS IAM roles for S3 access
  • Secure API key management
  • No hardcoded credentials in source code

📁 Project Structure

Advanced-Research-Agent/
├── advanced-agent/
│   ├── main.py                 # Main application entry point
│   ├── src/
│   │   ├── workflow.py         # Workflow orchestration
│   │   ├── models.py           # Data models
│   │   ├── prompts.py          # LLM prompts
│   │   ├── firecrawl.py        # Web content extraction
│   │   ├── pdf_notetaker.py    # PDF processing
│   │   └── s3_pdf_service.py   # S3 PDF management
│   ├── pyproject.toml          # Project configuration
│   └── README.md               # Agent-specific documentation
├── simple-agent/               # Simplified version
├── .gitignore                  # Git ignore rules
└── README.md                   # This file

🤝 Contributing

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

📄 License

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

🙏 Acknowledgments

📞 Support

For questions or support, please open an issue in the GitHub repository.

Note: This is a research and development tool. Please ensure compliance with relevant terms of service and data privacy regulations when using third-party APIs and services.