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.

• 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.

• 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

• 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

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

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

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

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

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

python main.py

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

• "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"

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

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

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

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

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

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

• Built with LangChain for LLM orchestration
• Uses Anthropic Claude for AI analysis
• Powered by Firecrawl for web content extraction
• PDF processing with pdfplumber and PyPDF2

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.