Deep Research AI Agent

Generate comprehensive research reports on any topic using AI-powered web search and synthesis.

Problem

Researching a topic thoroughly is time-consuming. You need to find sources, evaluate credibility, synthesize information, and format citations—all before you even start writing.

Solution

This tool automates the research workflow:

1. Search — Queries Tavily API for relevant sources (filters out social media noise)
2. Cache — Stores results in ChromaDB with semantic search and smart TTL
3. Synthesize — LLM drafts structured reports in your chosen style
4. Export — Download as PDF, Word, Markdown, or BibTeX

Live Demo →

Architecture

flowchart LR
    subgraph Input
        Q[Query]
        C[Config]
    end

    subgraph LangGraph["LangGraph Workflow"]
        R[Research Node]
        D[Draft Node]
    end

    subgraph Storage
        V[(ChromaDB<br/>Vector Store)]
        CA[(Joblib Cache)]
    end

    subgraph External
        T[Tavily API]
        O[OpenRouter LLM]
    end

    Q --> R
    C --> D
    R --> |search| T
    R --> |check cache| V
    R --> |store| V
    R --> |cache query| CA
    R --> D
    D --> |generate| O
    D --> Export[PDF/Word/MD/BibTeX]

Key Design Decisions:

• Two-node state machine: Research and Draft are decoupled for testability
• Vector memory with TTL: News content expires in 3 days; evergreen in 30 days
• Cross-encoder reranking: Improves retrieval precision over raw similarity
• Domain filtering: Excludes Reddit, Twitter, TikTok by default

Quick Start

# Clone
git clone https://github.com/saksham-jain177/AI-Agent-based-Deep-Research.git
cd AI-Agent-based-Deep-Research

# Install
pip install -r requirements.txt

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

# Run
streamlit run app.py

Required API Keys:

Optional:

ENABLE_VECTOR_STORE=true    # Enable ChromaDB caching
PREFER_CACHE_RESULTS=false  # Prefer cached over fresh results

Configuration

Performance

Measured on typical queries with ChromaDB caching enabled.

Non-Goals

This tool is not:

• A fact-verification engine — always verify critical claims
• Real-time streaming — uses section-by-section progress
• A citation authority — check sources before academic submission
• Optimized for legal/medical/financial advice

Limitations

• Rate limits: Tavily free tier has daily limits
• Model availability: OpenRouter free models may be rate-limited
• Citation accuracy: Auto-generated citations should be manually verified
• Language support: Best results in English; ES/DE are functional but less tested

Project Structure

├── app.py              # Streamlit UI
├── main.py             # LangGraph workflow orchestration
├── research_agent.py   # Tavily search + vector store integration
├── draft_agent.py      # LLM prompting and section generation
├── vector_store.py     # ChromaDB with reranking and TTL
├── cost_estimator.py   # Token/cost estimation with uncertainty
├── citation_formatter.py # APA/MLA/IEEE/BibTeX generation
└── tests/              # Pytest test suite

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/your-idea)
3. Run tests (pytest tests/ -v)
4. Submit a pull request

See CONTRIBUTING.md for detailed guidelines.

License

MIT License — use freely for personal or commercial projects.

Questions? Open an issue or start a discussion.