# Research Brief Generator

This project builds a LangGraph agent that produces a cohesive research brief comparing three entities (e.g., vector databases).  
The agent:

1. Generates comparison criteria using an LLM.  
2. Performs iterative web searches with Tavily for each entity‑criterion pair.  
3. Aggregates findings into a concise research brief.  
4. Provides a recommendation verdict.

## Prerequisites

- Python 3.10+
- An OpenAI API key (set in `OPENAI_API_KEY` environment variable).  
- A Tavily API key (set in `TAVILY_API_KEY` environment variable).

## Setup

```bash
# Clone the repository
git clone https://github.com/yourusername/research-brief.git
cd research-brief

# Create a virtual environment
python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create a .env file with your API keys
echo "OPENAI_API_KEY=your_openai_key" >> .env
echo "TAVILY_API_KEY=your_tavily_key" >> .env
```

## Usage

Run the CLI with default entities (Chroma, FAISS, Qdrant):

```bash
python -m src.main
```

Provide custom entities:

```bash
python -m src.main --entities "EntityA, EntityB, EntityC"
```

The output will display the research brief followed by the verdict.

## Project Structure

```
src/
├── cli.py          # CLI entry point
├── graph.py        # LangGraph workflow
├── main.py         # Package entry
├── nodes.py        # Node implementations
└── state.py        # State schema
```

## Extending

- Replace the LLM with a local model (e.g., Ollama) by adjusting the `llm` initialization in `nodes.py`.  
- Add more sophisticated parsing or error handling as needed.

## License

MIT License