Deep Research

A multi-agent AI research system that transforms user queries into structured research reports.

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Overview

Deep Research is a FastAPI-based application that uses a multi-agent approach to perform comprehensive research on user queries. The system consists of three specialized agents:

1. Planner Agent: Transforms user queries into structured research plans
2. Researcher Agent: Executes research tasks using web search tools
3. Synthesizer Agent: Compiles findings into a cohesive final report

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Features

• REST API for submitting research queries
• Multi-agent architecture for specialized task handling
• Integration with OpenAI's GPT models
• Web search capabilities via Tavily API
• Docker support for easy deployment

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Tech Stack

• Python 3.9+
• FastAPI
• LangChain & LangGraph
• OpenAI API
• Tavily Search API
• Poetry for dependency management
• Docker & Docker Compose

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Installation

Prerequisites

• Python 3.9 or higher
• Poetry
• Docker & Docker Compose (optional)

Local Setup

1. Clone the repository:

   git clone <repository-url>
   cd deep-research

2. Install dependencies:

   poetry install

3. Create a .env file with your API keys:

   openai_api_key=your_openai_api_key
   tavily_api_key=your_tavily_api_key

4. Run the application:

   poetry run uvicorn src.main:app --host 0.0.0.0

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Docker Setup

1. Build and run with Docker Compose:

   docker-compose up --build

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API Usage

Health Check

GET /health

Response:

{
  "message": "running..."
}

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Submit Research Query

POST /research

Request Body:

{
  "query": "What are the latest advancements in quantum computing?"
}

Response:

{
  "plan": "...",
  "research": "...",
  "final_report": "...",
  "messages": [...]
}

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Project Structure

deep-research/
├── src/
│   ├── agents/        # Multi-agent system components
│   ├── api/           # FastAPI routes and schemas
│   ├── core/          # Core configuration and logic
│   ├── prompts/       # Agent prompt templates
│   ├── tools/         # Research tools
│   ├── utils/         # Utility functions
│   └── main.py        # Application entry point
├── tests/             # Test suite
├── Dockerfile         # Docker configuration
├── docker-compose.yml # Docker Compose configuration
├── pyproject.toml     # Poetry dependencies
└── README.md          # This file

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License

[License information]

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Contributors

• Alexander Zwerner