🎓 Multi-Agent Research System

A powerful AI-driven system designed to assist researchers in generating IEEE-standard research papers. This project leverages a team of specialized AI agents to handle different stages of the research process, from topic suggestion to drafting and peer review.

🚀 Features

The system employs a multi-agent architecture to simulate a real-world research workflow:

1. Topic Suggester Agent: Suggests high-potential, practical research topics based on your area of interest. Focuses on topics with high impact and manageable innovation risk.
2. Researcher Agent: Conducts deep research on the selected topic, gathering relevant information, methodologies, and context.
3. Writer Agent: Synthesizes the research material into a structured IEEE-standard paper draft.
4. Reviewer Agent: Acts as a peer reviewer, providing critical feedback and suggestions to improve the draft.

🛠️ Technology Stack

• Python: Core programming language.
• Streamlit: For the interactive web interface.
• Google Generative AI (Gemini): Powering the intelligence of the agents.
• python-dotenv: For environment variable management.

📋 Prerequisites

• Python 3.8 or higher
• A Google Cloud API Key with access to Gemini models.

📦 Installation

1. Clone the repository:

   git clone <repository-url>
   cd Multi-Agent-Research-System

2. Create and activate a virtual environment (optional but recommended):

   python -m venv .venv
   # Windows
   .venv\Scripts\activate
   # macOS/Linux
   source .venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Configuration:

   • Create a .env file in the root directory.
   • Add your Google API key:

     GOOGLE_API_KEY=your_google_api_key_here

🖥️ Usage

You can run the system in two modes: Web Interface (recommended) or Command Line Interface (CLI).

Option 1: Web Interface (Streamlit)

This provides a user-friendly interface to interact with the agents.

streamlit run app.py

1. Open the URL provided in the terminal (usually http://localhost:8501).
2. Step 1: Enter your area of interest and click "Suggest Topics".
3. Step 2: Copy a topic or enter your own, then click "Start Research & Writing".
4. The system will display progress as each agent completes its task.
5. Download the generated Draft Paper and Review Feedback.

Option 2: Command Line Interface (CLI)

Run the script directly in your terminal for a text-based experience.

python main.py

Follow the on-screen prompts to enter your area of interest and select a topic. The output files (_draft.md and _review.txt) will be saved in the current directory.

📂 Project Structure

Multi-Agent-Research-System/
├── agents/                 # Agent implementations
│   ├── base_agent.py       # Base class for agents
│   ├── researcher.py       # Researcher agent logic
│   ├── reviewer.py         # Reviewer agent logic
│   ├── topic_suggester.py  # Topic suggester logic
│   └── writer.py           # Writer agent logic
├── app.py                  # Streamlit web application
├── main.py                 # CLI entry point
├── config.py               # Configuration and API setup
├── requirements.txt        # Project dependencies
└── README.md               # Project documentation

📄 License

MIT License