AI Hangout - Multi-Agent Chat System

A sophisticated multi-agent chat system that brings together four unique AI personalities in a dynamic group conversation. Built with CrewAI and LangGraph, this project demonstrates the power of collaborative AI with specialized agents working together to create engaging discussions.

About the Project

AI Hangout allows you to chat with four distinct AI personalities simultaneously:

• 🖥️ Tech Enthusiast: Passionate about technology and innovation
• 🎬 Film Critic: Expert in cinema and storytelling
• 💪 Fitness Coach: Focused on health and wellness
• 🧠 Philosopher: Asks thought-provoking questions

Each agent has their own expertise, communication style, and perspective, creating rich, multi-faceted conversations on any topic.

For a detailed explanation of the concepts behind this project, check out my Medium article: Digital Water Cooler Talk: Creating AI Friends That Communicate Using CrewAI and LangGraph

Features

• Multi-Agent Collaboration: Four distinct AI agents with unique personalities and expertise
• Dynamic Conversations: Real-time responses from all agents
• Web Interface: Clean, modern UI for easy interaction
• API Backend: RESTful API for extensibility
• Docker Support: Easy deployment and consistent environments
• Persistent Storage: Conversation history saved locally

Tech Stack

• Backend: Python, CrewAI, LangGraph
• Frontend: Flask, HTML, CSS, JavaScript
• LLM Provider: Groq (Llama 3)
• Deployment: Docker, Docker Compose

Prerequisites

• Python 3.10+
• Docker and Docker Compose (for containerized deployment)
• Groq API key

Installation

Local Development

1. Clone the repository:

   git clone https://github.com/Ibzie/AI_Crew_Chatroom.git
   cd ai-hangout

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt
   pip install flask

4. Create a .env file with your Groq API key:

   GROQ_API_KEY=your_api_key_here
   

Running Locally

1. Start the backend API:

   python main.py --mode api

2. In a new terminal, start the Flask frontend:

   cd frontend
   python app.py

3. Open your browser and navigate to http://localhost:5000

Docker Deployment

1. Build and run with Docker Compose:

   docker-compose build
   docker-compose up

2. Access the application at http://localhost:5000

Project Structure

ai_hangout/
├── agents/                 # AI agent configurations
├── config/                 # Configuration settings
├── frontend/              # Flask web interface
│   ├── app.py            # Flask application
│   ├── static/           # CSS and JavaScript
│   └── templates/        # HTML templates
├── models/                # Data models
├── services/              # External service integrations
├── utils/                 # Utility functions
├── workflows/             # LangGraph workflows
├── docker-compose.yml     # Docker configuration
├── Dockerfile            # Container definition
├── main.py               # Main application entry
└── requirements.txt      # Python dependencies

Usage

1. Enter a topic to discuss
2. All four AI agents will respond with their unique perspectives
3. Continue the conversation by sending your own messages
4. Each agent maintains their personality throughout the discussion

API Endpoints

• POST /api/start-conversation: Start a new conversation
• GET /api/continue-conversation/{id}: Get agent responses
• POST /api/add-message/{id}: Add a user message

Configuration

Key settings can be adjusted in config/settings.py:

• DEFAULT_MODEL: The Groq model to use (default: llama3-8b-8192)
• MAX_ITERATIONS: Maximum conversation turns per round
• DEFAULT_TEMPERATURE: Controls response creativity

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Acknowledgments

• CrewAI for the multi-agent framework
• LangGraph for conversation flow management
• Groq for fast LLM inference

Author

Ibrahim Akhtar

• Medium: @Ibzie
• LinkedIn: Ibrahim Akhtar
• GitHub: @Ibzie

Support

If you find this project helpful, please give it a ⭐️ on GitHub!

For questions or support, please open an issue in the repository.

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