🚀 Omni Multi-Agent System

Advanced AI Multi-Agent System with Persistent Memory, Real-time Chat, and Specialized Agent Coordination

Omni Multi-Agent is a cutting-edge AI system that orchestrates multiple specialized AI agents through an intelligent routing system. Built with enterprise-grade architecture, it provides seamless integration of conversational AI, image generation, web search, and document processing capabilities with persistent session management.

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🌟 Key Features

🧠 Multi-Agent Architecture

• Intelligent Router: Automatically routes requests to the most suitable agent
• Specialized Agents: Research, Math, Planning, Image Generation, and more
• Agent Coordination: Seamless handoffs and collaborative problem-solving

💾 Persistent Memory System

• Session Management: Maintain conversation context across sessions
• Message History: Full conversation persistence with SQLAlchemy
• Context Retrieval: Smart context loading for enhanced responses

🎨 Advanced Capabilities

• Image Generation: High-quality image creation with Stable Diffusion XL
• Document Processing: PDF analysis and content extraction
• Web Search: Real-time web information retrieval
• Speech Integration: Text-to-speech and speech-to-text support

🌐 Modern Tech Stack

• Backend: FastAPI with async/await support
• Frontend: React with modern UI components
• Database: SQLAlchemy with async ORM
• Vector DB: Qdrant for semantic search
• AI Framework: LangGraph + LangChain for agent orchestration

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🚀 Quick Start

Option 1: Docker Deployment (Recommended)

# Clone the repository
git clone https://github.com/tantran24/Omni-Multi-Agent.git
cd Omni-Multi-Agent

# Start all services with Docker Compose
docker-compose up -d

# Access the application
# Frontend: http://localhost
# Backend API: http://localhost:8000
# API Documentation: http://localhost:8000/docs

Option 2: Manual Installation

Prerequisites

• Python 3.11+
• Node.js 18+
• Ollama
• Git

Backend Setup

# Navigate to backend directory
cd backend

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

# Install dependencies
pip install -r requirements.txt

# Initialize database
python init_db.py

# Start the server
uvicorn main:app --reload --host 0.0.0.0 --port 8000

Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

Ollama Setup

# Install Ollama (if not already installed)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull required models
ollama pull llama2
ollama pull gemma:2b

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📱 Usage Guide

Basic Chat Interaction

1. Start a Conversation: Open the app and type your message
2. Session Management: Your conversations are automatically saved
3. Switch Sessions: Use the session manager to navigate between chats
4. Persistent History: All messages are preserved across browser sessions

Advanced Features

Image Generation

Generate an image of a futuristic city at sunset
Create a logo for a tech startup
Draw a cute cartoon cat wearing a spacesuit

Document Analysis

• Upload PDF files for analysis and Q&A
• Extract key information from documents
• Summarize long documents

Web Search

Search for the latest news about artificial intelligence
Find information about climate change solutions
Look up the current stock price of Tesla

Mathematical Problem Solving

Solve the equation: 2x + 5 = 15
Calculate the derivative of x^2 + 3x + 2
What is the area of a circle with radius 5?

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🛠️ Development Guide

Project Structure

Omni-Multi-Agent/
├── backend/                 # FastAPI backend
│   ├── services/           # Core business logic
│   ├── utils/             # Utilities and agents
│   ├── config/            # Configuration files
│   ├── database/          # Database models and migrations
│   └── main.py           # Application entry point
├── frontend/              # React frontend
│   ├── src/
│   │   ├── components/    # React components
│   │   ├── services/      # API services
│   │   └── utils/        # Frontend utilities
│   └── public/           # Static assets
├── docker-compose.yml     # Docker orchestration
└── README.md             # This file

API Documentation

The backend provides comprehensive API documentation:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc
• OpenAPI Schema: http://localhost:8000/openapi.json

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🎯 Specialized Agents

🤖 Chat Agent

• General conversation handling
• Context-aware responses
• Memory integration

🎨 Image Agent

• Text-to-image generation
• Style and quality optimization
• Multiple format support

📚 RAG Agent

• Document Q&A
• Semantic search
• Context extraction

🔍 Research Agent

• Web search integration
• Information synthesis
• Fact verification

📊 Planning Agent

• Task breakdown
• Project planning
• Goal-oriented assistance

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⚙️ Configuration

Environment Variables

# Backend Configuration
OLLAMA_BASE_URL=http://localhost:11434
HUGGINGFACE_API_KEY=your_hf_api_key
DATABASE_URL=sqlite:///./database/app.db

# Frontend Configuration
VITE_API_URL=http://localhost:8000

Customization

The system is highly configurable through:

• backend/config/config.py - Backend settings
• frontend/.env - Frontend environment variables
• backend/config/prompts.py - Agent prompts and behaviors

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🔒 Security Features

• CORS protection configured
• Input validation and sanitization
• Secure file upload handling
• Environment-based configuration
• Docker security best practices

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🤝 Community & Support

Getting Help

• 📖 Documentation
• 💬 Discussions
• 🐛 Issue Tracker

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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🏆 Acknowledgments

• LangChain Team for the amazing agent framework
• FastAPI for the high-performance web framework
• React Community for the frontend ecosystem
• Ollama for local LLM infrastructure
• Hugging Face for AI model hosting

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Made with ❤️ by the Omni Multi-Agent Team

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