NOVA

Multi-Agent Network Operations AI Platform

An advanced framework for building intelligent, collaborative multi-agent systems that seamlessly integrate the Model Context Protocol (MCP) and Agent2Agent Protocol (A2A).

🚀 Overview

This system provides a robust architecture for deploying and managing AI agents that can communicate with each other and access external tools and data sources. The framework enables complex workflows where specialized agents collaborate to solve problems that would be difficult for any single agent to handle.

✨ Key Features

• Protocol Integration: Bridges Anthropic's MCP and Google's A2A protocols
• Agent Orchestration: Manages agent discovery, task routing, and workflow execution
• Context Integration: Provides standardized access to data sources and tools
• Agent Registry: Maintains a catalog of available agents and their capabilities
• Security & Governance: Implements access controls, audit logging, and compliance monitoring
• User Interface: Web-based tools for task creation, workflow design, and monitoring

🏗️ Architecture

!Architecture Diagram

The system is built on a modular architecture with the following components:

• Agent Orchestration Layer: Implements A2A protocol for inter-agent communication
• Context Integration Layer: Implements MCP protocol for tool and data access
• Agent Registry: Catalogs agents and their capabilities
• Security and Governance Module: Enforces access controls and monitors system activities
• User Interface Layer: Provides user-friendly interfaces for interacting with the system

🛠️ Getting Started

Prerequisites

• Python 3.10+
• Docker and Docker Compose
• Kubernetes (for production deployment)

Installation

# Clone the repository
git clone https://github.com/yourusername/multi-agent-system.git
cd multi-agent-system

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

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your settings

# Start the system (development mode)
python main.py --env development

📚 Documentation

For detailed documentation, see the docs directory:

• Architecture Overview
• API Reference
• Integration Guide
• Deployment Guide
• Security Model

🧩 Example Use Cases

• Data analysis workflows
• Customer service automation
• Content generation and refinement
• Research assistance
• Business process automation

🧪 Examples

Check out the examples directory for sample implementations:

• Basic Workflow
• Database MCP Server
• Data Analysis Agent
• Integrated System

📝 License

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

🤝 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/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request