AI Agent Operating System (AOS)

A comprehensive framework for building, deploying, and managing AI agents across various domains including executive coaching, customer success, and business operations.

🚀 Overview

This repository contains a complete AI Agent Operating System designed to help organizations deploy specialized AI agents for different business functions. The system includes:

• Executive Team Agents: Specialized agents for different C-level roles (CEO, COO, CFO, CSO, etc.)
• Coaching Agents: AI-powered coaches based on renowned personalities (Tony Robbins, Grant Cardone, etc.)
• Business Operations: Agents for sales, customer success, product management, and more
• Integration Framework: Seamless integration with popular business tools and platforms

🏗️ Architecture

The AOS follows a modular architecture with the following key components:

Core Components

• Agent Framework: Base classes and utilities for building AI agents
• Integration Layer: APIs and connectors for external services
• Web Interface: Modern web-based chat interfaces for agent interactions
• Deployment System: Containerized deployment with Docker and cloud platforms

Agent Types

1. Executive Agents (agent-os/exec-team/)

   • Capital, Commercial, COO, CFO, CSO
   • Customer Success, Dev Mindshare, Founder Coach
   • Latency Ops, Product, Sales, Ship Velocity
   • Strategist, YC Office

2. Coaching Agents (agent-os/coaches/)

   • Tony Robbins Coach
   • Grant Cardone Agent
   • Sam Altman Agent
   • Stephen Hayes Agent

3. Specialized Agents (agents/)

   • Core agent framework
   • Integration examples
   • Testing utilities

🛠️ Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• Docker (optional)
• OpenAI API key

Installation

1. Clone the repository

   git clone https://github.com/your-org/ai-agent-os.git
   cd ai-agent-os

2. Set up environment

   cp agent-os/env.example .env
   # Edit .env with your API keys and configuration

3. Install dependencies

   cd agent-os
   make install

4. Run a sample agent

   cd coaches/tony-robbins
   make run

Environment Variables

Create a .env file with the following variables:

# OpenAI Configuration
OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4-turbo-preview

# Optional: Other AI Providers
ANTHROPIC_API_KEY=your_anthropic_key_here

# Arcade.ai Integration (optional)
ARCADE_API_KEY=your_arcade_key_here
ARCADE_USER_ID=your_user_id_here

# Database (optional)
DATABASE_URL=postgresql://user:password@localhost/dbname

# Redis (optional)
REDIS_URL=redis://localhost:6379/0

📚 Documentation

Getting Started

• Quick Start Guide
• Agent Overview
• Deployment Guide

Agent Development

• Core Framework
• Integration Examples
• Advanced Patterns

Security & Compliance

• Security Policy
• Production Checklist

🔧 Development

Project Structure

ai-agent-os/
├── agent-os/                 # Main agent operating system
│   ├── exec-team/           # Executive team agents
│   ├── coaches/             # Coaching agents
│   └── create_agents.sh     # Agent creation script
├── agents/                  # Core agent framework
├── agents-examples/         # Example implementations
├── coo-agent-orchestrator/  # COO agent orchestrator
├── cso-agent/              # CSO agent implementation
└── docs/                   # Documentation

Building Agents

1. Use the agent creation script

   cd agent-os
   ./create_agents.sh

2. Manual agent creation

   mkdir my-agent
   cd my-agent
   cp ../coaches/tony-robbins/server/app.py ./server/
   cp ../coaches/tony-robbins/frontend/* ./frontend/
   # Customize the agent

Testing

# Run tests for specific agent
cd coo-agent-orchestrator
python -m pytest tests/

# Run all examples
cd agents-examples
python explore.py

🚀 Deployment

Docker Deployment

# Build and run with Docker
docker build -t ai-agent-os .
docker run -p 3000:3000 -p 5057:5057 ai-agent-os

Cloud Deployment

The system supports deployment to various cloud platforms:

• Render: See render.yaml configuration
• AWS: Use the provided CloudFormation templates
• Google Cloud: Kubernetes deployment configurations included

Production Considerations

• Set up proper environment variables
• Configure monitoring and logging
• Implement rate limiting and security measures
• Set up backup and recovery procedures

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Development Workflow

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

Code Standards

• Follow PEP 8 for Python code
• Use TypeScript for frontend components
• Write comprehensive tests
• Document all public APIs

📄 License

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

🙏 Acknowledgments

• OpenAI for the GPT models and API
• The open-source community for various libraries and tools
• Contributors and users who provide feedback and suggestions

📞 Support

• Documentation: Check the docs folder for detailed guides
• Issues: Use GitHub Issues for bug reports and feature requests
• Discussions: Use GitHub Discussions for questions and community support

🔗 Related Projects

• OpenAI Agents Python SDK
• Agent Examples
• COO Agent Orchestrator

---

Note: This is an open-source project. Please ensure you have proper API keys and follow all terms of service for integrated services.