AI Hypervisor

Overview

This repository contains the core code for the AI Hypervisor, a central component designed to manage and coordinate the activities of lower-level agents within an advanced AI system. The hypervisor is responsible for orchestrating the execution of logic and thought agents, ensuring efficient communication between components, and optimizing resource allocation for high-performance AI operations.

Features and Capabilities

• Agent Management: Provides robust mechanisms for lifecycle management, scheduling, and execution of various AI agents.
• Resource Optimization: Dynamically allocates resources to ensure optimal performance and scalability of AI tasks.
• Inter-Agent Communication: Facilitates seamless communication between logic agents, thought agents, and other components of the AI system.
• Monitoring and Logging: Implements comprehensive monitoring and logging to track the performance and health of the AI system.

Applications

The AI Hypervisor plays a critical role in the following applications:

• Complex Decision Making: Coordinates multiple AI agents to perform sophisticated decision-making processes.
• Distributed AI Systems: Manages distributed AI components to ensure cohesive operation and collaboration.
• Resource-Intensive Tasks: Optimizes the execution of resource-intensive AI tasks by effectively managing computational resources.

Installation

To install the AI Hypervisor, follow these steps:

1. Clone the repository:

   git clone https://github.com/oracleagent/ai-hypervisor.git
   cd ai-hypervisor

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

Initial Setup

1. Initialize the Hypervisor: Start the hypervisor to begin managing AI agents.

   python hypervisor.py

2. Configure Agents: Define and configure the AI agents that the hypervisor will manage.

Running the Hypervisor

1. Start an Agent: Use the hypervisor to start a specific AI agent.

   python hypervisor.py --start-agent agent_name

2. Monitor Agent Performance: Monitor the performance and health of running agents.

   python hypervisor.py --monitor

3. Allocate Resources: Dynamically allocate resources to agents as needed.

   python hypervisor.py --allocate-resources

Next Steps

After setting up and initializing the AI Hypervisor, you can:

1. Integrate Additional Agents: Add new logic and thought agents to extend the AI system's capabilities.
2. Enhance Communication Protocols: Improve inter-agent communication mechanisms for better coordination and efficiency.
3. Optimize Resource Management: Continuously optimize resource allocation strategies to enhance system performance.

Contact

For further inquiries, technical support, or collaboration opportunities, please open an issue on the GitHub repository.