This framework structures complex projects using multiple AI agents, mimicking real-world team management to overcome LLM context window limitations. It's designed for users collaborating with AI on large projects, enabling longer, more productive sessions by maintaining context and ensuring smooth transitions between AI agents.

How It Works

APM employs a Manager Agent and specialized Implementation Agents, coordinated through a Memory Bank (shared project logbook) and Handover Protocols. This approach addresses context loss and agent forgetfulness by using structured workflows, detailed task assignment prompts, and context retention techniques, aiming for efficient, cost-effective AI collaboration with user oversight.

Quick Start & Requirements

• Install: Clone the APM GitHub repository or use the template to create a new project repository. Place the cloned/templated files at the root of your project workspace.
• Initiation: Copy the content of prompts/00_Initial_Manager_Setup/01_Initiation_Prompt.md and paste it as the first message to your AI assistant.
• Prerequisites: An AI assistant (e.g., Cursor IDE) capable of processing markdown prompts and maintaining conversational context. Cursor IDE users can optionally leverage pre-defined Cursor Rules for enhanced reliability.
• Resources: No specific hardware requirements are listed beyond a functional AI assistant environment.
• Docs: Full APM Documentation

Highlighted Details

• Mimics real-world team roles and processes for intuitive AI collaboration.
• Features a Memory Bank system for persistent project logging and context retention.
• Implements Handover Protocols for seamless transitions between AI agents when context limits are reached.
• Offers optional Cursor Rules for enhanced agent reliability and workflow efficiency within the Cursor IDE.

Maintenance & Community

The project is open-source, welcoming contributions for prompt improvements, documentation, features, and bug reporting. Details for contributing are available in CONTRIBUTING.md.

Licensing & Compatibility

Licensed under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

The effectiveness of APM is dependent on the underlying AI model's capabilities and the user's ability to guide the agents. While designed to mitigate context loss, extreme project complexity or prolonged sessions may still encounter limitations.