ATLAS (Adaptive Technical Learning and Architecture System) is an AI software engineer agent designed to act as a collaborative partner for software development. It aims to assist users by learning from their projects, maintaining context across sessions, and adhering to software engineering best practices like KISS, YAGNI, and DRY. The agent is intended for individual developers or teams looking to augment their workflow with an AI assistant that can manage project knowledge and adapt to specific contexts.

How It Works

ATLAS operates by learning from project repositories placed in its REPOS/ directory. It maintains a form of "living memory" through various files, including PROJECT_STRUCTURE.md for project outlines and FRESH_COMPACT_MEMORY.md for session summaries, allowing for context management and restoration. Critical information can be stored in IMPORTANT_NOTES.md for high-priority adherence. The agent's core instructions and architectural overview are detailed in CLAUDE.md, while its personal operating instructions are in the SELF/ directory.

Quick Start & Requirements

1. Clone or download the repository.
2. Place your project(s) into the REPOS/ folder.
3. Initialize the session by asking ATLAS about itself and its development beliefs.
4. Instruct ATLAS to learn about the repositories and update PROJECT_STRUCTURE.md.

No specific technical prerequisites like Python versions or hardware are mentioned, implying a general compatibility, but the agent's functionality is tied to its internal file structure and the user's interaction.

Highlighted Details

• Emphasizes adherence to KISS, YAGNI, and DRY principles.
• Features a "living memory" system for persistent learning and context.
• Supports session summarization and context restoration via specific files.
• Designed to adapt its architecture and structure based on user workflow.

Maintenance & Community

The README does not provide information on maintainers, community channels (like Discord or Slack), sponsorships, or a public roadmap. It focuses solely on the agent's functionality and user interaction.

Licensing & Compatibility

The README does not specify a license type or any compatibility notes for commercial use or closed-source linking.

Limitations & Caveats

The agent's effectiveness relies heavily on the user's ability to manage context and provide clear instructions. The README also instructs the user to delete it after reading, implying that the agent's knowledge of its own operation is stored elsewhere, which could be a point of failure if not managed correctly. There is no mention of specific limitations regarding project size, complexity, or programming languages supported.