self-improving-agent

AI agents learn from interactions by using a self-improvement system with semantic, episodic, and working memory that analyzes feedback loops to self-correct errors and automatically evolve their codebase.

The best way to implement lifelong learning is a universal self-improvement system that captures all skill experiences via execution hooks, enabling continuous adaptation and automatic codebase evolution.

To trigger a self-improvement cycle, instruct the AI agent to self-evolve, which initiates an analysis of recent experiences to update guidance and prevent future errors.

No dependencies are required. The self-improving agent integrates directly with skill execution through pre-start, post-completion, and error event hooks to capture learning data.

Yes, the self-correction mechanism analyzes root causes after a debugging session and automatically updates other skills with new patterns to prevent similar errors in the future.

The multi-memory architecture requires sufficient interaction data from skill execution hooks to build effective feedback loops, making continuous adaptation dependent on active usage volume.