self-improving-agent

Automate LLM agent self-improvement by extracting patterns from every skill interaction and updating related skills. The system uses automatic hooks, memory layers, and manual triggers to refine guidance with traceable provenance.

A lifelong learning pipeline for autonomous agents requires persistent feedback loops across semantic, episodic, and working memory. This approach applies evolutionary learning to extract patterns and safely validate changes through versioned templates.

Automatic hooks for self-correction in LLM workflows are triggered before_start, after_complete, and on_error. These hooks adapt agent behavior automatically by feeding interaction data into multi-layer memory systems.

Manage semantic, episodic, and working memory for AI agents by capturing patterns, experiences, and context in a multi-memory architecture. This enables memory consolidation and indexing for safe self-correction and validation.

The self-improving agent approach works with manual triggers by supporting human-in-the-loop validation. Users can prompt the agent to guide improvements, validate changes, and apply self-validation templates for safe memory consolidation.