self-improving-agent

An agent learns from past interactions automatically by using hooks like before_start, after_complete, and on_error to extract patterns and consolidate experiences into semantic, episodic, and working memory after every skill execution.

Multi-memory architecture for agent self-improvement is a system using semantic, episodic, and working memory to store patterns, experiences, and current context. It allows an agent to consolidate interactions and apply learned patterns across different skills.

Propagate updates across related agent skills automatically by triggering hooks after skill completion to extract insights and apply cross-skill updates, ensuring coherent improvements throughout the framework.

Yes, you can trace agent memory updates and pattern evolution for audits using evolution markers. The system records source-based tracing for automatic updates, supporting governance by linking consolidated patterns back to their original skill interactions.

No, cross-skill learning does not require manual configuration for each agent skill. Once integrated, the hooks-driven system autonomously detects emerging patterns and propagates updates across related skills within the framework automatically.