openclaw-continuous-learning

You can analyze agent sessions to generate atomic learnings by running nightly session analysis, which detects recurring actions and preferences to convert observed behaviors into self-improvement instincts.

Atomic learnings are memory structures called instincts created with confidence scores. They guide agent self-improvement by capturing detected behavioral patterns and suggesting optimizations for future sessions.

Instincts with confidence scores are created by analyzing agent session history to surface recurring patterns, automatically generating memory structures that enable guided adaptation for the agent.

Yes, continuous learning integrates with agent-self-improvement workflows to incorporate user feedback and external signals, ensuring proposed optimizations align with both observed behavior and user input.

Yes, session analysis examines the history of OpenClaw agents to suggest performance optimizations, automatically proposing updates based on observed behaviors and detected recurring patterns.