continual-learning

Continual learning solves this by using a hook that auto-initializes on the first session to persist experiences and reflections across sessions. It maintains local memory for project-specific conventions and common pitfalls to prevent forgetting.

Continual learning for coding agents is a mechanism that retains and refines knowledge across sessions using memory storage strategies and reflection hooks. It works by persisting global cross-project patterns and local project-specific experiences to improve long-term performance.

You can set up memory management for coding agents by installing the continual learning hook, which auto-initializes on the first session. It applies memory storage strategies and compaction rules to safely persist tool usage patterns and reflections.

Yes, this continual learning approach supports both global memory for cross-project patterns and preferences, and local memory for project-specific conventions. This dual strategy ensures stable, high-quality learnings across different development workflows.

A key consideration for using reflection patterns for agent memory persistence is ensuring stable, high-quality learnings through compaction rules. The system must provide safe, auditable behavior to prevent unstable or low-quality insights from degrading agent performance.