continual-learning

AI coding agents retain memory across sessions through global and local storage that captures project conventions and patterns. Automated hooks and SQL-based learnings persist this context, enabling continual learning without manual intervention.

Continual learning for AI coding agents is the ability to retain useful patterns, conventions, and past mistakes over time. It solves context loss between sessions by persisting memory, which improves agent reliability and performance on recurring tasks.

Automatically capture project-specific conventions by copying memory hooks into your repository. These hooks trigger during active coding sessions to record local patterns, team preferences, and SQL-based learnings without requiring manual documentation.

Memory persistence supports both global and local storage. Global memory retains cross-project patterns and tool insights, while local memory stores project-specific conventions, mistakes, and team preferences for targeted context retrieval.

Memory decay works through compaction rules that balance retention and growth. The system applies decay logic to manage memory size, ensuring that outdated or less useful learnings are pruned while retaining critical project patterns over time.

The best way to prevent repeating mistakes is persisting them via local memory storage and automated hooks. By retaining past errors as learnings, the agent retrieves these guardrails in future sessions to avoid identical failures.