continual-learning

Continual learning enables AI coding agents to retain knowledge across sessions by capturing outcomes through hooks, reflecting on them, and persisting them into memory stores for later application in development workflows.

Managing memory across multiple projects is best achieved through a two-tier memory system utilizing global and local stores. This balances cross-project knowledge with repository-specific data, maintaining distinct context for AI agents.

Automatically capture learnings by installing hook-based learning mechanisms that extract actionable insights from task outcomes. These hooks persist data into SQL-backed memory stores, automating the knowledge retention process for AI agents.

Yes, this memory management approach supports SQL-based memory integration. It utilizes SQL-backed stores for both automatic and manual storage strategies, enabling structured querying and compaction of persisted learnings.

Compaction works by consolidating stored memory in SQL-backed stores to prevent bloat. It follows best-practice guidelines to ensure persisted learnings remain actionable, efficient, and optimized for agent retrieval during tasks.

Use local memory stores for repository-specific knowledge and global memory stores for broader cross-project learnings. This two-tier approach ensures general best practices are shared globally while specific codebase context remains isolated.