memory-bank

To persist project context across AI sessions, you need a dedicated memory store acting as the single source of truth. The memory-bank approach maintains project objectives and decisions in structured files so the assistant can reliably reload context at task start.

Your AI assistant forgets technical decisions because it lacks persistent cross-session memory. By enforcing a workflow to read, verify, execute, and update structured memory files, the assistant avoids repeating outdated decisions and maintains context consistency.

The best way to maintain project documentation for AI workflows is using a memory-driven planning approach. This involves reading and verifying memory files before executing tasks, then recording decision changes to keep your project documentation consistently aligned with user intent.

You trigger memory file updates by applying an update trigger policy during your prompting workflow. You should enforce updating the relevant files when patterns change, major modifications occur, users request an update, or the current context becomes unclear.

Yes, you can use memory-bank for tracking architecture refactoring decisions. After refactoring, the assistant re-reads progress, active context, and system patterns to avoid repeating outdated decisions and automatically updates the memory to reflect what changed.

You should avoid using a persistent AI memory store for isolated, one-off tasks that require no historical context or decision tracking. If your workflow does not need structured memory hygiene or cross-session context reloading, this approach adds unnecessary file management overhead.