Reviewable memory you can control

What memory means in LiraLora

Memory is the product layer that preserves meaningful continuity across runs: project direction, recurring preferences, working patterns, style choices, corrections, references, and workflow habits.

It is not meant to be a hidden dump of everything you typed. Useful details can become memory candidates with plain-language summaries and review actions before they are allowed to shape future work.

Memory is different from stuffing more text into a prompt. LiraLora retrieves scoped memory when it is relevant, applies it to the workflow, and keeps the effect explainable through run details and memory traces.

Review before memory becomes durable

Workflow results can produce pending memory candidates. A candidate is a suggestion, not an automatic rule.

The review loop lets users approve what should carry forward and reject or dismiss what should not. That matters because a successful run may reveal a useful preference, but not every model observation deserves to guide future work.

This keeps memory useful without letting old or accidental context quietly accumulate into a noisy profile.

Approved, archived, restored, and manual memory

Approved memories become available for future runs. If a memory should stop influencing work, it can be archived or disabled rather than treated as irreversible.

Archived memory can be restored when it becomes useful again, and manual memory creation lets users add important project context directly instead of waiting for an automatic candidate.

When permanent deletion is available, it should be explicit and confirmed. The general product posture is to make memory lifecycle actions clear, intentional, and auditable.

Scoped by the work you are doing

A music preference, an image style preference, a coding convention, and a project-wide correction should not all behave like the same invisible global prompt.

LiraLora separates memory by project and workflow domain so the right context can help the right kind of run. Applied-memory traces make it easier to understand whether general, project, image, music, coding, or chat-related context affected the result.

Memory works with history and replay

Memory becomes more valuable when it is paired with workflow history. A run can show what was requested, which provider handled the work, what memory was applied, and what result was produced.

That makes successful work easier to reuse. Instead of starting from scratch, future workflows can continue from approved direction, prior decisions, and project-specific context.

Trust and boundaries

Memory is meant to be reviewable and scoped, not a black-box profile. Project access boundaries control which memory belongs to which workspace or project.

Local provider traffic goes to the endpoints you configure. LiraLora’s memory layer is separate from local model execution, so using memory does not require handing every AI task to a hosted model.

Important continuity effects should remain understandable in plain language, so the product feels helpful instead of silently steered by old context.