elite-longterm-memory

To preserve AI agent context across sessions, you can use file-based memory structures like SESSION-STATE.md and daily logs to keep active context and decisions human-readable and easy to review.

Semantic recall for long-term AI memory works by using LanceDB-style vector search to retrieve past decisions and context, while Mem0 extraction captures this data token-efficiently to reduce waste.

Yes, this long-term memory workspace workflow supports Cursor, Claude, ChatGPT, Copilot, and OpenClaw by maintaining session state, daily logs, semantic recall, and knowledge graph storage.

The best way to store permanent AI decisions in a project is by using git-notes persistence for cold memory, keeping permanent context durable across restarts alongside warm LanceDB vectors.

To initialize persistent memory files, ask the AI to activate the memory skill in your workspace so it creates the necessary files, records ongoing decisions, and prepares the system for recall.

File-based AI context management requires durable storage and manual memory hygiene, utilizing write-ahead logging to maintain consistency and prevent data loss during complex recall operations.