elite-longterm-memory

You can add persistent long-term memory to an AI agent by using a multi-layer architecture that stores and retrieves context across sessions. This involves hot RAM, warm semantic stores, and cold knowledge graphs to preserve decisions and enable durable recall.

Restoring context for an AI agent after a restart requires write-ahead logging (WAL) and vector search. This combination guarantees durability and accessibility, enabling rapid context restoration from warm stores and cold knowledge graphs without losing previous decisions.

Vector search retrieves agent memories by using LanceDB to index and query persistent context across tasks and projects. This enables instant recall of decisions, preferences, and knowledge stored in the warm semantic store during coding workflows.

Yes, you can use a Git-Notes knowledge graph to store branch-aware decisions for AI agents. This approach provides auditability and collaboration by structuring persistent context alongside your coding workflows and daily logs.

To initialize a unified memory system in your workspace, run the command `npx elite-longterm-memory init`. This sets up the multi-layer memory architecture, including hot RAM, warm store, cold knowledge graph, and optional cloud backup.

Yes, AI agent memory supports optional cloud backup for cross-device sync. This ensures that durable context, decision logs, and the structured knowledge graph remain accessible and synchronized across different devices and sessions.