elite-longterm-memory

To add long-term memory to an AI agent and prevent context loss, use a multi-layer system with hot RAM, LanceDB vectors, and Git-Notes to retain decisions, logs, and preferences across sessions.

Vector search with LanceDB provides a warm store for AI agent memory recall by indexing context as vectors, enabling semantic search and auto-injection of relevant historical data into active sessions.

Yes, you can use Git-Notes as a cold store knowledge graph for coding assistants to preserve structured decisions and context, ensuring critical development data survives across project resets.

The best way to reduce token usage when extracting facts from agent memory is applying Mem0 auto-extraction, which automatically isolates key information to minimize context size during recall.

Yes, write-ahead-memory survives RAM resets and context compaction by acting as a hot store that continuously logs active memory changes before they are permanently archived in vector or markdown formats.

To sync AI agent memory across multiple devices, activate an optional cloud backup layer that synchronizes your local MEMORY.md archives and LanceDB vectors for cross-device context retrieval.