agent-memory

An Ebbinghaus forgetting curve enables AI agent memory persistence by automatically decaying relevance scores over time. This tiered approach prioritizes recent interactions while keeping older data accessible for deeper analysis and creative recall.

Implement tiered search for AI agent knowledge management by scanning and initializing markdown files. This Skill supports heartbeat, normal, deep, and creative search protocols to match different context depths and recall requirements.

Yes, you can use markdown files for persistent AI agent memory management. The system scans, initializes, decays, and touches markdown files to maintain knowledge bases, providing detailed memory health statistics for monitoring.

The best way to manage knowledge relevance decay in AI agents is applying an Ebbinghaus-inspired forgetting curve. Running a decay process on your vault directory updates relevance scores and tiers, ensuring information prioritization adjusts automatically over time.

You need a tiered recall system for AI agent memory when recalling past interactions or stored knowledge with varying context depths. It ensures relevant information is prioritized while older data remains accessible for deep analysis or creative recall.