memory-system

A decay-aware memory system organizes conversation context by classifying entries into permanent memories and decay-laden memories. Permanent memories retain critical information, while decay-laden memories gradually lose relevance based on a forgetting curve until refreshed or archived.

You classify and archive SQL memories by applying defined SQL schemas to categorize entries as permanent or decay-laden, then route aging entries into weekly archives. This enables historical memory recovery and structured recall by topic.

Yes, you can automate weekly digest workflows for conversation memory management. The system generates weekly summaries of memory updates, tracks decay state changes, and archives aged memories to maintain context relevance across conversations.

You need decay-laden memory classification for chat assistants when managing long-term personalized dialogues where daily information becomes stale. It prevents context bloat by automatically degrading unused memories while preserving permanent knowledge.

You retrieve archived memories and refresh decay states by querying topics through the SQL schema interface. When events reoccur or topics are revisited, the system actively refreshes decay-laden memories, restoring their active status for immediate recall.

Limitations of a decay-aware memory system include strict behavior rules for retention and deletion that require precise SQL schema adherence. Memories not actively refreshed will eventually decay and archive, potentially losing granular context if not properly tagged.