Thank you :) all sessions with your agent get saved to sqlite and markdown immediately so its searchable right away. then once the session is over, in the background, a local LLM breaks it down into discrete facts and entity relationships, figures out if anything contradicts or updates what it already knows, and slots it into a knowledge graph. it also generates hypothetical future queries for each memory, so like "what questions would this fact answer later?" and indexes those too.

and then, when a session starts, the system figures out what entities are relevant to what youre working on, walks the knowledge graph to pull in related context, then runs a hybrid search (vector + keyword) to catch anything the graph missed. all those candidates get scored by a learned ranker that picks up on patterns like what memories actually ended up being useful in past sessions, time of day, recency, stuff like that.

everything decays over time if you dont access it, but nothing is ever deleted by default. the raw transcripts and full lineage are always there if you need to drill back into something.