memory-recall

Persistent cross-session memory solves this by extracting and compiling user and project facts from past conversations into a structured store. The agent then injects this compiled memory artifact at the start of each new session for immediate historical context recall.

Semantic recall works by matching current queries against a structured fact store that features importance scoring and deduplication. This mechanism retrieves relevant historical facts and compiles them into a memory document loaded at the start of each session.

The best approach uses automated session summarization to turn recent conversation turns into rolling digests. These digests feed into a structured fact store with importance scoring and decay, ensuring ongoing projects maintain seamless context and preference retention.

Yes, cross-session memory answers follow-ups like what did I tell you before by applying time-layered compilation to past conversations. It retrieves specific user facts and project context from the structured store to provide an accurate, historically grounded response.

Yes, fact extraction records reusable facts with importance scores, categories, and deduplication to manage memory efficiently. This prevents redundant data and ensures the compiled memory artifact remains compact and highly relevant when injected at session start.