agent-memory-systems

LLM agent memory across sessions is managed by coordinating in-context, working, long-term, and episodic memory stores. This preserves user preferences, past decisions, and conversation history in persistent storage like a vector store or database.

The best way to persist user preferences is using long-term memory stores backed by a vector store or database. This ensures agent preferences and learnings survive beyond a single dialogue session.

Memory consolidation for LLM context management summarizes and prunes old data from memory stores. This process keeps the agent's active context within token budgets while retaining essential historical information.

Episodic memory logs are maintained to create an audit trail of agent actions for compliance and analysis. These logs track the sequence of events and decisions across interactions.

Agent memory persistence operates independently with no external dependencies. It structures memory types and applies namespace isolation to safely handle sensitive data across single and multi-session dialogues.

AI agents forget task state because working memory is typically session-bound. Implementing working memory with bridging queues allows task state to persist and transfer between agents across separate dialogues.