memory-systems

To persist agent memory across sessions, you design layered memory architectures combining working, long-term, entity, and temporal knowledge graphs. This ensures cross-session continuity and prevents agents from acting on stale, time-sensitive information.

Choosing between Mem0, Zep/Graphiti, Letta, LangMem, or Cognee involves comparing their production memory framework capabilities. You evaluate them based on retrieval strategy support, entity tracking, and temporal accuracy requirements.

Implementing hybrid retrieval strategies combines semantic, entity-graph, and temporal filtering to match query shapes. This approach improves accuracy by pulling just-in-time context rather than bulk preloading data into the agent's context window.

Handling fact conflicts and stale facts in agent memory requires invalidation-first consolidation logic. This prevents unbounded memory growth and provides graceful recovery from empty retrieval results or contradictory entity tracking data.

You can use a simpler file-system approach for agent persistence instead of a full knowledge graph. This works for basic cross-session retention but requires manual handling of temporal validity, entity tracking, and consolidation logic.