memory-systems

To persist agent memory across sessions, you model a multi-layer memory architecture—working, short-term, long-term, and entity memory—and wire it into the agent's context loader to preserve knowledge continuity.

A temporal knowledge graph for agent memory is a structure that tracks entity validity periods and relationships over time, enabling temporal reasoning and cross-session continuity for historical data.

You prevent knowledge drift in a vector store by applying memory consolidation and retrieval patterns, managing the growing knowledge base by loading relevant memories just-in-time to balance latency and persistence.

Yes, the multi-layer agent memory approach works with numpy, which is the required dependency for implementing the vector storage with metadata and memory consolidation logic.

The best way to track entities across multiple tool interactions is using an entity memory layer combined with a temporal knowledge graph, preserving continuity and tracking relationships across sessions and tools.