memory-systems

Persist agent state across sessions by implementing a multi-layer memory model that stores and retrieves working, short-term, long-term, and knowledge-graph memories. This architecture maintains context and continuity between interactions.

A temporal knowledge graph for entity tracking is a memory structure that records relationships and events over time. It enables time-aware decision making by managing when memories were stored and how they evolve across interactions.

Yes, you can use numpy for memory consolidation and context integration. The system relies on numpy as its core dependency to process numerical data arrays required for managing and consolidating multi-layered agent memory states.

To store and retrieve memories for a specific entity across sessions, you start a named memory session, save the entity data with a current timestamp, and then query the memory system using that entity identifier.

Using file-system-as-memory for cross-session reasoning may introduce latency and scaling constraints compared to graph databases. It is best suited for localized entity tracking rather than highly concurrent, large-scale knowledge graph deployments.

You need privacy safeguards for agent memory consolidation when storing sensitive entity tracking data in long-term memory. Safeguards ensure that personal or confidential information is protected during context integration and cross-session retrieval.