memory-systems

To persist agent memory across sessions, you use a layered memory architecture combining working, short-term, and long-term memory layers. This enables entity tracking and continuity by storing facts in vector-based and graph-based stores for later retrieval.

Temporal memory in a knowledge graph tracks entity changes over time using validity periods. You need it when agents must reason about past interactions and time-aware knowledge, rather than just storing static facts across conversations.

You store and retrieve memories for specific entities by initializing a session, recording facts like an entity's last seen state, and querying the graph-based store. This ensures consistent entity data and cross-session persistence for tailored responses.

Yes, you can use numpy to support vector-based memory stores for fast retrieval. The architecture combines these vector stores with knowledge graphs to enable structured reasoning over accumulated knowledge for your AI assistant.

The best way to manage memory consolidation is using privacy-aware retention policies that control data growth and exposure. This prevents unbounded memory expansion while maintaining necessary continuity across conversations.

Agents lose state and produce inconsistent entity data across conversations when they lack temporal memory and cross-session persistence. Implementing a layered memory architecture with knowledge graphs resolves this by retaining facts and tracking changes over time.