memory-systems

You can persist AI agent memory across sessions by implementing a layered architecture that uses vector stores and temporal knowledge graphs to maintain state, track entities, and enable cross-session reasoning for memory-informed decisions.

A temporal knowledge graph for agent memory is a structure that preserves entity relationships and enables time-aware queries. It supports temporal validity to maintain accurate context across long-running sessions and complex reasoning tasks.

You can implement layered memory architecture by structuring data into working, short-term, long-term, and entity memory layers. This setup supports just-in-time memory loading and consolidation when integrated with your context modules.

Yes, the memory system supports cross-agent memory coordination. It integrates with context systems to enable memory consolidation and coordinate shared state information across multiple AI agents working within the same environment.

Yes, you need numpy installed as a core dependency to set up and operate these vector stores and temporal knowledge graphs. It provides the underlying numerical processing required for memory retrieval operations.

You should use a memory graph instead of a standard vector store when you need to preserve complex entity relationships and perform time-aware queries. Graphs enable temporal validity tracking across long-running sessions where basic vector retrieval falls short.