memory-systems

Persist agent memory across sessions by storing and retrieving accumulated knowledge using layered memory, vector stores, and knowledge graphs. This maintains consistent entity tracking and dynamic context continuity beyond a single conversation.

A temporal knowledge graph preserves entity relationships and applies temporal validity to facts for reasoning over accumulated knowledge. It prevents outdated information from interfering with the current context during time-aware decisions.

Implement working, short-term, long-term, and temporal memory layers to balance latency and persistence in AI agents. These layers support consolidation pipelines and selective context injection for dynamic context maintenance.

Yes, memory consolidation supports just-in-time memory loading through retrieval pipelines that enable selective context injection. This ensures AI agents retrieve relevant memories dynamically without overloading the working memory context.

Track entities and preserve relationships across interactions by combining vector stores with knowledge graphs. This approach maintains continuity, supports temporal reasoning, and enables consistent context retrieval across sessions.

Outdated information interferes with current context when memory lacks temporal validity. Prevent it by applying temporal memory layers to assign validity periods to facts, ensuring only currently relevant knowledge is injected into the agent's context.