memory-systems

Memory architectures for agents use layered storage—working, short-term, long-term, and persistent—to retain knowledge across sessions. Vector stores enable semantic retrieval, knowledge graphs support entity relationships, and temporal knowledge graphs track time-aware facts, allowing agents to access consolidated information on demand.

Vector stores retrieve semantically similar information through embeddings. Knowledge graphs traverse entity relationships and connections. Temporal knowledge graphs add time validity to facts, enabling agents to distinguish current from outdated information and validate fact recency.

Use vector stores for semantic similarity and retrieval-augmented generation tasks. Use knowledge graphs when you need entity relationship traversal and structured reasoning. Combine both: vector stores for semantic matching and knowledge graphs for relational logic in multi-turn conversations and task continuity.

Temporal knowledge graphs attach validity windows and version history to facts, allowing agents to determine whether information is current or outdated. This supports pattern-based consolidation and just-in-time integration with contextual attention during multi-turn agent interactions.

Yes. Agents can combine vector stores for semantic retrieval with knowledge graphs for entity traversal within the same or adjacent memory layers. This hybrid approach satisfies both semantic and relational requirements for cross-session task continuity and complex reasoning.

Plan your memory layers upfront: identify which information belongs in working, short-term, long-term, or persistent storage. Prepare embedding models for vector stores, define entity and relationship schemas for knowledge graphs, and determine temporal validity rules for time-aware fact handling.