memory-systems

You can persist agent state across sessions by implementing a layered memory architecture that uses a vector store and temporal knowledge graph for storage, retrieval, and context integration. This ensures entity continuity as interactions evolve.

A temporal knowledge graph is a memory layer that tracks how entities and facts evolve over time. You need it for autonomous agents that must reason over evolving knowledge, maintaining relational context beyond simple static retrieval.

To store and retrieve user preferences, you save entity-specific facts to the memory system, such as noting a user prefers coffee. You then query the memory store for that user to retrieve related facts and personalize responses.

Yes, numpy is the required dependency for building this persistent memory architecture. It provides the numerical operations needed to support the vector store implementation for scalable memory storage and retrieval.

The best way to structure agent memory is using a layered model that balances latency, persistence, and reasoning. This architecture separates working, short-term, long-term, and temporal graphs to manage just-in-time loading and memory growth.

Your chatbot loses entity continuity because it lacks a persistent memory layer. Implementing memory consolidation and context integration with a vector store ensures state survives across sessions and maintains knowledge consistency.