memory-systems

You implement agent memory systems by designing persistence architectures that utilize vector stores, knowledge graphs, and temporal knowledge graphs to retain context. This allows AI agents to maintain and retrieve accumulated knowledge across multiple distinct user sessions.

Choosing between Mem0, Letta, and Zep/Graphiti involves comparing these production frameworks based on their specific persistence architectures. The decision depends on whether your agent requires vector stores, standard knowledge graphs, or temporal knowledge graphs for cross-session retention.

Temporal knowledge graphs improve agent memory persistence architectures by tracking the time-sensitive relationships between entities. This allows AI agents to maintain evolving context and retrieve historically accurate knowledge during cross-session interactions.

Vector stores are a core component for building AI agent memory systems with frameworks like LangMem and Cognee. They provide the underlying persistence architecture required to embed and retrieve semantic knowledge across different sessions.

A standalone vector store is insufficient for agent memory when your application requires complex relational context or temporal awareness. In these cases, standard or temporal knowledge graphs are necessary to map evolving entity relationships across sessions.