Memory System Design

Memory provides the persistence layer that allows agents to maintain continuity across sessions and reason over accumulated knowledge. Simple agents rely entirely on context for memory, losing all state when sessions end. Sophisticated agents implement layered memory architectures that balance immediate context needs with long-term knowledge retention. The evolution from vector stores to knowledge graphs to temporal knowledge graphs represents increasing investment in structured memory for improved retrieval and reasoning.

When to Activate

Activate this skill when:

• Building agents that must persist knowledge across sessions
• Choosing between memory frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee)
• Needing to maintain entity consistency across conversations
• Implementing reasoning over accumulated knowledge
• Designing memory architectures that scale in production
• Evaluating memory systems against benchmarks (LoCoMo, LongMemEval, DMR)
• Building dynamic memory with automatic entity/relationship extraction and self-improving memory (Cognee)

Core Concepts

Think of memory as a spectrum from volatile context window to persistent storage. Default to the simplest layer that meets retrieval needs, because benchmark evidence shows tool complexity matters less than reliable retrieval — Letta's filesystem agents scored 74% on LoCoMo using basic file operations, beating Mem0's specialized tools at 68.5%. Add structure (graphs, temporal validity) only when retrieval quality degrades or the agent needs multi-hop reasoning, relationship traversal, or time-travel queries.