memory-systems

# 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) Do not activate this skill for adjacent work owned by other skills: - File-backed scratchpads, run logs, and tool-output offloading: `filesystem-context`. - Conversation compaction or human-readable handoff summaries: `context-compression`. - Masking, prefix caching, token budgets, or retrieval scoping inside one trajectory: `context-optimization`. - Formal belief/desire/intention models over RDF s...