What problem does it solve? Agents lose all state when sessions end, forcing users to repeat context and preventing long-term knowledge accumulation. This Skill guides the design of layered memory architectures that persist facts, entities, and relationships across sessions with reliable retrieval. ## Core Features & Use Cases - Framework Selection: Compares production memory frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee) with benchmark data from LoCoMo, LongMemEval, DMR, and HotPotQA to match retrieval patterns to the right tool. - Layered Memory Design: Provides decision tables for working, short-term, long-term, entity, and temporal knowledge graph layers so you add complexity only when retrieval quality demands it. - Reference Implementations: Ships Python building blocks for vector stores with metadata indexing, property graphs with entity registries, temporal knowledge graphs with validity intervals, and memory consolidation logic. - Use Case: When building a support agent that must remember user preferences and past issues across conversations, use this Skill to choose between Mem0's managed vector memory and Zep's temporal knowledge graph, then prototype with the included IntegratedMemorySystem script. ## Quick Start Ask the agent to help you choose and implement a memory framework for persisting user preferences across sessions, for example by saying "implement long-term memory for my agent using Mem0 or Zep".
