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 persistence architectures that retain 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 frameworks to retrieval patterns. - 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. - Use Case: You are building a support agent that must remember user preferences and past issues across conversations. Use this Skill to choose between Mem0 and Zep, implement temporal validity tracking, and set up consolidation triggers. ## Quick Start Ask the agent to design a memory architecture for a chatbot that must remember user preferences across sessions and recommend a framework.
