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 frameworks to retrieval patterns. - Layered Architecture Guidance: Provides decision tables for working, short-term, long-term, entity, and temporal knowledge graph memory layers, plus retrieval strategies (semantic, entity-based, temporal, hybrid). - Runnable Implementation: Ships a Python module with VectorStore, PropertyGraph, TemporalKnowledgeGraph, and an IntegratedMemorySystem for prototyping before committing to infrastructure. - Use Case: When building a support agent that must remember user preferences and track changing facts (e.g., addresses, subscriptions) across months of conversations, use this Skill to choose a framework, design temporal validity tracking, and prototype the storage layer. ## Quick Start Ask the agent to design a memory architecture for a chatbot that must remember user preferences across sessions and recommend whether to use Mem0, Zep, or a simple file-based store.
