agent-memory-systemslisted

# Agent Memory Systems Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). ## Principles - Memory quality = retrieval quality, not storage quantity - Chunk for retrieval, not for storage - Context isolation is the enemy of memory - Right memory type for right information - Decay old memories - not everything should be forever - Test retrieval accuracy before production - Background memory formation beats real-time ## Capabilities - agent-memory - long-term-memory - short-term-memory - working-memory - episodic-memory - semantic-memory - procedural-memory - memory-retrieval - memory-formation - memory-decay ## Scope - vector-database-operations → data-engineer - rag-pipeline-architecture → llm-architect - embedding-model-selection → ml-engineer - knowledge-graph-design → knowledge-engineer ## Tooling ### Memory_frameworks - LangMem (LangChain) -