Agent skill · research science · aiskillstore
agent-memory-systems
Memory is the cornerstone of intelligent agents. Without it, every
Why this skill is useful
Provides domain-specific knowledge on cognitive architectures and memory management strategies that the AI wouldn't reliably generate on its own.
What it needs
About 9k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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 Memoryframeworks LangMem (LangChain) - When: LangGraph agents with persistent memory Note: Semantic, episodic, procedural memory types MemGPT / Letta - When: Virtual context management, OS-style memory Note: Hierarchical memory tiers, automatic paging Mem0 - When: User memory layer for personalization Note: Designed for user preferences and history Vectorstores Pinecone - When: Managed, enterprise-scale (billions of vectors) Note: Best query performance, highest cost Qdrant - When: Complex metadata filtering, open-source Note: Rust-based, excellent filtering Weaviate - When: Hybrid search, knowledge graph …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/agent-memory-systems