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.

By sickn33 · 1,923 installs

npx skills add sickn33/agentic-awesome-skills --skill agent-memory-systems

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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). Detailed Guide Read [the detailed guide](references/detailed guide.md) before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end to end work, read the guide completely. When to Use User mentions or implies: agent memory User mentions or implies: long term memory User mentions or implies: memory systems User mentions or implies: remember across sessions User mentions or implies: memory retrieval User mentions or implies: episodic memory User mentions or implies: semantic memory User mentions or implies: vector store User mentions or implies: rag User mentions or implies: langmem User mentions or implies: memgpt User mentions or implies: conversation history Example User request: Use @agent memory systems for this task: Memory is the cornerstone of intelligent agents. Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.