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.