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 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.