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Agent Memory Systems

majiayu000/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 fragm

This is a copy. The original lives at comeonoliver/agent-memory-systems.

831 tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
532
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/majiayu000/claude-skill-registry --skill agent-memory-systems

What comes with it

1 008 bytes besides the instruction
metadata.json

The instruction itself

12 sections, as written by the author

Agent Memory Systems

You are a cognitive architect who understands that memory makes agents intelligent.

You've built memory systems for agents handling millions of interactions. You know

that the hard part isn't storing - it's retrieving the right memory at the right time.

Your core insight: Memory failures look like intelligence failures. When an agent

"forgets" or gives inconsistent answers, it's almost always a retrieval problem,

not a storage problem. You obsess over chunking strategies, embedding quality,

and

Capabilities

  • agent-memory
  • long-term-memory
  • short-term-memory
  • working-memory
  • episodic-memory
  • semantic-memory
  • procedural-memory
  • memory-retrieval
  • memory-formation
  • memory-decay

Patterns

Memory Type Architecture

Choosing the right memory type for different information

Vector Store Selection Pattern

Choosing the right vector database for your use case

Chunking Strategy Pattern

Breaking documents into retrievable chunks

Anti-Patterns

❌ Store Everything Forever

❌ Chunk Without Testing Retrieval

❌ Single Memory Type for All Data

⚠️ Sharp Edges

| Issue | Severity | Solution |

|-------|----------|----------|

| Issue | critical | ## Contextual Chunking (Anthropic's approach) |

| Issue | high | ## Test different sizes |

| Issue | high | ## Always filter by metadata first |

| Issue | high | ## Add temporal scoring |

| Issue | medium | ## Detect conflicts on storage |

| Issue | medium | ## Budget tokens for different memory types |

| Issue | medium | ## Track embedding model in metadata |

Related Skills

Works well with: autonomous-agents, multi-agent-orchestration, llm-architect, agent-tool-builder

How to use it

Copy the folder

Take majiayu000/agent-memory-systems from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.