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Memory Systems Design

Guides an agent to design persistent memory: cross-session recall, entity tracking, and retrieval over stored facts.

Source Download skill
Repository
muratcankoylan/Agent-Skills-for-Context-Engineering
Stars
18,282
Licence
MIT
Files
3 files

What it does

When installed, the agent gets guidance on how to give AI agents long-term memory that survives between sessions. It helps choose between approaches (plain files, vector search, entity registries, temporal knowledge graphs) and frameworks such as Mem0, Zep/Graphiti, Letta, LangMem and Cognee, and it points to memory benchmarks for comparing them. It also includes a Python helper module (memory_store.py) with simple vector store, property graph and temporal graph building blocks for prototyping.

Great for

  • Developers building an agent that must remember users or facts across sessions
  • Teams choosing between memory frameworks and wanting a structured comparison
  • Engineers prototyping a memory layer before committing to a production framework

Skip it if

  • Simple within-session context management, which belongs to the related filesystem-context or context-compression skills (the skill itself redirects there)
  • Production deployments without testing: the bundled code is a simple prototype and benchmark figures are dated and must be rechecked
  • Non-technical users looking for a ready-made personal memory app; this is design guidance, not a product

Try asking

  • “Design a memory architecture for a customer support agent that remembers each customer across sessions”
  • “Compare Zep/Graphiti and Mem0 for an enterprise agent that needs temporal reasoning”
  • “Prototype an entity registry so 'John Doe' is recognised as the same person across conversations”
  • “Help me pick the simplest memory layer that meets my retrieval needs”
Step-by-step guide: how to use Memory Systems DesignInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 3 original files, the MIT licence and an attribution note.

    memory-systems.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip memory-systems.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip memory-systems.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Start with the simplest layer (files or key-value store) and add vector or graph structure only when retrieval measurably fails
  • Treat the bundled memory_store.py as a prototype; review it and test retrieval quality before using it with real user data
  • Re-check benchmark numbers from the source before making product or purchasing decisions

What's inside

  • references/implementation.md
  • scripts/memory_store.py
  • SKILL.md

Redistributed unmodified from muratcankoylan/Agent-Skills-for-Context-Engineering under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.