Guide · Agent skill
How to use the Memory Systems Design agent skill
Updated · written with AI from the project's own documentation and checked against our catalogue data
The memory-systems skill gives an AI agent design guidance for persistent memory: knowledge that survives between sessions, entity tracking, facts with validity periods, and retrieval over graphs or vectors. It is aimed at developers who are building an agent that must remember users or facts over time, or who are choosing between memory frameworks. After this guide you will know how to install the skill, trigger it with a natural request, and understand what it can and cannot do for you.
Memory Systems Designmuratcankoylan/Agent-Skills-for-Context-Engineering · MIT Download the skill Quick start
- 1Download and unzip. Download the skill ZIP from this page and unzip it into ~/.claude/skills/ or a project's .claude/skills/ folder.
- 2Or upload in the Claude apps. In the Claude apps, open Settings, go to Capabilities, then Skills, and upload the skill.
- 3Install Python 3 and numpy. Make sure Python 3 is available and install the numpy library, which memory_store.py imports.
- 4Describe your memory problem. Ask in plain language about cross-session memory, entities, temporal facts or a framework comparison.
- 5Choose a layer and retrieval strategy. Let the agent use the skill's tables to pick the shallowest memory layer and a matching retrieval strategy.
- 6Prototype and measure. Use memory_store.py to build a small prototype and test retrieval quality before choosing a production framework.
What does the memory-systems skill make the agent do?
An agent skill is a folder of instructions that an AI agent loads when a task matches the skill's description. This one covers persistent semantic memory. When it is active, the agent can compare memory approaches such as plain files, vector search, entity registries and temporal knowledge graphs, and explain which one fits a given retrieval need.
The skill also points to frameworks it names (Mem0, Zep/Graphiti, Letta, LangMem and Cognee) and to memory benchmarks (LoCoMo, LongMemEval and DMR) for comparing them. It recommends starting with the simplest layer that meets retrieval needs and adding structure only when a simpler layer fails. It routes file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.
- Choose a memory layer: working, short-term, long-term, entity or temporal knowledge graph.
- Pick a retrieval strategy: semantic, entity-based, temporal or hybrid.
- Compare frameworks by retrieval shape and read benchmark results as dated evidence.
- Prototype with the bundled memory_store.py building blocks.
How do you install the memory-systems skill?
Download the ZIP for this skill from this page and unzip it. For a personal setup, place the unzipped skill folder in ~/.claude/skills/. For a single project, place it in that project's .claude/skills/ folder.
You can also upload the skill in the Claude apps. Open Settings, go to Capabilities, then Skills, and upload the skill there.
- Personal install: ~/.claude/skills/
- Project install: .claude/skills/ inside your project
- Claude apps: Settings → Capabilities → Skills
Which requirements do you need before running it?
The skill needs Python 3 and the numpy library, which is imported by memory_store.py. Frameworks such as Mem0, Zep/Graphiti, Letta, LangMem or Cognee are optional and only needed if you decide to adopt one of them.
The skill runs scripts and does not use the network, according to its listing. The licence is MIT.
- Required: Python 3
- Required: numpy
- Optional: Mem0, Zep/Graphiti, Letta, LangMem or Cognee
How do you trigger the skill with natural requests?
You do not need a special command. Describe the memory problem in plain language and the agent should load the skill when your request matches its description. Mention persistence, cross-session memory, entities, or the frameworks by name to make the match clearer.
If your request is really about a scratchpad, a handoff summary or token budgets, the skill will redirect you to the matching related skill instead.
- "Design long-term memory so my support agent remembers each customer across sessions."
- "Compare Mem0 and Zep for an agent that tracks facts that change over time."
- "Which memory benchmark should I use to test retrieval for my agent?"
- "Prototype a simple vector store for remembering user preferences."
What does a typical design session look like?
Give the agent your workload: who the agent serves, what must persist, and how facts change. The agent should map this to a memory layer and a retrieval strategy from the skill's tables. You can then ask it to build a prototype using the helper module.
The bundled memory_store.py includes a simple vector store, property graph and temporal graph building blocks. The reference file implementation.md contains the vector store code, including a cosine similarity function and a VectorStore class with add and related methods.
- Start with the shallowest layer and a simple retrieval test.
- Add graph or temporal structure only if retrieval quality degrades or you need multi-hop or time-based queries.
- Recheck any benchmark figures before making claims.
What are the safety notes for scripts and the network?
The skill includes scripts, so review memory_store.py before running it, as you would any code you did not write. The listing says the skill does not use the network, but your own framework choice may: Mem0, Zep, Letta, LangMem and Cognee are separate products with their own setup and data handling, which you should check yourself.
The bundled code is a simple prototype. Test it thoroughly before using it in production.
- Read scripts/memory_store.py before executing anything.
- The skill is listed as not using the network; any frameworks you add are separate.
- Do not treat the prototype as production-ready.
What people use it for
- Designing a support agent that remembers each customer's preferences across sessions.
- Comparing Mem0, Zep/Graphiti and Letta for a team that must pick one memory framework.
- Prototyping an entity registry so that the same person is recognised across conversations.
- Planning a temporal knowledge graph for facts that change over time and need history preserved.
- Selecting benchmarks such as LoCoMo or LongMemEval to test a memory system before launch.
Frequently asked questions
Is the memory-systems skill free?
The skill is released under the MIT licence, as stated on this page. The frameworks it mentions, such as Mem0, Zep/Graphiti, Letta, LangMem and Cognee, are separate projects with their own terms, which you should check before adopting one.
What do I need to install the memory-systems skill?
You need the skill folder unzipped into ~/.claude/skills/ or a project's .claude/skills/ folder, or uploaded under Settings, Capabilities, Skills in the Claude apps. The skill itself requires Python 3 and numpy.
Does the memory-systems skill use the internet?
The listing states that the skill does not use the network. It does run scripts, so review memory_store.py before running it.
Is this a ready-made personal memory app?
No. It is design guidance plus a small Python prototype module. It is not a product that stores personal memories for you.
What is the difference between this skill and filesystem-context or context-compression?
memory-systems covers persistent semantic memory across sessions. File-backed scratchpads go to filesystem-context, and handoff summaries or conversation compaction go to context-compression. Token budgets inside one task go to context-optimization.
Why does the skill recommend starting with plain files?
The skill says to default to the simplest layer that meets retrieval needs, because benchmark evidence suggests tool complexity matters less than reliable retrieval for some memory workloads. Add graphs or temporal validity only when retrieval degrades or you need multi-hop reasoning or time-based queries.
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Our verdict
Use this skill if you are a developer designing an agent that must remember users or facts across sessions, or a team that wants a structured comparison of memory frameworks. Pick something else if you only need within-session context handling, which filesystem-context or context-compression cover, or if you want a finished personal memory app, since this is design guidance and a prototype module rather than a product.
Download the skill