MemOS is a memory operating system that stores, retrieves, and manages long-term memory for large language models and AI agents. It supports context-aware interactions by organizing text, images, tool traces, personas, and knowledge bases for reuse across tasks.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add MemTensor/MemOS --skill memos-memory-guidegit clone --depth 1 https://github.com/MemTensor/MemOSWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/memtensor/memos/memos-memory-guide)<a href="https://agentmods.dev/skills/memtensor/memos/memos-memory-guide"><img src="https://agentmods.dev/badge/skills/memtensor/memos/memos-memory-guide/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/memtensor/memos/memos-memory-guide"><img src="https://agentmods.dev/badge/skills/memtensor/memos/memos-memory-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 37 Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.
- medium Excessive Agency · line 78 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00131 | $0.03387 |
| Opus 5.5 | $0.00052 | $0.01355 |
| Sonnet 5.5 | $0.00026 | $0.00677 |
| Haiku 4.5 | $0.00013 | $0.00339 |
Grade A, and why
memos-memory-guide scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- curl How it starts
The opening of the file, as written. The whole thing — 235 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MemOS Local Memory — Agent Guide
This skill describes how to use the MemOS memory tools so you can reliably search and use the user's long-term conversation history, query team-shared data, share tasks, and discover or pull reusable skills.
Two sharing planes exist and must not be confused:
- Local agent sharing: visible to agents in the same OpenClaw workspace only.
- Team sharing: visible to teammates through the configured team server.
How memory is provided each turn
- Automatic recall (hook): At the start of each turn, the system runs a memory search using the user's current message and injects relevant past memories into your context. You do not need to call any tool for that.
- When that is not enough: If the user's message is very long, vague, or the automatic search returns no memories, you should generate your own short, focused query and call
memory_searchyourself. - Memory isolation: Each agent can only see its own local private memories and local
publicmemories. Team-shared data only appears when you search withscope="group"orscope="all".
Tools — what they do and when to call
memory_search
- What it does: Search long-term conversation memory for past conversations, user preferences, decisions, and experiences. Returns relevant excerpts with
chunkIdand optionallytask_id. Only returns memories belonging to the current agent or marked as public. - When to call:
- The automatic recall did not run or returned nothing.
- The user's query is long or unclear — generate a short query yourself and call
memory_search(query="..."). - You need to search with a different angle (e.g. filter by
role='user').
- Parameters:
query(string, required) — Natural language search query.scope(string, optional) —'local'(default) for current agent + local shared memories, or'group'/'all'to include team-shared memories.maxResults(number, optional) — Increase when the first search is too narrow.minScore(number, optional) — Lower slightly if recall is too strict.role(string, optional) — Filter local results by'user','assistant','tool', or'system'.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 235 lines · 131 tokens per session scan A 3c131ce68982
memos-memory-guide is a skill published in the GitHub repository MemTensor/MemOS (11,880 stars, last pushed 6d ago), licensed Apache-2.0. It adds 131 tokens to every session and 3,387 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
memory-triage
Persistent long-term memory protocol powered by mem0. Evaluate conversations for durable facts worth storing via memoryadd. Handles identity, preferences, decisions, configurations, rules, projects, and relationships. Loaded by the openclaw-mem0 plugin when skills mode is active.
mem0
Mem0 Platform SDK for adding persistent memory to AI applications. TRIGGER when: user mentions "mem0", "MemoryClient", "memory layer", "remember user preferences", "persistent context", "personalization", or needs to add long-term memory to chatbots, agents, or AI apps. Covers Python SDK (mem0ai), TypeScript SDK…
mem0-cli
Mem0 CLI -- the command-line interface for mem0 memory operations. TRIGGER when: user mentions "mem0 cli", "mem0 command line", "@mem0/cli", "mem0-cli", "pip install mem0-cli", "npm install -g @mem0/cli", or is running mem0 commands in a terminal/shell (mem0 add, mem0 search, mem0 list, mem0 get, mem0 init, mem0…
mem0-vercel-ai-sdk
Mem0 provider for Vercel AI SDK (@mem0/vercel-ai-provider). TRIGGER when: user mentions "vercel ai sdk", "@mem0/vercel-ai-provider", "createMem0", "retrieveMemories", "addMemories", "getMemories", "searchMemories", "mem0 vercel", "AI SDK provider", "AI SDK memory", or is using generateText/streamText with mem0. Also…
mem0-status
Diagnoses mem0 connectivity, API key validity, and memory read/write functionality. Use when memory operations fail, searches return empty, addmemory errors occur, or to verify the plugin is working correctly.
mem0-tour
Browses all stored memories grouped by category with full content display. Use when reviewing all project memories, exploring stored knowledge, onboarding to a project, or getting an overview of captured decisions, conventions, and learnings.