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 agentmods add skills/memtensor/memos/memos-memory-guidenpx skills add MemTensor/MemOS --skill memos-memory-guidegit clone --depth 1 https://github.com/MemTensor/MemOSWhat 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 | $0.00131 | $0.03387 |
| Opus 5 | $0.00066 | $0.01693 |
| Sonnet 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 yesterday.
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.
- yesterday First seen · 235 lines · 131 tokens per session scan A 3c131ce68982
memos-memory-guide is a skill published in the GitHub repository MemTensor/MemOS (11,091 stars, last pushed 4d ago), licensed Apache-2.0. It adds 131 tokens to every session and 3,387 once invoked, about $0.0007 per session on Opus 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.
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