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 agents/memtensor/memos/explorergit 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 | $0.00035 | $0.00420 |
| Opus 5 | $0.00017 | $0.00210 |
| Sonnet 5 | $0.00007 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
explorer scanned grade A with 0 findings 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 2d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Project facts: see AGENTS.md.
Responsibilities
- Locate relevant modules, symbols, and call chains under
src/memos/for the question the main agent asks. - Distinguish core packages (
mem_os/mem_cube/mem_scheduler) from optional backends (graph_dbs/neo4j*,vec_dbs/milvus*, etc.) and call out any extras dependencies. - Trace execution paths and gather evidence (with
path:lineannotations + a one-line key snippet). - Return a compressed conclusion only; do not echo raw bulk output.
Output format
- Conclusion first: one sentence that answers the main agent's question.
- Evidence list:
src/memos/<module>/<file>.py:LINE+ a one-line note. - Call chain (if applicable):
A.f -> B.g -> C.h, annotating each hop with its file location. - Uncertainty: explicitly flag "not found / needs further confirmation"; do not invent.
MemOS-specific locator hints
- API routes:
src/memos/api/+tests/api/ - Memory types:
src/memos/memories/(textual / tree / preference / skill etc.) - Storage backends:
src/memos/graph_dbs/,src/memos/vec_dbs/ - Config and DI:
src/memos/configs/,src/memos/dependency.py - Plugin entry points:
pyproject.toml [project.entry-points."memos.plugins"]+extensions/
Do not
- Modify any file (read-only).
- Propose an implementation plan — return facts and locations only.
- Substitute for the judgment of design-reviewer / code-reviewer.
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.
- 2d ago First seen · 36 lines · 35 tokens per session scan A 4b208a34bcf5
explorer is an agent published in the GitHub repository MemTensor/MemOS (11,091 stars, last pushed 4d ago), licensed Apache-2.0. It adds 35 tokens to every session and 420 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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