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 ThinkfleetAI/memmesh --skill benchmarkgit clone --depth 1 https://github.com/ThinkfleetAI/memmeshWrote 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/thinkfleetai/memmesh/benchmark)<a href="https://agentmods.dev/skills/thinkfleetai/memmesh/benchmark"><img src="https://agentmods.dev/badge/skills/thinkfleetai/memmesh/benchmark/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/thinkfleetai/memmesh/benchmark"><img src="https://agentmods.dev/badge/skills/thinkfleetai/memmesh/benchmark.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00072 | $0.00452 |
| Opus 5.5 | $0.00029 | $0.00181 |
| Sonnet 5.5 | $0.00014 | $0.00090 |
| Haiku 4.5 | $0.00007 | $0.00045 |
Grade A, and why
benchmark 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 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.
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
benchmark
Put numbers on the comparison. MemMesh ships a real benchmark harness that runs the public LOCOMO dataset end-to-end against competing systems.
What it compares
Systems: thinkfleet (MemMesh) vs full_context vs naive_rag, and — with keys
— Mem0 / Zep. Metrics: answer accuracy (rubric-scored), tokens consumed, latency,
and cost per conversation.
Run it
The harness lives in the engine repo at crates/eval/competitive/:
cd crates/eval/competitive
python bench.py --systems thinkfleet,mem0,full_context --dataset locomo
# results land in results/
(Set the competitors' API keys via env for a head-to-head; without them you still get MemMesh vs full-context vs naive-RAG.)
Report honestly
MemMesh's positioning is calibration over raw accuracy — "80% means 80%" and honest abstention beat a slightly higher accuracy with overconfident wrong answers. So report the full picture:
- accuracy and calibration error,
- tokens / latency / cost (MemMesh's retrieval is far cheaper than full-context),
- where MemMesh abstained vs. where a competitor answered confidently and wrong.
Don't cherry-pick a single accuracy number. If a competitor wins on one axis, say so, and show where MemMesh's calibration/cost advantage pays off.
Cost gating
Prove the win on the cheap tiers (LOCOMO, BEAM-100K) before spending on BEAM-1M/10M — a single 10M-token conversation is expensive. Escalate tiers only once the cheaper tier shows a clear, defensible lead.
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 · 50 lines · 72 tokens per session scan A 1b4bf7a8c72a
benchmark is a skill published in the GitHub repository ThinkfleetAI/memmesh (405 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 72 tokens to every session and 452 once invoked, about $0.0003 per session on Opus 5.5. 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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Generate vector embeddings via 9Router /v1/embeddings using OpenAI / Gemini / Mistral / Voyage / Nvidia / GitHub embedding models for RAG, semantic search, similarity. Use when the user wants embeddings, vectors, RAG, semantic search, or to embed text.
similarity-search-patterns
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
embedding-strategies
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
edd
Eval-driven development loop for this repo (rag-loop, an aircraft-systems RAG app on LangGraph, OpenAI, Qdrant Cloud, and TruLens). Use when the user wants to run an experiment, inspect feedback metrics, or decide keep-or-reject on a config/prompt change.