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 memmesh-sdkgit 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/memmesh-sdk)<a href="https://agentmods.dev/skills/thinkfleetai/memmesh/memmesh-sdk"><img src="https://agentmods.dev/badge/skills/thinkfleetai/memmesh/memmesh-sdk/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/memmesh-sdk"><img src="https://agentmods.dev/badge/skills/thinkfleetai/memmesh/memmesh-sdk.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 | $0.00176 | $0.01679 |
| Opus 5.5 | $0.00070 | $0.00672 |
| Sonnet 5.5 | $0.00035 | $0.00336 |
| Haiku 4.5 | $0.00018 | $0.00168 |
Grade A, and why
memmesh-sdk 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.
How it starts
The opening of the file, as written. The whole thing — 167 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MemMesh TypeScript SDK
MemMesh is not just a store-and-recall memory layer. It is a memory +
calibrated-prediction + behavior-discovery engine over a bi-temporal
knowledge graph. The SDK talks to the hosted platform (app.memmesh.ai) over
REST; for a zero-infra local setup, drive the same engine through the CLI +
MCP server instead (see memmesh-cli).
Mental model:
observe(feed raw text — the engine decides what to save) →search/buildContext(retrieve) →predict(forecast the subject's next move, with a calibrated confidence and provenance).
Step 1 — install and authenticate
npm install @thinkfleet/memory-sdk
export MEMMESH_API_KEY="mm-your-api-key" # from app.memmesh.ai
Step 2 — initialize
import { ThinkFleetMemory } from "@thinkfleet/memory-sdk";
const memory = new ThinkFleetMemory({
apiKey: process.env.MEMMESH_API_KEY, // or a Cognito JWT via `token`
// baseUrl defaults to https://app.memmesh.ai
});
Step 3 — the core loop: observe → retrieve → (predict)
Observe — the engine decides what to save
Unlike layers where you judge "is this worth saving?", you feed MemMesh raw text and its extractor (regex + structural rules + optional LLM refinement) decides. Cheap, idempotent, silent on filler.
await memory.memory.observe({
text: "Alice is vegetarian and allergic to nuts. She books gym classes on Mondays.",
userId: "alice",
projectId: "myapp",
});
There are also typed intake helpers: observeImage, observeVoice,
observeDocument, ingestMedia.
Retrieve — search or a full context bundle
const hits = await memory.memory.search({ query: "dietary restrictions", userId: "alice" });
// Or the synthesized, token-budgeted bundle (profile + patterns + predictions + top memories):
const ctx = await memory.context.build({ subjectKind: "user", subjectId: "alice", maxTokens: 2000 });
The moat — predict anything, with calibration + abstention
This is what a vector-recall layer cannot do. Predictions carry a calibrated
confidence ("80% means 80%"), provenance (evidenceMemoryIds), and a
first-class abstention ("I don't know yet" is a valid, honest answer).
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 · 167 lines · 176 tokens per session scan A ef38fa2e612f
memmesh-sdk is a skill published in the GitHub repository ThinkfleetAI/memmesh (405 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 176 tokens to every session and 1,679 once invoked, about $0.0007 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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