memmesh-sdk

memmesh-sdk is a skill for Claude Code, Codex from ThinkfleetAI/memmesh. It costs 176 tokens per session (1,679 once invoked), scanned A, original, Apache-2.0.

A TypeScript software development kit for MemMesh, a hosted service that stores memories, retrieves them, and can make confidence-rated predictions. It connects an application to MemMesh over the web.

In plain words
What is it for?
Use it to save observations, search and list memories, build context, make predictions, record decisions and outcomes, and discover behavior patterns. It also covers authentication and service health.
Why use it?
It provides the code needed to add persistent memory and related prediction features without building those service calls yourself. It also describes a local command-line and MCP option for running the same engine without hosted infrastructure.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to save observations, search and list memories, build context, make predictions, record decisions and outcomes, and discover behavior patterns. It also covers authentication and service health.

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Install with agentmods
npx agentmods add skills/thinkfleetai/memmesh/memmesh-sdk
Install

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.

Any agent
npx skills add ThinkfleetAI/memmesh --skill memmesh-sdk
Clone the repo
git clone --depth 1 https://github.com/ThinkfleetAI/memmesh

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for memmesh-sdk

README.md
[![agentmods](https://agentmods.dev/badge/skills/thinkfleetai/memmesh/memmesh-sdk/github.svg)](https://agentmods.dev/skills/thinkfleetai/memmesh/memmesh-sdk)
Your own site
<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.

agentmods 80×15 button for memmesh-sdk

Your own site · 80×15
<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>
Per session 176 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,679 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 1mo ago against content hash ef38fa2e612f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

skills/memmesh-sdk/SKILL.md · 167 lines

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).

Read the full file on GitHub · 167 lines

Changes

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.

  1. 1mo ago First seen · 167 lines · 176 tokens per session scan A ef38fa2e612f

Subscribe to this mod's changes

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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