thinkfleetai/memmesh/memmesh-sdk
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MemMesh TypeScript SDK reference (@thinkfleet/memory-sdk) for the hosted platform at app.memmesh.ai. Covers the ThinkFleetMemory client — observe / search / list, the predict + lattice prediction surface, closed-loop learning (recordDecision / recordOutcome), emergent behavior discovery, and the health / financial vertical packs. TRIGGER when: user is writing code that calls the MemMesh SDK, mentions "@thinkfleet/memory-sdk", "ThinkFleetMemory", "memmesh sdk", "lattice.predict", "predictTarget", or wants to add memory OR prediction to a TS/JS app. DO NOT TRIGGER for: the local MCP observe/recall loop (that's the always-on `memmesh` skill), CLI usage (use `memmesh-cli`), or wiring into an existing repo (use `memmesh-integrate`).
Install
npx vskill@latest install thinkfleetai/memmesh/memmesh-sdkSKILL.md
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'snext move, with a calibrated confidence and provenance).
Step 1 — install and authenticate
Step 2 — initialize
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.
There are also typed intake helpers: observeImage, observeVoice, observeDocument, ingestMedia.
Retrieve — search or a full context bundle
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).
Closed-loop learning — make predictions get better
Record the decision you made and the outcome that followed; the engine feeds that back into calibration and effectiveness reporting.
Emergent behavior discovery — patterns nobody predefined
Knowledge graph (bi-temporal)
Vertical packs
Compliance & consent (regulated use)
Scoping model
Six-level hierarchy: platform › project › location › agent › user › session. Pass projectId / userId / agentId / sessionId to scope any call. Lifecycle: pending → confirmed → superseded → rejected (the engine supersedes on contradiction — you don't hand-manage it).
Language support
TypeScript/JavaScript is the shipping distributed SDK today. For non-TS stacks, use the MCP server (any MCP-capable agent) or the REST API directly (llms.txt / OpenAPI at docs.memmesh.ai). A Python SDK is on the roadmap.
Ground truth (fetch before relying on ambient knowledge)
- Docs index (agent-ready): https://docs.memmesh.ai/llms.txt
- SDK examples:
predict-anything.ts,financial-demo.ts,next-best-offer.ts - Related skills:
memmesh(MCP loop),memmesh-cli,memmesh-integrate
Security
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Source
- SKILL.md in the repositoryCheckingskills/memmesh-sdk/SKILL.md
Popularity
- Repository stars
- 441
- Repository forks
- 475
- 7-day trend score
- 30
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