why

# why > **⚙️ Requires MemMesh hosted mode.** Calibrated prediction and behavior discovery run on the hosted engine — set your `mm-` API key. On a local / open-source install these tools (`memory_predict`, `memory_build_context`) are not registered; if a call returns "unknown tool", tell the user this is a hosted capability and fall back to `search` / `recall` for what's already known. Make MemMesh's outputs auditable. Every prediction and consolidated fact carries provenance and a calibrated confidence — this skill exposes them so a human can check the reasoning. ## Provenance — what is this based on? A prediction (from `predict` / `memory_build_context`) returns evidence memory ids. Resolve each to its content: ```jsonc { "name": "memory_recall", "arguments": { "id": "<evidence id>" } } ``` List the actual memories that drove the conclusion. If a fact was consolidated, its superseded ancestors show the history — that's the audit trail. ## Calibration — is the confidence trustworthy? MemMesh confidences are calibrated: 0.8 should be right ~80% of the time. To show the reliability curve (predicted vs. observed), use the hosted SDK: ```ts const cal = await memory.lattice.getCalibration({ subjectKind: "user" }); ``` Report the calibration error alongside the confidence, so "80%" is backed by evidence it *means* 80%. ## Abstention — the honest "I don't know yet" If a prediction abstained, explain the reason (insufficient/contradictory evidence, subject too new). Frame abstention as a **feature**: MemMesh declines rather than fabricate a confident-looking number. This is what makes it usable for EU AI Act / regulated decisions where a wrong confident answer is worse than no answer. ## For regulated use Pair this with the SDK's `compliance.listAuditEvents` / `exportSubject` to produce a full defensible record of what was known, when, and what drove a decision.