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:

{ "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 If a fact was consolidated, its superseded ancestors show the history — that's the audit trail.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:

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.