thinkfleetai/memmesh/predict

predict

⚙️ 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.

Turn accumulated memory into a forward forecast. Unlike search ("what do we know"), predict answers "what happens next" — and it tells you how confident it honestly is, or abstains.

Forward behavior prediction (local MCP)

{ "name": "memory_predict",
  "arguments": { "subjectKind": "user", "subjectId": "<id>",
                 "horizonDays": 30, "minConfidence": 0.5, "limit": 20 } }

Returns ranked predictions, each with a confidence decayed over the horizon and the provenance behind it. Confidence is calibrated — 0.8 means it's right ~80% of the time — not a raw model logit.

Read the result honestly

• Present the top predictions with their confidence and horizon.
• If a prediction abstains (not enough evidence), say so plainly — "not

enough signal yet" is a valid, correct answer, and the point of MemMesh.

• Cite the evidence ids so the user can trace why. Use the why skill to dig

into calibration/provenance.

Predict ANY target (hosted / SDK)

The declarative "predict anything" surface (lattice.predictTarget with target.kind ∈ event_occurrence | numeric | event_time | anomaly) lets you add a new prediction with no code change — just name the target. It runs on the hosted gRPC/SDK path:

await memory.lattice.predictTarget({
  subject: { kind: "account", externalId: "acme" },
  target:  { kind: "event_occurrence", name: "churn" },
});

Prereq

Predictions come from mined behavior_pattern memories. If memory_predict returns nothing, the subject may not have enough observed history yet — feed more via observe, or check what patterns exist with the behaviors skill.