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) ```jsonc { "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: ```ts 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.