behaviors

name	behaviors
description	Surface emergent behavior patterns MemMesh has mined from a subject's history — recurring habits nobody predefined, each with prevalence, stability, and the evidence behind it. Use when the user asks "what patterns do you see", "what are this user's habits", or wants the patterns that drive predictions.

behaviors

⚙️ 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.

Show the patterns MemMesh discovered on its own. These behavior_pattern memories are what predict projects forward — inspecting them explains the forecasts.

List mined patterns (local MCP)

{ "name": "memory_search",
  "arguments": { "type": "behavior_pattern", "projectId": "<repo>", "limit": 50 } }

Or scope to one subject and read them out of the context bundle:

{ "name": "memory_build_context",
  "arguments": { "subjectKind": "user", "subjectId": "<id>", "include": ["patterns"] } }

Discover new patterns (hosted / SDK)

The discovery pass that finds patterns nobody predefined runs on the SDK:

const behaviors = await memory.behaviors.discover({ projectId: "myapp" });
// each: { pattern, prevalence, stability, evidenceMemoryIds }

Present them

For each pattern show: the behavior, how often it holds (prevalence), how stable it is over time (stability), and a couple of evidence memories. Rank by stability × prevalence — the strongest, most reliable habits first.

Why it matters

A vector-recall memory layer can only return facts you already stated. MemMesh derives structure — "books gym classes on Mondays", "reorders ~every 6 weeks" — from raw observations. That derived structure is the input to predict and the reason the predictions have provenance.