thinkfleetai/memmesh/behaviors

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.