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) ```jsonc { "name": "memory_search", "arguments": { "type": "behavior_pattern", "projectId": "<repo>", "limit": 50 } } ``` Or scope to one subject and read them out of the context bundle: ```jsonc { "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: ```ts 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.