context-loader

# context-loader > **⚙️ 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. Prime the session with the right memory before you act. ## General project/session context ```jsonc { "name": "memory_search", "arguments": { "projectId": "<repo>", "limit": 20 } } ``` Skip only on pure pleasantries; the moment the task is substantive, load first. ## A specific subject — the synthesized bundle When you're about to reason about one entity (a user, contact, account), don't fire five searches — get the assembled picture in one call: ```jsonc { "name": "memory_build_context", "arguments": { "subjectKind": "user", "subjectId": "<id>", "maxTokens": 2000, "include": ["profile","patterns","predictions","memories"] } } ``` This returns the profile, active behavior patterns, **forward predictions**, and top memories — with provenance ids — ready to drop at the top of your prompt. That prediction section is the differentiator: you enter the task already knowing what the subject is likely to do next. ## Anticipatory follow-on Once you're working with a few memories, pull what's most likely needed next via spreading activation over the graph: ```jsonc { "name": "memory_prefetch_related", "arguments": { "seedMemoryIds": ["<id1>","<id2>"], "limit": 10 } } ``` ## Cite what you use When a loaded memory shapes your response, mention it briefly so the user can correct stale info.