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

{ "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:

{ "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:

{ "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.