The fastest way to understand OpenClawBrain is to watch one question travel through the system. This page traces a single conversation turn from the moment your agent receives it to the moment a correction makes the guide smarter for next time.
OpenClaw receives a user message and keeps control of the hot path.
OpenClaw resolves the active pack and compiles bounded context from it.
If the promoted manifest requires learned routing, the compile must use that pack’s learned route_fn and expose routerIdentity in diagnostics.
Relevant package surface:
@openclawbrain/activation@openclawbrain/compiler@openclawbrain/openclawOpenClaw assembles the final prompt from the compiled context and serves the model call.
OpenClaw sends the response and can keep serving even if learner refresh is stale or unavailable.
The turn is written into normalized interaction and feedback events for later learning.
Relevant package surface:
@openclawbrain/events@openclawbrain/event-export@openclawbrain/openclawOpenClawBrain materializes candidate packs, refreshes learned routing artifacts, and stages or promotes them behind the hot path. That is asynchronous candidate-pack refresh, not proof of live mutation of the currently active pack.
Relevant package surface:
@openclawbrain/learner@openclawbrain/activation@openclawbrain/pack-formatFor one useful turn-level proof bundle, keep at least:
turn_idThis worked example proves the intended package boundary and learning-first operator story.
It does not by itself prove comparative benchmark performance, full benchmark coverage inside this repo, full live runtime plasticity on the active pack, or Brain Ground Zero route-function / QTsim proof parity.