robot-training

Robot Training

Robot training turns one real browser demonstration into durable artifacts:

• interaction timeline from Crawlio session recording
• DOM/storage state snapshots streamed into extension-owned IndexedDB
• CDP network entries and response bodies
• portable request/response traces in flows.jsonl
• replay inputs for agent_session_* background sessions

Use this when speed and repeatability matter. The human can teach the flow once; agents later replay by API contract or semantic browser actions instead of primary mouse events.

Capture Protocol

Start with a fresh run:

{
  "action": "training_start",
  "url": "https://target.example/",
  "outputDir": "/absolute/path/to/runs/target-take-1",
  "maxDurationSec": 600,
  "maxInteractions": 500,
  "active": true,
  "captureStorageValues": false
}

Call observe with action: "training_start" before the user begins. The extension opens a fresh tab, starts network capture and recording, installs an event-driven monitor for the current and future documents, and retains the run locally. The MCP server writes manifest.json, but it does not own collection.

During the demonstration, poll with:

{ "action": "training_status", "runId": "rt_..." }

Call observe with action: "training_stop" and the same runId after the demonstration. The extension fetches bounded/redacted response bodies before stopping Network, returns its resident export, and the server materializes:

• raw-dump.json
• recording.json
• network.json
• bodies.json
• state-log.json
• state.json
• flows.jsonl
• causal-graph.json
• CAUSAL.md
• recipe.json
• REGISTRY.md
• api.openapi.yaml
• manifest.json

If MCP disconnects during the demonstration, do nothing in the page. The extension continues collecting. Reconnect, query training_status, and stop with the original runId.

After the canonical files have been verified, delete the extension-retained record only when the user asks to remove it: