Where does the browser automation code run?
An external worker, a hosted browser Function, a browser-local app action and a hosted agent are different execution models. The choice determines who deploys the code, starts the browser, records the result and stops the run.
A scheduled job should return a verified result and stop its owned browser even when an action fails. The scheduler's successful HTTP trigger is not proof that the browser completed the business operation.
Compare the execution model
Notte: Functions deploy Python code with a run() entry point. Invoke them over HTTP or with an SDK, inspect run history, and roll back code by redeploying an earlier version as the latest version.
Anchor Browser: Automation Tasks produce versioned workflows from a prompt or a recorded demonstration. Test and edit the workflow, then invoke it through the task API. Compare this authoring model with writing a handler directly.
Compare deployment ownership
Notte: Use a remote browser from your existing process or deploy the browser automation as a Notte Function. These choices put lifecycle, scheduling and monitoring responsibilities in different places.
Anchor Browser: Use a browser session from existing code or deploy a reusable Automation Task. The task path includes workflow editing and versioning; direct CDP leaves execution ownership with your application.
Compare the evidence from a run
Notte: Live view shows an active browser and can be embedded in an application. Recordings let you review a finished session. Function runs have their own history and results, so keep the run ID alongside the browser session ID.
Anchor Browser: Anchor documents MP4 session recordings and a live browser used during demonstrations. Keep the session artifact alongside the task's version, inputs and completion result.
How to evaluate this choice
Keep an external runner when you already operate one and need portability. A native execution layer can remove deployment work; browser-local code can reduce repeated network calls. Compare actual workload behavior before assuming either always wins.
- Confirm the supported language, entry point and dependency packaging.
- Trace deployment, version selection, invocation and cancellation.
- Keep the handler runtime limit separate from browser session duration.
- Decide whether scheduling is native or supplied by another service.
- Associate results and logs with both the code version and browser session.
Requirements side by side
| Requirement | Notte | Anchor Browser |
|---|---|---|
| Code execution | Python Functions | Automation Tasks |
| Scheduled execution | Native | Task execution path |
| Recordings / replay | Documented | Documented |
Sources: docs.notte.cc [1] ↗docs.notte.cc [2] ↗docs.notte.cc [3] ↗docs.notte.cc [4] ↗docs.notte.cc [5] ↗docs.notte.cc [6] ↗docs.anchorbrowser.io [7] ↗docs.anchorbrowser.io [8] ↗docs.anchorbrowser.io [9] ↗docs.anchorbrowser.io [10] ↗docs.anchorbrowser.io [11] ↗docs.anchorbrowser.io [12] ↗
Which approach fits?
Choose Notte when your browser workflow needs saved logins, credential fill or scheduled Python code alongside the browser.
Notte tradeoff: A saved profile restores browser state; it does not prove that a website still considers the user signed in. Use Managed Auth when the workflow needs connection readiness before it starts.
Choose Anchor Browser when you want managed sign-in and an automation task you can demonstrate, edit and invoke from your application.
Anchor Browser tradeoff: A demonstrated automation task and an arbitrary Python handler are different authoring models. Evaluate how much control you need over the code, dependencies and exact workflow.
A useful acceptance check
Write a pass/fail result for the real task before running the evaluation. Record the account, configuration and code version next to that result. Repeat failures need an explanation, not an automatically different browser configuration.