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.
Lightpanda: The reviewed documentation establishes a browser engine and hosted access, not a deployed Python Function contract. A separate automation process must have its own deployment, triggers and monitoring design.
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.
Lightpanda: Compare hosted Lightpanda access with operating the engine yourself. The engine's supported APIs and website compatibility remain relevant in either deployment.
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.
Lightpanda: Check support for the artifacts the workflow needs, such as screenshots or rendered-page inspection, against Lightpanda's engine documentation. A successful text-based navigation test does not validate a visual automation task.
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 | Lightpanda |
|---|---|---|
| Code execution | Python Functions | External worker |
| Scheduled execution | Native | External scheduler |
| Recordings / replay | Documented | Not established |
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] ↗lightpanda.io [7] ↗
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 Lightpanda when your workflow can use Lightpanda's supported browser APIs and you want to evaluate that engine on the pages you actually automate.
Lightpanda tradeoff: CDP connectivity does not establish complete Chromium compatibility. Validate rendering-dependent steps, downloads and browser features with the selected engine before a migration.
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.