Mimic remains a general CDP browser runtime. Optimize is an occasional training operation for repeatable workloads: it records a successful browser environment, validates local replay, and searches for acquisitions the workload can survive without. Candidate search never falls back to the live website.

Same task. A smaller request graph.
app.js
reviews.js
/api/product
/api/reviews
analytics.js
experiments.js
/api/recommendations
Unused images
Your workload
still passes.

Your own assertions decide
what can stay out.

Interactive illustration, not a benchmark. What is removable depends on your workload.

Train once, then run normally
mimic optimize --name shop -- node scraper.js

mimic --profile shop

The command can be Node, Python, a test runner or any executable using Mimic over CDP. No language SDK or Mimic assertion API is required. Optimize supplies a private endpoint through MIMIC_CDP_URL. Your existing Mimic instance can keep running. See endpoint setup in the guide if your script uses a hardcoded address.

Beyond broad resource categories

Keep the scripts and API responses the workload needs, and test specific resources and combinations it may not need. Blocking a parent can activate a fallback request; Optimize explores the resulting branch rather than treating the request graph as a fixed list. Where acquisition must remain, headers-only Fetch responses and classic-script execution exclusions provide additional experimental actions.

Check the difference, then reuse it

Optimize measures the result, saves what passed, and explains what changed. Your normal assertions define success. See the measured results and methodology. Savings depend on the workload and the state of the website.

Assertions and evidence, not a promise about tomorrow

A passing profile preserves the behavior verified in its recorded states. Unknown documents and request inputs use general Mimic, but destructive exclusions cannot be rolled back after they happen. Profiles are explicitly empirical: retain your live assertions and retrain when relevant states change.