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If the agent generated a flow for you and you want to understand what it produced, here’s what each part does — and how those same parts extend once you’re testing the same behavior on more than one platform. If your team has written Playwright or Appium tests before, QA Wolf flows will look familiar. The selectors, interactions, and assertions work the same way. What’s different is the wrapper around them. Every flow is wrapped in flow() from the @qawolf/flows package — it tells the runner what to run, where to run it, and which runtime objects to inject into the callback.

AAA framework

QA Wolf flows follow the Arrange-Act-Assert (AAA) format.
  • Arrange — sets up state before the interaction.
  • Act — performs the interaction being tested.
  • Assert — verifies the expected outcome.
iOS and Android use driver.$(...) calls instead of page calls.

Import statement

Import from the @qawolf/flows subpath that matches the platform you are testing.
For web flows, always import expect from @qawolf/flows/web — not from @playwright/test, which causes assertions to bypass QA Wolf’s reporting. iOS, Android, and Node flows don’t have this pitfall: expect is wired to the QA Wolf runner automatically.

Flow wrapper

  • Name — what this flow is called in your results, bug reports, and QA Wolf dashboard. Make it descriptive enough that a failing flow name tells you where to look.
  • Configuration — where the flow runs and how it starts. See Launch styles below, and Target literals for the values target accepts.
  • Callback — the async function containing your test logic. See Callback parameters.

Launch styles

Declarative launch Recommended Pass launch: true to use the default startup, or an options object to customize it. Either way, QA Wolf starts the browser or app before your callback runs, and the callback receives the platform object ready to use.
If you’ve used Playwright directly, this is the equivalent of calling await browser.launch() — QA Wolf handles that setup for you, so your callback starts with a ready-to-use page.
Pass an options object instead of true when you need to customize startup, such as reusing a browser profile. See the Web API Reference for the full list of options.
Explicit launch Omit launch from the configuration and call launch() inside the callback when startup options aren’t known until the flow runs. The callback receives no platform object: launch() returns a result you narrow with isPersistent(), isAnonymous() or isElectron() before reading page or context. See the Web API Reference for details. For Electron desktop app testing, see Testing Electron apps.

Callback parameters

Every flow callback receives three parameters regardless of platform:

inputs

Values passed into the flow from outside. Use this to read data published by another flow in the same run. Keys are uppercase by convention, e.g. inputs["EMAIL"].

setOutput(...)

Publishes one or more key-value pairs that a later flow in the same run can read via its inputs. Keys are uppercase by convention. You can publish multiple values in a single call:
A consumer flow will not run until its producer has called setOutput. See Passing data between flows for how producers and consumers work together.

test(...)

Wraps a named sub-step, grouping actions and assertions under a label that appears in your results. When a step fails, the label tells you exactly where in the flow the failure happened. All four parameters in one callback:

Platform object

A launch-enabled flow also receives the platform object, which differs by platform: For the full callback context shape, see the API Reference for Web, Android, and iOS.

Environment variables

An environment variable is set on the environment and holds the same value for every run, such as a base URL or a test account’s password. inputs holds values another flow published during the run. Read one with process.env.VAR_NAME, usually in the Arrange section before any interactions begin.
Set them under Workspace settings → Environments, on the environment’s Environment variables tab.

Platform differences

Prefer a separate flow per platform, each importing from its own @qawolf/flows subpath. When one flow has to cover several platforms, put the difference in a helper function so the flow stays linear. Log capture is a typical case:
Helpers like these sit at module level, which holds only imports, constants and pure functions. To branch on the platform at runtime instead, read platform.target — sparingly.
launch(), device and platform.target are stubs the runner replaces at execution time, so they work only inside the flow callback. At module level, platform.target throws.
Last modified on September 18, 2026