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webwright

Run long-horizon, multi-step browser automation by delegating to the Microsoft webwright CLI, which writes and executes Playwright scripts to drive a real Chromium browser. Use for logins, multi-page forms, checkout/wizard flows, and repeatable site navigation; not for simple single-page reads.

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Webwright

Delegate browser tasks to the webwright CLI. Webwright is a code-as-action web agent: an LLM writes and runs Playwright scripts to drive a real Chromium browser, which is more robust and repeatable than click-by-click automation.

When to use

  • Multi-step web flows: logins, multi-page forms, checkout/wizard flows.
  • Long-horizon navigation across several pages where state must be carried.
  • Repeatable site tasks where you want a reusable generated script as the artifact.

When NOT to use

  • Simple single-page reads or extraction — use web_fetch / built-in web tools.
  • Anything that does not actually require a driven browser.

Preflight (do this before the first run)

This skill is opt-in. It only becomes available once skills.entries.webwright.enabled is true (an unset value counts as off), so enable it first:

openclaw config set skills.entries.webwright.enabled true

Run webwright doctor next as a setup check. Note its key check is OpenAI-specific: it always runs an OpenAI Key check, so if you intend to use Anthropic or OpenRouter, a doctor OpenAI Key FAIL is expected and is NOT a blocker — verify your chosen provider's key separately (step 3). Then confirm:

  1. webwright is on PATH (this skill is gated on it). If missing, see references/setup.md.
  2. Chromium is installed for Playwright: playwright install chromium.
  3. A provider API key is set in the environment for the model config you pick: OPENAI_API_KEY (default model_openai.yaml), ANTHROPIC_API_KEY (model_claude.yaml), or OPENROUTER_API_KEY (model_openrouter.yaml).
  4. The python, python3, and playwright that webwright's generated scripts will invoke must be webwright's own install (with Chromium). If you installed webwright in a venv, activate it (or prepend its bin to PATH) before running — otherwise generated scripts can pick up a different system Python that lacks the browser. See references/setup.md.
  5. Choose an output directory INSIDE the current workspace. Never write into ~/.openclaw, $OPENCLAW_STATE_DIR, or any active OpenClaw state directory.

Invocation

The CLI uses a main subcommand:

webwright main \
  -t "TASK INSTRUCTION" \
  --start-url "https://example.com" \
  -c base.yaml -c model_openai.yaml \
  --task-id my_task \
  -o ./webwright-out/my_task

-c defaults to base.yaml model_openai.yaml. You can also stack inline overrides, e.g. -c agent.step_limit=20 to cap the agent's step budget.

Then read the run artifacts (generated final_script.py, plan.md, step logs, screenshots under final_runs/run_<id>/) from the -o directory and report the generated script path to the user.

See references/cli.md for every flag, config stacking, and the output layout.

Hard rules

  • Always pass -o pointing inside the workspace; never inside OpenClaw state dirs.
  • Always pass --start-url and a specific, scoped -t task.
  • Browser runs take real actions on live sites and spend API tokens. Only run on user-authorized tasks and sites, and avoid destructive actions.
  • Never put secrets in the -t task text. The task is sent to the model provider and recorded in run artifacts (trajectories, logs, process args), so passwords or tokens placed there are exposed. For login-walled tasks, pass credentials via environment variables and tell the agent the env var NAMES to read in its generated script (e.g. "log in using $SITE_USER / $SITE_PASS"), or pre-authenticate a persistent browser profile — never the literal secret values.
  • Pick exactly one model config (model_openai.yaml, model_claude.yaml, model_openrouter.yaml) whose matching API key is present in the environment.

Examples

Search flights and read the results:

webwright main -t "Search flights SEA to JFK departing 2026-08-15 returning 2026-08-20" \
  --start-url "https://www.google.com/flights" \
  -c base.yaml -c model_openai.yaml --task-id flights -o ./webwright-out/flights

Extract a table behind a multi-step navigation (Claude model):

webwright main -t "Open the docs, go to the pricing page, and list every plan and price" \
  --start-url "https://example.com" \
  -c base.yaml -c model_claude.yaml --task-id pricing -o ./webwright-out/pricing