Natural-language UI test runner
Project description
playwright-use
Natural-language UI tests, no imperative code. Playwright + AI.
https://github.com/user-attachments/assets/1aa826fd-e05b-462b-a717-8a4b448a38ed
Setup
python -m venv .venv && . .venv/Scripts/activate # Windows PowerShell
pip install -r requirements.txt
python -m playwright install chromium
Configure Azure OpenAI
Create a .env in project root:
AZURE_OPENAI_API_KEY=...
AZURE_OPENAI_ENDPOINT=https://<resource>.openai.azure.com/
AZURE_OPENAI_DEPLOYMENT=<deployment_name>
AZURE_OPENAI_API_VERSION=2024-08-01-preview
LLM_PROVIDER=azure-openai
Other providers
- OpenAI:
LLM_PROVIDER=openai
OPENAI_API_KEY=...
# optional
OPENAI_MODEL=gpt-4o-mini
OPENAI_BASE=https://api.openai.com/v1
- Anthropic (Claude):
LLM_PROVIDER=anthropic
ANTHROPIC_API_KEY=...
ANTHROPIC_MODEL=claude-3-haiku-20240307
- Groq:
LLM_PROVIDER=groq
GROQ_API_KEY=...
GROQ_MODEL=llama3-8b-8192
Run a goal
Headless (default):
python main.py goals/login.goal.yaml
Headed (recommended for debugging):
python main.py goals/login.goal.yaml --headed
Or, when installed as a package (see Packaging):
playwright-use goals/login.goal.yaml --headed
Artifacts are written to runs/<GoalName_Timestamp>/:
report.html,report.jsonevents.logtrace.zip,*.webmstep_*.png,step_fail_*.png
Goal file format
name: "Login and Checkout Flow"
url: "https://www.saucedemo.com"
steps:
- description: "Open the site"
- description: "Log in with username 'standard_user' and password 'secret_sauce'"
- description: "Add 'Sauce Labs Backpack' to the cart"
- description: "Go to cart"
- description: "Click checkout"
- description: "Fill in first name 'Toni', last name 'Ramchandani', zip code '411001'"
- description: "Click Continue"
- description: "Click Finish"
assertions:
- "The final page shows a confirmation that order is placed"
- "URL contains 'checkout-complete'"
Notes:
${var}placeholders can be used inside descriptions/assertions and are replaced from an optionalvars:map.
Headed vs Headless
- Headed: launches maximized; viewport inherits OS window size for realistic layout.
- Headless: uses a deterministic 1280x800 viewport for reproducibility.
How it works (high-level)
- Each step’s natural-language description is converted into a JSON action plan by Azure OpenAI (
core/planner.py). - Actions are executed via Playwright with robust element resolution (
core/healer.py) and resiliency in inputs and checkboxes (core/executor.py). - Assertions check URL fragments or use a strict LLM oracle (
core/oracle.py). - Reports are generated by
core/reporter.py.
Aliases (self-learning)
- The runner caches successful hint→selector mappings per host in
fixtures/aliases.yaml. - You can also predefine entries there. File is hot‑reloaded.
Packaging
Build and install locally:
pip install --upgrade build twine
python -m build
pip install dist/*.whl
Run via console script:
playwright-use goals/login.goal.yaml --headed
Publish to PyPI (requires credentials):
twine upload dist/*
License
This project is licensed under the MIT License. See LICENSE.
See also: ARCHITECTURE.md for internals and diagrams.
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