Know what to test.Know whether to ship.
PlaywrightGen connects requirements, test intent, reviewable automation, execution evidence, and failure intelligence so your team can act on quality risk instead of guessing from disconnected AI output.
Quality evidence map
Illustrative project workflow
Requirement
Approved intent
Test Cases
Linked scenarios
Automation
Browser + API
Run Evidence
Immutable attempts
Highest-value action
Review the failed run and its evidence before approving the automation or making a release decision.
One product, two ways to start
Explore freely. Build trust in Workspace.
Free tools help you reach a useful first result quickly. Workspace turns that one-time result into versioned, reviewable project evidence.
Free tools
Fast, preliminary analysis
Use a focused tool when you want a disposable draft or a quick second opinion. These results do not claim durable project coverage.
Workspace
The system of record for quality
Use Workspace when evidence must survive beyond one prompt: assign ownership, preserve versions, review automation, record execution, and keep every decision inside the correct organization and project.
The quality evidence loop
From approved intent to an explainable release decision
Each stage produces a record the next stage can reference. Nothing important has to live only in a chat transcript or a copied code block.
Define approved intent
Keep acceptance criteria, ownership, review state, and every material revision inspectable.
Make coverage explicit
Link business intent to structured scenarios, expected outcomes, priority, and review history.
Generate reviewable code
Create separate Browser and API artifacts with deterministic validation and human approval.
Preserve what happened
Keep each result, step outcome, environment, failure detail, and evidence link immutable.
Act on the real gaps
See missing coverage, unresolved failures, stale evidence, and the next highest-value action.
Works where your team already works
Your editor writes the code. PlaywrightGen keeps the proof.
Every editor now has an assistant that writes Playwright. What it cannot know is which behaviour your team agreed on, and whether the last run proved it. That is what PlaywrightGen gives it.
Your editor
Approved tests, inside VS Code, Cursor and Claude Code
Connect your editor's AI assistant to a project over MCP. It writes tests against the approved test case instead of a guess, keeps the version marker that links results back, and pulls reviewed automation into your repository.
Read-only · personal token · revoked when access ends
Your CI
Your runners, your secrets, our evidence
Tests run in your own GitHub Actions. Only a bounded summary of results comes back, and each one attaches to the exact approved version it exercised.
Signed reports · nothing of yours executes here
Your team
A second pair of eyes on every approval
Members write requirements, test cases and automation. Leads approve them. Nobody approves their own submission while someone else can, and every step shows who did it and when.
Leads · Members · Viewers · review queue
# In VS Code, Cursor or Claude Code
you Using PlaywrightGen, write the test for our checkout test case.
assistant Read “Customer completes checkout” — approved, version 3, 4 steps. Writing tests/customer-completes-checkout.spec.ts with the title [pwg:7c1e…] so your CI results attach to version 3.
Designed for the whole delivery conversation
One shared view of quality, different decisions for each role
QA and SDET leads
Connect planning, automation, execution, and failure review without maintaining separate evidence spreadsheets.
Developers
Get a clear next test, inspect generated Playwright code, and understand failures without losing engineering context.
Engineering managers
See what is covered, what is unresolved, and which evidence is still missing before a release decision.
Trust model
AI assistance without invisible authority
The goal is not maximum automation at any cost. The goal is faster quality work that remains reviewable, attributable, and safe to challenge.
Evidence before confidence
Every durable quality claim should link back to the Requirement, Test Case, artifact, run, or finding that supports it.
AI proposes. Teams approve.
AI can review, generate, and diagnose. It cannot silently change approved intent or turn generated output into trusted automation.
History stays inspectable
Versions and execution attempts are preserved so a later edit never changes what the team previously reviewed or ran.
Start with the evidence you have
Turn the next quality question into a durable team decision.
Try a focused free review, or enter Workspace when the result needs ownership, history, approval, and project context.