Cypress tests break when the UI changes. agent-qa re-plans — and remembers.
Cypress made JavaScript E2E pleasant to write, with a paid cloud for the rest. agent-qa removes the script layer entirely: plain-English tests, self-adapting runs, memory in your repo, no cloud required.
Try agent-qa, the open-source way to write tests in English and keep the memory of every run.
agent-qa vs Cypress
| Capability | agent-qa | Cypress | Details |
|---|---|---|---|
| Plain-English authoring | Cypress tests are JavaScript/TypeScript. agent-qa tests are natural-language YAML — reviewable by anyone who understands the product, not just the framework. | ||
| Survives UI changes | Cypress selectors fail on markup changes and wait for a human. agent-qa re-plans from intent and records what changed. | ||
| Execution memory | agent-qa builds file-backed product memory across runs; Cypress runs are stateless. | ||
| Open source | The Cypress runner is open source (its Cloud is a paid product). agent-qa's whole workflow — runs, artifacts, memory, dashboard — is repo-local. | ||
| Local and CI execution | Both execute locally and in CI. agent-qa adds coding-agent execution via MCP as a first-class path. | ||
| Mobile app testing | Cypress is web-only by design. agent-qa runs native Android and iOS flows with the same YAML contract. | ||
| Coding-agent native | agent-qa ships MCP tools, Skills, and structured artifacts so coding agents can author, run, and triage tests autonomously. | ||
| Parallelization without paid cloud | Cypress's turnkey parallelization and analytics live in its paid Cloud. agent-qa's runs, artifacts, and local dashboard need no vendor cloud. |
Why teams move from Cypress to agent-qa
Flake stops being a lifestyle
Cypress flake usually traces to timing waits and selectors encoding a UI that moved. agent-qa executes from intent with adaptive planning, cached known-good plans, and memory of how your product actually behaves — attacking the root causes of flake rather than retrying around them.
No cloud upsell in your critical path
The open-source Cypress runner is real, but parallelization, analytics, and flake detection push you toward the paid Cloud. agent-qa keeps the complete workflow — execution, artifacts, memory, local dashboard — free and repo-local.
One harness for web and mobile
Cypress stops at the browser. If your product has an app — and it does — agent-qa covers those flows with the same plain-English format, instead of forcing a second framework and a second skill set.
Cypress improved the developer experience of writing test scripts. agent-qa removes the scripts. English in, verified flows out, memory retained — on web and the mobile app Cypress can't touch.
Frequently asked questions
Is agent-qa a good Cypress alternative?
Yes, particularly if your Cypress pain is flake, selector maintenance, or missing mobile coverage. agent-qa replaces scripted specs with plain-English YAML tests that self-adapt to UI changes, remember prior runs, and extend to Android and iOS.
What does agent-qa cost compared to Cypress?
The Cypress runner is free with a paid Cloud for parallelization and analytics. agent-qa is free and open source end to end — you pay only your chosen LLM provider's token costs, moderated by plan caching.
How do I migrate from Cypress to agent-qa?
Rewrite intent, not code: each Cypress spec describes a user journey, and that journey — stated in plain English — is a complete agent-qa test. Most teams port their smoke suite first and retire specs as the equivalent agent-qa tests prove out.
Does agent-qa have a dashboard like Cypress Cloud?
agent-qa ships a local dashboard for runs, tests, suites, hooks, memory, insights, and live execution — running on your machine or CI artifacts, not a hosted subscription.
Can agent-qa handle component testing like Cypress?
No — component testing stays with your unit-level tooling. agent-qa focuses on user-level E2E flows across web and mobile, which is where scripted approaches are most expensive to maintain.
Write tests in natural language
Define actions and assertions in human language while agents work from visible roles, labels, and screen state.
Learn about natural language tests
test-id: t_slice-cart-bane-deep-fold-prim-paar-baru-nable-kayname: Check Linear issue creation flowtarget: linear-webuse: browser: name: chromiumsteps: - Click on the Create issue icon. - | Verify that the Create issue modal is shown. - | Enter "Fix mobile login" in the "Issue title" input field. - | Select "Engineering" from the Team selector and select "Todo" from the Status field. - Click on the Create issue button. - | Verify that the created issue is shown with title "Fix mobile login" and status "Todo".
Evolves with every run
With every test run, agent-qa builds execution memory from product, suite, and test observations, then adds that context to future runs. agent-qa also curates memory from steps that were healed during execution, helping future runs avoid the same mistake.
Learn about memory
Built for Humans
Top-tier developer experience with a beautiful dashboard, intuitive CLI, and clear workflows for authoring, running, and debugging tests.
Learn about the dashboard
Runs
❯ agent-qa run tests/linear/create-issue.yamlRunning 1 test(s)...✓ Click on the Create issue icon. 5s Sub-actions: 1 total (1 succeeded, 0 failed)✓ Verify that the Create issue modal is shown. 4s Sub-actions: 1 total (1 succeeded, 0 failed)✓ Enter "Fix mobile login" in the "Issue title" input field. 5s Sub-actions: 1 total (1 succeeded, 0 failed)✓ Select Engineering from Team and Todo from Status 6s Sub-actions: 2 total (2 succeeded, 0 failed)✓ Click on the Create issue button. 3s Sub-actions: 1 total (1 succeeded, 0 failed)✓ Verify created issue title and Todo status 6s Sub-actions: 2 total (2 succeeded, 0 failed) PASS Check Linear issue creation flow 29sRun ID: r_lined-frig-schema-main-depart-hing-aline-balls-cran-dess Memory: 1 added (3s)Run attributes: agent-qa.trigger=cli agent-qa.runner=localTests: 1 of 1 passedSteps: 6 passed, 6 totalCache: 6 hits, 0 missesTime: 29s
Built for Machines
The same primitives are exposed through MCP and skills so coding agents can discover schemas, author YAML, enqueue runs, inspect artifacts, and triage failures.
Learn about MCP
Accelerate runs with smart Cache
The action cache reuses validated plans across similar subsequent test runs, reducing planner work, token usage, and runtime overhead.
Learn about caching
Execution Speed
42s -> 8s
Cached action plans skip redundant planner work on similar subsequent runs.
Reduced Token Usage
fewer planner tokens
Validated steps reuse prior reasoning when the flow and screen state still match.
Run sandboxed hooks during tests
Run Node, Bun, Python, or Bash hooks in isolated Docker containers to set up environments, call APIs, seed fixtures, tear down state, or pass structured outputs back into the active test run.
Learn about hooks
// emits CHECKOUT_EUR_TOTAL_CENTS for the active test runconst response = await fetch("https://api.frankfurter.app/latest?from=USD&to=EUR,GBP")const { rates } = await response.json() const fixture = { plan: "team", currency: "USD", subtotal_cents: 2900, eur_total_cents: Math.round(2900 * rates.EUR), gbp_total_cents: Math.round(2900 * rates.GBP), seat_limit: 12, fixture_at: "2026-05-07T00:00:00Z",} const env = Object.entries(fixture) .map(([key, value]) => `CHECKOUT_${key.toUpperCase()}=${value}`) .join("\n") await Bun.write("/tmp/agent-qa.env", `${env}\n`)console.log(JSON.stringify({ checkoutFixture: fixture }, null, 2))
Review your QA like code
Tests, configs, hooks, memory, and suite logic all live as version-controlled code, so every change can be diffed, reviewed, reused, and shared across teams.
Learn about configuration
Self-healing test execution
When any sub-action, such as click, fill, or select, fails, agent-qa re-observes the UI and tries a different path in the same run. Tests recover from UI drift and flaky interactions instead of failing on the first broken action.
Learn about self-healing
Bring your own LLM
Run tests with the model of your choice via OpenAI- and Anthropic-compatible endpoints, Gemini, local or open-source models, and subscriptions like Codex and Claude Code.
Learn about LLM providers

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