Applitools sees pixels; agent-qa remembers product behavior from code.
Applitools is strongest around visual AI validation; agent-qa focuses on repo-owned natural-language E2E flows, agent execution, hooks, and artifact review.
Try agent-qa, the open-source way to remember behavior, not just compare pixels.
agent-qa vs Applitools
| Capability | agent-qa | Applitools | Details |
|---|---|---|---|
| Open source | Applitools is not positioned as a repo-owned open-source testing framework; agent-qa is built for teams that want the core QA workflow inspectable from day one. | ||
| Repo-owned YAML | agent-qa keeps test intent, config, hooks, memory, and suites beside product code instead of treating QA as a separate product surface. | ||
| Coding-agent native | agent-qa gives coding agents MCP tools, Skills, CLI runs, artifacts, memory, and cache so verification happens in the same loop as code changes. | ||
| Bring your own LLM | agent-qa lets teams choose model providers and compatible endpoints instead of binding QA execution to a vendor-controlled runtime. | ||
| Local and CI execution | agent-qa can run from a developer machine, CI job, or agent workflow with the same source-controlled command path. | ||
| Web and mobile QA | agent-qa targets web and mobile E2E work while keeping the same natural-language authoring and runtime evidence model. | ||
| Memory, cache, hooks | agent-qa combines execution memory, smart cache, and sandboxed hooks so test runs get faster, richer, and easier to debug over time. | ||
| No platform lock-in | agent-qa keeps the durable QA contract in your repo, not behind a hosted editor or a vendor-only control plane. |
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

* This comparison is based on publicly available information. Product capabilities and pricing can change; verify details with each vendor before making a purchase decision.