Katalon ships a suite. agent-qa ships a QA agent that learns your product.
Katalon is a broad test automation platform with its own IDE and per-seat licensing. agent-qa is the lightweight repo-native path when the goal is agent-run E2E living beside the code.
Try agent-qa, the source-available way to keep E2E tests and memory in one repo loop.
agent-qa vs Katalon
| Capability | agent-qa | Katalon | Details |
|---|---|---|---|
| Source access | Katalon is not positioned as a repo-owned framework with published source, so behaviour you disagree with is a support ticket. With agent-qa it is a pull request. | ||
| Repo-owned YAML | Katalon keeps the test intent inside its own product. agent-qa keeps intent, config, hooks, memory and suites beside the code they cover, where your engineering process already works. | ||
| Coding-agent native | A coding agent cannot click through a hosted editor. agent-qa ships MCP tools, packaged Skills and a CLI, so the agent that changed the code writes the test, runs it and reads the failure without leaving the loop. | ||
| Bring your own LLM | Whoever picks the model sets your quality ceiling and your bill. agent-qa lets you point at any provider, any compatible endpoint, or a model on your own hardware, and change it in one line. | ||
| Local and CI execution | One command on a laptop, in CI, and from an agent. No run depends on somebody else's control plane being up, and nothing queues behind another tenant. | ||
| Web and mobile QA | Web, Android and iOS from the same natural-language flow and the same evidence model. The surface is a target named in a file, not a different product tier. | ||
| Memory, cache, hooks | Execution memory, a validated action cache and sandboxed hooks compound. A suite that has been running a month is faster, cheaper and better informed about your app than the day it was written. | ||
| No platform lock-in | Every durable asset stays in your repository. Cancel agent-qa tomorrow and the tests, the memory and the evidence are still there and still readable. |
Why teams switch from Katalon
Built for coding agents, not dashboards
Your team already ships code with coding agents, and a coding agent cannot click around Katalon's interface. agent-qa ships MCP tools, packaged Skills and a CLI, so Claude Code, Cursor and their peers author the test, run it and triage the failure inside the same loop that wrote the change.
No license fee, no seat math
Katalon runs on per-seat licensing across a studio IDE and platform tiers, and that number grows with the coverage you add. agent-qa has no paid tier or licence fee for FSL-permitted use and its source is available under FSL-1.1-ALv2. You pay for tokens and infrastructure you control, on whichever provider is cheapest this quarter, and the cache cuts that too.
The IDE is the lock-in
Katalon coverage means Katalon Studio projects, Katalon formats, Katalon runtime engines. agent-qa coverage means YAML files any editor opens, any engineer reviews, and any coding agent extends, the suite sprawl never starts.
Katalon optimizes for covering every testing category. agent-qa optimizes for the loop that matters: code changes, agent verifies, memory compounds. Pick the tool shaped like your workflow.
Frequently asked questions
Is agent-qa a good Katalon alternative?
Yes, and the reason is structural rather than a feature count. Katalon is a broad test automation platform and IDE covering web, mobile, API, and desktop with per-seat licensing, which means the asset you are building lives on their side of the line. agent-qa is a source-available QA agent with no paid tier, governed by FSL-1.1-ALv2: tests are plain-English YAML in your repository, runs execute on your laptop, in your CI, or from your coding agent, and every run writes back into memory committed beside the tests. The suite gets better at your app whether or not you renew anything.
How much does agent-qa cost compared to Katalon?
Katalon is priced on per-seat and platform-tier licensing, so the bill tracks how much you test. agent-qa has no paid tier, no seats and no platform fee; FSL-1.1-ALv2 governs permitted use. You pay for the model tokens and infrastructure you already control, on the provider you choose, and the validated action cache takes roughly 60% of the tokens off a matched rerun. Adding coverage does not add a line item.
How do I migrate from Katalon to agent-qa?
You are re-describing intent, not porting code, which is why this is far smaller than a normal test migration. Katalon test cases encode user flows under studio-specific structure; strip them back to intent and each becomes a short agent-qa YAML file. Run npx agent-qa init, write each critical flow as a plain-English YAML test, and let the runtime work out the selectors and the recovery. Most teams move a smoke suite in an afternoon, and there is nothing to un-pick later because the output is files in your own repository.
Does agent-qa cover web and mobile like Katalon?
Yes, and from the same file. agent-qa runs end-to-end tests on web, Android and iOS with one natural-language format, one memory store and one evidence model, so a flow written once survives being pointed at another surface. Katalon covers mobile through its studio tooling; agent-qa covers it through the same plain-English YAML and CLI you use for web, no separate tooling to learn.
Is agent-qa lighter to adopt than Katalon?
Substantially. There is no IDE to install, no project format to learn, and no license to assign: npx agent-qa init scaffolds the workspace, your first plain-English test runs minutes later, and everything lives in the repo your team already works in.
Sources
This page is based on public product and documentation sources. Verify current features and pricing with each vendor before making a purchase decision.
Where agent-qa pulls ahead of Katalon
The parts of agent-qa that answer what Katalon leaves you carrying.
Natural-language tests
Describe actions and assertions in natural language. agent-qa resolves them against the live interface using visible roles, labels, and screen state.
Learn about natural language testsNatural-language YAML
Write the behavior and expected outcome in plain English. The test stays as reviewable YAML in your repository.
Targets users recognize
Refer to “New issue,” “Checkout,” or the “Issues table.” agent-qa finds the matching control in the live interface.
One format, every surface
Use the same natural-language structure across web, Android, and iOS without maintaining selector-heavy variants.
Web, Android, and iOS
Point the same natural-language flow at a web target, an Android build, or an iOS build. Playwright and Appium are execution kernels here, not code generators: no test script is ever produced. The agent decides on each action from what it can currently see, and the kernel performs it, whether that is a click in Chromium, a tap on a local emulator, or a gesture on a remote device.
Learn about mobile testingExecution kernels
Playwright and Appium only perform the actions the agent decides on. No test script is generated, and nothing is replayed from a recording.
Three web engines
Run the same flow on Chromium, Firefox, or WebKit, and override the engine, viewport, or headless mode per test.
Native and remote devices
Drive real Android and iOS builds by app package or bundle ID, on a local emulator, a simulator, or a remote device.
Four products, four platforms
Each row names its own target, and a target is what decides where a test runs. This one names a web target, so it carries a URL.
Version controlled, built for teams
Tests, suites, hooks, and the workspace config are files you write. What it learns about your app, the bugs it files, the rules you assert, and the skills an authoring agent uses are files agent-qa writes, in a visible directory beside your tests rather than in a database you cannot read. All of it is committed, so a new memory bundle arrives as a diff in a pull request with the evidence that justified it. Only derived indexes and binary run artifacts are gitignored, because they rebuild from what is committed. A teammate, a coding agent, and CI check out one commit and get the same brief.
Learn about configurationFiles, not a database
What you author and what the agent learns sit next to each other as committed files. Nothing it knows is locked in a store you cannot open.
Learning arrives as a diff
A new memory bundle or a filed issue shows up in a pull request, with the evidence behind it, and is approved the way any other change is.
Everyone reads the same commit
A teammate, a coding agent, and CI work from one checkout, so no run is quietly using state that nobody else has.
- Tests
- Configs
- Memory
- Self improvement
- Knowledge
- Engineeragent-qa
- QA engineeragent-qa
- Coding agentagent-qa
- CIagent-qa
Bring your own model
The model is a setting, not a rewrite. Point a workspace at an OpenAI or Anthropic compatible endpoint, at Gemini, at an open-weight model running on your own hardware, or at a subscription your team already pays for like Codex or Claude Code, and override it on the one test that needs something stronger than the rest. Nothing about how a test is written changes when the model does, because the test says what should happen and the model is only what works out how to get there. There is no vendor to be locked to and no key of ours to buy.
Learn about LLM providersSwap it in one line
The provider, the endpoint, and the model are config. Change them and every test you have already written runs against the new one, unedited.
The seat you already pay for
Account-backed authentication works where the provider allows it, so a Codex or Claude Code subscription can drive runs without a second bill.
Or nothing leaves the building
Point it at an open-weight model on your own hardware when the code under test is the kind that cannot go to somebody else's API.
Compatible endpoints and Codex subscription workflows.
Compatible endpoints and Claude Code subscription workflows.
Gemini configs with named credentials.
Cloud and open model workflows through compatible endpoints.
Open model workflows through compatible endpoints.
Cloud and open model workflows through compatible endpoints.
Cloud model access through compatible endpoints.
Local models through compatible endpoints.
Desktop local model workflows via compatible servers.
Route compatible requests across a broad hosted model catalog.
MiMo model workflows through compatible endpoints.
Hunyuan model workflows through compatible endpoints.
DeepSeek model workflows through compatible endpoints.
GLM model workflows through compatible endpoints.
MiniMax model workflows through compatible endpoints.
Nemotron open models through compatible endpoints.
Step model workflows through compatible endpoints.
Ling open models through compatible endpoints.
* This comparison is based on publicly available information. Product capabilities and pricing can change; verify details with each vendor before making a purchase decision.