testRigor made English tests easy. agent-qa makes them learn.
testRigor pushes no-code authoring through a managed platform. agent-qa makes plain-English tests pull-request artifacts, reviewed like code, run by coding agents, remembered across runs.
Try agent-qa, the source-available way to turn plain-English runs into reusable memory.
agent-qa vs testRigor
| Capability | agent-qa | testRigor | Details |
|---|---|---|---|
| Source access | testRigor 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 | testRigor 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 testRigor
Runs compound instead of resetting
testRigor runs a test and forgets. Every run starts from nothing, which is why the hundredth run costs exactly what the first one did. agent-qa writes what it learned into memory committed beside your tests, so the next run starts where the last one finished and the suite gets better at your app on its own.
Your tests stop being hostages
Every test you write in testRigor makes leaving testRigor more expensive. That is not an accident, it is the business model. Every test you write with agent-qa is a YAML file in your repository: reviewed in a pull request, portable to any runner, and still yours the day you cancel.
No-code has a ceiling; code review doesn't
Plain English is the right authoring format. testRigor got that part right. But serious teams need English tests that go through pull requests, diff cleanly, and sit next to hooks and config. That's a repo workflow, not a platform feature.
testRigor proved plain-English testing works. agent-qa is what it looks like when plain English meets source control, coding agents, and memory, without the platform in between.
Frequently asked questions
Is agent-qa a good testRigor alternative?
Yes, and the reason is structural rather than a feature count. testRigor is a no-code platform where plain-English tests are authored and executed inside the vendor's product, 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 testRigor?
testRigor is priced on tiered platform subscriptions, 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 testRigor to agent-qa?
You are re-describing intent, not porting code, which is why this is far smaller than a normal test migration. testRigor tests are already written in plain English, which makes them the easiest kind to port. The intent transfers almost verbatim into agent-qa YAML. 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 testRigor?
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. testRigor supports mobile through its platform as well; agent-qa's difference is that the same YAML contract, memory, and artifacts serve web and mobile from your repo.
Both use plain English, what's actually different?
Where the English lives and what happens after a run. testRigor stores tests in its platform and each run stands alone. agent-qa stores tests in your repo, routes them through code review, and feeds every run's findings into memory and cache so execution gets faster and more reliable over time.
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 testRigor
The parts of agent-qa that answer what testRigor leaves you carrying.
Natural-language tests
Write actions and assertions in plain English; agent-qa resolves them against the live interface.
Learn about natural language testsPlain-English YAML
Describe the behavior once; it stays as reviewable YAML in your repository.
Learn moreExecution memory
Turn successful runs into reviewable, evidence-backed memory that makes every future run faster.
Learn about memoryProven before it is used
New knowledge must pass replay and live evidence before guiding a run.
Learn moreFaster warm runs
Reuse proven flows and supersede stale facts instead of rediscovering them.
Learn moreA run leaves evidence
Elements that resolved, a flow that worked, and timings that are real, all attached to the steps that produced them.
Version controlled, built for teams
Tests, knowledge, and rules stay as reviewable files shared by teammates, agents, and CI.
Learn about configurationFiles, not a database
Tests, config, memory, and rules stay as files your team can inspect and own.
Learn moreLearning arrives as a diff
New memory and issues arrive as pull-request diffs, with their evidence.
Learn moreOne commit everywhere
Humans, coding agents, and CI share the same knowledge from one commit.
Learn more- Tests
- Configs
- Memory
- Self improvement
- Knowledge
- Engineeragent-qa
- QA engineeragent-qa
- Coding agentagent-qa
- CIagent-qa
Bring your own model
Switch providers, endpoints, or models in config without rewriting a single test.
Learn about LLM providersUse your existing seat
Run with supported Codex or Claude Code subscriptions, with no second bill.
Learn more
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.