BaseRock talks business signals. agent-qa remembers how your product actually behaves.
BaseRock frames testing around business-signal validation. agent-qa focuses on source-owned web and mobile E2E execution that engineers and coding agents run directly, with proof that accumulates.
Try agent-qa, the source-available way to keep E2E proof and memory in engineering hands.
agent-qa vs BaseRock
| Capability | agent-qa | BaseRock | Details |
|---|---|---|---|
| Source access | BaseRock 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 | BaseRock 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 BaseRock
Runs compound instead of resetting
BaseRock 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.
You pick the model, not the vendor
BaseRock decides which AI runs your tests, when it changes, and what it costs you. agent-qa is bring-your-own-model: swap providers in one line, put a cheap model on smoke tests and a strong one on the flow that matters, or run against the internal endpoint your security team already signed off.
Business signals aren't E2E proof
Signal-level validation tells you something moved; it doesn't tell you the checkout flow works on the build you're about to ship. agent-qa produces behavioral proof, step-by-step runs with artifacts, tied to the exact source state that produced them.
BaseRock reports on your product from the outside. agent-qa proves your product from the inside, run by run, release by release, in files you keep.
Frequently asked questions
Is agent-qa a good BaseRock alternative?
Yes, and the reason is structural rather than a feature count. BaseRock is an AI testing product framed around validating business signals, 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 BaseRock?
BaseRock is priced on vendor platform pricing, 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 BaseRock to agent-qa?
You are re-describing intent, not porting code, which is why this is far smaller than a normal test migration. Identify the user-visible behaviors behind each BaseRock signal, then write those behaviors as agent-qa YAML tests. You go from monitoring signals to proving flows. 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 BaseRock?
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. agent-qa executes true device-level flows on Android and iOS as well as web, not just signal checks.
Can agent-qa results feed business reporting like BaseRock?
agent-qa emits structured run results, artifacts, and failure classifications you can pipe anywhere, dashboards, CI gates, Slack, or a coding agent's triage loop. The difference is the underlying evidence is a real E2E run, stored with your code.
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 BaseRock
The parts of agent-qa that answer what BaseRock leaves you carrying.
Execution 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.
Self-improvement
agent-qa diagnoses each run, learns only from valid evidence, and proves every lesson before using it.
Learn about the curatorIt never rewrites itself
It updates app knowledge and proposes rules, never its own prompts or code.
Learn moreEvery run is evidence
A finished run hands over its steps, artifacts, and timings. The verdict is already final, so nothing downstream can change whether it passed.
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.