Autonoma connects to your repo. agent-qa moves in, and remembers every run.
Autonoma focuses on connected agent testing from its platform. agent-qa makes the repo the control plane: YAML, MCP, Skills, cache, hooks, and memory that compounds where your code lives.
Try agent-qa, the source-available control plane where every run adds repo memory.
agent-qa vs Autonoma
| Capability | agent-qa | Autonoma | Details |
|---|---|---|---|
| Source access | Both publish source. What differs is how much of the product that covers: agent-qa's engine, authoring format, memory store and agent workflow are all in the repo you clone, under FSL-1.1-ALv2. | ||
| Repo-owned YAML | agent-qa makes plain-English YAML the contract of record, so changing a test is a diff a reviewer can argue with rather than something somebody did in an app. | ||
| 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 Autonoma
Built for coding agents, not dashboards
Your team already ships code with coding agents, and a coding agent cannot click around Autonoma'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.
Runs compound instead of resetting
Autonoma 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.
Connected-to is not the same as owned-by
Autonoma reaches into your repo from its platform. agent-qa inverts that: the repo is the platform, and everything, tests, hooks, memory, cache, artifacts, is a file your team can read, review, and keep.
Autonoma and agent-qa agree that agents should run QA. They disagree on who should own the result. agent-qa's answer: you, in your repo, forever.
Frequently asked questions
Is agent-qa a good Autonoma alternative?
Yes, and the reason is structural rather than a feature count. Autonoma is an agent-testing product that connects to your repositories from its own platform, 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 Autonoma?
Autonoma is priced on vendor-set product 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 Autonoma to agent-qa?
You are re-describing intent, not porting code, which is why this is far smaller than a normal test migration. Since Autonoma already works at the level of user intent, rewriting its flows as agent-qa YAML tests preserves the intent while gaining code review. 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 Autonoma?
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. Autonoma covers app testing through its product; agent-qa runs web and mobile from the same repo-owned YAML and CLI path.
Both are agentic, why does repo ownership matter?
Because agents are only as good as the context they keep. agent-qa's memory, cache, and artifacts are files beside your code, versioned with it and visible to every coding agent you run. A platform-side agent's learning stays on the platform's side of the fence.
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 Autonoma
The parts of agent-qa that answer what Autonoma 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-healing execution
When an action fails, agent-qa re-observes the screen and finds another route to the same outcome.
Learn about self-healingRefund the $128.00 payment to the customer.
order #4417
Timeline
A step, and the screen it runs on
A payment for order #4417, and a step that asks for it to go back. Nothing about the screen is unusual and the control it needs is where it should be.
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.