Autosana and Momentic both write tests in plain language and onboard your coding agent over MCP. Momentic differs in what runs after that: cached steps that finish in under 500ms with no model call, a suite that gates every merge at Notion and Quora, and Mo, an agent that finds the bugs no test covers yet.
Web, iOS and Android · CLI · CI · MCP
How Momentic and Autosana compare across the dimensions teams evaluate most.
| Category | Momentic | Autosana |
|---|---|---|
| Approach | Agent-native, plain-English steps, with AI actions for the flows you cannot script | Agent-native, natural-language flows, vision based |
| What the agent sees | A screenshot, the accessibility tree, and the DOM on web or the screen XML on mobile | A screenshot; Autosana describes itself as vision based |
| Step speed | Cached steps replay with no model call; over 99% finish in under 500ms | No published benchmark |
| Product context | Knowledge base holds your terms, agent rules and flows; Memory replays past AI decisions | Per-flow variables and hooks |
| Exploratory QA | Mo explores the app, reproduces each bug in a clean session and files it | Flows generated from your code diffs |
| Platforms | Web, iOS and Android | iOS, Android, mobile web and desktop web |
| Authoring | Cursor, Claude Code or Codex write the YAML into your repo over MCP; the CLI runs it | Natural-language flows, created from code diffs, onboarded over MCP |
| Where tests live | YAML files in your repo | Their dashboard, unless you opt that repo in to files. Environment variable values stay with them either way |
| From commit to run | The YAML in your working copy is the test, and the CLI runs it on any branch | A GitHub sync copies the files into their platform, on the default branch only |
| Execution | Momentic runs locally and in CI: exit code, JUnit XML, sharding, a video of every failed run and a status comment on the pull request | Their cloud, or a local run with their Python CLI, with video proof in the pull request |
| Scale | Thousands of parallel browsers, hundreds of parallel simulators and emulators | Cloud runs, scale not published |
| Track record | Notion, Retool, Webflow, Xero and Quora run on Momentic | Y Combinator, early stage |
| Best fit | Teams that need one suite to gate every merge at scale | Small teams that want the agent to close the loop |
Autosana writes end-to-end tests as natural-language flows for iOS, Android, mobile web and desktop web. It is vision based, so it drives the app the way a person would rather than reading the code. It creates and updates flows from your code diffs, keeps them as files in your repo through GitHub sync, onboards over MCP, and loops with your coding agent locally until the tests pass. In a pull request it runs its cloud agents and attaches video proof of the flow. Its flow language covers taps, swipes, pinch, long press, deeplinks, locale and location changes, variables and hooks. Of the tools on this list, it works the most like Momentic, so the run model and the scale rows matter more here than the approach. In their code-managed mode the repo becomes the source of truth, the flow turns read-only in the dashboard, and their CLI validates the YAML before you push. You turn that mode on per repo. The sync then runs on a push to your default branch, so a pull request shows a preview for a suite file, no check for a hook file, and nothing at all for a fork.
01
Notion, Retool, Webflow, Xero and Quora gate releases with Momentic. Quora cut a 7-hour QA pass to 30 minutes. The infrastructure runs thousands of parallel browsers and hundreds of parallel simulators and emulators. Autosana is earlier, and does not publish a parallel limit.
02
After the first successful run, a Momentic step caches the signals it used to find its target. Repeat runs replay from that cache with no model call: a cached click averages 250ms, a type step 340ms, and over 99% of steps finish in under 500ms. An agent that reads a screenshot on every step pays a model call on every step, and a suite that gates every merge pays it on every run.
03
Momentic resolves each step against a screenshot, the accessibility tree, and the DOM on web or the screen XML on mobile, and infers from the step description which signal matters. 'The Sign in button' resolves from accessibility attributes and text. 'The red Cancel button below the order summary' resolves from position and pixels. Autosana is vision based, so the screenshot is its only input. An element the screenshot does not show, or two that look alike, still resolves on Momentic.
04
An AI action takes a goal, such as 'complete checkout with a saved card', and the agent chooses the clicks at run time. It caches the resolved steps after the first pass, so the next run replays them instead of planning again, and it records a branch when the UI differs between runs. Preset steps stay fast and deterministic; you reach for an AI action only where the flow changes.
05
Momentic's MCP server gives Cursor, Claude Code or Codex the tools to write a test, run it in a live browser, read the screenshots and the failure, and fix the test or the code until it passes. When a run fails on a branch, the momentic-maintain skill has the agent read the saved classification (bug, intended change, test issue or infrastructure), inspect the run evidence, and repair the test or hand the bug back to the coding agent. Autosana loops with your agent locally as well. The difference is what the loop leaves behind: a YAML file the CLI replays in CI with no agent attached, a video of every failed run, and a status comment the Momentic GitHub App posts on the pull request.
06
The knowledge base holds your terminology, rules for a specific agent and your known flows, and every AI-assisted step retrieves the relevant entries. Memory feeds the agent its own decisions from past runs, so 'the selected tab' resolves the same way this week as it did last week. The suite gets more consistent the longer it runs.
07
Mo takes a target and a brief, plans the test cases, and runs each one in its own hosted browser, simulator or emulator, a hundred or more at once. A suspected bug goes to a reproducer agent that starts from a clean session, and only a reproduced bug reaches the report, with a recording and repro steps. One session ran 116 agents and returned 27 bugs. Autosana generates flows from your code diff; Mo covers the flows the diff did not touch.
08
Autosana files still go through a sync to reach their platform: it runs on your default branch, a pull request only previews a suite file, and one invalid file blocks every change for that repo until you fix it. Momentic has no sync. The CLI reads the YAML on your current branch and runs it, the exit code blocks the merge, and JUnit XML goes into the report you already have.
09
Momentic re-resolves a moved target during the run, retries through the transient failure, and writes the repair back to the test file. A failure that survives the retries comes back classified as a bug, an intended application change, a test issue or an infrastructure problem, with the reasoning and a triage action attached. You act on the category instead of reading every failure.
Autosana is close to Momentic, so start with the authoring column. The way an agent writes a test, what it reads on each step, and where that file runs shape the rest of the suite.
| Tool | Authoring | Runs on | Best fit |
|---|---|---|---|
| Momentic | Plain-English steps as YAML in your repo. Cursor, Claude Code and Codex write them over our MCP server, and the CLI runs them | Web, iOS and Android; cloud browsers, simulators and emulators | The suite lives in the repo and gates CI, with no framework to maintain |
| Autosana | Natural-language flows in their dashboard. You can opt a repo in to files, and the sync copies them on the default branch only | iOS, Android, mobile web and desktop web; local and cloud runs | Small teams who want the agent to close the loop |
| MobileBoost (GPT Driver) | Their platform holds each test as a record. You change it through their REST API, or record it in their no-code editor | iOS and Android; simulators and real devices, 100+ in parallel | Mobile-only teams who need simulator features nobody else exposes |
| Ranger | Scenarios through their CLI. An approved feature becomes a test inside the Ranger suite, which they host | Browsers only; hundreds in parallel on demand | Verifying a browser feature your coding agent just wrote |
| Playwright | Code: TypeScript, Python, Java or C# | Chromium, Firefox and WebKit; your runners | Engineers who want full control and accept selector upkeep |
| Maestro | YAML flows | Simulators, emulators and devices; local or Maestro Cloud | Mobile teams who want readable flows and a fast local loop |
| Appium | Code, on the WebDriver protocol | iOS and Android, simulators, emulators and devices | Selenium teams extending the same protocol to mobile |
| mabl | Low-code recorder aimed at non-technical authors | Web and mobile web; vendor cloud | Authoring leaves engineering, and the tests leave your repo |
| testRigor | Plain-English scripts in a vendor web app | Web and mobile; vendor cloud | Teams who accept tests living outside version control |
| QA Wolf | Their engineers write your Playwright tests | Web and mobile web; their infrastructure | Buying coverage as a service instead of hiring |
Choose Momentic
Choose Momentic if the suite must gate every merge, as it does at Notion, Retool, Webflow, Xero and Quora, with cached steps that finish in under 500ms, a knowledge base the agents read on every step, and Mo to bug bash the flows no test covers.
Choose Autosana
Choose Autosana if you need Firefox or Edge coverage today, or if you want flows generated straight from your code diffs.
Trusted by teams who made the switch.
“Momentic gave us a fast and reliable way to validate Poe.com's AI responses, even when they weren't deterministic.”
30 min
daily test execution, down from 7 hours
500+
manual test cases replaced
100%
critical tests created in one month
They are close. Both write tests in plain language, keep them in your repo and onboard coding agents over MCP. The differences are what the agent reads on each step, how fast a cached step runs, the knowledge base and Memory behind the agents, Mo for the flows no test covers, and which teams already gate merges with each.
About as fast as the Playwright script it replaces. A cached step runs with no model call, and over 99% of steps finish in under 500ms. In our published login benchmark, cached Momentic steps ran 52ms slower on average than the equivalent Playwright calls. The first run of an AI-assisted step is slower, 4 to 8 seconds to locate an element, and that cost is paid once.
A screenshot, the accessibility tree, and the DOM on web or the screen XML on mobile. AI assertions read all three, so a check on a GenAI feature with no fixed output still resolves. Autosana describes itself as vision based.
Yes, with Mo. You give Mo a target and a brief, it plans the cases and runs them across hosted browsers, simulators or emulators at the same time, and it reports only the bugs a separate agent reproduced from a clean session. A Momentic test covers the flow on every merge; Mo covers the flows nobody has written a test for.
Yes, if you turn on their code-managed mode for that repo. Autosana then treats the repo as the source of truth, and it syncs the files on a push to your default branch. Momentic has no sync step. The YAML in your working copy is the test, and the CLI runs it on any branch, locally and in CI.
Autosana lists Chrome, Firefox, Edge and Chromium. Momentic runs Chromium and Chrome today, with Safari and Firefox on the roadmap. If a Firefox gate decides your choice, say so early.
Both test iOS and Android. Momentic adds hosted simulators and emulators at high parallel counts, which matters when a full regression must finish inside a release window.
Yes. Momentic has a free tier and pay-as-you-go from $125 per month, so you can run the same flow in both and compare the reports.
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