Give /automate a task in plain English and it drives a real browser to do it: navigate a site, click through a multi-step flow, fill a form, reach a page that only renders after interaction. The result streams back in one API call. It's an API you call, not a framework you install. Browser and LLM included, nothing to host, no concurrency ceiling. Accessibility-tree automation spends 60 to 80% fewer tokens than screenshot-based agents. Built by Mozilla. Ephemeral, no training on your data.
Most web automation tools hand you a browser and leave the hard parts to you: hosting it, driving it, and paying for vision tokens on every screenshot. Automate flips that. You send a task in plain language plus a URL, we run the browser on our side, and you get finished output back in one streaming API call. Drop it into an agent or wire it into an app you already ship in a matter of a few minutes.
It can book a meeting, fill a multi-step form, or pull data from a page that only renders after you click around. The engine reads the page's accessibility tree instead of screenshots, so it spends 60 to 80% fewer tokens than screenshot-based agents. That's a different cost model once you're running this at scale.
If you're building agents or adding web actions to an app, I'd genuinely love to know: what's the first task you'd point it at? I'll be here all day answering everything. Thank you for taking a look 🙏
Being able to just type '/automate' and dictate what I want to be done is an incredible UX. I'm going to explore hooking this up to my own agents today!
Congrats on #3! What’s been the hardest part of building reliable browser automation for AI agents?
Not having to host the browser removes the exact part of web automation that always breaks in production. How are you handling sites with heavy bot detection, is that abstracted away or still on the developer side? Reading the accessibility tree instead of screenshots is a smart cost move too, that token math adds up fast at scale.
Mozilla shipping something this developer-focused and privacy-conscious feels like a nice return to form. Loving that robots.txt compliance and ephemeral processing are defaults, not afterthoughts.
Schema-defined extraction is the bit I was missing, and it worked cleanly on a couple of messy product pages. The robots.txt compliance and no-training stance from Mozilla make it easy to ship without a long legal check.
How does the pricing scale if my agent is firing hundreds of these calls per hour, and is there a way to cap costs before things get out of hand?
the stateless-per-call design makes sense for reliability but I'm curious how it handles flows that need to stay logged in across steps - like a site where you need a session cookie from step one to do anything in step two. do you pass auth state back in yourself each call, or is there something built in for that
How does the ephemeral processing actually work in practice, like is there any retention window for debugging failed calls or is it truly gone the second the response lands?
How does the pricing actually work for the research calls versus the simpler Markdown or extraction endpoints, since those seem like pretty different workloads?
finally tried this and the schema extraction felt shockingly clean, called it for a few messy product pages and got structured JSON back without babysitting.
Curious how it handles sites that block headless browsers or rely heavily on client-side rendering since you mentioned no browser infra on my end.
The robots.txt compliance baked in by default is a really thoughtful touch, especially from Mozilla. Feels like they actually thought through the trust layer for devs shipping agents, not just the shiny API surface.
Mozilla backing this is what got me to try it, and the schema extraction actually nailed a messy product page on the first try. Glad my data isn't getting fed into some training pipeline either.
Finally tried Tabstack and the schema-defined extraction is genuinely useful, no more wrestling with messy scraped HTML. Love that robots.txt and ephemeral processing come baked in by default.
Spent a few minutes poking around the schema extraction and it actually nailed a messy recipe page on the first try. The robots.txt compliance detail is a nice touch for anyone tired of sketchy scrapers.
How does this handle sites with heavy bot protection or JS-only content that needs real browser interaction, and is that tier priced differently than simple fetches?
how does the robots.txt compliance work when an agent needs data from a page that's blocked but technically accessible via the rendered DOM?
Tried it on a couple research queries and the cited multi-source output was solid. Nice that it returns clean Markdown instead of forcing me to parse raw HTML.
how does the robots.txt compliance actually work when an agent needs to interact with a page that blocks scraping, does the API just refuse or is there a way to get the structured data another way
About Tabstack Browser Automation on Product Hunt
“Automate the web in your app or agent, no browser to host”
Tabstack Browser Automation launched on Product Hunt on July 1st, 2026 and earned 388 upvotes and 117 comments, earning #3 Product of the Day. Give /automate a task in plain English and it drives a real browser to do it: navigate a site, click through a multi-step flow, fill a form, reach a page that only renders after interaction. The result streams back in one API call. It's an API you call, not a framework you install. Browser and LLM included, nothing to host, no concurrency ceiling. Accessibility-tree automation spends 60 to 80% fewer tokens than screenshot-based agents. Built by Mozilla. Ephemeral, no training on your data.
Tabstack Browser Automation was featured in API (98.4k followers), Developer Tools (515.4k followers) and Artificial Intelligence (473.1k followers) on Product Hunt. Together, these topics include over 191.1k products, making this a competitive space to launch in.
Who hunted Tabstack Browser Automation?
Tabstack Browser Automation was hunted by fmerian. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Reviews
Tabstack Browser Automation has received 2 reviews on Product Hunt with an average rating of 5.00/5. Read all reviews on Product Hunt.
Want to see how Tabstack Browser Automation stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Hey Product Hunt 👋 Tessa here from @Tabstack by Mozilla
Most web automation tools hand you a browser and leave the hard parts to you: hosting it, driving it, and paying for vision tokens on every screenshot. Automate flips that. You send a task in plain language plus a URL, we run the browser on our side, and you get finished output back in one streaming API call. Drop it into an agent or wire it into an app you already ship in a matter of a few minutes.
It can book a meeting, fill a multi-step form, or pull data from a page that only renders after you click around. The engine reads the page's accessibility tree instead of screenshots, so it spends 60 to 80% fewer tokens than screenshot-based agents. That's a different cost model once you're running this at scale.
Free to get started at tabstack.ai/browser-automation
If you're building agents or adding web actions to an app, I'd genuinely love to know: what's the first task you'd point it at? I'll be here all day answering everything. Thank you for taking a look 🙏