Launching today
Tabstack is a web data and automation API that delivers reliable structured output. Pass a URL and a schema, get back JSON that matches every time. Run research in one call and get cited answers back. Automate browsers without running infrastructure. The intelligence is built into every API call. No scraper to build, maintain, or watch break when a site changes. Built at Mozilla.
Tabstack
Hey Builders! 👋 I'm Tessa, founding [technical] GTM at Tabstack.
Tabstack is a web data and automation API with intelligence built into every call. You don't get raw content back to parse, clean, or run through another LLM. You get the output your product or agent needs, already done.
Five endpoints:
/extract/json — pass a URL and a schema, get back JSON that matches it
/extract/markdown — clean markdown from any URL
/generate/json — custom instructions, structured output back
/research — multi-source research with citations, one call, no orchestration
/automate — managed browser agent for JS-heavy pages, forms, multi-step flows
No scraper to maintain. No pipeline to build. No Monday morning incident because a site changed its data structure.
I joined this team because Mozilla has always believed the web should stay open and your data should stay yours. Ephemeral data, zero model training, robots.txt compliant. That's not a feature—it's the foundation.
Add it to Claude, Cursor, or Claude Code via MCP in 30 seconds. Check out the docs →
What use case are you reaching for first? I vastly improved a messy data parsing pipeline the first time I tried it.
A few other things I've built since joining Tabstack just 4 weeks ago:
Rival — open source competitive intelligence tool powered by Tabstack. Tracks competitors daily, detects changes across their site, pricing, docs, jobs and social, and surfaces live intel via MCP whenever you need it for strategy. Uses all five Tabstack endpoints.
LocalPlate — open source self-hosted meal planner. Imports recipes from any URL using Tabstack's extraction and automation endpoints.
Scout — prospect intelligence, signal feed and CRM. Uses Tabstack to enrich prospects with structured profile data, synthesize ICP fit scores and outreach briefs, and run deep-dive research — all automated.
@tessak22 🐐
@tessak22 the "schema you pass, JSON you get back" framing is the actual axis to compare these tools on. we hit similar territory building a voice→form widget — when the source is messy (transcribed speech, not HTML), the gap between "got something parseable" and "got exactly the schema fields, every time" is where the work actually lives.
the case that usually exposes it: a field in the schema that's genuinely missing from the source. whether the API returns null, hallucinates a guess, or explicitly surfaces the absence — that's the decision that determines whether downstream code can trust the output.
Tabstack
@webappski EXACTLY! I'm so glad you noticed that. Its really hard to position these types of tools when there are so many options out there right now. If you give it a try, please let me know what you think.
RiteKit Company Logo API
@tessak22 That's the key challenge with any browser automation tool. Tabstack uses real browser instances rather than headless requests, which helps bypass some detection methods, but sites with sophisticated bot detection (like Cloudflare's advanced rules) will still present obstacles. The best approach is usually to build in delays and rotate through different browser configurations to stay under detection thresholds.
GPT Food Cam
We're using Tabstack internally with some of our products b/c it out-performs many of the others we have tried against sites that have traditionally been harder to extract structured data from.
Congrats on the launch!
Tabstack
@mobileraj felt the same exact way when I first tried the product and swapped it out for my complicated data extraction pipeline. It was a game-changer in terms of decreasing LLM costs AND vastly improving the quality of the results, too.
@mobileraj amazing! make sure to leave a review here: https://www.producthunt.com/products/tabstack/reviews/new
"Pass a URL and a schema, get back JSON that matches every time" — what does "every time" mean in practice? Does the schema act as a strict contract where the call fails if a field can't be populated, or does it return partial data with nulls? Would change how I'd design error handling around it.
Also The "cited answers" from research calls is the detail I keep coming back to. Are the citations actual source URLs pulled from the pages it visited, or more like attribution to the top-level domain? Big difference if you're building something where the downstream user needs to verify the source.
Tabstack
@sounak_bhattacharya Best-effort, not strict. The schema defines the shape you want; the API extracts what it can find and returns nulls for fields it can't populate. The call itself won't fail because a field is missing from the page. What will fail is a bad request (400/422) if your schema is malformed.
Actual source URLs, not domains. Each citedPage in the complete event's metadata.citedPages array has a url field (full page URL, always present) and a claims array that maps specific assertions to that source. If you're building something where users need to verify, you get enough to link directly to the page and show which claims came from it.
Asa.team
The schema-as-contract model is the right call. Most scraping tools dump raw content and make you figure out the mess. Curious what happens when a field can't be populated though, does the call fail hard or return a partial result with nulls? That distinction matters a lot for pipelines that chain multiple calls.
Tabstack
@ng_junsheng I agree and have witnessed the same. Its a chaotic mess, and then when data structures changes, things break. To answer your question, though, partial result with nulls, not a hard fail. The call succeeds as long as the request is valid and the page is reachable. Fields that can't be populated come back as null.
Tabstack
@anusuya_bhuyan you should give it a try and find out! I wasn't able to get data from G2 or LinkedIn, but otherwise, I've found success on a lot of tricky websites.
@anusuya_bhuyan @tessak22 Is there a risk of getting banned by LinkedIn for TOS violations?
Tabstack
@anusuya_bhuyan @robert_douglass no, because thus far, LinkedIn is not something I've cracked the code on yet (with extract anyways). I'm experimenting a ton though and can come back here and comment if I find a way. You could take matters into your own hands and use the automate endpoint and feed it your logged in LinkedIn cookie but I would be SUPER careful because you can get banned for that. LinkedIn really just needs to provide a self-serve API—they'd make a lot of money from it I think.
Tech Marketing Framework
congrats on the launch! Does it require any integrations or extensions for Mozilla specifically or browser agnostic?
Tabstack
@j1ngg Nope! Its a stand-alone product. Should be super simple to setup, too! Reach out if you need anything. 😊
Interesting, does it handle different site format?
Also dynamic sites?
@tessak22 I have tried to use it on E-commerce sites. Could you please guide me throught it. Thanks
Tabstack
@hamza_addi I would try running it locally, you can change the threshold settings and configure things slightly differently that may make it easier to do. The playground is configured a specific way and ecomm doesn't seem to be a great fit for how its currently configured.
Tabstack
@hamza_addi it sure does! That's how we're different than other tools. Other tools you're dealing with the data extraction pipeline and when something changes, your scraper is broken. Tabstack does all of your task needs inside the API call so it handles the dynamic changes, adapts, and still delivers the end results you need—markdown or json. Give it a try and let me know how it goes.