Paste an apps.shopify.com listing and get it back as a row: the app name, who publishes it, the rating and review count, the pricing label, the one-line tagline, the listing link and the icon. Point it at a single app for the cleanest result, or at a search or category page to sweep a whole segment.
The input is the listing page - a single app for one clean row, or a search or category page to take a whole segment at once.
One row per app, each tagged with the query it came from - so a sweep across several categories still reconciles back to your input list.
This is the one thing worth knowing before you plan a job. A single /<app> listing URL is the cleanest input - one row per app - and it runs on the standard pool. Search and category pages render their listings client-side, so those need a residential connection. Same scraper, different requirement depending on what you point it at.
developer arrives in its own column rather than buried in the listing title, which is what makes it possible to group a whole category by who builds the apps in it - and to notice when one studio quietly owns six of the top listings.
pricing carries the label the listing shows rather than a number - the app store describes plans in words, and this column keeps that wording instead of flattening it into a figure that would not survive the translation.
Each row is one app store listing: what it is called, who publishes it, how it is rated, what it costs, what it says it does, and where to find it.
The column list below is the header row of real run exports and the platform's own published list, which agree exactly. Read the note under it before you write a parser - the rating, the review count and the price are all text, and the price is a phrase rather than a figure.
The export's header row, in order, confirmed against the platform's published column list. Descriptions say what each field is for; read the note underneath before you rely on any of them arriving filled.
Two things to know before you write code against this. First, the types: rating, reviews and pricing are all text, and pricing is a phrase the store wrote rather than a number you can compare - cast the first two and parse the third only if you really need to. Second, and this decides how you plan a job: a direct app listing URL runs on the standard pool, while search and category pages render client-side and need a residential connection. The single-app URL is also the cleanest input, one row per app. Beyond that this page describes what each column is for rather than promising it will be populated - the listing fills what it fills, and a free-tier run against your own URLs is the way to find out.
A list of URLs and a limit. What you point it at is the real decision - a single app listing, or a search or category page that sweeps a whole segment.
A few examples of how teams use app store listing data to answer a question they actually have.
Sweep a category and group by developer. One studio holding several of the listings in a segment is a market structure you cannot see from the store front page, and it changes who you think you are competing with.
Every listing gets one line to say what it does. Pulling a category's taglines into a single column is the fastest way to see which promises are crowded and which are unclaimed - positioning research that would otherwise be forty browser tabs.
Re-run a list of app URLs on a schedule and diff two exports. A rating drifting, a review count jumping or a pricing label changing are all things worth knowing about a competitor before they show up in a sales call.
No subscription, no minimum, no recurring bill. Your first 500 rows are on us - after that, pay-as-you-go at the same flat rate as every other scraper here.
Every new account, one-time. No credit card required. Per-query limits, file upload and every export format included.
Roughly $2 per 1,000 apps. The pre-flight estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.
Volume pricing, dedicated workers and an SLA for continuous monitoring or very large sweeps. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.Ratings are a number; reviews are the reason behind it. Useful when a listing's average has moved and you want to know what changed.
Where a lot of these apps launched. Pairing a launch profile with the current store listing shows what a product promised and what it became.
Short answer: yes for the public listings - and this export contains no merchant, no install and no revenue data.
App store listings are published to be found. The name, the developer, the rating, the review count, the pricing label, the tagline and the icon are shown to anyone who opens the page, signed in or not - that is what a store listing is for. Collecting publicly published catalogue data is long-established practice, and nothing here touches a login, a merchant account or the Partner dashboard.
There is no personal data in this output. The developer column names a publisher, which is a company or a studio rather than an individual customer, and there are no merchant names, no install counts and no revenue figures, because none of that is public. Whatever a Partner account can see about its own apps is not what this reads.
Shopify's own terms restrict automated access, so this is a terms question as well as a legal one - if you are a Shopify Partner, check them. We run no third-party trackers on the data layer, and your exports auto-delete after 30 days.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
500 one-time free rows on every new account - no expiry. After that it is $0.002 per row, pay-as-you-go - no card on file until you say so.
Livescraper's Shopify Apps Scraper turns app store listings into a table. You paste apps.shopify.com URLs one per line - a single app listing for the cleanest result, or a search or category page to sweep a segment - or upload them as a CSV, XLSX, TXT or Parquet file, cap the rows per query, and download the result as a clean CSV, Excel or JSON file.
Each row is one listing: the app name, the developer who publishes it, the rating and the review count, the pricing label as the store words it, the one-line tagline, the listing link and the icon. The developer arrives in its own column rather than folded into the title, which is what makes grouping a category by who builds the apps in it a single operation.
Strategy teams sweep a category and group by developer to see who really owns a segment. Product and marketing teams pull a category's taglines into one column to find which promises are crowded and which are unclaimed. Competitive teams re-run a shortlist on a schedule and diff two exports, where a drifting rating or a changed pricing label is worth knowing before it comes up in a sales call.
One practical note that shapes how you plan a job: a direct app listing URL runs on the standard pool, while search and category pages render their listings client-side and need a residential connection. Beyond that, the rating, review count and pricing all arrive as text, and pricing is a phrase rather than a figure. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.