Paste walmart.com product URLs and get the listings back as rows: the item id, the name, the brand, the price and its currency, the rating and review count, the stock status, and - the one most people come for - which seller is actually shipping it. Fifteen columns, one row per product.
The input is the listing page, not the item number - you name the products you want and the job reads what each page publishes.
One row per product, each tagged with the query it came from - so a run across a long list still reconciles back to your input.
A walmart.com listing is not always sold by Walmart. The seller column names the merchant behind the buy box, so a marketplace listing is distinguishable from a first-party one without opening the page - which is usually the difference between a competitor and a reseller.
Both walmart.com/ip/625493716 and the longer slug form of the same link are accepted, one per line. Whichever your list already holds is the right input, and every row carries item_id parsed from the URL - so two differently-worded links to one product still join on a single column.
availability arrives as a machine-shaped uppercase token rather than as display prose, which is what makes it safe to filter and group on directly. Cast the numbers first though - the price, rating and review count all arrive as text.
Each row is one listing as Walmart publishes it: how it is identified, what it costs, how it is rated, whether it is in stock and who is selling 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: several of these fields are text where you would expect a number, and one is a number where its neighbours are text.
The export's header row, in order, confirmed against the platform's published column list. Where a value shape has actually been observed it is described; where it has not, the entry says what the field is for and stops.
currency, not in this column.Two things to know before you write code against this. First, the types are not what they look like: price, rating and review_count all come back as text while position comes back as a number, so a naive sum over the price column concatenates instead of adding. Second, join on item_id rather than on query: the same product can be submitted as a bare id URL or as the longer slug URL, and query faithfully preserves whichever you sent. Beyond the types, this page describes what each column is for and not what it will contain - a listing fills what it fills, and the way to find out is a free-tier run against your own URLs.
A list of URLs and a limit. That is the whole form - there is no sort order and no proxy setting to choose on this one.
A few examples of how teams use marketplace listing data to answer a question they actually have.
Pull your own products and read the seller column. A listing for your item sold by a merchant you have never heard of is the whole finding, and it arrives as a value in a table rather than as something somebody had to notice while browsing.
Re-run a list on a schedule and diff two exports. Price moving is one signal; the stock token flipping is another, and the pair tells you more than either alone - a competitor out of stock is a different situation from a competitor discounting.
Join on the item id to reconcile your own catalogue against what the marketplace actually shows - the title, the brand and the category as published. The url column makes anything that looks wrong checkable at the source in one click.
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 products. 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 catalogue pulls. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.The same listings, from the buyer's side. Both carry the Walmart item id, so a catalogue export and a review export join on one column without any matching work.
The other marketplace. Running both on an item you sell is how you see whether a price or a reseller problem is channel-specific or everywhere.
Short answer: yes for the public catalogue - and this export contains no account, no customer and no order data.
The fields collected here are the ones Walmart publishes on its own public product pages, for shoppers and shopping tools to read. The title, the price, the rating, the stock status and the seller are shown to anyone who opens the page, signed in or not. Collecting publicly published catalogue data is long-established practice, and nothing here touches a login, a cart or a checkout.
There is no personal data in this output at all - it is a product table. The seller column names a merchant, which is a business rather than a person, and there are no customer names, no order history and no contract pricing, because none of that is public. Whatever your account sees when signed in is not what this reads.
Walmart's own terms restrict automated access, so this is a terms question as well as a legal one - if you have a commercial relationship with them, check it. 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 Walmart Products Scraper turns a list of listing URLs into a product table. You paste walmart.com product URLs one per line - with or without the descriptive slug - or upload them as a CSV, XLSX, TXT or Parquet file, cap the rows per query if you want to, and download the result as a clean CSV, Excel or JSON file.
Each row is one listing: the Walmart item id, the title, the brand, the price and its currency, the rating and review count, the stock status, the selling merchant, the category, the description, the image, the product link and the position it fell at in the run. The item id is parsed from the URL, so a run assembled from links in different formats still joins on a single column.
Brand teams pull their own products and read the seller column to find out who is reselling them. Pricing teams re-run a list on a schedule and diff two exports, where a moving price and a flipping stock token tell different stories. Catalogue teams join on the item id to reconcile a product master against what the marketplace actually shows, with the url column making anything suspicious checkable at the source.
One practical note up front: the price, rating and review count come back as text while the position comes back as a number, so cast before you total or sort, and take the unit from the currency column rather than assuming it. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.