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ItemInfo Products Scraper

A category URL in,
a priced catalogue out.

Point it at an ItemInfo product page or a whole search category and get the catalogue back as rows: name, price, currency, availability, brand, and the identifiers that make an export joinable - sku, mpn and gtin. Fifteen columns, one row per product. And unlike several scrapers here, there is no proxy for you to set up.

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500 free rows, one-time$0.002 per row after that15 columnsCSV · XLSX · JSON
How it works

Five steps,
and no setup.

One input and one control. There is no proxy to configure on this one, so the whole job is a list of links and a number.

  1. STEP 1Sign in to the platform.
  2. STEP 2Open the ItemInfo Products Scraper.
  3. STEP 3Paste ItemInfo product URLs or category and search URLs, one per line - or upload a CSV, XLSX or TXT file.
  4. STEP 4Set a limit per query, or leave it empty to take everything, then click Get Data.
  5. STEP 5Download the result as CSV, XLSX or JSON.

The limit is per query, so five category URLs with a limit of two hundred is two hundred products from each - not two hundred altogether.

Why teams use it

Nothing to configure,
and the right keys.

No proxy to bring

Requests go out through our pool rather than your own address, and this retailer is not one of the four the tool flags as needing a residential proxy on top. So there is nothing to buy, nothing to configure, and no second bill - which is not true of every scraper in this catalogue, and is the reason this page is shorter than its siblings.

Built for catalogue matching

sku, mpn and gtin sit on the same row as price and availability. Those are the fields that let an export line up against your own item master or another distributor's list - matching on product names is guesswork, matching on a manufacturer part number is not.

A search page is a valid input

You do not need a list of product URLs to start. Hand it a search or category URL and it comes back expanded, one row per product - which is exactly how the tool's own example for ItemInfo is written, pointing at a whole section rather than a single item.

What you get

Fifteen columns,
one row per product.

Each row is one item: what it is called, what it costs and in what currency, whether it is available, who makes it, the identifiers that pin it down, and how it is presented.

The column list below is the header row of real run exports - forty-five of them, all identical - corroborated by the column array the run screen renders. The names are as solid as they get. What comes after the table matters more than usual, though, because this service shares one pipeline with several others and we have never pointed that pipeline at ItemInfo ourselves. The note says exactly what that does and does not let us tell you.

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Data dictionary

Fifteen columns,
and an honest ceiling.

The names are exact and doubly confirmed. The descriptions say what each column is for - not what it will contain, because that is the retailer's decision rather than ours, and the note below explains why.

query
The URL you submitted for this row - the product, category or search page it came from. Written by the scraper, so it is there on every row.
service
Which retailer this row came from. This scraper shares one pipeline with several other product services, and the one you picked is stamped onto every row - useful if you keep more than one retailer in the same table.
name
The product name as the page declares it.
price
The price the page declares.
currency
The currency that price is in, as a separate column - so you are not parsing a symbol out of the price field.
availability
The stock status the page declares.
brand
The brand the page declares.
sku
The retailer's own stock code for the item.
mpn
The manufacturer part number. Along with gtin, this is what makes an export joinable to a catalogue that is not ItemInfo's.
gtin
The global trade item number - the barcode identifier.
rating
The product rating, if the page publishes one. No scale is stated here; we have not seen a value.
reviews
The review count. A number of reviews, not the review text - there is no review body in this export.
image
The product image the page declares.
url
The product link.
description
The product description the page declares.

Two things about the evidence behind this page. First, the mechanism: products are read from the JSON-LD a page publishes about itself - the structured data retailers embed for search engines. That sets a ceiling this page will not pretend past. Every column after service exists only if ItemInfo declares that field, so a blank means not published, not run failed. Second, the gap: we have never run this against ItemInfo. We hold forty-five exports from the shared pipeline behind this service and not one is an ItemInfo run - forty-four came back empty and the only populated one is a different retailer. That export is worth one honest data point about the tool rather than about ItemInfo: query and service filled on every row, because the scraper writes them itself - and six of the fifteen were empty on every single row. Expect gaps here too, expect them to fall differently because a different retailer publishes different structured data, and let your first free-tier run tell you which. That is also why this page prints no example values and no formats: with no ItemInfo run, we would be inventing them.

Run one category free
Run controls

One dial,
and a list of links.

A line is a product page, a category, or a search. Beyond that there is a single number to set and one way to hand over a longer list - and nothing else to configure.

Product URL Category URL Search URL One per line Limit per query Empty or 0 takes all CSV upload XLSX upload TXT upload No proxy needed
Where the fields come from

The page declares it,
we report it.

This scraper reads the structured data a product page publishes about itself - the JSON-LD block retailers embed so search engines can show a price and a stock status. That is why the columns read like they do: sku, mpn, gtin, brand, availability and currency are the standard vocabulary for describing a product, not names we invented.

The practical consequence is the one worth planning around. A column is only as good as the retailer's own markup. If a field is missing from the page's structured data it comes back blank, and no amount of re-running changes that - it is not a failure, it is an absence. In the one populated export we hold from this shared tool, six of the fifteen columns were blank on every row.

If you need something the markup does not carry, that is a different job - the Universal AI Scraper reads the page itself and returns the attributes you name, rather than only what was declared for search engines.

livescraper.app · the ceiling
Read from the page's JSON-LD✓
query and service: written by us✓
The other thirteen: declared by the page✓
Blank = not published, not failed!
Check the gaps on your first run✓
Structured data is a retailer's choice, and it differs between them.
Handing over the list

Uploads add,
and don't de-duplicate.

An uploaded file does not clear the box - the lines are appended to whatever you had already typed, and duplicates are not removed. Upload the same file twice and those URLs really will be fetched twice, and billed twice.

For a CSV or TXT the whole line is taken as one URL, so a spreadsheet with extra columns is not what you want here. Only an XLSX is read column by column, and there it takes column A.

The picker lists Parquet alongside CSV, XLSX and TXT, but it cannot actually read one in the browser and will tell you so - use one of the other three.

livescraper.app · the upload
CSV · XLSX · TXT✓
Duplicates are NOT removed!
CSV and TXT: the whole line✓
XLSX: column A✓
Appended to what you typed✓
Check the box before you run - a doubled list is a doubled bill.
Common workflows

Three jobs people
run here most.

A few of the ways teams turn a distributor catalogue into something a spreadsheet can answer.

Procurement

Price your own parts list

Export the categories you buy from and join the result to your item master on mpn or gtin rather than on names. What you get is a line-by-line comparison against what you are actually paying, which is the version of that question a finance team will accept.

Procurement · Finance
Catalogue

Fill the gaps in your own data

If your product records are missing manufacturer numbers or barcodes, a category export is a source for them. Brand, sku, mpn and gtin arrive together on one row, which is usually enough to reconcile an item you already stock.

Data · Merchandising
Availability

Watch a category over time

Run the same category on a schedule and keep the exports. Price and availability moving across a whole section tells you more about a supplier than any single item does - and because there is no proxy in the loop, a recurring job here costs only its rows.

Ops
Get started
Pricing

Pay for the products
you actually pull.

No subscription, no minimum, no recurring invoice - and on this one, no proxy bill either. Your first 500 rows are on us.

Free tier

500 rows free - $0

On every new account, one time. No credit card. Per-query limits, file upload and every export format included - and nothing to supply on top, because this service needs no proxy of your own.

$0 forever
Pay as you go

$0.002 per row, after the free tier

The same flat rate as every other scraper on the platform, and a row here is one product. The estimator shows the row count and credit cost before a run starts - no surprise bills, no compute units to translate.

Most popular
Volume

Custom · high volume

Volume pricing, dedicated workers and an SLA for ongoing monitoring or very large catalogue pulls. Tell us your numbers and we will quote it.

Talk to us
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10% off your first paid run.Use code LIVESCRAPER10 at checkout.
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Pairs well with

The same pipeline,
other shelves.

One is this scraper's twin on a retailer that does need a proxy; the other fills the fields a page never declared.

Fastenal Products Scraper

The same fifteen columns from the same pipeline, on an industrial supplier. Worth knowing before you plan a run: that one is on the list of retailers you need your own residential proxy for.

Explore

Universal AI Scraper

This page is limited to what a product page declares in its structured data. That one reads the page itself and returns the attributes you name - the answer when the field you need was never marked up.

Explore
The legal bit

Is it legal to scrape
product catalogues?

Short answer: yes for public catalogue data - and this is about as low-risk as scraping gets on the privacy side, because there are no people in it.

Everything in this export is shown on a public product page to anyone who opens it: the name, the price, the currency, the availability, the brand, the identifiers, an image and a description. More than that, most of it is published in structured form precisely so that machines will read it - that is what JSON-LD is for. Collecting it for price research and catalogue work is long-settled practice, and nothing here touches a login, an account price or a checkout.

There is no personal data in these fifteen columns at all - no names, no reviewers, no contact details. That makes the usual privacy analysis short. What replaces it is commercial: prices and catalogue structure are a supplier's business information, and using them to inform your own buying is ordinary, while republishing a distributor's catalogue as your own is a different matter that no export makes acceptable.

One thing this page will not do is read the absence of a proxy requirement as an invitation. That a retailer does not block datacentre addresses says nothing about what its terms permit, so read them before you scale a recurring job up. We run no third-party trackers on the data layer, and your exports self-delete after 30 days.

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livescraper.app · principles
Public catalogue pages only✓
No logins, no account pricing, no checkout✓
No personal data in any column✓
No block is not the same as permission!
Exports self-delete (30 days)✓
Check ItemInfo's own terms before you scale up.
FAQ

What people ask
before signing up.

The questions we get most. Anything else? Talk to us - humans write the answers, not a bot.

How do I scrape ItemInfo product data?+
Using the ItemInfo Products Scraper:
  1. Sign in to the platform.
  2. Open the ItemInfo Products Scraper.
  3. Paste ItemInfo product URLs or category and search URLs, one per line - or upload a CSV, XLSX or TXT file.
  4. Set a limit per query, or leave it empty to take everything, then click Get Data.
  5. Download the result as CSV, XLSX or JSON.
Do I need a proxy for this one?+
No - and that is worth saying because several scrapers on this platform do. Requests go out through our shared pool rather than your real address, and the tool names four retailers that need a residential proxy of your own on top of that. ItemInfo is not one of them, so there is nothing for you to set up. That is the tool's own assessment rather than a promise about every run.
Can I point it at a whole category, or only single products?+
Either. A line can be one product URL or a category or search URL, and a category returns one row per product in it. The example the tool itself puts in the box for ItemInfo is a search URL covering a whole section, so working a category at a time is the expected way to use it.
Where does the data actually come from?+
From the JSON-LD that the page publishes about itself - the structured data retailers embed for search engines. That is worth knowing because it sets the ceiling: this scraper reports what ItemInfo declares, so a column comes back blank when the page never declared that field, not because the run failed.
Will all fifteen columns be filled?+
Almost certainly not, and we will not pretend otherwise. Because the fields are read from each page's structured data, which fields are present is the retailer's choice. In the one populated export we hold from this shared tool - a different retailer, not ItemInfo - six of the fifteen were empty on every row. Expect gaps, and let your first free run tell you which ones apply here.
Have you run this against ItemInfo yourselves?+
No. We hold forty-five run exports from the shared product scraper behind this service and not one of them is an ItemInfo run. So the fifteen column names are solid - two independent sources agree on them - and what ItemInfo specifically returns is something your first free-tier run will establish before we would.
What is the service column for?+
It records which retailer a row came from. This scraper is one of several services sharing a single product pipeline, and the service you picked is sent with the job and written onto every row - so if you keep exports from more than one retailer in the same table, that column is what keeps them apart.
How much does it cost?+
The first 500 rows are free and one-time, with no credit card. After that it is $0.002 per row - the same flat rate as every other scraper on the platform. A row is one product, and there is no proxy bill on top.

Your first 500 products,
on the house.

500 free rows, one time, on every new account - no expiry. After that it is $0.002 per row, pay as you go, with no card on file until you decide. Nothing else to supply: this one needs no proxy of your own.

Start free See full pricing
Live in seconds · no card

Scrape ItemInfo product data at scale

Livescraper's ItemInfo Products Scraper turns catalogue pages into a spreadsheet. You paste ItemInfo product URLs, category URLs or search URLs - one per line, or uploaded as a CSV, XLSX or TXT file - set how many products to take per query, and download the result as a clean CSV, Excel or JSON file, one row per product. There is no proxy to configure and nothing to install.

Each row carries the name, the price and its currency as separate columns, availability, brand, and the three identifiers that make catalogue work possible: sku, mpn and gtin. Those last two are the reason this export is useful rather than merely interesting - a manufacturer part number joins to your item master, where a product name only approximately does. Rating, review count, image, link and description round out the fifteen.

Procurement teams export the categories they buy from and join the result to what they are already paying, line by line. Data and merchandising teams use it to fill in manufacturer numbers and barcodes their own records are missing. Operations teams run the same category on a schedule and watch price and availability move across a whole section rather than one item - cheap to repeat here, because rows are the only cost.

One limit is stated plainly rather than buried. The fields are read from the structured data each page publishes about itself, which means a blank column is a field the retailer never declared rather than a run that failed; in the one populated export we hold from this shared tool, from a different retailer, six of the fifteen columns were empty throughout. We have not run this against ItemInfo ourselves, so the fifteen column names are what we stand behind and the rest is what your first free-tier run will show you. Start free: your first 500 rows cost nothing and need no card.