RockAuto Scraper
We do not have a dedicated RockAuto data API. What we do have is an AI scraper that reads a rockauto.com page you point it at, turns it into the columns you asked for and hands them back by REST, CSV or Excel — on a schedule, priced per page.
- rockauto.com
How to scrape RockAuto
RockAuto publishes no read API for its catalogue. There is no key to apply for and no endpoint to call, which is why the page is the interface. RockAuto is the cheapest published price for most aftermarket parts, which makes it the floor every other parts retailer is measured against. The way to get the data is to read the page the way a customer sees it: point the AI scraper at a rockauto.com URL, declare the columns you want in plain English, and schedule it. Auto parts only compare when the fitment matches, so a part number and a vehicle application are not nice-to-have columns: without them the price is unusable.
What the AI scraper does with a rockauto.com link
The same three steps as any other page you point it at. Nothing about RockAuto is pre-built, which is exactly why it works on pages nobody wrote a connector for.
Paste the link
Give it a rockauto.com product or listing URL. Market, currency and assortment all come from the page itself, so there is no region setting to get wrong — the URL decides.
Declare the columns
Name each field, give it a type and write a line telling the AI what to look for. “brand” and “product_title” are the two most people start with. The schema is yours and you can change it between runs.
Schedule it and collect
Run it hourly, daily or on demand. Every run returns the same columns in the same order with a timestamp on each row, so the history builds itself.
Set it up once. The data keeps coming.
Save your search once and it runs by itself. Every run lands in your own Scrapewise database, and you pick how to read it.
Set it up once
Add your ASINs, keywords, places or apps to a scraper in the web app. New accounts get 5 free requests.
Runs on your schedule
Pick daily, weekly on the days you choose, every few days, or the first or last day of the month. Scheduled runs start at night, European time. Need it more often? Start runs from your own code.
Saved in your database
Each run adds dated rows, so this week sits next to last week. Rows are kept for 90 days.
Use it your way
Open the table in the web app and download the latest run as an Excel file. Ask your own AI assistant about it. Or pull the rows into your code with an API key.
Ask your AI assistant about your own data
Connect Claude Desktop or Claude Code with a read-only key. The assistant reads the rows you've already collected, at no extra cost on Scrapewise. With a full-access key it can start a run for you too.
- Which of my ASINs lost the Buy Box this week?
- Which competitor cut prices the most since Monday?
- Show the keywords where I dropped out of the top 10.
What you send and what you get
You send a link and a column list. Everything else — market, currency, which products are on the page — comes from the page, so there is nothing to configure twice.
- Link to a RockAuto pagerequired
Open the product or category page in your browser and copy the address. Any RockAuto country domain works the same way.
https://www.rockauto.com/ - Your column listrequired
Field name, type and one line of plain English per column. Written once, reused on every run.
brand, product_title, part_number, oe_number - How often it runsoptional
Hourly, daily, weekly or on demand through the API. Daily is what most price monitoring uses.
Daily at 06:00 UTC
One row per part on the page, 12 columns each
- brand
- product_title
- part_number
- oe_number
- fitment
- price
- core_charge
- currency
- availability
- warranty
- product_url
- checked_at
REST API, CSV or Excel. The column set stays the same between runs, so downstream jobs do not re-map fields.
The columns a RockAuto run comes back with
We are not going to print a table of invented auto parts prices and call it real output. RockAuto did not return a readable product page to an ordinary request, so we have no run of our own to quote from. So what is published here is the column list you declare before the run, which is the same list every run comes back with.
brandBrand as the site labels itproduct_titlePart title as the site writes itpart_numberSite's or manufacturer's part numberoe_numberOriginal-equipment reference, where the page prints onefitmentVehicle application the page statespricePrice the site is charging todaycore_chargeCore or surcharge quoted separately, where showncurrencyCurrency the page quotedavailabilityStock state as the page states itwarrantyWarranty term as statedproduct_urlLink back to the page the row came fromchecked_atWhen this run collected the page (UTC)Every column here is one you name yourself in the schema. If a field is not on the page on the day of the run, it comes back empty rather than guessed.
What a RockAuto page costs
Pay-as-you-go from your wallet. No plan, no monthly fee, no seat count.
Measured on 3 October 2026, not assumed. A plain HTTP request for a rockauto.com page comes back with the page, so most runs never need a browser and never need a residential exit.
500 RockAuto pages checked once a day for a month is 15,000 pages.
about EUR 7.50 for the month
You are charged for what a run actually used, not what it was expected to need, and your balance never expires.
See the full price list →What this page is not promising
We would rather say this here than in a support ticket.
No dedicated RockAuto endpoint
There is no RockAuto API in our catalogue and no pre-built RockAuto schema. The AI scraper reads the page you point it at — that is the whole mechanism, and it is why it works on pages nobody built a connector for.
Nothing behind a login
It reads what a visitor can see. Account pricing, contract pricing and anything behind a sign-in are out of scope.
No field the page does not show
If the page does not print it, the scraper cannot return it. Identifiers like EAN or GTIN only come back where the retailer publishes them.
A price is a point in time
Every row carries the timestamp of the run that collected it. A price without one is not evidence, which is why the column is in the default schema.
RockAuto has one specific trap
The same part appears at several brand tiers with very different prices, so brand has to be part of the row or the comparison is meaningless.
Not a bulk dump of the catalogue
You give it the URLs you care about. It does not crawl rockauto.com end to end, and a schedule that tried to would cost more than the answer is worth.
Typical fields the AI scraper extracts from a RockAuto page
Treat this as a starting point rather than a fixed schema. What comes back is whatever the page actually shows on the day of the run.
Part number and fitment
The only basis on which two auto-parts prices are comparable at all.
- Manufacturer or site part number
- Original-equipment reference where printed
- Vehicle make, model and year the page states
- Interchange or cross-reference numbers, where listed
- Position or side, where relevant
The price behind the price
Core charges, warranty tiers and shipping change the landed cost materially in this category.
- Listed price
- Core charge or surcharge quoted separately
- Warranty term and whether a longer term costs more
- Delivery charge where quoted
- Any was-price or promotional price
Brand tier and stock
An OE-branded part and an economy-branded part at the same price are not the same observation.
- Brand and brand tier as the page presents it
- Stock state as the page states it
- Delivery window shown
- Quantity required per vehicle, where stated
- Rating and review count, where shown
The column list is one you write: name each field, give it a type and a line telling the AI what to look for. The schema belongs to your run, not to us. How Custom Schema works →
What it cannot give you If a field is not visible on the page, the scraper cannot invent it. Identifiers such as EAN or GTIN only come back when RockAuto publishes them.
What people use RockAuto data for
Part-number price benchmarking
One schema keyed on part number across several parts retailers gives a table that joins exactly, which is rare and is why this category is worth scraping properly.
Brand-tier price mapping
What the OE brand, the mid tier and the economy brand each cost for the same application. That spread is the pricing decision.
Core charge and landed cost analysis
A headline price that excludes a core charge is not the price. Capturing it separately is what makes a landed-cost comparison possible.
Fitment coverage research
Which applications a competitor actually lists a part for, which is a range decision disguised as a catalogue question.
Supplier price-floor checks
If you manufacture parts, this is how you see what your part numbers are listed at across the retailers that carry them.
Feeding your own pricing rules
The export is a normal REST API or CSV, so part number, fitment and price land in whatever pricing model you already run.
What makes RockAuto harder than an ordinary storefront
Here is what the scraper is actually up against on rockauto.com, measured rather than assumed.
Access
- Plain HTTP requests are answered, but the answer is the shell of the page rather than the priced version of it
- No named protection vendor on the response, which is why this one lands on a cheaper tier than most of the cohort
- robots.txt carries a blanket disallow, so the scheduling decision is yours to make and document
Fitment is the join key
- A price without a part number and a vehicle application is not comparable to anything
- The same part is listed under several brands and tiers, which are different products commercially
- Core charges and warranty tiers sit outside the headline price
- Catalogues are enormous and organised by vehicle, so a sweep is a long list of URLs rather than one request
What the page publishes
- No product-level structured data is published, so every field is read off the rendered page
- The same part appears at several brand tiers with very different prices, so brand has to be part of the row or the comparison is meaningless.
- Price is one of the last things the page settles on, so a read taken too early records the placeholder rather than the number
Keeping a history
- A single read is a snapshot; the value is in the series, which means the run has to be scheduled and the rows kept
- Every row carries the timestamp of the run that produced it, so two days can be compared without guesswork
- Columns stay stable between runs, so a dashboard written once does not break when the site redesigns
RockAuto scraping — questions
What people ask before pointing the scraper at rockauto.com.
RockAuto publishes no read API for its catalogue. There is no key to apply for and no endpoint to call, which is why the page is the interface.
Ready to pull RockAuto data into your stack?
Start free — or talk to our team about your exact fields, refresh cadence and volume.