Paste Home Depot product URLs, one per line, and the review section comes back as a table - who wrote it, the rating, the headline, the review text and the date, alongside the product's overall rating and review count. Ten columns, one row per review, sorted the way you choose.
One required field and two controls. The sort is the one worth thinking about.
The limit is per query, not per run: five products with a limit of a hundred is a hundred reviews from each, not a hundred altogether.
Photo Reviews narrows to the ones where someone actually photographed the thing installed. For a product that arrives flat-packed or gets fitted into a wall, those are the reviews that tell you what it looks like in a real house rather than in a studio.
Most review pages only let you go newest-first. Having Oldest as a sort is what lets you read the launch-period complaints - the ones that reveal whether a problem was fixed in a later production run or has simply been buried under newer reviews.
aggregate_rating and review_count describe the product rather than the individual review - that is what the names say, and it is why they sit in a per-review row at all. Kept beside rating, they are what let you weigh one review against the average it sits in: a two-star among four hundred fives reads very differently from a two-star among nine. Whether they are filled in on every row or only where the export chooses to is the kind of thing to check on your first run; we have never seen a populated one.
These names are confirmed twice: they are the service's own declared list, shared with the other review scrapers on this pipeline, and they are the header row of every export we hold. What each column is for is below. What each will contain is not - the note explains why.
The names are solid. Everything past the names is your first run's job, and here that caveat is unusually strong. We hold three run exports for this service and none of them contains a Home Depot review. Every review column - author, rating, title, review, date, aggregate_rating, review_count - was empty on all three rows; only query and status carried anything at all. Two of the three had been pointed at a placeholder address rather than at Home Depot, and came back http 404. The third was pointed at a real Home Depot product and still returned no review. So those runs tell you the export's shape and nothing whatsoever about its contents. What they do confirm is the column list: the header row is identical across all three workbooks and matches the service's declared set exactly. Everything else - what a rating looks like, what format a date arrives in, whether aggregate_rating is a number or a string - is deliberately absent rather than guessed. The form also warns that anti-bot sites return a blocked status on the free pool, which is residential-only; that is the platform's own warning about the shared pipeline and it is worth planning for. Run the free tier against one real product and read the header row and the first few values before you build on them.
All of them start from a product link you can copy out of the site.
DIY products fail in ways a lab test does not catch - a bracket that does not line up, a finish that marks, an instruction sheet that skips a step. Sort by Lowest rating and read the text, and the same three complaints usually appear again and again.
A competing product's review section is the most candid description of it you will get, and it is public. Point the scraper at their listing and you get the same ten columns you get for your own, so the two files compare directly.
The Photo Reviews sort narrows to reviews where a buyer attached an image. For a returns investigation or a supplier conversation, a handful of photographed complaints carries more weight than a thousand star ratings - and it keeps the row count, and the bill, small.
No subscription, no minimum, no per-seat licence. Your first 500 rows are on us - after that it is pay-as-you-go.
For every new account, one time. No credit card. All scrapers unlocked. Given that no run we hold has returned a real review, this is the part that matters most here: spend a few rows establishing what this export actually contains before you plan around it.
Roughly $2 per 1,000 reviews, the same flat rate as every other scraper on the platform. The limit is per query, so ten products with a limit of a hundred is a thousand rows - worth the arithmetic before you start, and a good reason to narrow the sort first.
Volume pricing, SLAs, dedicated workers and tailored onboarding for teams tracking a category rather than a single product. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.Reviews land wherever the item is sold. These return them in the same shape.
The same item usually sells on both, and the two audiences complain about different things - delivery damage on one, fitting problems on the other. Running both separates the product's faults from the channel's.
Another big-box retailer carrying much of the same catalogue. A third data point is what turns two conflicting review sections into an actual signal.
The nearest competitor's catalogue rather than its reviews - price, availability and identifiers. Useful for putting a review next to what the same item costs across the road.
Short answer: yes, when you only collect what is publicly visible on a product page - and that is all this service reads.
A product's review section is public. Anyone can open the listing without an account and read it, and collecting publicly visible information for product and market research is long-settled practice. As long as the data is publicly available and the process does not disrupt the service, there are no federal laws against it.
Reviews are written by people, though, and author is a display name. Publicly visible is not the same as free of obligation: if you store it, you are handling personal data, and the GDPR and similar regimes apply to you regardless of where you got it. Reading complaints to fix a product is an easy case; building a durable file on individual reviewers is not, and this service is not intended for it.
Home Depot's terms restrict automated access, so this remains a question of terms. We touch nothing behind a login, read only what an ordinary visitor sees, run no third-party trackers on the data layer, and your exports self-delete after 30 days.
The questions we hear most. Something else? Talk to us - humans write the answers, not bots.
Paste one product URL, sort by Photo Reviews, and see what buyers actually posted. Your first 500 rows are free.
Reviews of a power tool or a bathroom fitting are worth more than reviews of most things, because the people writing them have installed the item and lived with it. They are also stuck inside a widget you can only scroll. This service turns a product's review section into a table: paste Home Depot product URLs, one per line, and each review comes back as a row - the author, the rating, the headline, the body text and the date, alongside the product's own overall rating and total review count. Ten columns, one row per review.
Two controls shape the run, and the sort is the interesting one. There are six options - Photo Reviews, Most Helpful, Oldest, Newest, Highest rating and Lowest rating - and two of those are rare enough to be the reason to use this at all. Photo Reviews narrows to buyers who photographed the thing in place, which is the only honest answer to what a finish or a fitting actually looks like. Oldest lets you read the launch-period complaints, which is how you tell a fixed problem from a buried one. The limit is per query rather than per run, so ten products at a hundred each is a thousand rows.
Keeping the review-level and product-level scores in separate columns is what makes the export analysable. rating is this review; aggregate_rating and review_count belong to the product the review sits on. A two-star review among four hundred five-stars is a different fact from a two-star among nine, and only the second pair of columns tells you which you are looking at. Exactly how those two are filled in across a product's rows is worth a glance on your first run, since we have never seen a populated export to describe.
One limit is stated plainly because it changes what to expect from this page. We hold three run exports for this service and none of them contains a Home Depot review: every review column was empty on all three. Two had been pointed at a placeholder address rather than at Home Depot and came back http 404; the third was pointed at a real Home Depot product and still returned no review. What survives is the column list, and it survives well - the header row is identical across all three workbooks and matches the service's declared set. So the ten names above are dependable, and everything past them - what a rating looks like, what format a date arrives in, whether the aggregate is a number or a string - is deliberately absent rather than invented. The form also warns that anti-bot sites return a blocked status on the free pool, which is residential-only; that is the platform's own warning about the shared pipeline and it is worth planning for. Run one product on the free tier and read the first few rows yourself; your first 500 cost nothing and need no credit card. See pricing for current rates.