Paste an Angi near-me search or a company-list URL and get the contractors back as rows: name, trade, full address, star rating, review count, recommended rate, six summarised sub-ratings, years in business and the amenity flags Angi puts on a profile. Ranked in the order Angi returned them.
The input is the listing page, not the company - you point it at a search you would otherwise scroll, and the job returns every result on it as a row.
One row per company, tagged with the query it came from and numbered in the order Angi returned it - so the ranking survives into the spreadsheet.
Not one star score but seven numbers: the headline rating plus summarised professionalism, punctuality, overall, responsiveness, quality and value. They come back as real numbers, so a sort works without cleaning the column first.
Emergency callouts, free estimates, warranties, veteran-owned, bilingual, small jobs welcome, commercial work - seven true/false columns, not a phrase you have to parse. Filtering a list down to who does emergency work is one expression.
A near-me search returns the firms that serve your postcode, and their own addresses sit in surrounding towns - in the runs we checked, results routinely carried postcodes other than the one searched. That is the point of the query, and worth knowing before you filter on it.
Company identity, a full postal address, the ratings block, years in business, the amenity flags, and Angi's own listing id and profile URL. One row per company, numbered in the order the listing returned it.
Two things about that number are worth saying before you read the table rather than after. Eight of the forty-two columns are duplicates - Angi returns the company name, four address parts, the profile URL and the review count twice each under different names, and each pair carried the same value in every export we checked. And three columns came back empty throughout: phone, hours and local_quotes. Counting honestly, that is around thirty-one distinct populated fields, not forty-two.
The duplicate pairs, the empty columns and the column types are properties of the export and hold regardless of what you query. How often a sparse field like address_street2 carries a value is a different matter - that moves with the trade and the area, and with how many rows you take. Run a free-tier job on your own category for that.
The header row of real exports, in export order. Duplicates are named as duplicates and empty columns are named as empty - read the note underneath before you build against this.
rating but not equal to it - the two disagreed on every row.Forty-two columns, but eight are copies and three never filled. title, address_street1, address_city, address_state, address_country, address_postalCode, listing_url and reviews each duplicate an earlier column and carried the same value as it; phone, hours and local_quotes came back empty throughout. That leaves roughly thirty-one fields carrying distinct information. Types are mixed here, unlike our kununu and Product Hunt exports: the seven amenity flags are real booleans, years_in_business, recommended_rate and the six summarised ratings are real numbers, and everything else - including rating and review_count - is a string. And do not merge rating into average_ratings_summarized_OVERALL: they are different figures and disagreed on every row we compared.
The input is a listing page. Point it at a near-me search for a trade and an area, or at a company-list page, and every result on it comes back as a row.
A few examples of what a filterable table of contractors is good for.
Suppliers, franchisors and SaaS vendors selling into the trades can pull every firm in a category and area, then filter on years in business and review count to separate established operators from brand-new listings.
The row order is the order Angi served it, so a repeat run shows movement. Alongside the ratings breakdown you can see which competitor is winning on punctuality and which on value.
Run the same category across a list of postcodes and you have a supply map: how many firms serve each area, how long they have traded, and how many offer emergency work.
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 companies. 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 running many categories across many areas on a schedule. Tell us your numbers and we will quote.
LIVESCRAPER10 at checkout.Angi's phone column came back empty on every row we pulled, and there is no website column at all. These two close that gap.
The same contractors on Maps, where the phone number and the website do come back. Match on company name and postcode and you have the contact details Angi withheld.
Once you have the websites, this crawls them for email addresses, phone numbers and social handles - the last step from a directory listing to someone you can actually contact.
Short answer: yes for the public listing - and what comes back is business information, not personal data.
Everything collected here is shown on a public Angi listing to any visitor, signed in or not: the company name, its trade, its business address, the star rating and review count, the summarised sub-ratings, years in business and the amenity flags. No login, no paywall, nothing behind an account.
It is also worth being clear about what this is not. There is no reviewer name, no review text and no customer detail anywhere in the export - you get counts and averages, so the output describes companies rather than the people who wrote about them. That keeps it on the business-data side of the line, which is a materially easier position than a scrape of review prose.
Angi's terms restrict automated access, so this is a terms question as well as a legal one - if you have a contractual relationship with the site, 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 Angi Scraper turns a listing page into contractor data. You submit Angi near-me search URLs or company-list URLs - typed one per line, or uploaded as a CSV, XLSX, TXT or Parquet file - set a limit per query if you want one, and download the results as a clean CSV, Excel or JSON file. One row per company, numbered in the order Angi returned it.
Each row carries the company's identity and a full postal address, its trade, the headline star rating and review count, the percentage of reviewers who would recommend it, six summarised sub-ratings covering professionalism, punctuality, overall, responsiveness, quality and value, how many years it has traded, and the amenity flags Angi shows on a profile - emergency callouts, free estimates, warranties, veteran-owned, bilingual, small jobs and commercial work.
Suppliers and vendors selling into the trades use it to build prospect lists they can filter by tenure and review volume. Contractors use it to see where they rank in their own area and which competitor is winning on which sub-rating. Researchers run one category across many postcodes to map how a trade is supplied across a region.
Two honest notes before you start, because a column count is not the same as a field count. Eight of the forty-two columns duplicate another column exactly, and three - phone, hours and local quotes - came back empty throughout, which leaves roughly thirty-one carrying distinct information. And a near-me search returns firms that serve your postcode rather than firms registered in it, so the address you get is theirs, not yours. Start free: your first 500 rows cost nothing and need no credit card, and after that it is $0.002 per row, flat.