⭐ Trustpilot Reviews Scraper: Ratings & Replies

Overview

Trustpilot Reviews Scraper pulls the full public review history for any business on Trustpilot. Give it a domain like amazon.com, booking.com, or nike.com and get back every review as structured JSON: reviewer name, star rating, title, body text, publish date, company reply, plus the business's overall TrustScore and total review count. Optionally filter to just 1-star and 2-star reviews to focus on complaints.

It beats Trustpilot's AWS WAF (which blocks almost every data-centre IP) using a residential proxy pool, so runs return actual data instead of a challenge page. No login, no API key, priced pay per review. Blocked pages, missing domains, and failed runs are never charged. You only pay for a review that was actually delivered.

✅ No login required | ✅ Residential proxy included | ✅ Pay per review returned | ✅ MCP-ready for AI agents

Features

Full review scrape. Every public review on the business page paginated to the end. Star filter. Return only 1-star and 2-star reviews for churn or complaint analysis. Company replies. Reply text and reply date included on every review that has one. Business summary. Overall TrustScore and total review count included per business. Ban-resistant. Residential proxy pool clears the AWS WAF that stops data-centre scrapers.

How it works

The actor takes a business domain (amazon.com), normalises it to the Trustpilot slug (https://www.trustpilot.com/review/amazon.com), and requests the review pages through Apify's residential proxy pool. Trustpilot fronts every page with an AWS WAF challenge that blocks almost every data-centre IP: residential IPs clear the challenge and get the real page.

Each page is parsed for reviews (reviewer, rating, title, text, date, reply) and paginated until the maxResults budget is hit or the business runs out of reviews. Star-rating filters are applied client-side, so you can pull only the 1-star and 2-star reviews for churn work without paying for the rest.

🧾 Input configuration

{
  "companyDomain": "amazon.com",
  "maxResults": 200,
  "starsFilter": ["1", "2"],
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountry": "US"
  }
}

📤 Output format

Each run pushes two kinds of records: one business summary record (once per run) and one record per review. Here is a real review record captured from a live run against amazon.com:

{
  "business_domain": "amazon.com",
  "reviewer_name": "John",
  "rating": 1,
  "title": "Amazon lost my package and then updated…",
  "text": "Amazon lost my package and then updated the package as undeliverable, then Amazon tried to blame Intelcom for losing the package, but Intelcom didn't even have the package ever in their possession. Amazon lied to me about who's fault it was. Then Amazon says I have to wait a week or more before they can return my money, because they say that's the waiting period to see if the package gets delivered, but the package tracking on the Amazon site clearly says the package is undeliverable. Amazon makes you wait for your refund when it's clearly their lies and fault the package was lost.",
  "review_date": "2026-07-15T00:30:31.000Z",
  "review_url": "https://www.trustpilot.com/reviews/6a56b8878caf7c42b6979634",
  "review_id": "6a56b8878caf7c42b6979634",
  "scraped_at": "2026-07-15T04:17:40.059Z"
}

And the business summary record from the same run:

{
  "_type": "business",
  "business_domain": "amazon.com",
  "business_name": "Amazon",
  "overall_rating": 1.6,
  "total_reviews": 47501,
  "trustpilot_url": "https://www.trustpilot.com/review/amazon.com",
  "scraped_at": "2026-07-15T04:17:39.772Z"
}

Fields on the business summary record:

Field	Description
🌐 business_domain	Company domain scraped (e.g. amazon.com)
🏢 business_name	Business name as displayed on Trustpilot
⭐ overall_rating	Overall TrustScore for the business
🔢 total_reviews	Total number of reviews on the business page
🔗 trustpilot_url	The Trustpilot review page scraped
🕒 scraped_at	ISO timestamp of the scrape

Fields on each review record:

Field	Description
🌐 business_domain	Company domain scraped (e.g. amazon.com)
🙋 reviewer_name	Display name of the reviewer
⭐ rating	Star rating (1 to 5)
✍️ title	Review headline
📝 text	Full review body text
📅 review_date	ISO date the review was published
🔗 review_url	Direct link to the review on Trustpilot
🆔 review_id	Trustpilot's internal review ID
💬 reply_text	Company reply text, present only when the business replied
🕒 scraped_at	ISO timestamp of the scrape

💼 Common use cases

Customer & churn insight Pull every 1-star and 2-star review for your product or a competitor and cluster the complaints. Feed reviews into an LLM to surface recurring themes and priority fixes.

Competitor teardown Compare TrustScores and review volume across a competitor set. Read the top complaints and top praise for a competitor to sharpen positioning.

Reputation monitoring Watch your own business page for new negative reviews on a daily cadence. Track how quickly and consistently the company responds to reviews.

Due diligence & investing Pull every review for a target acquisition to gauge product and support quality. Spot patterns of fraud, delivery issues, or refund refusals before buying in.

🚀 Getting started

1. Open the actor and enter a companyDomain (e.g. booking.com).
2. Set maxResults for the number of reviews to pull.
3. Optionally set starsFilter (e.g. ["1", "2"]) to focus on negative reviews.
4. Leave the residential proxy on (required to clear the AWS WAF).
5. Click Start. Download as JSON, CSV, or Excel, or pull the dataset via API or MCP.

FAQ

Why do I need a residential proxy? Trustpilot fronts every page with an AWS WAF challenge that blocks almost every data-centre IP. Residential IPs from real consumer ISPs clear the challenge and return the real page. Data-centre proxies will just return a challenge page.

How many reviews can I pull for one business? As many as the business has publicly. The actor paginates to the end within your maxResults budget.

How much does it cost? Pay per review returned, pay as you go. No subscription, no monthly minimum.

Can I use it in an AI agent? Yes. It's exposed as an MCP tool. See below.

Use in Claude, ChatGPT & any MCP agent

https://mcp.apify.com/?tools=themineworks/trustpilot-reviews

Or call it programmatically with the Apify client:

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });

const run = await client.actor('themineworks/trustpilot-reviews').call({
  companyDomain: 'amazon.com',
  maxResults: 100,
  starsFilter: ['1', '2'],
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

🛠️ Complete your competitor & reputation pipeline

Got the reviews. Now build the wider picture:

• Google Trends Scraper: track brand interest for the same business over time.
• Meta Ad Library Scraper: see how the brand is running paid creative right now.
• LinkedIn Company Scraper: profile the business with size, industry, HQ, and website.

Typical flow: trustpilot-reviews reveals what customers actually think, google-trends shows demand, meta-ad-library shows the creative response.

Questions or need a custom field set? Reach out through the Apify profile.