How to use Trustpilot Reviews Scraper

Step by step: in the Apify Store, over the API, from JavaScript or Python, in n8n, and from AI agents through MCP or LangChain.

$0.52 per 1,000 reviews on the Free plan, down to $0.27 on higher plans (live Apify price on 2026-11-02).

1. In the Apify Store (no code)

1. Open arcadium/trustpilot-reviews and sign in to Apify (a free account works).
2. Fill in the input form. The fields below are the ones the example uses.
3. Set a maximum cost per run in the run options if you want a hard cap.
4. Start the run. Rows appear in the Output tab as they arrive.
5. Export the dataset as JSON, CSV or Excel, or connect an integration.

Example input:

{
  "companies": [
    "www.bestbuy.com"
  ],
  "maxReviewsPerCompany": 20,
  "sort": "recency",
  "verified": false,
  "withReplies": false,
  "includeCompanyInfo": true,
  "splitFilters": true,
  "companySort": "most_relevant",
  "claimedOnly": false,
  "maxCompaniesPerCategory": 20,
  "onlyNewReviews": false
}

2. Over the Apify API

The synchronous endpoint runs the actor and returns its rows in one call (for runs up to about five minutes; use the asynchronous run endpoint for longer ones).

curl -X POST "https://api.apify.com/v2/acts/arcadium~trustpilot-reviews/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"companies":["www.bestbuy.com"],"maxReviewsPerCompany":20,"sort":"recency","verified":false,"withReplies":false,"includeCompanyInfo":true,"splitFilters":true,"companySort":"most_relevant","claimedOnly":false,"maxCompaniesPerCategory":20,"onlyNewReviews":false}'

3. From JavaScript

// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/trustpilot-reviews').call({
  "companies": [
    "www.bestbuy.com"
  ],
  "maxReviewsPerCompany": 20,
  "sort": "recency",
  "verified": false,
  "withReplies": false,
  "includeCompanyInfo": true,
  "splitFilters": true,
  "companySort": "most_relevant",
  "claimedOnly": false,
  "maxCompaniesPerCategory": 20,
  "onlyNewReviews": false
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

4. From Python

# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/trustpilot-reviews").call(run_input={
    "companies": ["www.bestbuy.com"],
    "maxReviewsPerCompany": 20,
    "sort": "recency",
    "verified": False,
    "withReplies": False,
    "includeCompanyInfo": True,
    "splitFilters": True,
    "companySort": "most_relevant",
    "claimedOnly": False,
    "maxCompaniesPerCategory": 20,
    "onlyNewReviews": False,
})
items = client.dataset(run.default_dataset_id).list_items().items  # apify-client 3.x returns a Run object
print(items)

5. In n8n

Paste this node into a workflow (it is a plain HTTP Request node). Create a Header Auth credential with name Authorization and value Bearer <APIFY_TOKEN>; each returned row becomes one item for the next node.

{
  "nodes": [
    {
      "name": "Run arcadium/trustpilot-reviews",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~trustpilot-reviews/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"companies\":[\"www.bestbuy.com\"],\"maxReviewsPerCompany\":20,\"sort\":\"recency\",\"verified\":false,\"withReplies\":false,\"includeCompanyInfo\":true,\"splitFilters\":true,\"companySort\":\"most_relevant\",\"claimedOnly\":false,\"maxCompaniesPerCategory\":20,\"onlyNewReviews\":false}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}

Ready-made workflow to import:

• Weekly Trustpilot reviews of competitors to Google Sheets New and changed reviews of a competitor list every Monday, a weekly TrustScore snapshot per company and a weekly digest row.

6. From an AI assistant (MCP)

Add Apify’s MCP server to Claude Desktop, Cursor, VS Code or any MCP client, limited to this actor:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/trustpilot-reviews",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}

More on the MCP page.

7. From LangChain

# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/trustpilot-reviews")
rows = tool.invoke({"run_input": {
    "companies": ["www.bestbuy.com"],
    "maxReviewsPerCompany": 20,
    "sort": "recency",
    "verified": False,
    "withReplies": False,
    "includeCompanyInfo": True,
    "splitFilters": True,
    "companySort": "most_relevant",
    "claimedOnly": False,
    "maxCompaniesPerCategory": 20,
    "onlyNewReviews": False,
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])

Sample output

One row, from the example in the actor’s README. Long text is shortened and lists are cut to two items.

{
  "rowType": "company",
  "source": "category",
  "businessUnitId": "5d2f4f05dc509f00010c9b44",
  "entityId": "itrustcapital.com",
  "name": "iTrustCapital",
  "url": "https://www.trustpilot.com/review/itrustcapital.com",
  "trustScore": 5,
  "stars": 5,
  "reviewCount": 9946,
  "primaryCategory": "Software Company",
  "country": "US",
  "hasConsumerAlert": true,
  "alertType": "InfoHighRiskInvestment",
  "listingCategory": "electronics_technology",
  "listingCountry": "US",
  "listingRank": 1
}

Tutorials

• Track competitor Trustpilot reviews weekly in Google Sheets with n8n

Schedule it

In Apify Console, create a Task from your input and add a Schedule (for example daily). Each run writes a new dataset; integrations can push the rows to Google Sheets, Slack, a webhook or your database.