# Trustpilot Review Monitor — New Reviews & Rating Drops (`outstanding_vegetable/trustpilot-review-monitor`) Actor

Watch companies on Trustpilot and get only NEW reviews since the last run (filter by star rating, e.g. 1-2 stars only) plus alerts when a TrustScore drops, with reviewer, date, verified flag and company reply. Daily schedule. No login. MCP-ready. $20 per 1,000 alerts.

- **URL**: https://apify.com/outstanding\_vegetable/trustpilot-review-monitor.md
- **Developed by:** [Peter Skotte](https://apify.com/outstanding_vegetable) (community)
- **Categories:** Lead generation, Automation, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $20.00 / 1,000 alerts

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Trustpilot Review Monitor — new reviews & rating-drop alerts

Watch any list of companies on Trustpilot and get **only what changed**: new reviews (optionally
just the 1–2 star ones) and drops in a company's TrustScore. The actor remembers every review it
has already reported, so a daily schedule produces a clean alert feed instead of a re-scrape.
Reputation-monitoring SaaS charges a monthly subscription for this; here you pay per alert.

### Use cases

- **Brand reputation**: get every new 1–2 star review about your company the morning it appears, with the full text, so support can reply before it spreads.
- **Agencies**: one monitor per client (`monitorId`), each with its own webhook into the client's Slack channel.
- **Competitor intelligence**: watch competitors' review streams and TrustScores; a `rating_drop` alert is an early signal of a service problem.
- **Due diligence / vendor risk**: keep a running log of complaints about suppliers, marketplaces or fintechs.

### How it works

1. Loads each company's Trustpilot review page (newest first) in a real browser.
2. Compares the reviews against the monitor's memory. Unseen reviews inside your star and language filters are emitted as `new_review`.
3. Compares the company's current TrustScore with the one stored from the previous run. A fall of at least `ratingDropThreshold` is emitted as `rating_drop` with `previousTrustScore`.
4. Saves the updated memory (up to 50,000 review IDs per monitor) and POSTs a summary to your webhook.

Memory lives in a named key-value store `trustpilot-monitor-<hash of monitorId>`, so it survives
across runs and tasks. Delete that store to reset a monitor.

### Input

| Field | Default | Notes |
|---|---|---|
| `companies` | `["amazon.com","tesla.com"]` | Company websites as shown in Trustpilot URLs |
| `minStarsToAlert` / `maxStarsToAlert` | `1` / `5` | Star band for `new_review` alerts. `1`/`2` = negative reviews only |
| `languages` | `["en"]` | Trustpilot language codes; `all` for every language |
| `maxNewPerCompany` | `5` | Cap on new-review alerts per company per run; the rest come out next run |
| `ratingDropThreshold` | `0.1` | Minimum TrustScore fall (1.0–5.0 scale) that triggers `rating_drop` |
| `firstRunMode` | `emitAll` | `emitAll` reports the newest reviews on the first run; `baseline` records them silently |
| `webhookUrl` | `""` | Optional POST target for the run summary |
| `monitorId` | `default` | One memory per ID — run several watchlists side by side |
| `maxItems` | `10` | Overall cap on alerts per run |
| `proxyConfiguration` | Apify residential (US) | Required, see "Why a browser" below |

### Recommended setup for a daily feed

1. Create a task with your `companies`, star band and a meaningful `monitorId` (`acme-negative`).
2. **First run: set `firstRunMode` to `baseline`.** It records the current reviews and TrustScores and emits nothing, so day one is not a backlog of old reviews.
3. Set `maxNewPerCompany` and `maxItems` high enough for a busy day (e.g. 50 / 500) and **schedule the task daily**. Companies with hundreds of reviews per day (large marketplaces) may deserve every 6 hours; the actor reads at most 100 newest reviews per company and language per run.
4. Point `webhookUrl` at Slack (incoming webhook), Zapier, Make or your own endpoint.

The default settings (`{}`) run in `emitAll` mode so you see real reviews on the first try.

### Example: negative-review alerts for two brands

```json
{
  "companies": ["revolut.com", "wise.com"],
  "minStarsToAlert": 1,
  "maxStarsToAlert": 2,
  "languages": ["en", "de"],
  "maxNewPerCompany": 50,
  "maxItems": 500,
  "ratingDropThreshold": 0.1,
  "firstRunMode": "baseline",
  "webhookUrl": "https://hooks.slack.com/services/XXX/YYY/ZZZ",
  "monitorId": "fintech-negative"
}
```

### Output

One record per alert. `changeType` is `new_review` or `rating_drop`.

```json
{
  "company": "amazon.com",
  "companyName": "Amazon",
  "trustScore": 1.6,
  "reviewCount": 49213,
  "reviewId": "68d9573bf1ddb15da6e70cbc",
  "rating": 1,
  "title": "Package never arrived",
  "text": "Ordered on the 20th, tracking said delivered but …",
  "reviewerName": "Jane D",
  "reviewerCountry": "US",
  "reviewDate": "2026-09-28T17:10:10.000Z",
  "experienceDate": "2026-09-27T00:00:00.000Z",
  "isVerified": false,
  "replyText": null,
  "replyDate": null,
  "reviewUrl": "https://www.trustpilot.com/reviews/68d9573bf1ddb15da6e70cbc",
  "changeType": "new_review",
  "previousTrustScore": null,
  "firstSeenAt": "2026-09-28T19:02:11.412Z",
  "monitorId": "default"
}
```

A `rating_drop` record carries the company fields, `trustScore` (current), `previousTrustScore` and
`reviewUrl` pointing at the company profile; the review fields are `null`.

### Webhook payload

POSTed once per run as `application/json`, also saved as `SUMMARY` in the run's key-value store:

```json
{
  "monitorId": "fintech-negative",
  "runAt": "2026-09-29T06:00:03.118Z",
  "newReviewCount": 7,
  "ratingDropCount": 1,
  "recorded": 0,
  "seenTotal": 412,
  "baseline": false,
  "companies": { "revolut.com": { "trustScore": 4.2, "reviewCount": 210331, "at": "2026-09-29T06:00:03.118Z" } },
  "records": [ { "...first 50 records, same shape as the dataset..." } ]
}
```

### Why a browser (and 4 GB of memory)

Trustpilot gates its pages behind a "Verifying Connection" check that blocks plain HTTP clients and
headless browsers. This actor runs a real, headed Chromium on an Apify **residential** proxy, which
passes the check. Run it with **4096 MB** of memory; a browser session is why the start fee is a
little higher than for a plain API monitor.

### Pricing

Pay per event: a small start fee plus a fee **per alert emitted**. A daily run that finds nothing new
costs only the start fee.

### Notes

- Reviews outside the star band are remembered too, so widening the band later does not replay old reviews.
- Companies without a Trustpilot profile log a warning and are skipped.
- The first run without state is the only one where `firstRunMode` matters.

# Actor input Schema

## `companies` (type: `array`):

Company websites as they appear in Trustpilot URLs, e.g. amazon.com, revolut.com, booking.com.

## `minStarsToAlert` (type: `integer`):

Only alert on reviews with at least this many stars. Set 1..2 (with max 2) to get negative reviews only.

## `maxStarsToAlert` (type: `integer`):

Only alert on reviews with at most this many stars.

## `languages` (type: `array`):

Trustpilot language codes to watch, e.g. en, de, fr, es. Use "all" for every language.

## `maxNewPerCompany` (type: `integer`):

Cap on new-review alerts per company in one run. Newest first; reviews beyond the cap stay unseen and are emitted next run.

## `ratingDropThreshold` (type: `number`):

Emit a rating\_drop alert when a company's TrustScore falls by at least this much since the previous run (TrustScore is 1.0-5.0).

## `firstRunMode` (type: `string`):

What to do when the monitor has no saved state yet. emitAll: report the newest reviews as new (good for a first test). baseline: silently record the current reviews and TrustScores and emit nothing, so the next scheduled run reports only what changed since.

## `webhookUrl` (type: `string`):

Optional. After each run a JSON summary {monitorId, runAt, newReviewCount, ratingDropCount, companies, records\[first 50]} is POSTed here (Slack/Zapier/Make/your API).

## `monitorId` (type: `string`):

Name of this watchlist. Each ID keeps its own memory of seen reviews and TrustScores, so you can run several monitors side by side.

## `maxItems` (type: `integer`):

Overall cap on records (new reviews + rating drops) emitted in one run.

## `proxyConfiguration` (type: `object`):

Residential proxy is required to pass Trustpilot's connection check.

## Actor input object example

```json
{
  "companies": [
    "amazon.com",
    "tesla.com"
  ],
  "minStarsToAlert": 1,
  "maxStarsToAlert": 5,
  "languages": [
    "en"
  ],
  "maxNewPerCompany": 5,
  "ratingDropThreshold": 0.1,
  "firstRunMode": "emitAll",
  "webhookUrl": "",
  "monitorId": "default",
  "maxItems": 10,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ],
    "apifyProxyCountry": "US"
  }
}
```

# Actor output Schema

## `records` (type: `string`):

Dataset of new-review and rating-drop alerts found in this run (JSON).

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "companies": [
        "amazon.com",
        "tesla.com"
    ],
    "languages": [
        "en"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("outstanding_vegetable/trustpilot-review-monitor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "companies": [
        "amazon.com",
        "tesla.com",
    ],
    "languages": ["en"],
}

# Run the Actor and wait for it to finish
run = client.actor("outstanding_vegetable/trustpilot-review-monitor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "companies": [
    "amazon.com",
    "tesla.com"
  ],
  "languages": [
    "en"
  ]
}' |
apify call outstanding_vegetable/trustpilot-review-monitor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,outstanding_vegetable/trustpilot-review-monitor"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/gIE3aORO2JNHNX3s5/builds/2rLI1TjeulWx6GbeW/openapi.json