How to use Review Monitor

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

$12.00 per 1,000 entities checked (live Apify price on 2026-10-31).

1. In the Apify Store (no code)

1. Open arcadium/reputation-monitor 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:

{
  "startUrls": [
    "https://www.booking.com/hotel/gb/the-savoy.html",
    "https://www.yelp.com/biz/katzs-delicatessen-new-york",
    "https://apps.apple.com/us/app/slack/id618783545"
  ],
  "maxReviewsPerEntity": 50
}

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~reputation-monitor/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrls":["https://www.booking.com/hotel/gb/the-savoy.html","https://www.yelp.com/biz/katzs-delicatessen-new-york","https://apps.apple.com/us/app/slack/id618783545"],"maxReviewsPerEntity":50}'

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/reputation-monitor').call({
  "startUrls": [
    "https://www.booking.com/hotel/gb/the-savoy.html",
    "https://www.yelp.com/biz/katzs-delicatessen-new-york",
    "https://apps.apple.com/us/app/slack/id618783545"
  ],
  "maxReviewsPerEntity": 50
});
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/reputation-monitor").call(run_input={
    "startUrls": ["https://www.booking.com/hotel/gb/the-savoy.html", "https://www.yelp.com/biz/katzs-delicatessen-new-york", "https://apps.apple.com/us/app/slack/id618783545"],
    "maxReviewsPerEntity": 50,
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
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/reputation-monitor",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~reputation-monitor/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"startUrls\":[\"https://www.booking.com/hotel/gb/the-savoy.html\",\"https://www.yelp.com/biz/katzs-delicatessen-new-york\",\"https://apps.apple.com/us/app/slack/id618783545\"],\"maxReviewsPerEntity\":50}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}

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/reputation-monitor",
      "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/reputation-monitor")
rows = tool.invoke({"run_input": {
    "startUrls": ["https://www.booking.com/hotel/gb/the-savoy.html", "https://www.yelp.com/biz/katzs-delicatessen-new-york", "https://apps.apple.com/us/app/slack/id618783545"],
    "maxReviewsPerEntity": 50,
}})
# 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": "summary",
  "status": "changed",
  "platform": "appstore",
  "entityId": "618783545-us",
  "input": "https://apps.apple.com/us/app/slack/id618783545",
  "name": "Slack",
  "url": "https://apps.apple.com/us/app/slack/id618783545",
  "checkedAt": "2026-10-21T22:59:28.653Z",
  "previousCheckAt": "2026-10-21T22:54:50.343Z",
  "rating": 4.14,
  "previousRating": 4.14,
  "ratingChange": 0,
  "reviewCount": 54143,
  "previousReviewCount": 54115,
  "reviewCountChange": 28,
  "newReviews": 6,
  "editedReviews": 0,
  "newNegativeReviews": 1,
  "newReviewsAvgRating": 4.5,
  "avgRating30d": 3.29,
  "previousAvgRating30d": 3.29,
  "reviews7d": 55,
  "reviews30d": 100,
  "reviewsPerWeek": 23.3,
  "avgRatingLifetime": 3.29,
  "lifetimeSource": "sample",
  "rating30dVsLifetime": 0,
  "ownerReplyRate90d": 0.05,
  "medianReplyLagDays": 0,
  "unansweredNegative30d": 35,
  "appVersions": [],
  "versionRegression": null,
  "windowComplete30d": false,
  "historyReviews": 100,
  "historySince": "2026-10-08",
  "stopReason": "onlyNew",
  "flags": [
    "new_negative",
    "unanswered_negative"
  ]
}

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.