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Export Yelp reviews for a list of businesses

Franchise owners, agencies and analysts often need the reviews of dozens of locations in one table, and Yelp’s website has no export.

arcadium/yelp-reviews reads the public reviews of each business you list, newest first, with the full text and the owner’s reply. Reviewer names and photos are never returned.

Cost of this example on the Apify Free plan: 500 × $0.0003 = $0.15 ($0.115 on the Apify Gold plan and higher), plus $0.001 per GB of memory when the run starts.

Open Yelp Reviews Scraper on the Apify StoreActor overview

What you need

Step 1: Run it in the Apify Store (no code)

  1. Open arcadium/yelp-reviews on the Apify Store and sign in (a free Apify account works).
  2. Paste the business links into Business URLs, one per line.
  3. Set Max reviews per business, for example 50.
  4. Turn on Include business summary row for one free row per business with its overall rating and review count.
  5. Optional: set a maximum cost per run in the run options as a hard cap.
  6. Click Start. Rows appear in the Output tab; export them as JSON, CSV or Excel.

The same input as JSON (paste it in the JSON tab of the input form):

{
  "startUrls": [
    {
      "url": "https://www.yelp.com/biz/tartine-bakery-san-francisco-3"
    }
  ],
  "maxReviewsPerBusiness": 50,
  "includeBusinessSummary": true
}

Step 2: Call it over the Apify API

One HTTP call runs the actor and returns its rows. It suits runs up to about five minutes; for longer runs start the run asynchronously and read the dataset when it finishes.

curl -X POST "https://api.apify.com/v2/acts/arcadium~yelp-reviews/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"startUrls":[{"url":"https://www.yelp.com/biz/tartine-bakery-san-francisco-3"}],"maxReviewsPerBusiness":50,"includeBusinessSummary":true}'

Step 3: Run it from Python

With apify-client 3.x, call() returns a Run object: read the dataset id as run.default_dataset_id (older examples use run["defaultDatasetId"], which no longer works).

# pip install "apify-client>=3"
import csv, os

from apify_client import ApifyClient

with open("businesses.csv") as f:  # one column named url
    urls = [{"url": r["url"]} for r in csv.DictReader(f) if r["url"].strip()]

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/yelp-reviews").call(run_input={
    "startUrls": urls, "maxReviewsPerBusiness": 50, "includeBusinessSummary": True,
})
rows = list(client.dataset(run.default_dataset_id).iterate_items())  # apify-client 3.x

keys = ["productName", "entityId", "date", "ratingOverall", "text", "vendorResponse", "language", "reviewUrl"]
with open("yelp-reviews.csv", "w", newline="") as f:
    w = csv.DictWriter(f, fieldnames=keys, extrasaction="ignore")
    w.writeheader()
    w.writerows(r for r in rows if r.get("rowType") == "review")
for e in (r for r in rows if r.get("rowType") == "error"):
    print("could not read:", e.get("input"), e.get("code"))

Step 4: Run it 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/yelp-reviews').call({
  "startUrls": [
    {
      "url": "https://www.yelp.com/biz/tartine-bakery-san-francisco-3"
    }
  ],
  "maxReviewsPerBusiness": 50,
  "includeBusinessSummary": true
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Step 5: Ask an AI assistant (MCP)

Apify’s hosted MCP server turns the actor into one tool. Add it to Claude, Cursor, VS Code or any MCP client:

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

Then ask, for example:

Use arcadium/yelp-reviews to read the 30 newest reviews of https://www.yelp.com/biz/tartine-bakery-san-francisco-3 and summarise what people praise and what they complain about.

More clients and the local setup on the MCP page.

Only new reviews next time

Add "onlyNewReviews": true and a stateKey (for example franchise-west) and run the same list weekly: reviews already delivered under that key are skipped and not charged.

Output fields

Every row has a rowType; filter on it first. Failed inputs come back as free rows with a code and message. The fields this tutorial uses, from the actor’s dataset schema:

FieldWhat it holds
rowTypeKind of row. Filter on this first. Values: 'review': one review (charged). 'business-summary': one row per business with its overall rating and review count (free). 'coverage': stated total vs delivered and why reading stopped (free). 'error': an input could not be read; see code and message (free).
productNameName of the reviewed hotel, business, product or app.
entityIdThe site's own id of the reviewed entity (place id, product id, app id).
reviewIdStable review id in the form '<Platform>___<native id>'. Use it to deduplicate between runs.
reviewUrlLink to the single review, null when the site has none.
dateDate the review was written, ISO format yyyy-mm-dd. Null if unknown.
ratingOverallOverall rating on a 0 to 5 scale (Booking: its 1-10 score divided by 2, original in extras.score10). Null when missing.
textReview text in the language it was written in. Null when the review has no text.
languageLanguage of the review text as a two-letter code, null if unknown.
vendorResponseReply of the owner, vendor, developer or employer, null when there is none.

All fields: Yelp Reviews Scraper guide.

Cost per run

Pay per event: you pay for the rows delivered, at the actor’s live Apify price on 2026-11-01. Error, coverage and summary rows are free.

WhatRowsPrice per row (Free plan)Subtotal
Review: 10 businesses x 50 reviews500$0.0003$0.15

Cost of this example on the Apify Free plan: 500 × $0.0003 = $0.15 ($0.115 on the Apify Gold plan and higher), plus $0.001 per GB of memory when the run starts. Higher Apify plans pay less per row. The Store page always has the current price.

Limitations

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