Yelp Reviews Scraper

Export public Yelp reviews for known business URLs, aliases or dedicated business IDs. Retain review text and ratings alongside business context, source dates, media, owner replies and available public reviewer metadata. Reviewer profile URLs are redacted and extended reviewer metrics are disabled by default.

Quick start

Choose a known business and a small per-business review cap. The existing example combines two businesses with rating, language and service-text filters:

{
  "startUrls": [
    { "url": "https://www.yelp.com/biz/gary-danko-san-francisco" },
    { "url": "bottega-louie-los-angeles" }
  ],
  "maxReviewsPerBusiness": 10,
  "sort": "DATE_DESC",
  "ratings": ["5", "4"],
  "language": "en",
  "reviewSearch": "service",
  "redactReviewerProfileUrls": true,
  "includeReviewerDetails": false
}

Prefer a video guide? This short Apify walkthrough shows you how to set up and run an Actor. It uses a different Actor, but the setup steps are the same.

Ready-to-run examples

Open these published examples from the Actor's Examples tab:

• Monitor the newest Yelp reviews for a business
• Find 1-star and 2-star Yelp reviews for a business
• Collect 5-star Yelp reviews for customer research
• Search Yelp reviews for service feedback
• Export English-language Yelp reviews to CSV or JSON
• Compare Yelp reviews across two businesses
• Build a Yelp reviews dataset for AI or RAG

What you get

One saved review row with stable review and business identity, source links, text, rating and available UTC/local dates. Rows can include owner replies, media, reactions and business-level rating distributions. Optional reviewer enrichment stays null unless enabled, and source totals do not establish that your bounded export covers every review.

Who is it for?

• Reputation-management agencies tracking review changes for client portfolios.
• Local chains monitoring customer feedback across branches.
• Market researchers comparing public sentiment across competitors.
• Data teams enriching dashboards, alerts, BI reports, and AI review-analysis workflows.

How you can use the data

• Prepare a recent-feedback review sheet. Use the Gary Danko URL with newest-first order and a small cap. Keep reviewId, reviewUrl, rating, text and collection time, then compare saved IDs in your own destination rather than assuming a run is a complete change feed.
• Compare low-rating samples across two venues. Use the existing Gary Danko and Bottega Louie aliases with one- and two-star filters. Keep businessAlias and source URL beside each review, and report sample size rather than treating filtered snippets as representative of all customers.
• Inspect a service topic with its replies. Apply the existing service keyword and English-language settings, retaining ownerReply, rating and available dates. Summaries and sentiment labels are a separate analysis step, not fields the Actor verifies.
• Build a privacy-conscious export. Keep reviewer profile URLs redacted and extended details off when text and business context suffice. Retain only the fields needed for your review process, without treating a public reviewer name as verified identity or contact permission.

Input settings

Field	Type	Description
startUrls	array	Yelp business URLs or Yelp aliases. Bare aliases are not treated as opaque IDs.
businessUrls	array	Additional Yelp business URLs or aliases.
businessIds	array	Optional dedicated field for opaque Yelp business IDs.
changedSince	string	Experimental ISO date/time cutoff applied when Yelp provides a parseable UTC timestamp.
maxReviewsPerBusiness	integer	Maximum unique reviews to save for each business.
sort	string	DATE_DESC, RELEVANCE_DESC, RATING_DESC, or RATING_ASC.
ratings	array	Optional list of star ratings to include, such as ["5", "4"].
language	string	Language code sent to Yelp. Omitted or blank uses en.
reviewSearch	string	Optional keyword or phrase to search inside reviews.
redactReviewerProfileUrls	boolean	Omit direct reviewer profile URLs by default for privacy-conscious exports.
includeReviewerDetails	boolean	Include public reviewer review/friend/photo counts, Elite year, and profile photo. Disabled by default.

Input options

Monitor newest reviews for one business

{
  "startUrls": [{ "url": "https://www.yelp.com/biz/gary-danko-san-francisco" }],
  "maxReviewsPerBusiness": 20,
  "sort": "DATE_DESC",
  "redactReviewerProfileUrls": true
}

Find low-rating feedback for a portfolio

{
  "startUrls": [
    { "url": "gary-danko-san-francisco" },
    { "url": "bottega-louie-los-angeles" }
  ],
  "maxReviewsPerBusiness": 50,
  "sort": "DATE_DESC",
  "ratings": ["1", "2"],
  "redactReviewerProfileUrls": true
}

Search reviews for a service topic

{
  "startUrls": [{ "url": "https://www.yelp.com/biz/gary-danko-san-francisco" }],
  "maxReviewsPerBusiness": 25,
  "reviewSearch": "service",
  "language": "en"
}

Output data

The existing shortened row illustrates historical source values rather than a fresh review lookup:

{
  "reviewId": "7byQK-mVsJdfCCb4MhzHYQ",
  "reviewPosition": 1,
  "reviewUrl": "https://www.yelp.com/biz/gary-danko-san-francisco?hrid=7byQK-mVsJdfCCb4MhzHYQ",
  "businessId": "WavvLdfdP6g8aZTtbBQHTw",
  "businessAlias": "gary-danko-san-francisco",
  "businessName": "Gary Danko",
  "businessUrl": "https://www.yelp.com/biz/gary-danko-san-francisco",
  "businessRating": 4.5,
  "businessReviewCount": 6133,
  "rating": 4,
  "text": "I've been to Gary Danko a few times over the years, and it continues to be one of San Francisco's classic fine dining restaurants.",
  "language": "en",
  "createdAt": "2026-07-31T05:31:29Z",
  "createdAtLocal": "2026-07-30T22:31:29-07:00",
  "reviewerName": "Johnny Z.",
  "reviewerProfileUrl": null,
  "reviewerReviewCount": null,
  "photos": [
    {
      "id": "KK2AtWft-qNmtAqAFZjgug",
      "url": "https://s3-media0.fl.yelpcdn.com/bphoto/KK2AtWft-qNmtAqAFZjgug/348s.jpg",
      "caption": null
    }
  ],
  "videos": [],
  "mediaCount": 10,
  "feedback": { "useful": 0, "funny": 0, "cool": 0 },
  "contentReactions": { "helpful": 0, "thanks": 0, "loveThis": 0, "ohNo": 0 },
  "ownerReply": null,
  "sourceUrl": "https://www.yelp.com/biz/gary-danko-san-francisco",
  "collectedAt": "2026-07-31T00:00:00.000Z"
}

This example is shortened for readability. Reviewer enrichment fields remain null unless includeReviewerDetails is enabled.

Field group	Fields	Description
Review	reviewId, reviewPosition, reviewUrl, rating, text, language, createdAt, createdAtLocal	Stable review identity, clean text, rating, language, and UTC/local dates.
Business	businessId, businessAlias, businessName, businessUrl, businessRating, businessReviewCount, businessPrimaryPhotoUrl	Business identity and aggregate context attached to each review.
Business distributions	businessReviewCountsByRating, businessReviewCountsByLanguage	Rating and language totals supplied by Yelp.
Reviewer	reviewerId, reviewerName, reviewerLocation, reviewerProfileUrl	Public reviewer identity fields. Profile URLs are redacted by default.
Optional reviewer enrichment	reviewerReviewCount, reviewerFriendCount, reviewerPhotoCount, reviewerEliteYear, reviewerProfilePhotoUrl	Public profile metrics returned only when includeReviewerDetails is enabled.
Owner reply	ownerReplyText, ownerReplyCreatedAt, ownerReply	Backward-compatible reply fields plus structured reply ID, author, role, and image.
Media	photos, videos, mediaCount	Attached review photos/videos with public URLs, captions, thumbnails, and counts.
Engagement	feedback, contentReactions, reactions	Useful/funny/cool and Helpful/Thanks/Love this/Oh no counts.
Visit metadata	checkInCount, isFirstReviewer, isReviewOfTheDay, hasPaidThroughYelp, hasPurchasedDeal, hasPlacedReservation, reservationProvider, previousReviewCount, experienceDate	Public context Yelp associates with the review or visit.
Provenance	sourceUrl, collectedAt	Input/resolved business URL and collection timestamp.

Download your results

Open your run's dataset, choose Export and select JSON, CSV or Excel. JSON keeps nested fields together. CSV and Excel are useful when you want to review the records in a spreadsheet.

See Apify's download and export guide for the available formats and field options.

Pricing

A run that begins extraction has a one-time start event. Each saved review has an item event. A started run can incur its start charge even when no reviews match.

Keep the per-business review cap and number of businesses small. The Actor stops when its remaining review-event budget is exhausted. Rating or keyword filters can reduce the matching sample without guaranteeing the requested count.

See the live Pricing tab for current rates. Platform usage can also apply.

Tips for best results

• Use the right identity field. Put URLs and aliases in startUrls or businessUrls, and opaque IDs in businessIds.
• Keep source dates beside the text. Older reviews can expose only local date text, so the experimental changedSince cutoff only applies when a UTC timestamp can be parsed.
• Start with privacy defaults. Enable extended reviewer metrics only when your purpose and retention policy require them.
• Keep portfolio batches manageable. Save business identity with each row and inspect failure details before retrying a smaller target list.
• Distinguish a filtered sample from complete coverage. Tight rating, language or keyword filters can return fewer reviews than requested.

Limits and caveats

• Yelp may show fewer reviews than requested for businesses with limited public review history or tight filters.
• Some old review dates are only available as Yelp-displayed local date text.
• Extended reviewer profile metrics are disabled by default. Enable them only when your use case and retention policy require that personal data.
• Multi-business runs retain useful rows after an isolated business failure and save failure details to key-value records. Essential dataset, billing or checkpoint-storage failures still fail the run.
• Restarting or resurrecting the same run does not resume extraction: existing execution, checkpoint or start-charge state stops further work, preserving saved rows and charges. This is not an exactly-once guarantee; a new run is a separate billable operation. The processing deadline uses the original run start time.
• Responses have a 4 MiB decompressed safety bound. Oversized responses remain extraction failures, not empty results. Large-run summaries keep bounded samples with omitted counts; pending-target previews include the original INPUT index/count rather than duplicating every target.
• For large portfolios, run smaller batches on a schedule to simplify monitoring and retries.
• If a run returns no items, inspect RUN_SUMMARY for no-match, unavailable-target, charge-budget or failure details before retrying a known public business URL with a low review limit.
• Yelp-confirmed unavailable aliases or business IDs are skipped with status: "unavailable", a per-target reason and an unavailableTargets count in RUN_SUMMARY. A challenge-free regional Yelp page-not-found response or Yelp's explicit unknown-business-ID response is required; this does not establish deletion, closure or privacy. Mixed batches keep useful review rows. Unavailable targets add no review charges; the existing run startup charge still applies.
• Generic HTTP errors, challenges, missing identity and unexpected review payloads remain failures, not unavailable-target success. Essential storage and billing failures still fail the run.

The experimental changedSince cutoff is applied only when Yelp supplies a parseable UTC timestamp. Local date text is retained when available but is not silently converted into a reliable cutoff timestamp. Reviewer profile URLs stay redacted by default, and extended reviewer metrics remain null unless explicitly enabled.

Business-level ratings and distributions are aggregate context, not statistics calculated from your saved sample. Public replies, media and reactions can be missing or change. Cross-run comparisons, alerts and sentiment analysis belong in your own workflow.

Automation and integrations

Save your settings for next time

Once you have an input that works for you, save it as an Apify task. This keeps your settings together so you can use them again without filling in the form from scratch.

Run on a schedule

Need fresh results regularly? You can schedule the Actor or a saved task to run at a time that suits you.

Each scheduled run collects the data again. If you want to compare results or highlight changes, add that step in your own workflow.

Send results to your other tools

Use n8n, Make or Zapier to connect this scraper to the rest of your workflow. For example, you could pass its results to an AI assistant or add records to a spreadsheet. Follow the Apify integration guides for your chosen tool.

For a custom connection, an Apify webhook can notify your app when a run finishes or fails. Your app can then fetch the results or handle the failure.

API usage

Want to collect this data from your own app or automation? The examples below send input to the scraper and show how to collect its results.

The Node.js and Python examples wait for the run to finish before reading its results. The cURL example starts the run and returns its details. Wait for that run to finish, then use its defaultDatasetId to fetch the results.

For setup instructions, see Apify's JavaScript client guide, Python client guide and Actor runs API reference.

cURL

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~yelp-reviews-scraper/runs?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{"startUrls":[{"url":"https://www.yelp.com/biz/gary-danko-san-francisco"}],"maxReviewsPerBusiness":10}'

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/yelp-reviews-scraper').call({
  startUrls: [{ url: 'https://www.yelp.com/biz/gary-danko-san-francisco' }],
  maxReviewsPerBusiness: 10,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

from apify_client import ApifyClient
import os

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/yelp-reviews-scraper').call(run_input={
    'startUrls': [{'url': 'https://www.yelp.com/biz/gary-danko-san-francisco'}],
    'maxReviewsPerBusiness': 10,
})
items = client.dataset(run['defaultDatasetId']).list_items().items
print(items)

MCP and AI agents

You can ask your AI assistant to collect data with Yelp Reviews Scraper and help you work with the results. The scraper returns the data, and your assistant can then summarize it, compare records or prepare notes.

Connect your assistant through the Apify MCP server. MCP gives your assistant a way to find this scraper, run it and read the results. Follow the official setup and sign-in guide for your app.

Easier setup with an Apify plugin

If you use a supported coding assistant, the official Apify plugin can set up the connection for you. It connects your assistant to Apify and includes instructions for common tasks. Follow the guide for your particular app.

Use Apify AI in Console

Prefer to keep everything in Apify? Open Apify AI in Console and ask it to find Fetch Cat's Yelp Reviews Scraper. You can describe the data you want and ask questions about the results.

Apify AI is currently in beta. It asks for confirmation before starting an Actor, and normal run charges apply.

Choose your assistant

• Claude and Claude Code (Anthropic): Apify setup guide.
• ChatGPT and Codex (OpenAI): Apify setup guides.
• Grok Bot (xAI): Apify plugin guide.
• Gemini CLI (Google): MCP server setup guide. Use the server URL below with the HTTP configuration supported by Gemini CLI.

Connect just this scraper

To keep your setup focused on this scraper, add this server configuration to a compatible MCP client:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com?tools=fetch_cat/yelp-reviews-scraper"
    }
  }
}

Sign in to Apify when your client asks. If your client uses a Bearer token instead, follow its setup guide and keep the token in its secure configuration. Configuration formats vary by app, so use the linked guide if your client needs a different format.

Use Apify's general server

Already connected to the general Apify MCP server at https://mcp.apify.com? You can use it to find this scraper without adding another server. Ask your assistant to use Yelp Reviews Scraper by Fetch Cat, or give it the identifier fetch_cat/yelp-reviews-scraper.

Your assistant can check the available settings and retrieve the results from the returned run. If the run is still working, it should follow that run rather than start it again. Both connection options use Apify's hosted service.

Example requests

• “Use Fetch Cat’s Yelp Reviews Scraper to collect up to 5 newest reviews for gary-danko-san-francisco. Keep review IDs, ratings, text and source dates, with reviewer profile URLs redacted and extended reviewer details off.”
• “Collect up to 5 one- or two-star reviews per business for gary-danko-san-francisco and bottega-louie-los-angeles. Keep each business alias beside its reviews without treating the filtered samples as all customer feedback.”
• “Retrieve up to 5 English-language reviews containing service for the Gary Danko Yelp business URL. Keep available owner replies and source links, with reviewer profile URLs redacted and extended details off.”

Normal Apify charges apply. Check the live Pricing tab before asking your assistant to run a larger batch.

Pay with x402

If your AI agent has a funded wallet, it can pay for Apify usage through x402. This experimental option lets an agent use Apify without a conventional account.

Apify AGI provides a prepaid API token with a spending cap after payment. Your agent can use that token to run this scraper through the Apify API or an authenticated MCP connection.

Some MCP setups also support paying for each request directly. That is a different payment option, so check the official guide for its availability.

Follow the official guide for wallet setup, supported payment options and current terms. Apify handles the payments. Keep wallet keys and payment tokens in your agent's secure configuration.

Other ways for agents to pay

Apify also supports MPP for prepaid access through AGI and Skyfire for eligible Actors. If your agent already uses one of these, see the agentic payment guide to choose the right setup.

FAQ

Can I scrape multiple Yelp businesses in one run?

Yes. Add multiple business URLs or aliases to startUrls. The actor keeps each output item tied to its source business.

Can I collect only one-star reviews?

Yes. Set ratings to ["1"] and choose the sort order that fits your workflow.

Does the actor return owner replies?

Yes, public owner reply text and reply timestamps are returned when Yelp provides them.

Can I use the output in dashboards or alerts?

Yes. Export the dataset as JSON, CSV, Excel, or consume it through the Apify API.

Does the actor include private Yelp data?

No. It extracts publicly available review data from business pages.

Related Actors

• Google Maps Reviews Scraper — Collect public Google Maps review text, ratings and available reviewer fields.
• Yandex Maps Reviews Scraper — Collect available public Yandex Maps review text, ratings and business references.
• Tripadvisor Reviews Scraper — Collect available public Tripadvisor review text, ratings and visit details.
• Trustpilot Reviews Scraper — Collect public Trustpilot business reviews, ratings and available company details.
• Facebook Reviews Scraper — Collect available public Facebook business recommendations and review text.

Support and feedback

For custom additions or integrations, email hello@fetchcat.dev.

Found an error or have a feature request? Open an issue with the run URL, a non-sensitive input example and what you expected.

Need help sharing it? See Apify’s guide to sharing a run when reporting an issue. Sharing a run can give the developer access to its input, logs and results, so use a run that contains no sensitive data. Please leave out API tokens, passwords, cookies, private documents and personal data. Use a public sample URL or a redacted example instead.

Tried this scraper? We'd love to hear how it worked for you. Leave a review to help others choose the right tool and help us improve it. Your feedback means a lot to us.

Made with care for curious people, AI agents and the possibilities in data. ♥