Amazon Reviews Scraper

Amazon Reviews Scraper exports public written reviews for amazon.com products from an ASIN or US product URL. Get review text, ratings, dates and available media as structured data for product research and repeatable exports. Coverage requires an exact ASIN match to a public Woot offer. The Actor checks the current catalog and can also recover some archived offers. Archived discovery is bounded and does not cover every product. Some products return fewer written reviews than Amazon displays; others cannot be resolved at all. Cubiker B0BLCBRBVZ remains unsupported by the demonstrated route and is not repaired by this release.

Quick start

Paste one covered ASIN, set a small review limit, and start the Actor. This AirPods example was verified on 29 October 2026; catalog availability can change:

{
  "asins": ["B0DGJ7HYG1"],
  "maxReviewsPerProduct": 20,
  "scrapeMode": "basic",
  "includeDiagnostics": true
}

Download saved reviews from the run's dataset as CSV, Excel or JSON. An exact-matched offer whose feed completes with no usable written records returns an empty dataset. An unresolved offer match, HTTP error or broken response remains a failure with diagnostics. This does not mean the product has no reviews.

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.

Example recipes

Use the JSON recipes below. Older saved/public examples may contain targets outside current coverage; replace their ASINs with a verified covered product before running.

What you get

One dataset row per available written review, carrying product identity, review text and any source-supplied rating, date or review link. Coverage provenance accompanies the rows. Per-product diagnostics help distinguish a completed check with no returned reviews from an input or execution failure. A product can have reviews on Amazon that this Actor cannot return.

Who is it for?

• Reputation teams tracking customer feedback for product lines.
• Product marketers comparing review sentiment across competing ASINs.
• Ecommerce analysts enriching dashboards with available public reviews.
• Agencies monitoring client and competitor review changes.
• Data teams that need repeatable exports instead of manual review copying.

How you can use the data

• Review feedback on a known US product. Supply a US Amazon product URL or ASIN with a small per-product cap. Export text, rating, reviewDate and productUrl, then classify themes in your own analysis rather than expecting built-in sentiment.
• Compare available feedback across ASINs. Keep asin and coverage provenance beside the review text. Inspect DIAGNOSTICS before comparing products, because a smaller export can reflect partial source coverage rather than less customer feedback.
• Keep a repeatable review sheet. When joining exports, use reviewId with asin where supplied. If the public source omits review IDs, use a content-based key in your downstream workflow to avoid double-counting.
• Separate unavailable coverage from no reviews. A successful check with no returned reviews does not establish that a product has none. Retain the coverage outcome with your research notes rather than turning it into a product-level rating or review total.

Input settings

You can provide one or both of these input types:

• productUrls - US amazon.com product or review URLs, including /dp/, /gp/product/, and /product-reviews/ links.
• asins - Amazon Standard Identification Numbers such as B0DGJ7HYG1.

Setting	API key	Purpose
Maximum reviews per product	maxReviewsPerProduct	Limit unique written reviews per ASIN, from 1 to 500. The form starts at 20. Set it explicitly in API requests.
Scrape mode	scrapeMode	basic (default), full, or max. Broader checks can find more available reviews, without guaranteeing completeness or newest-first order.
Save coverage diagnostics	includeDiagnostics	Save per-ASIN coverage details when processing reaches the diagnostics step. Enabled by default.

At least one valid ASIN or supported URL is needed. Both input lists can be combined. Invalid entries are skipped when valid targets remain. No login or cookies are required.

Quick ASIN check

{
  "asins": ["B0DGJ7HYG1"],
  "maxReviewsPerProduct": 20,
  "scrapeMode": "basic",
  "includeDiagnostics": true
}

Product URL export

{
  "productUrls": [
    { "url": "https://www.amazon.com/dp/B0F7PX6RSB" }
  ],
  "maxReviewsPerProduct": 50,
  "scrapeMode": "full",
  "includeDiagnostics": true
}

Deeper deduplicated coverage check

{
  "asins": ["B0DGJ7HYG1", "B0F7PX6RSB"],
  "maxReviewsPerProduct": 150,
  "scrapeMode": "max",
  "includeDiagnostics": true
}

Output data

Each saved dataset item represents one public review record. Fields include:

Field	Description
asin, productUrl	Product identifier and canonical Amazon URL.
reviewId, reviewUrl	Native review identifier and permalink when supplied; currently null on the demonstrated Woot feed. Deduplicate downstream using ASIN plus review content, author, date and rating when IDs are absent.
title, text, rating, author	Core review content.
reviewedIn	Original public country-and-date label.
reviewCountry, reviewCountryCode	Parsed country and ISO alpha-2 code.
reviewDateText, reviewDate, reviewTimestamp	Raw date, normalized YYYY-MM-DD date, and Unix UTC timestamp.
helpfulVotes, isVerifiedPurchase, isVineReview	Public trust and engagement signals.
imageUrls, mediaUrls, videoUrls, videoPosterUrls	Normalized review media links.
marketplace, scrapedAt	Marketplace and ISO extraction timestamp.
source, sourceMarketplace, sourceCoverage	Source and coverage provenance.

More example output

Illustrative output shape, not a newly collected review. Fields depend on the public source and can be null or empty.

{
  "asin": "B0DGJ7HYG1",
  "productUrl": "https://www.amazon.com/dp/B0DGJ7HYG1",
  "reviewId": null,
  "reviewUrl": null,
  "title": "Works well",
  "text": "The product matched the listing and arrived quickly.",
  "rating": 5,
  "author": "Amazon Customer",
  "reviewedIn": "Reviewed in the United States on July 20, 2026",
  "reviewCountry": "United States",
  "reviewCountryCode": "US",
  "reviewDateText": "July 20, 2026",
  "reviewDate": "2026-07-20",
  "reviewTimestamp": 1784505600,
  "helpfulVotes": 2,
  "isVerifiedPurchase": true,
  "isVineReview": false,
  "imageUrls": [],
  "mediaUrls": [],
  "videoUrls": [],
  "videoPosterUrls": [],
  "marketplace": "amazon.com",
  "scrapedAt": "2026-08-01T15:00:00.000Z",
  "source": "woot_reviews_ajax",
  "sourceMarketplace": "amazon.com",
  "sourceCoverage": "partial_woot_public_coverage"
}

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

The Review saved (review) event is charged for each review item saved to the dataset. There is no separate configured start event. Coverage diagnostics are not review items, and completed checks with no saved reviews do not emit review-item charges.

Current and scheduled rates

Prices below are USD per 1,000 saved reviews, with no start fee. Current rates remain active until 13 November 2026 at 00:00 UTC. The scheduled increase applies from that time, not before.

Apify plan / API tier	Current	From 13 Nov 2026, 00:00 UTC
FREE	$0.08	$0.70
BRONZE	$0.08	$0.60
SILVER	$0.07	$0.50
GOLD	$0.06	$0.40
PLATINUM	$0.05	$0.40
DIAMOND	$0.04	$0.40

GOLD, PLATINUM and DIAMOND share the same scheduled Business price. Charges are per saved review: the scheduled per-review rates are $0.00070, $0.00060, $0.00050, $0.00040, $0.00040 and $0.00040 respectively. Coverage and output limitations remain unchanged.

Review pricing is increasing to support reliable collection. You will continue to pay only for reviews saved, with lower rates on higher-volume plans.

Confirm the current and scheduled rates in the live Apify Pricing tab before running.

Tips for best results

• Start with 20 reviews per product. Check whether a known ASIN returns useful written reviews before increasing limits.
• Use raw ASINs for repeated product checks. Keep each ASIN with the returned rows and diagnostics.
• Enable coverage diagnostics for your downstream workflow. Review them before interpreting a small or empty export.
• Use review and product identity when merging exports. Compare reviewId plus asin where available, with a content-based key when IDs are missing.

Limits and caveats

• Bounded runs. Extraction stops after four minutes or 720 request attempts. Reviews already saved remain available. Split large product lists into smaller runs.
• Low-yield pages. Paging stops within a feed after two consecutive pages add no new written reviews. Other feeds selected by your scrape mode are still checked. Diagnostics identify this limit, so a stopped feed should not be treated as complete review history.
• Review budgets. The scraper checks the remaining review budget before fetching and saving more rows. It stops if billing is partially fulfilled and reports the saved and charged counts. Billing or storage errors fail the run rather than silently continuing.
• Restarted runs. Start a new run instead of resurrecting or restarting an old one. Existing saved reviews are preserved and the old run will not repeat extraction or billing.

This actor supports US amazon.com product identifiers. It rejects non-US Amazon marketplace URLs so the dataset does not imply unsupported marketplace coverage.

Coverage is partial by ASIN and may differ from the reviews visible on Amazon. Use scrapeMode: max for the broadest available coverage check. Broader modes do not guarantee more results, all reviews, or any particular final sort order. The actor saves unique written reviews, not rating-only entries. It does not calculate sentiment, product-level rating totals or AI review summaries.

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~amazon-reviews-scraper/runs' \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  --data @- <<'JSON'
{
  "asins": [
    "B0DGJ7HYG1"
  ],
  "maxReviewsPerProduct": 20,
  "scrapeMode": "basic",
  "includeDiagnostics": true
}
JSON

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/amazon-reviews-scraper').call({
  "asins": [
    "B0DGJ7HYG1"
  ],
  "maxReviewsPerProduct": 20,
  "scrapeMode": "basic",
  "includeDiagnostics": true
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Python

import os
from apify_client import ApifyClient

client = ApifyClient(os.environ['APIFY_TOKEN'])
run = client.actor('fetch_cat/amazon-reviews-scraper').call(run_input={'asins': ['B0DGJ7HYG1'], 'maxReviewsPerProduct': 20, 'scrapeMode': 'basic', 'includeDiagnostics': True})
print(client.dataset(run['defaultDatasetId']).list_items().items)

MCP notes

If you use Apify MCP tools, run the actor with the same JSON input shown above and read items from the default dataset. Diagnostics are available from the default key-value store record named DIAGNOSTICS.

MCP and AI agents

You can ask your AI assistant to collect data with Amazon 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 Amazon 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/amazon-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 Amazon Reviews Scraper by Fetch Cat, or give it the identifier fetch_cat/amazon-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 Amazon Reviews Scraper to collect up to 5 written reviews for US ASIN B0DGJ7HYG1, with diagnostics enabled. Show text, rating, date and any review link, and explain the available coverage.”
• “Collect up to 5 reviews per product for the two US Amazon product URLs I provide. Keep each ASIN and coverage outcome with the text, then compare recurring feedback themes in a separate analysis.”
• “Retrieve a small batch of up to 10 written reviews for this US Amazon ASIN using the broadest supported coverage check. Keep product and review identifiers for comparison with my earlier export, and do not treat zero coverage as proof the product has no reviews.”

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.

Diagnostics

When includeDiagnostics is enabled and processing reaches the save step, the actor attempts to save a DIAGNOSTICS key-value store record. Archived-offer lookups also save OFFER_DISCOVERY with metadata, forum and offer stage outcomes and available HTTP status codes. An unavailable search result can use one public product-page metadata check; neither route guarantees a match. Fatal failures can stop the run before these records are written. Use the run log in that case. Each saved ASIN diagnostic has a status and coverage counters:

• ok - public review rows were found and parsed.
• no_coverage - the request completed, but the supported public route returned no rows for that ASIN.
• failed - the ASIN could not be processed after retries.
• rate_limited - the public source limited requests.
• upstream_changed - the response no longer matched the expected review structure.

Diagnostics include attempted combinations, raw rows, unique rows, and a short message.

FAQ

Does it support every Amazon product?

No. Coverage depends on whether the supported public route returns review rows for the ASIN.

Does it support non-US marketplaces?

No. The actor currently supports US amazon.com URLs and ASINs only.

What happens when an ASIN has no coverage?

The run can still succeed. The actor records a no_coverage diagnostic so your workflow can track the check.

Can I use this as an Amazon reviews API?

Yes. Call fetch_cat/amazon-reviews-scraper with the input keys above, then read the default dataset. It is not an official Amazon API and has partial coverage. No Amazon login is required.

How are duplicates handled?

The actor saves each unique review once per ASIN within a run. Repeated runs can return the same review again.

Related Actors

More Amazon scrapers

• Amazon Products & Search Scraper — Collect public Amazon search listings or look up ASINs and product URLs with available product metadata.
• Amazon Seller Profiles Scraper — Retrieve one public Amazon seller profile with identity, storefront links and available feedback or business details.
• Ecommerce Product Page Scraper — price and availability snapshots.
• Facebook Ads Library Scraper — collect public Meta ad copy, advertiser details, creative links and available transparency ranges.
• TikTok Ads Library Scraper — collect public TikTok Creative Center Top Ads text, creative links and available performance indicators.

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. ♥