Amazon Product Scraper — Price, BSR, Reviews & Seller Data

Amazon Product Scraper, published as khadinakbar/amazon-product-intelligence, is an Apify Actor usable through Apify and Apify MCP. It accepts a keyword, a list of ASINs, or direct Amazon product or search URLs, and returns one record per scraped product. Each record can include ASIN, title, brand, price, original price, discount percent, rating, review count, BSR rank and category, Prime eligibility, seller details, fulfillment by Amazon, availability, images, description, bullet points, marketplace, scrape timestamp, and source URL.

Best fit and connected workflows

This Actor fits workflows that begin with Amazon product discovery and end with a structured product record. Common routing patterns include:

• Keyword-led research: start from a product idea or category term and collect matching Amazon listings.
• ASIN refreshes: monitor known products by passing exact ASIN codes.
• URL-led extraction: process a known product page or search results page directly.
• Marketplace comparison: run the same input across a chosen Amazon marketplace to compare local price, BSR, Prime, and seller fields.
• Review follow-up: use this Actor first for product-level context, then connect the returned record to a review-focused workflow for deeper sentiment analysis.

Practical scenario

Maya manages an ecommerce sourcing sheet. She starts with a short ASIN list from a supplier and runs the Actor with reviews enabled for a small sample. The returned records include price, bsr_rank, rating, review_count, prime_eligible, seller_name, and fulfilled_by_amazon. She uses those fields to compare listings, then sends the selected product records into her next review workflow for sentiment reading.

Input

Choose one input method or combine them.

Field	Type	Purpose
searchQuery	string	Keyword search for Amazon, such as a product type or topic.
asins	array of strings	Specific Amazon Standard Identification Numbers to scrape.
startUrls	array of request objects	Direct Amazon product pages or Amazon search result pages.
country	string	Amazon marketplace to scrape: US, UK, DE, FR, CA, ES, IT, JP, AU, or IN.
maxResults	integer	Maximum number of products to extract. Each product counts as one billable event.
includeReviews	boolean	Loads customer review pages for each scraped product.
maxReviews	integer	Maximum number of reviews per product when reviews are enabled.
proxyConfiguration	object	Proxy settings, with Apify Residential proxies as the default recommendation.

Focused input example

{
  "searchQuery": "wireless earbuds",
  "country": "US",
  "maxResults": 3,
  "includeReviews": true,
  "maxReviews": 5
}

Output

The default dataset stores one record per scraped product.

Field	Type	Purpose
asin	string or null	Amazon Standard Identification Number.
title	string or null	Full product title.
brand	string or null	Product brand name.
price	number or null	Current selling price.
currency	string or null	Currency code or symbol.
original_price	number or null	Original price before discount.
discount_percent	number or null	Discount percentage off original price.
rating	number or null	Average customer rating.
review_count	number or null	Total number of customer reviews.
bsr_rank	number or null	suitable Sellers Rank number.
bsr_category	string or null	suitable Sellers Rank category name.
prime_eligible	boolean or null	Whether the product has a Prime badge.
seller_name	string or null	Seller name.
seller_id	string or null	Seller identifier on Amazon.
fulfilled_by_amazon	boolean or null	Whether the item is fulfilled by Amazon.
availability	string or null	Stock availability text.
images	array or null	Product image URLs.
description	string or null	Full product description text.
bullet_points	array or null	Feature bullet points from the product page.
marketplace	string or null	Amazon marketplace domain.
scraped_at	string or null	ISO 8601 scrape timestamp.
source_url	string or null	Source Amazon URL.

Illustrative output record

{
  "asin": "B07DF46NW9",
  "title": "Example Product Title",
  "brand": "Example Brand",
  "price": 29.99,
  "currency": "USD",
  "original_price": 39.99,
  "discount_percent": 25,
  "rating": 4.6,
  "review_count": 1243,
  "bsr_rank": 18,
  "bsr_category": "Electronics",
  "prime_eligible": true,
  "seller_name": "Example Seller",
  "seller_id": "A1EXAMPLE",
  "fulfilled_by_amazon": true,
  "availability": "In Stock",
  "images": [
    "https://m.media-amazon.com/images/I/example.jpg"
  ],
  "description": "Example product description.",
  "bullet_points": [
    "Example feature one",
    "Example feature two"
  ],
  "marketplace": "amazon.com",
  "scraped_at": "2026-01-01T12:00:00Z",
  "source_url": "https://www.amazon.com/dp/B07DF46NW9"
}

How it works

The Actor accepts keyword, ASIN, or URL input and runs against the selected Amazon marketplace. The live contract describes a stealth Playwright approach with residential proxies, and the input defaults point to Apify Residential proxies. It supports 10 Amazon marketplaces: US, UK, DE, FR, CA, ES, IT, JP, AU, and IN.

When includeReviews is enabled, the Actor loads each product's reviews page separately and returns up to maxReviews reviews per product according to the execution settings. The dataset schema is product-centric, so each row represents one scraped product. The output contract also exposes results, runOutput, and runSummary for automation and monitoring.

Pricing

This Actor uses Pay per event pricing. Each scraped product counts as one Product Scraped event, and Apify platform usage is billed separately according to your Apify plan and resource consumption.

For example, a run that returns ten products creates ten billable product events. Open the live Pricing tab on the Actor page for the current event pricing and the platform usage that applies to your run.

Use with AI agents (MCP)

This Actor is usable through Apify MCP. It accepts structured product discovery input and returns dataset records that AI agents can read directly.

Precise tool description: retrieve Amazon product intelligence from a keyword, ASIN list, or direct Amazon URL, then read the resulting dataset items for product fields such as price, BSR, ratings, seller information, Prime status, bullet points, and images.

Exact Actor identity: khadinakbar/amazon-product-intelligence

Find Amazon product intelligence for the US marketplace from this ASIN list, return the dataset items, and summarize price, BSR, rating, review count, seller, Prime status, and availability for each product.

Output interpretation: each dataset row represents one scraped product. source_url provides provenance, scraped_at shows when the record was collected, and fields such as price, bsr_rank, rating, review_count, seller_name, seller_id, prime_eligible, and fulfilled_by_amazon are useful for downstream reasoning.

Scope and pagination guidance: use searchQuery for topic-led discovery, asins for exact product IDs, and startUrls for known Amazon pages. maxResults sets the product count for a run, and each product is charged as one Product Scraped event.

API example

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({
  token: process.env.APIFY_TOKEN,
});

const run = await client.actor('khadinakbar/amazon-product-intelligence').call({
  searchQuery: 'wireless earbuds',
  country: 'US',
  maxResults: 3,
  includeReviews: false,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Best results and outcome guidance

Start with a small maxResults value when testing a new keyword, ASIN list, or URL set. Use asins when you already have exact product identifiers, searchQuery when you want Amazon-discovered listings for a topic, and startUrls when the source page is already known.

If you need marketplace-specific data, match country to the Amazon domain you are studying. When reviews matter, enable includeReviews and choose a focused maxReviews value that matches the depth you need for downstream analysis.

Continue the workflow

• Then use Amazon Product Scraper to extend Amazon Product Scraper — Price, BSR, Reviews & Seller Data research with a complementary content contract.
• Then use Amazon Product Search Scraper — Full Detail Data to extend Amazon Product Scraper — Price, BSR, Reviews & Seller Data research with a complementary discovery contract.

Design note

I found that the live dataset contract includes seller_id, fulfilled_by_amazon, and source_url, which makes each record useful for routing, fulfillment checks, and provenance tracking.

FAQ

When should I use searchQuery instead of asins?

Use searchQuery when you are starting from a keyword or category idea. Use asins when you already have exact Amazon product identifiers.

When should I use startUrls?

Use startUrls when you already have Amazon product pages or Amazon search result pages and want those exact pages processed.

How do I connect this Actor to review workflows?

Use this Actor to capture the product record first, then route the returned product into a review-focused Actor when you want deeper review and sentiment analysis.

Which marketplace should I choose?

Pick the Amazon marketplace that matches the region you want to inspect, such as US, UK, DE, FR, CA, ES, IT, JP, AU, or IN.

What should I look at first in the output?

For product screening, price, bsr_rank, rating, review_count, prime_eligible, seller_name, and fulfilled_by_amazon are often the first fields to review.

Responsible use

Use this Actor for publicly available Amazon product information and handle the resulting data in line with Amazon's terms, local law, and any applicable data protection requirements. Keep runs focused on the records you need, store the data responsibly, and respect platform and marketplace policies when automating collection.