Amazon Scraper

Amazon Scraper — search by keyword and export every product with its full detail page

Turn a keyword like charger into a clean, structured list of products — then download it as JSON, CSV, Excel, or XML. Each product comes back with its title, price, rating, review count, images, bullet points, brand, seller, availability, category, and sales rank. Search several terms at once, or paste product URLs/ASINs to pull specific products.

What can Amazon Scraper do?

• Search Amazon with one or more keywords and return the matching products
• Enrich every product with its full detail page: bullets, all images, seller, category, sales rank, and more
• Go beyond a keyword's search results by following each product's related items (on by default)
• Scrape specific products by URL or ASIN, without searching
• De-duplicate results and cap each search so a run stays predictable
• Export results to JSON, CSV, Excel, or XML
• Run via the API, schedule runs, and integrate through webhooks or MCP

What data can I extract?

What you get

Features

Products — asin, url, title, brand, price, price_value, list_price, list_price_value, discount_percent, currency, in_stock, rating, rating_breakdown, reviews_count, review_summary, availability, seller, seller_id, seller_url, ships_from, category, color, size, style, model_number, manufacturer, item_weight, product_dimensions, date_first_available, upc, variation_attributes, attributes, image_url, images, gallery_thumbnails, bullets, description, aplus_images, aplus_text, best_sellers_rank, best_sellers_ranks, variants, variant_details, prime, sponsored, coupon, delivery, condition, return_policy, support, is_amazon_choice, amazon_choice_text, videos_count, answered_questions, has_reviews, reviews_url, monthly_purchase_volume, location, important_information, brand_store_url, search_query, position, scraped_at attributes — the long tail of category-specific specs (e.g. Wattage, Connector Type, Material, Capacity) as a key/value object, since they differ from one product type to another.

Search by keyword, or paste product URLs / ASINs Full detail page per product (on by default) Related-product discovery to widen the catalog Listing-only mode for fast, cheaper runs Export to JSON, CSV, Excel, XML API access, webhooks, SDKs LLM-ready output for MCP, ChatGPT, Claude

How to use Amazon Scraper

1. Create a free Apify account.
2. Open Amazon Scraper in Apify Console.
3. Enter one or more Search queries (the form is prefilled with charger).
4. Set Max results per search; leave Fetch full product details and Discover related products on.
5. Click Save & Start.
6. Download results in JSON, CSV, Excel, or XML.

To pull specific products instead, open the Paste product URLs instead* section and add product URLs or ASINs.

Input

One run does one of two things. By default it searches the terms you give it. If you paste anything into Paste product URLs instead*, the run switches to those exact products and ignores the search queries — the two modes cannot be combined.

Getting more products. Amazon only exposes a limited number of result pages per keyword, so a single search term returns a finite set of products. There are two ways to get more:

1. Discover related products (on by default) — the Actor follows the related items shown on each product page, so a run keeps finding new products past the search results. Search results always come first; related products are only used once the search pages are exhausted, or once they stop returning new products.
2. Add more search queries — several specific terms (e.g. 65w gan charger, usb c charger cable, car charger) each surface a different set, which is the most reliable way to reach a larger catalog.

Both work together; the whole run is still capped by Max results per search.

• search_queries — the keywords to search, one per line.
• max_results — cap on products returned per search term (default 50).
• fetch_details — fetch each product's full detail page (default on). Turn off for a fast listing-only run.
• discover_related — follow related items to find products beyond the search pages (default on; requires fetch_details).
• product_urls — product URLs or ASINs to scrape directly; wins over search_queries when filled.
• language — request locale (default English).

Example input

{
  "search_queries": ["charger"],
  "max_results": 50,
  "fetch_details": true,
  "discover_related": true
}

See the Input tab above for every parameter.

Output

Results land in a dataset under the Storage tab. View them in the Overview table, download in JSON, CSV, Excel, or XML, or pull them via the API.

{
  "asin": "B0FQN62VY2",
  "url": "https://www.amazon.com/dp/B0FQN62VY2",
  "title": "Ergonomic Office Chair Desk Chair with Adjustable Lumbar Support",
  "brand": "Nexthro",
  "price": "$94.99",
  "price_value": 94.99,
  "list_price": "$119.99",
  "list_price_value": 119.99,
  "discount_percent": 21,
  "currency": "USD",
  "in_stock": true,
  "rating": 4.5,
  "rating_breakdown": { "1": 5, "2": 2, "3": 6, "4": 8, "5": 79 },
  "reviews_count": 190,
  "review_summary": "Customers find this office chair to be a great choice for home use, appreciating its adjustable lumbar support and easy-to-adjust armrests.",
  "prime": false,
  "sponsored": true,
  "coupon": "",
  "availability": "In Stock",
  "seller": "",
  "ships_from": "Amazon",
  "category": "Home & Kitchen > Furniture > Home Office Furniture > Home Office Chairs > Home Office Desk Chairs",
  "color": "Black",
  "size": "",
  "style": "",
  "model_number": "23-1L",
  "manufacturer": "Nexthro",
  "item_weight": "",
  "product_dimensions": "",
  "date_first_available": "",
  "upc": "",
  "variation_attributes": { "color": "Black" },
  "attributes": {
    "Brand Name": "Nexthro",
    "Additional Features": "Adjustable Height",
    "Age Range Description": "Adult",
    "Assembly Instructions Description": "Yes, assembly is required"
  },
  "image_url": "https://m.media-amazon.com/images/I/81ErKg+7tEL._AC_SL1500_.jpg",
  "images": [
    "https://m.media-amazon.com/images/I/81ErKg+7tEL._AC_SL1500_.jpg",
    "https://m.media-amazon.com/images/I/81Qa4nC0CvL._AC_SL1500_.jpg",
    "https://m.media-amazon.com/images/I/81IQlKwGybL._AC_SL1500_.jpg"
  ],
  "bullets": [
    "【Ergonomic Office Chair】The Nexthro ergonomic office chair is designed to alleviate back pain, offering a comfortable seating experience with adjustable lumbar support.",
    "【Adjustable Lumbar Support】Our computer chair's lumbar support system adjusts 1\" forward and backward and 3.15\" up and down, conforming to the natural curve of the lower back."
  ],
  "description": "",
  "best_sellers_rank": "#6,963 in Home & Kitchen ( See Top 100 in Home & Kitchen ) #16 in Home Office Desk Chairs",
  "variants": ["B0F6XNPWYY", "B0FQMWXP4L", "B0FQMXYFMC"],
  "search_query": "office chair",
  "position": 3,
  "scraped_at": "2026-09-20T16:48:45Z"
}

Field names are lowercase snake_case, and the input keys match them. Switch dataset views — Overview, Pricing, Details, Media & bullets, Attributes & reviews — to focus the columns. The images, bullets, and variants arrays and the attributes / variation_attributes / rating_breakdown objects each render as a single column in those views.

What can you do with the data?

Each recipe names the exact input and fields to use.

1. Price and rating comparison across a category

1. Add several Search queries (e.g. 65w charger, gan charger, usb c charger).
2. Set Max results per search to 100 and keep Fetch full product details on.
3. Sort the dataset by price and rating, and filter on reviews_count to drop untested listings.
4. Track best_sellers_rank to see which products lead their category.

2. Product catalog and enrichment

1. Paste your product list into Paste product URLs instead* (URLs or ASINs).
2. Run with defaults to get one full row per product.
3. Use bullets, images, and category to populate a catalog or feed a model.

How much does Amazon Scraper cost?

Pricing uses two pay-per-event events, so you only pay for what a run actually produces:

• basic-result — charged once for every product returned, with the listing fields (title, price, rating, reviews, image). $3 per 1,000 products ($0.003 each).
• detailed-result — charged in addition for every product whose full detail page was fetched (bullet points, description, images, seller, category, sales rank).

A run with Fetch full product details on charges both events per product; a listing-only run charges only basic-result. You pay only for products saved to the dataset, so a smaller max_results costs less, and new Apify accounts get free monthly usage. See the Pricing tab for current rates and plan discounts.

FAQ

Do I need an account, cookies, or an API key? No. The Actor reads publicly available product pages, so you can run it straight away.

Can I scrape private or restricted content? No. It returns only what is publicly visible to a signed-out visitor.

How many results can I get? Set Max results per search per keyword. Amazon only exposes a limited set of result pages per term, so to reach a larger catalog either keep Discover related products on (it follows related items past the search results) or add several narrower keywords. Pasting product URLs/ASINs has no such limit.

Is it legal to scrape Amazon? The Actor collects publicly available data only. Check Apify's guidance on legal and ethical scraping and your own obligations before use.

Can I use it with the API / SDKs / MCP? Yes — see the API tab above, or connect through the Apify MCP server.

Something isn't working. Individual products that fail are skipped and logged; the run keeps going. Check the run log for skipped <ASIN>: ... lines, then open an issue if a problem persists.

Notes and limitations

• attributes is intentionally not fixed — Amazon specs differ per category (a coffee maker has Wattage/Voltage, a chair has Seat Material/Frame Material Type, a cable has Connector Type/Data Transfer Rate). Everything that is not a near-universal field goes into the attributes object, so the keys vary from product to product.
• Price and availability can be empty for products with no direct buy box (only third-party offers) or with region restrictions. list_price is only set when it is a genuine discount above the current price.
• rating_breakdown is only present when the page shows the per-star percentage breakdown.
• description is often empty on modern product pages, where the description is shown as images instead; bullets and attributes cover that content.
• rating/reviews_count are 0 for new products with no reviews yet, and review_summary is only present when a short review summary exists.
• Search is finite per keyword — Amazon returns a capped number of results per term; deeper pages mostly repeat. Two ways around it: Discover related products (default on) follows related items past the search results, and adding more specific keywords each surfaces a different set.
• Fields vary by category and locale; not every product exposes seller, ships_from, color/size, or variants.
• Detail runs are slower than listing-only runs because each product is fetched individually.

Run locally

go run ./cmd/actor

Input is read from the Apify key-value store; see INPUT_SCHEMA.json.

Support

Found a bug or have feedback? Open an issue in the Issues tab.