Amazon Products & Search Scraper

Use this Amazon products scraper to export public Amazon search results into structured data for assortment research, price monitoring, sponsored-placement analysis, and product discovery. Search ten Amazon marketplaces through an Amazon product search API workflow and export Amazon products with rank, ASIN, title, price, rating, reviews, Prime status, image, and source provenance.

Example input

{
  "queries": ["wireless headphones"],
  "marketplace": "US",
  "sort": "relevance",
  "maxItems": 20,
  "enrichDetails": false,
  "detailCacheMaxAgeMinutes": 60,
  "proxyMode": "auto"
}

Example output

{
  "rank": 1,
  "sponsored": true,
  "asin": "B012345678",
  "productTitle": "Wireless noise-cancelling headphones",
  "brand": "Example Brand",
  "price": 79.99,
  "priceDisplay": "$79.99",
  "currency": "$",
  "prime": true,
  "rating": 4.5,
  "reviewCount": 1234,
  "imageUrls": ["https://m.media-amazon.com/images/I/example.jpg"],
  "sourceType": "search",
  "sourceQuery": "wireless headphones",
  "marketplace": "US",
  "url": "https://www.amazon.com/dp/B012345678",
  "scrapedAt": "2026-07-10T12:00:00.000Z",
  "diagnostics": { "detailEnriched": false, "pageNumber": 1 }
}

What data can I export?

Each dataset row represents one Amazon product listing:

• Search provenance: organic rank, sponsored status, source query, and marketplace.
• Product identity: ASIN, parent and variant ASINs, title, brand, categories, URL, and image URLs.
• Commercial signals: numeric/display/list price, currency, deal, savings, Prime, shipping, availability, seller, and Buy Box seller.
• Social proof: numeric rating and review count.
• Provenance: source type/query, marketplace, page number, enrichment status, and scrape timestamp.

Who is it for?

This Actor is designed for ecommerce analysts, marketplace sellers, brand-protection teams, pricing researchers, retail agencies, and developers building product intelligence workflows.

Use cases

• Compare product assortment across Amazon marketplaces.
• Monitor visible prices and Prime placement for selected searches.
• Measure sponsored versus organic placement.
• Build product research and category-analysis datasets.
• Feed structured Amazon listings into spreadsheets, databases, agents, or BI tools.

Input settings

Verified public example tasks

These Apify Store tasks use small, QA-verified inputs and can be opened or cloned directly:

• Enrich Amazon Product Search Results — verified run 6S71PHVBgdtnLxpCj produced 5 dataset items.
• Find Lowest Price Usb C Chargers — verified run ptSxQtqxjqbzdau4R produced 10 dataset items.
• Research Top Reviewed Coffee Grinders — verified run BrewwM2UIWfcYJJAv produced 10 dataset items.
• Search Amazon Uk Products — verified run 6xPlzxoEb1AmDDX11 produced 10 dataset items.
• Search Amazon Wireless Headphones — verified run Dq5a3KRb4QAQomi5H produced 10 dataset items.

Input recipes

US product research

{ "queries": ["standing desk"], "marketplace": "US", "maxItems": 20 }

UK brand monitoring

{ "queries": ["Sony headphones"], "marketplace": "UK", "maxItems": 10, "enrichDetails": true }

Tips

• Begin with 10 products to validate the fields for your workflow.
• Use a specific brand or model when you need focused results.
• Keep the marketplace consistent when comparing ranking over time.
• Keep proxyMode on auto for the best cost/reliability balance. Use datacenter only when the lowest platform cost matters more than fallback reliability.
• Keep the one-hour detail cache enabled for repeated ASIN research. Set detailCacheMaxAgeMinutes to 0 when every enriched field must come from a new product-page request.
• Export JSON for automation or CSV/XLSX for spreadsheet analysis.

Limits and practical notes

Amazon can vary visible products, prices, delivery promises, and rankings by time and location. Search rank and search-page fields are observed during the current run. Optional detail fields may be reused within detailCacheMaxAgeMinutes; diagnostics.detailSource and diagnostics.detailFetchedAt show whether they came from a live request or the bounded cache. The Actor collects public product search information and does not require an Amazon account. Scrape only data you have a lawful purpose to use, respect applicable privacy and database laws, and comply with Amazon's terms.

API usage

curl -X POST "https://api.apify.com/v2/acts/fetch_cat~amazon-products-search-scraper/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"queries":["wireless headphones"],"marketplace":"US","maxItems":10}'

Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('fetch_cat/amazon-products-search-scraper').call({
  queries: ['wireless headphones'], marketplace: 'US', maxItems: 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/amazon-products-search-scraper').call(
    run_input={'queries': ['wireless headphones'], 'marketplace': 'US', 'maxItems': 10}
)
items = client.dataset(run['defaultDatasetId']).list_items().items
print(items)

The run response includes a dataset ID. Download rows through the dataset API after the run succeeds.

Slack + n8n research workflow

Use Amazon Products & Search Scraper as a research input when a teammate asks a bounded question in Slack. Connect an MCP-compatible assistant through the hosted Apify MCP server, using the same documented queries, marketplace, maxItems, and enrichment inputs. Keep maxItems small for an interactive brief. The batch run writes structured results to its Apify dataset; use n8n or an Apify webhook to send a comparison with source URLs to Slack, Sheets, Notion, or your CRM.

Research prompt: “Search Amazon US for standing desk; return 20 results with sponsored status, price, rating, and review count.”

This Actor returns data and does not install or operate a Slack bot. Review the dataset and source URLs before acting on an AI-generated summary. See the shared Slack research brief recipes with Apify MCP + n8n.

MCP and agents

Connect through the hosted Apify MCP server, select this Actor in the configurator, and ask an agent to research products for a marketplace and query.

Example prompts:

• “Search Amazon US for wireless headphones, return 10 products, and include sponsored placement and price.”
• “Compare the first 20 Amazon UK results for standing desk with detail enrichment enabled.”

claude mcp add apify-amazon -- npx -y @apify/actors-mcp-server --actors fetch_cat/amazon-products-search-scraper

Example MCP client configuration:

{
  "mcpServers": {
    "apify-amazon": {
      "command": "npx",
      "args": ["-y", "@apify/actors-mcp-server", "--actors", "fetch_cat/amazon-products-search-scraper"],
      "env": { "APIFY_TOKEN": "YOUR_APIFY_TOKEN" }
    }
  }
}

Support

If a run fails or a field is missing, open an issue on the Actor page. Include the run ID, your input JSON, expected output, actual output, and a reproducible public URL from Amazon. Do not include passwords, cookies, or other private credentials.