How to use Amazon Product & Best Sellers Scraper

Step by step: in the Apify Store, over the API, from JavaScript or Python, in n8n, and from AI agents through MCP or LangChain.

$0.54 per 1,000 search results (live Apify price on 2026-11-01).

1. In the Apify Store (no code)

1. Open arcadium/amazon-products and sign in to Apify (a free account works).
2. Fill in the input form. The fields below are the ones the example uses.
3. Set a maximum cost per run in the run options if you want a hard cap.
4. Start the run. Rows appear in the Output tab as they arrive.
5. Export the dataset as JSON, CSV or Excel, or connect an integration.

Example input:

{
  "searches": [
    "wireless earbuds"
  ],
  "categories": [],
  "asins": [],
  "urls": [],
  "marketplace": "US",
  "maxResultsPerSearch": 20,
  "includeSponsored": true
}

2. Over the Apify API

The synchronous endpoint runs the actor and returns its rows in one call (for runs up to about five minutes; use the asynchronous run endpoint for longer ones).

curl -X POST "https://api.apify.com/v2/acts/arcadium~amazon-products/run-sync-get-dataset-items" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searches":["wireless earbuds"],"categories":[],"asins":[],"urls":[],"marketplace":"US","maxResultsPerSearch":20,"includeSponsored":true}'

3. From JavaScript

// npm install apify-client
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });
const run = await client.actor('arcadium/amazon-products').call({
  "searches": [
    "wireless earbuds"
  ],
  "categories": [],
  "asins": [],
  "urls": [],
  "marketplace": "US",
  "maxResultsPerSearch": 20,
  "includeSponsored": true
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

4. From Python

# pip install apify-client
import os
from apify_client import ApifyClient

client = ApifyClient(os.environ["APIFY_TOKEN"])
run = client.actor("arcadium/amazon-products").call(run_input={
    "searches": ["wireless earbuds"],
    "categories": [],
    "asins": [],
    "urls": [],
    "marketplace": "US",
    "maxResultsPerSearch": 20,
    "includeSponsored": True,
})
items = client.dataset(run.default_dataset_id).list_items().items  # apify-client 3.x returns a Run object
print(items)

5. In n8n

Paste this node into a workflow (it is a plain HTTP Request node). Create a Header Auth credential with name Authorization and value Bearer <APIFY_TOKEN>; each returned row becomes one item for the next node.

{
  "nodes": [
    {
      "name": "Run arcadium/amazon-products",
      "type": "n8n-nodes-base.httpRequest",
      "typeVersion": 4,
      "position": [
        0,
        0
      ],
      "parameters": {
        "method": "POST",
        "url": "https://api.apify.com/v2/acts/arcadium~amazon-products/run-sync-get-dataset-items",
        "authentication": "genericCredentialType",
        "genericAuthType": "httpHeaderAuth",
        "sendBody": true,
        "specifyBody": "json",
        "jsonBody": "{\"searches\":[\"wireless earbuds\"],\"categories\":[],\"asins\":[],\"urls\":[],\"marketplace\":\"US\",\"maxResultsPerSearch\":20,\"includeSponsored\":true}",
        "options": {
          "timeout": 300000
        }
      }
    }
  ],
  "connections": {}
}

6. From an AI assistant (MCP)

Add Apify’s MCP server to Claude Desktop, Cursor, VS Code or any MCP client, limited to this actor:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=arcadium/amazon-products",
      "headers": {
        "Authorization": "Bearer <APIFY_TOKEN>"
      }
    }
  }
}

More on the MCP page.

7. From LangChain

# pip install langchain-apify   (reads APIFY_API_TOKEN from the environment)
from langchain_apify import ApifyActorsTool

tool = ApifyActorsTool("arcadium/amazon-products")
rows = tool.invoke({"run_input": {
    "searches": ["wireless earbuds"],
    "categories": [],
    "asins": [],
    "urls": [],
    "marketplace": "US",
    "maxResultsPerSearch": 20,
    "includeSponsored": True,
}})
# or give `tool` to an agent: create_react_agent(llm, [tool])

Sample output

One row, from the example in the actor’s README. Long text is shortened and lists are cut to two items.

{
  "rowType": "searchResult",
  "marketplace": "US",
  "domain": "amazon.com",
  "currency": "USD",
  "searchTerm": "wireless earbuds",
  "page": 1,
  "position": 2,
  "organicPosition": null,
  "asin": "B0C1BTXM9W",
  "sponsored": true,
  "title": "Wireless Earbuds, Bluetooth 5.4 Ear Buds LED Power Display Headphones Bass Stereo ...",
  "url": "https://www.amazon.com/dp/B0C1BTXM9W",
  "imageUrl": "https://m.media-amazon.com/images/I/614gb9uXQuL.jpg",
  "price": 19.99,
  "priceText": "$19.99",
  "listPrice": 28.49,
  "listPriceText": "$28.49",
  "rating": 4.3,
  "ratingCount": 14589,
  "badges": [],
  "boughtPastMonth": 6000,
  "boughtPastMonthText": "6K+ bought in past month",
  "deliveryText": "Join Prime to get FREE delivery Tomorrow, Oct 23",
  "scrapedAt": "2026-10-22T08:00:00.000Z"
}

Tutorials

• Track Amazon Best Sellers ranks with Python

Schedule it

In Apify Console, create a Task from your input and add a Schedule (for example daily). Each run writes a new dataset; integrations can push the rows to Google Sheets, Slack, a webhook or your database.