# Amazon Deals Scraper with Smart Ranking (`amazonscraping/amazon-deals-scraper`) Actor Scrape top-performing products from Amazon bestsellers across multiple countries and categories, based on rating, review count, price, and bestseller status to help you identify trending deals. Key Features: Multi-Country Support Smart Filtering Advanced Ranking Hierarchy Output - **URL**: https://apify.com/amazonscraping/amazon-deals-scraper.md - **Developed by:** [Sara](https://apify.com/amazonscraping) (community) - **Categories:** Automation, Agents, Other - **Stats:** 7 total users, 3 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $5.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## ⭐️ What is Amazon Best Sellers Scraper? **Amazon Best Sellers Scraper** lets you extract the top-selling products from Amazon Best Sellers pages across multiple countries and categories. It outputs structured data in JSON, CSV, Excel, or XML — ready for analysis, dashboards, or integrations. With this scraper you can: - **Extract from 6 Amazon domains**: Saudi Arabia (.sa), Egypt (.eg), UAE (.ae), United States (.com), United Kingdom (.co.uk), and Germany (.de). - **Get full product details**: title, price, rating, number of reviews, URL, thumbnail image, and bestseller rank. - **Scrape any category and its subcategories** — Beauty, Electronics, Fashion, Home, Baby Products, and many more. - **Smart filtering & ranking** — products are automatically filtered by quality (minimum rating & reviews) and ranked by a weighted score so you get the best items first. - **Control output size** — set `maxResults` to return exactly how many products you need (10–5,000). - **Hierarchical output** — results are organized in a category tree structure so you can see exactly where each product sits in the Amazon taxonomy. --- ### 📚 How do I scrape Amazon Best Sellers? This scraper is designed for an easy start — no coding required. Here's how: 1. **Create a free Apify account** using your email. 2. **Open Amazon Best Sellers Scraper** on Apify. 3. **Choose your category** — pick from the dropdown: Electronics, Beauty, Fashion, Home, or select "All" for every category. 4. **Select the Amazon country** (Saudi Arabia, Egypt, UAE, US, UK, or Germany). 5. **Set the maximum number of products** you want (default: 1,000). 6. **Click "Start"** and wait for the data to be extracted. 7. **Download your data** in JSON, CSV, Excel, XML, or HTML. --- ### 📦 How many results can I get? You can extract up to **5,000 products per run**, ranked by quality score. The default is **1,000 products**. Results cover the selected category and all of its subcategories, giving you a comprehensive view of top sellers. The actual number of results depends on: - The category you choose (some have more products than others). - The Amazon marketplace (each country has different inventory). - Product availability and Amazon's own listings. The best way to know for your specific use case is to do a quick test run. --- ### ⬇️ Input parameters | Parameter | Description | Default | |-----------|-------------|---------| | **Category** | Product category — Electronics, Beauty, Fashion, Home, and more. Choose "All" to scrape every category. | Electronics | | **Country** | Amazon marketplace — US, SA, EG, AE, UK, DE | United States | | **Max Results** | Maximum number of products to return (10–5,000) | 1,000 | --- ### ⬆️ Output example Results are saved as a structured dataset. Each product includes: ```json [ { "title": "Apple AirPods Pro (2nd Generation)", "price": 189.99, "currency": "USD", "rating": 4.7, "reviews": 98520, "url": "https://www.amazon.com/dp/B0D1XD1ZV3", "thumbnail": "https://images-na.ssl-images-amazon.com/images/I/...", "category_path": "Electronics > Headphones, Earbuds & Accessories > Earbuds", "rank": 1, "quality_score": 97.3 }, { "title": "Samsung Galaxy S24 Ultra Case", "price": 14.99, "currency": "USD", "rating": 4.5, "reviews": 34200, "url": "https://www.amazon.com/dp/B0CS5THKR7", "thumbnail": "https://images-na.ssl-images-amazon.com/images/I/...", "category_path": "Electronics > Cell Phones & Accessories > Cases", "rank": 2, "quality_score": 91.8 } ] ```` Products are also organized in a **category hierarchy tree** (saved to Key-Value Store) so you can browse results by subcategory. *** ### ☝️ Tips and tricks - **Start with a small test** — set `maxResults` to 50 to quickly preview results before running a full extraction. - **Compare across countries** — run the same category on different Amazon domains to compare bestsellers across markets. - **Use the hierarchy output** — the Key-Value Store contains the full category tree with products embedded, perfect for building dashboards or reports. - **Quality filtering is automatic** — products with very low ratings or too few reviews are filtered out before ranking, so you only get reliable bestsellers. *** ### ❓ FAQ #### Do I need proxies to scrape Amazon? No setup needed on your end. This scraper uses **Apify's residential proxies** automatically to ensure reliable results without getting blocked. #### Can I integrate this scraper with other apps? Yes! Amazon Best Sellers Scraper can be connected with almost any cloud service or web app through Apify integrations — including **Make**, **Zapier**, **Google Sheets**, **Slack**, **LangChain**, and more. You can also use **webhooks** to trigger actions whenever a scrape finishes (e.g., send a notification or update a spreadsheet). #### Can I use it with an API? Absolutely. The **Apify API** gives you programmatic access to run this scraper, schedule recurring runs, and fetch results. Use the `apify-client` package for **Node.js** or **Python**. Check out the [Apify API reference](https://docs.apify.com/api) for full details, or click the **API tab** in the Actor page for ready-to-use code examples. #### What can I do with Amazon Best Sellers data? - **Market research** — identify top trends and best-selling products across categories. - **Competitive analysis** — track what's selling best and adjust your product strategy. - **Price monitoring** — compare prices across different Amazon marketplaces. - **Product discovery** — find trending products and emerging categories. - **E-commerce intelligence** — use data to inform pricing, inventory, and marketing decisions. *** ### 📬 Your feedback We're always working on improving performance. If you have technical feedback or found a bug, please create an issue on the Actor's **Issues** tab in Apify Console. # Actor input Schema ## `category` (type: `string`): The product category to scrape. Choose 'All' to scrape every category. ## `country` (type: `string`): Which Amazon marketplace to scrape from ## `maxResults` (type: `integer`): Maximum number of products to return (ranked by quality score) ## Actor input object example ```json { "category": "Electronics", "country": "com", "maxResults": 1000 } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("amazonscraping/amazon-deals-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = {} # Run the Actor and wait for it to finish run = client.actor("amazonscraping/amazon-deals-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call amazonscraping/amazon-deals-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=amazonscraping/amazon-deals-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Deals Scraper with Smart Ranking", "description": "Scrape top-performing products from Amazon bestsellers across multiple countries and categories, based on rating, review count, price, and bestseller status to help you identify trending deals.\n\nKey Features:\nMulti-Country Support\nSmart Filtering\nAdvanced Ranking\nHierarchy Output", "version": "1.0", "x-build-id": "AaHUdU9dAivEsRKfk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/amazonscraping~amazon-deals-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-amazonscraping-amazon-deals-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/amazonscraping~amazon-deals-scraper/runs": { "post": { "operationId": "runs-sync-amazonscraping-amazon-deals-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/amazonscraping~amazon-deals-scraper/run-sync": { "post": { "operationId": "run-sync-amazonscraping-amazon-deals-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "category" ], "properties": { "category": { "title": "Category", "enum": [ "All", "Amazon Devices & Accessories", "Automotive", "Baby Products", "Beauty", "DIY & Tools", "Electronics", "Fashion", "Gift Cards", "Grocery", "Health", "Home", "Office Products", "Patio, Lawn & Garden", "Pet Supplies", "Sporting Goods", "Toys", "Videogames" ], "type": "string", "description": "The product category to scrape. Choose 'All' to scrape every category.", "default": "Electronics" }, "country": { "title": "Amazon Country", "enum": [ "com", "sa", "eg", "ae", "co.uk", "de" ], "type": "string", "description": "Which Amazon marketplace to scrape from", "default": "com" }, "maxResults": { "title": "Maximum Products", "minimum": 10, "maximum": 5000, "type": "integer", "description": "Maximum number of products to return (ranked by quality score)", "default": 1000 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```