# Amazon Product Search Scraper (`dromb/amazon-product-search-scraper`) Actor Extract Amazon.com product search results with titles, ASINs, prices, ratings, reviews, sellers, images, and product URLs for market research, price monitoring, and product discovery. - **URL**: https://apify.com/dromb/amazon-product-search-scraper.md - **Developed by:** [Dmitriy Gyrbu](https://apify.com/dromb) (community) - **Categories:** E-commerce, Developer tools, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.22 / 1,000 amazon product rows This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ## Amazon Product Search Scraper Extract Amazon.com product search results with product titles, ASINs, prices, ratings, review counts, sellers, images, and product URLs. Use this scraper for marketplace research, competitor tracking, product discovery, price monitoring, listing research, and ecommerce data enrichment. This Actor is unofficial and is not affiliated with Amazon. ### Why Use It Amazon search results show which products are visible for a product, category, brand, or niche query. This Actor turns those result pages into structured rows you can export, compare, monitor, or feed into your own ecommerce workflow. Use it to: - research product niches and competing listings; - collect ASINs for follow-up analysis; - monitor price, rating, review count, and seller changes; - compare brands and product categories; - build product lead lists for ecommerce, sourcing, or affiliate workflows; - create repeatable Amazon search datasets without manually copying result pages. ### What You Get Each dataset item is one Amazon product result. ```json { "query": "wireless earbuds", "title": "Wireless Earbuds, Bluetooth Headphones...", "url": "https://www.amazon.com/...", "asin": "B0H33STTGC", "imageUrl": "https://m.media-amazon.com/...", "currency": "$", "price": 19.99, "oldPrice": 29.99, "rating": 4.5, "reviewsCount": 1240, "seller": "Amazon.com", "sellersCount": 1, "additionalInfo": "Limited time deal", "boughtCount": "1K+ bought in past month", "source": "amazon_product_search", "collectedAt": "2026-07-01T00:00:00+00:00" } ```` The run also stores an `OUTPUT` summary with status, requested queries, successful queries, failed queries, pushed item count, and structured errors. ### Input ```json { "searchQueries": ["wireless earbuds"], "maxProductsPerQuery": 20, "maxPagesPerQuery": 1, "deduplicate": true } ``` #### Input Fields - `searchQueries`: Product, brand, category, or niche searches to run on Amazon.com. - `maxProductsPerQuery`: Maximum number of product rows saved for each search query. - `maxPagesPerQuery`: Maximum result pages to request for each query. Use `1` for fast checks and up to `3` for broader collection. - `deduplicate`: Keeps only the first occurrence of each ASIN or product title across the run. ### Example Workflows #### Quick product search Best for a fast first pass on one product category. Saved Apify task: `amazon-product-search-quick-products` ```json { "searchQueries": ["wireless earbuds"], "maxProductsPerQuery": 20, "maxPagesPerQuery": 1, "deduplicate": true } ``` #### Market research Best for comparing several related product categories or niches. Saved Apify task: `amazon-product-search-market-research` ```json { "searchQueries": ["standing desk", "ergonomic office chair"], "maxProductsPerQuery": 25, "maxPagesPerQuery": 1, "deduplicate": true } ``` #### Price and listing monitoring Best for repeated runs where you track prices, ratings, review counts, and sellers over time. Saved Apify task: `amazon-product-search-price-monitoring` ```json { "searchQueries": ["protein powder vanilla", "collagen peptides"], "maxProductsPerQuery": 30, "maxPagesPerQuery": 1, "deduplicate": true } ``` ### Result Counting Each Amazon product row is one dataset item. A run with one query and `maxProductsPerQuery` set to `20` can produce up to 20 paid result rows. Failed or blocked queries are reported in `OUTPUT.errors` and do not create fake dataset rows. ### How To Use 1. Enter one or more Amazon search queries. 2. Choose how many product rows and pages to collect. 3. Run the Actor. 4. Export the dataset as JSON, CSV, Excel, XML, or connect it through the Apify API. ### Practical Tips - Start with one query and one page when testing a new niche. - Use specific product terms for cleaner result sets. - Keep `deduplicate` enabled when comparing related searches. - Run the same input on a schedule if you want price or review-count monitoring. ### Limits And Notes - This Actor currently targets Amazon.com search results. - Search results, prices, ratings, review counts, and sellers can change frequently. - Displayed currency can vary with Amazon localization and proxy location. - Some product fields may be missing when Amazon does not show them in search results. - The Actor returns product search rows, not full product-detail pages. - Failed queries are reported in `OUTPUT.errors` instead of being silently ignored. # Actor input Schema ## `searchQueries` (type: `array`): Product, brand, category, or niche searches to run on Amazon.com. ## `maxProductsPerQuery` (type: `integer`): Maximum number of product rows saved for each search query. ## `maxPagesPerQuery` (type: `integer`): Maximum Amazon result pages to request for each query. Use 1 for fast tests and up to 3 for broader collection. ## `deduplicate` (type: `boolean`): Keep only the first occurrence of each ASIN or product title across the run. ## Actor input object example ```json { "searchQueries": [ "wireless earbuds" ], "maxProductsPerQuery": 50, "maxPagesPerQuery": 1, "deduplicate": true } ``` # 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("dromb/amazon-product-search-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("dromb/amazon-product-search-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 dromb/amazon-product-search-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dromb/amazon-product-search-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Product Search Scraper", "description": "Extract Amazon.com product search results with titles, ASINs, prices, ratings, reviews, sellers, images, and product URLs for market research, price monitoring, and product discovery.", "version": "0.1", "x-build-id": "OyXLE0h1CjxgHbm71" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dromb~amazon-product-search-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dromb-amazon-product-search-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/dromb~amazon-product-search-scraper/runs": { "post": { "operationId": "runs-sync-dromb-amazon-product-search-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/dromb~amazon-product-search-scraper/run-sync": { "post": { "operationId": "run-sync-dromb-amazon-product-search-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", "properties": { "searchQueries": { "title": "Search Queries", "type": "array", "description": "Product, brand, category, or niche searches to run on Amazon.com.", "items": { "type": "string" }, "default": [ "wireless earbuds" ] }, "maxProductsPerQuery": { "title": "Max Products Per Query", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of product rows saved for each search query.", "default": 50 }, "maxPagesPerQuery": { "title": "Max Pages Per Query", "minimum": 1, "maximum": 3, "type": "integer", "description": "Maximum Amazon result pages to request for each query. Use 1 for fast tests and up to 3 for broader collection.", "default": 1 }, "deduplicate": { "title": "Deduplicate Products", "type": "boolean", "description": "Keep only the first occurrence of each ASIN or product title across the run.", "default": true } } }, "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 } } } } } } } } } } ```