# Amazon Reviews Scraper (`jdtpnjtp/amazon-reviews`) Actor Scrape full Amazon customer reviews with NO login and NO cookie. Provide ASINs or product URLs - get every review (rating, title, text, author, date, verified, helpful votes, photos) as clean structured rows. Pay only per review, no subscription. - **URL**: https://apify.com/jdtpnjtp/amazon-reviews.md - **Developed by:** [Data Forge](https://apify.com/jdtpnjtp) (community) - **Categories:** E-commerce, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.10 / 1,000 reviews 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 Reviews Scraper **Pull hundreds of real Amazon customer reviews per product as clean structured rows - across all five star ratings, deduped and newest-first.** Drop in product IDs or paste product URLs, pick a marketplace, star filter and sort order, and export sortable, filterable rows ready for CSV, Excel or JSON. No HTML parsing, no anti-bot headaches. No subscription, pay only per review. --- ### What it does Give it any mix of ASINs and/or Amazon product URLs and it pulls the customer reviews for each product (up to 800 each), with every field structured: - ⭐ **Star rating** the reviewer gave (1-5) - 📝 **Title** and 💬 **full review text** - 👤 **Reviewer name** and 📅 **date** - ✅ **Verified Purchase** flag and 🍃 **Amazon Vine** flag - 👍 **Helpful votes** - 🖼 **Review photos** (all attached images) - 🌍 **Country** the review was written in Narrow the result with the **star filter** (all stars, a single rating, or positive / critical sentiment) and choose **most recent** or **most helpful** ordering. Set a **soft cap per product** to keep runs quick and cheap, or pull up to 800 per product for a deep dataset. Flip on **featured reviews** to also grab the highlighted reviews shown on the product page. Reviews are deduped across all five star ratings and both sort orders. Smaller products come back in full; very large products return the most-recent and most-helpful slice. ### Input ```json { "products": ["B0CHX1W1XY", "https://www.amazon.com/dp/B09B8V1LZ3"], "country": "us", "starFilter": "all_stars", "sort": "recent", "maxReviewsPerAsin": 300, "includeTopReviewsFallback": false, "stopOnError": false } ```` | Field | What it does | |---|---| | **Products (ASINs or URLs)** | The products to pull reviews for - paste an ASIN (10-char code, e.g. B0CHX1W1XY) or a full product URL. Mix both freely. | | **Country** | Amazon marketplace: us, uk, de, fr, it, es, ca, jp. | | **Star filter** | all\_stars (default) / five\_star / four\_star / three\_star / two\_star / one\_star / positive / critical. | | **Sort** | recent (default) or helpful. | | **Max reviews per product** | Soft cap on reviews returned per product (1-800). A lower cap runs faster and cheaper - you only pay for reviews returned. | | **Also pull featured reviews** | Additionally grab the highlighted reviews shown on the product page. | | **Stop on error** | Abort the whole run on the first failed product, instead of skipping it. | Provide at least one product - an ASIN or a product URL - or the run fails fast with a clear message. ### Output One flat row per review, with the full raw review object under `data`. The dataset ships with a ready-made **Reviews** table tab, so the results read as a clean spreadsheet you can sort, filter and export to CSV / Excel / JSON in one click. A real review row: ```json { "asin": "B0CHX1W1XY", "author": "Jordan M.", "rating": 5, "title": "Best earbuds I've owned", "text": "Battery life is excellent and the noise cancelling is...", "date": "Reviewed in the United States on March 14, 2026", "country": "us", "verified_purchase": true, "vine": false, "helpful_count": 12, "images": ["https://m.media-amazon.com/images/I/..."], "image_url": "https://m.media-amazon.com/images/I/...", "data": { "...": "full review object" } } ``` Console columns on the **Reviews** tab include `asin`, `rating`, `title`, `text`, `author`, `date`, `verified_purchase`, `vine`, `helpful_count` and `country`. Error rows carry an `error_code` and are **free** - you only pay for real reviews. A run-summary record (`OUTPUT` key) reports the review count, error count and estimated cost. Live per-event pricing is shown on this actor's Apify Store page. ### Why this actor - **Hundreds of reviews per product** - across all five star ratings, deduped and newest-first, up to 800 each. - **Every review field structured** - rating, title, text, author, date, verified-purchase, Vine, helpful votes, photos, country. - **8 marketplaces** - us, uk, de, fr, it, es, ca, jp. - **Star + sentiment filters** - all stars, any single rating, or positive / critical, sorted by recent or helpful. - **ASINs or URLs in one field** - feed product IDs or paste product links; the ASIN is extracted for you. - **Fast cached responses** - repeat pulls return quickly; competitors scrape live at 3+ seconds. - **You only pay for results** - errors and your own misconfigurations are never charged. ### Need help? Enterprise plans? Custom scraping? I personally support every customer - drop a message on any channel. [![Telegram](https://img.shields.io/badge/Telegram-2CA5E0?style=for-the-badge\&logo=telegram\&logoColor=white)](https://t.me/j4dtpnj2tp) [![WhatsApp](https://img.shields.io/badge/WhatsApp-25D366?style=for-the-badge\&logo=whatsapp\&logoColor=white)](https://wa.me/380686031542) [![Email](https://img.shields.io/badge/Email-D14836?style=for-the-badge\&logo=gmail\&logoColor=white)](mailto:jdtpnjtp@gmail.com) # Actor input Schema ## `products` (type: `array`): The products to pull reviews for. Paste an Amazon product URL (any /dp/ or /gp/product/ link) or just its 10-char ASIN (e.g. `B0CHX1W1XY`) - mix both freely, one entry per product. Each product returns its reviews as individual rows, up to the per-product cap below. ## `country` (type: `string`): Which Amazon marketplace to read reviews from. ## `starFilter` (type: `string`): Which reviews to keep. Default returns the full deduped set across all stars; pick a single rating or sentiment to narrow it. ## `sort` (type: `string`): Order of the returned reviews. ## `maxReviewsPerAsin` (type: `integer`): How many reviews to return for EACH product, up to 800. A lower value returns fewer reviews and runs a faster, cheaper harvest - you only pay for reviews actually returned. 50 = quick sample, 300 = solid dataset, 800 = maximum coverage. ## `includeTopReviewsFallback` (type: `boolean`): When on, also pulls the featured (highlighted) reviews shown on the product page, in addition to the full set. Useful for a fast first batch of highlighted reviews. Each review billed as one `review` event. ## `stopOnError` (type: `boolean`): If on, the run aborts as soon as one product fails (e.g. UPSTREAM\_BLOCKED). If off (default), the failure is logged as a free diagnostic row and the run continues to the next product. ## Actor input object example ```json { "products": [ "B0CHX1W1XY" ], "country": "us", "starFilter": "all_stars", "sort": "recent", "maxReviewsPerAsin": 300, "includeTopReviewsFallback": false, "stopOnError": false } ``` # Actor output Schema ## `dataset` (type: `string`): The default dataset. Each row is one Amazon review with the common fields flattened to top-level columns and the full raw object under `data`. Use the Reviews tab for a clean table. ## `summary` (type: `string`): A single JSON object written to the default key-value store under the OUTPUT key. Contains country, star\_filter, sort, reviews (count), errors, total\_rows, estimated\_cost\_usd, limit\_reached, stopped\_on\_error and actor\_version. # 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 = { "products": [ "B0CHX1W1XY" ], "maxReviewsPerAsin": 300 }; // Run the Actor and wait for it to finish const run = await client.actor("jdtpnjtp/amazon-reviews").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 = { "products": ["B0CHX1W1XY"], "maxReviewsPerAsin": 300, } # Run the Actor and wait for it to finish run = client.actor("jdtpnjtp/amazon-reviews").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 '{ "products": [ "B0CHX1W1XY" ], "maxReviewsPerAsin": 300 }' | apify call jdtpnjtp/amazon-reviews --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jdtpnjtp/amazon-reviews", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Reviews Scraper", "description": "Scrape full Amazon customer reviews with NO login and NO cookie. Provide ASINs or product URLs - get every review (rating, title, text, author, date, verified, helpful votes, photos) as clean structured rows. Pay only per review, no subscription.", "version": "1.0", "x-build-id": "KQMm2LuzsO3hxWYdi" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jdtpnjtp~amazon-reviews/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jdtpnjtp-amazon-reviews", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jdtpnjtp~amazon-reviews/runs": { "post": { "operationId": "runs-sync-jdtpnjtp-amazon-reviews", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jdtpnjtp~amazon-reviews/run-sync": { "post": { "operationId": "run-sync-jdtpnjtp-amazon-reviews", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "products": { "title": "Products (ASINs or URLs)", "uniqueItems": true, "type": "array", "description": "The products to pull reviews for. Paste an Amazon product URL (any /dp/ or /gp/product/ link) or just its 10-char ASIN (e.g. `B0CHX1W1XY`) - mix both freely, one entry per product. Each product returns its reviews as individual rows, up to the per-product cap below.", "items": { "type": "string" } }, "country": { "title": "Country (Amazon marketplace)", "enum": [ "us", "uk", "de", "fr", "it", "es", "ca", "jp" ], "type": "string", "description": "Which Amazon marketplace to read reviews from.", "default": "us" }, "starFilter": { "title": "Star filter", "enum": [ "all_stars", "five_star", "four_star", "three_star", "two_star", "one_star", "positive", "critical" ], "type": "string", "description": "Which reviews to keep. Default returns the full deduped set across all stars; pick a single rating or sentiment to narrow it.", "default": "all_stars" }, "sort": { "title": "Sort order", "enum": [ "recent", "helpful" ], "type": "string", "description": "Order of the returned reviews.", "default": "recent" }, "maxReviewsPerAsin": { "title": "Max reviews per product", "minimum": 1, "maximum": 800, "type": "integer", "description": "How many reviews to return for EACH product, up to 800. A lower value returns fewer reviews and runs a faster, cheaper harvest - you only pay for reviews actually returned. 50 = quick sample, 300 = solid dataset, 800 = maximum coverage.", "default": 300 }, "includeTopReviewsFallback": { "title": "Also pull featured (top) reviews", "type": "boolean", "description": "When on, also pulls the featured (highlighted) reviews shown on the product page, in addition to the full set. Useful for a fast first batch of highlighted reviews. Each review billed as one `review` event.", "default": false }, "stopOnError": { "title": "Stop the whole run on the first error", "type": "boolean", "description": "If on, the run aborts as soon as one product fails (e.g. UPSTREAM_BLOCKED). If off (default), the failure is logged as a free diagnostic row and the run continues to the next product.", "default": false } } }, "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 } } } } } } } } } } ```