# Airtex Reviews Scraper (`getdataforme/airtex-reviews-scraper`) Actor The Airtex Reviews Scraper efficiently extracts detailed customer reviews from Airtex product pages, offering insights into consumer opinions.... - **URL**: https://apify.com/getdataforme/airtex-reviews-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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 ## Airtex Reviews Scraper ### Introduction The Airtex Reviews Scraper is an efficient tool designed to extract customer reviews from Airtex product pages. It provides valuable insights into consumer opinions, helping businesses make informed decisions based on real user feedback. ### Features - **Comprehensive Data Extraction**: Collects detailed review data including ratings, author details, and timestamps. - **High-Quality Data**: Ensures reliable and accurate information for analysis. - **Customizable Limits**: Allows setting a maximum number of reviews per product to suit your needs. - **Efficient Performance**: Optimized for fast and effective scraping operations. - **User-Friendly Interface**: Easy setup and configuration through the Apify Store. ### Input Parameters | Parameter | Type | Required | Description | Example | |--------------|----------|----------|-----------------------------------------------------------------------------|-------------------------------------------------------------------------| | `startUrls` | array | Yes | URLs of Airtex product pages to scrape reviews from. | `[{"url": "https://airtex.in/products/men-premium-t-shirt-light-grey-and-black-new"}]` | | `maxItems` | integer | No | Maximum number of reviews to collect per product. | `100` | ### Example Usage #### Input JSON ```json { "startUrls": [ { "url": "https://airtex.in/products/men-premium-t-shirt-light-grey-and-black-new" } ], "maxItems": 100 } ```` #### Output JSON ```json [ { "review_id": "bd0f93c7-ac6d-41b0-a8db-bfe1a9436e78", "verified_buyer": false, "product_title": "Men premium T Shirt Light Grey and Black New", "product_url": "https://airtex.in//products/men-premium-t-shirt-light-grey-and-black-new", "rating": 5, "author": "Rohan Verma", "title": "very fast service and super quality", "body": "This T-shirt feels like second skin. The stitching is flawless and the fit is just right. Definitely worth the price for the comfort it offers.", "timestamp": "2025-06-14 06:08:18 UTC", "review_pics": [ "https://review-images.judge.me/airtex/1749881298__whatsappimage2025-06-13at94745am__original.jpeg?quality=80&width=160" ], "transparency_badge": "review_collected_from_store_visitor", "scraped_at": "2026-07-03T08:28:23.197Z" } ] ``` ### Use Cases - **Market Research and Analysis**: Gain insights into customer preferences and trends. - **Competitive Intelligence**: Understand competitor strengths and weaknesses through user feedback. - **Price Monitoring**: Track pricing strategies based on reviews and ratings. - **Content Aggregation**: Compile review data for content creation or marketing purposes. - **Academic Research**: Use real-world data for studies in consumer behavior. - **Business Automation**: Integrate with business processes to automate decision-making. ### Installation and Usage 1. Search for "Airtex Reviews Scraper" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is a JSON array of review objects, each containing: - `review_id`: Unique identifier for the review. - `verified_buyer`: Boolean indicating if the reviewer is verified. - `product_title`: Title of the product being reviewed. - `product_url`: URL of the product page. - `rating`: Rating given by the reviewer (1 to 5). - `author`: Name of the reviewer. - `title`: Title of the review. - `body`: Full text of the review. - `timestamp`: Date and time when the review was posted. - `review_pics`: Array of URLs for any images included in the review. - `transparency_badge`: Indicates the source of the review collection. - `scraped_at`: Timestamp indicating when the data was scraped. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [Contact Us](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `startUrls` (type: `array`): URLs of Airtex product pages to scrape reviews from ## `maxItems` (type: `integer`): Maximum number of reviews to collect per product ## Actor input object example ```json { "startUrls": [ { "url": "https://airtex.in/products/men-premium-t-shirt-light-grey-and-black-new" } ], "maxItems": 100 } ``` # Actor output Schema ## `results` (type: `string`): No description # 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 = { "startUrls": [ { "url": "https://airtex.in/products/men-premium-t-shirt-light-grey-and-black-new" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/airtex-reviews-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 = { "startUrls": [{ "url": "https://airtex.in/products/men-premium-t-shirt-light-grey-and-black-new" }] } # Run the Actor and wait for it to finish run = client.actor("getdataforme/airtex-reviews-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 '{ "startUrls": [ { "url": "https://airtex.in/products/men-premium-t-shirt-light-grey-and-black-new" } ] }' | apify call getdataforme/airtex-reviews-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/airtex-reviews-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Airtex Reviews Scraper", "description": "The Airtex Reviews Scraper efficiently extracts detailed customer reviews from Airtex product pages, offering insights into consumer opinions....", "version": "0.0", "x-build-id": "8L3K5PuVgfzykdfpR" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~airtex-reviews-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-airtex-reviews-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~airtex-reviews-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-airtex-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~airtex-reviews-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-airtex-reviews-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "startUrls" ], "properties": { "startUrls": { "title": "Product URLs", "type": "array", "description": "URLs of Airtex product pages to scrape reviews from", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "maxItems": { "title": "Max Reviews", "minimum": 1, "type": "integer", "description": "Maximum number of reviews to collect per product", "default": 100 } } }, "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 } } } } } } } } } } ```