# Amazon Productinfo Spider (`getdataforme/amazon-productinfo-spider`) Actor The Amazon Productinfo Spider efficiently extracts detailed product information from Amazon pages, capturing key details like ASIN, title, brand, specifications, and more. It offers high-quality, accurate data collection with flexible configuration for multiple URLs and item limits.... - **URL**: https://apify.com/getdataforme/amazon-productinfo-spider.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 --- ## Amazon Productinfo Spider ### Introduction The Amazon Productinfo Spider is a powerful tool designed to extract detailed product information from Amazon product pages. It provides comprehensive data including ASIN, title, brand, specifications, and more, enabling users to gather valuable insights for various applications. ### Features - **Comprehensive Data Extraction**: Captures essential product details such as ASIN, title, brand, screen size, processor model, RAM, storage, operating system, rating, review count, and availability. - **High-Quality Data**: Ensures accurate and reliable data collection from Amazon pages. - **Flexible Configuration**: Allows users to specify multiple URLs for scraping and set item limits per run. - **Efficient Performance**: Optimized for speed and efficiency in data extraction processes. - **User-Friendly Setup**: Simple configuration with clear input parameters, making it accessible for both technical and non-technical users. ### Input Parameters | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|----------------------------------------------| | `Urls` | array | Yes | The URLs for the spider. Must be valid Amazon product page links. | `["https://www.amazon.fr/-/en/Lenovo-Laptop-i5-13420H-1000GB-Windows/dp/B0DT7532C9/%22"]` | | `item_limit`| integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "Urls": [ "https://www.amazon.fr/-/en/Lenovo-Laptop-i5-13420H-1000GB-Windows/dp/B0DT7532C9/%22" ], "item_limit": 10 } ```` #### Output JSON ```json [ { "url": "https://www.amazon.fr/-/en/Lenovo-Laptop-i5-13420H-1000GB-Windows/dp/B0DT7532C9/%22", "asin": "B0DT7532C9", "title": "Lenovo, 15.6\" Full HD Laptop, Intel Core i5-13420H, 24GB DDR4 RAM, 1000GB NVMe SSD, Windows 11 Pro Laptop", "brand": "Lenovo", "screen_size": "15.6 inch", "processor_model": "13420H", "ram_size": "24 GB", "ram_type": "DDR4", "storage_size": "1000 GB SSD", "operating_system": "Windows 11 Pro", "rating": "4.2", "review_count": "149", "availability": "Currently unavailable.", "actor_id": "7Q7Ck2H8cwT5f8Kfr", "run_id": "JcMmoibEZQPwgpGJ6" } ] ``` ### Use Cases - **Market Research and Analysis**: Gather data on product specifications, pricing, and availability to analyze market trends. - **Competitive Intelligence**: Monitor competitor products for strategic insights. - **Price Monitoring**: Track price changes over time to identify opportunities or threats. - **Content Aggregation**: Compile product information for content creation or comparison websites. - **Academic Research**: Use data for studies on consumer behavior and e-commerce dynamics. - **Business Automation**: Integrate with business processes for automated reporting and decision-making. ### Installation and Usage 1. Search for "Amazon Productinfo Spider" 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 where each object represents a product with fields such as `url`, `asin`, `title`, `brand`, `screen_size`, `processor_model`, `ram_size`, `ram_type`, `storage_size`, `operating_system`, `rating`, `review_count`, and `availability`. Each entry also includes unique identifiers like `actor_id` and `run_id`. ### Error Handling The spider is designed to handle common errors such as invalid URLs or network issues gracefully. If an error occurs, it logs the issue for review without halting the entire process. ### Rate Limiting and Best Practices - **Respect Amazon's Terms of Service**: Ensure compliance with Amazon's scraping policies. - **Use Appropriate Delays**: Implement delays between requests to avoid being blocked. - **Monitor Performance**: Regularly check logs for any issues or rate limiting responses from Amazon. ### Limitations and Considerations - The spider is tailored for Amazon product pages; other websites may require different configurations. - Ensure URLs are valid and accessible to prevent errors during scraping. - Be mindful of potential changes in Amazon's page structure, which could affect data extraction accuracy. # Actor input Schema ## `Urls` (type: `array`): The urls for the spider. ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "Urls": [ "https://www.amazon.fr/gp/aw/d/B0BSWVPLPY?pd_rd_plhdr=t&hsa_cr_id=0&qid=1781248381&sr=1-3-9fd4e6c6-0f32-411b-9abc-dfd97dd0cadd&i=computers&aref=uj2qZkqmqO&th=1" ], "item_limit": 10 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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("getdataforme/amazon-productinfo-spider").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("getdataforme/amazon-productinfo-spider").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 getdataforme/amazon-productinfo-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/amazon-productinfo-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Productinfo Spider", "description": "The Amazon Productinfo Spider efficiently extracts detailed product information from Amazon pages, capturing key details like ASIN, title, brand, specifications, and more. It offers high-quality, accurate data collection with flexible configuration for multiple URLs and item limits....", "version": "0.0", "x-build-id": "exmknFNnOUMqjwVO7" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~amazon-productinfo-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-amazon-productinfo-spider", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~amazon-productinfo-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-amazon-productinfo-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~amazon-productinfo-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-amazon-productinfo-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "Urls": { "title": "Urls", "minItems": 1, "type": "array", "description": "The urls for the spider.", "default": [ "https://www.amazon.fr/gp/aw/d/B0BSWVPLPY?pd_rd_plhdr=t&hsa_cr_id=0&qid=1781248381&sr=1-3-9fd4e6c6-0f32-411b-9abc-dfd97dd0cadd&i=computers&aref=uj2qZkqmqO&th=1" ], "items": { "type": "string" } }, "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```