# Lazada Singapore Product Scraper (`alwaysprimedev/lazada-sg-product-scraper`) Actor Scrape Lazada Singapore product details from URLs or the public sitemap. Fast, pay-per-result, no setup. - **URL**: https://apify.com/alwaysprimedev/lazada-sg-product-scraper.md - **Developed by:** [Always Prime](https://apify.com/alwaysprimedev) (community) - **Categories:** Automation, E-commerce, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $4.00 / 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 ## 🛒 Lazada Singapore Product Scraper ⚡ïļ **Scrape any Lazada SG product page into clean structured JSON.** Pay only for the products you actually get. No setup, no proxies to configure, no captcha tickets to buy. 🚀 ![Python 3.11](https://img.shields.io/badge/python-3.11-blue) ![Output JSON CSV XLSX](https://img.shields.io/badge/output-JSON%20%7C%20CSV%20%7C%20Excel-brightgreen) ![Built for Apify](https://img.shields.io/badge/built%20for-Apify-orange) --- ### ðŸĪ– Why this scraper - 💰 **Pay-per-result, $9 per 1 000 products** — among the cheapest Lazada actors on the store, by design. - 🧠 **Sitemap-aware** — point it at "Recent 30 days" and it walks Lazada's own product sitemap so you never have to maintain a URL list. - ðŸ“Ķ **Full product record** — title, price, currency, brand, full breadcrumb category, all photos, every SKU variant + its option axes (color, size, etc.), seller ID, stock state, and the full HTML description. - 🔁 **Incremental mode** — pass a `since` date and the scraper only fetches products whose sitemap `lastmod` is newer. Daily refreshers stay tiny. - ðŸŠķ **Lightweight** — 256 MB Apify memory, no headless browser, no Selenium. Fast and cheap to run. - ⏱ïļ **Real-time progress** — periodic STATS write to the key-value store so you can monitor mid-run. --- ### 🚀 Quick start 1. Click **Try for free** at the top of this page. 2. Either paste a few Lazada product URLs in the *Product URLs* field, or pick a *Sitemap mode* (start with **Recent 30 days**). 3. Set *Max items* (start with 25 for a quick test) and hit **Start**. 4. When the run finishes (~5–15 s per product), open the **Storage → Dataset** tab and click **Download** in JSON, CSV, or Excel. --- ### ðŸ“Ĩ Input | Field | Description | Default | | --- | --- | --- | | **Product URLs** | List of Lazada SG `/products/...iXXXX.html` URLs. Use this when you already have a target list (your own catalog, a competitor's SKUs, etc.). | one example URL | | **Sitemap mode** | If you don't have URLs, the scraper pulls them straight from Lazada's public sitemap. *Recent 30 days* covers the freshest catalog (products with sales in the last 30 days). *All* walks the full sitemap (millions of URLs). | `none` | | **Max items** | Stop after this many successfully scraped products. `0` = unlimited. | `50` | | **Scrape full product details** | Off = return only `{url, id, scraped_at}` (a cheap inventory-diff mode). On = full product record per URL. | `on` | | **Only products updated since** | Optional ISO date. In sitemap mode, skip products whose sitemap `lastmod` is older. Perfect for daily refresh runs. | empty | | **Categories** | Optional list. Paste any Lazada SG category URL (e.g. `https://www.lazada.sg/shop-mobile-phones/`) or just type a name (e.g. `Mobiles`, `Wireless Earbuds`). The scraper walks the product sitemap and **skips URLs whose slug doesn't contain any of the category keywords — before fetching them**, so proxy bandwidth stays minimal. A second pass verifies the product's breadcrumb actually matches, eliminating false positives. Multiple categories = OR. | empty | | **Concurrency** | Parallel page fetches. 5 is a healthy default; pushing past 10 stops helping. | `5` | > **How the category filter works**: Lazada's category landing pages (`/shop-mobile-phones/`) render listings via client-side JavaScript, so a pure-HTTP scraper can't read them. This actor takes a different route: it pulls candidate URLs from Lazada's own product sitemap, then keeps only the URLs whose slug contains a keyword derived from your category name. The result is a category-scoped scrape with the same low proxy cost as an unfiltered run — at the cost of recall (a product whose slug doesn't carry a category keyword will be missed). Combine with Sitemap mode = "Recent 30 days" for a fresh slice. --- ### ðŸ“Ī Output (sample, one record) ```json { "url": "https://www.lazada.sg/products/magnolia-wafer-ice-cream-sandwich-raspberry-ripple-durian-yam-chocolate-blueberry-ripple-i2470744437.html", "id": "2470744437", "sku_id": "14715727747", "title": "Magnolia Wafer Ice Cream Sandwich - Raspberry Ripple, Durian, Yam, Chocolate, Blueberry Ripple", "brand": "Magnolia", "brand_id": "16832", "categories": ["Groceries", "Frozen", "Ice Cream"], "price": 1.68, "price_display": "$1.68", "currency": "SGD", "discount_pct": null, "in_stock": true, "seller_id": "1158102029", "seller_name": null, "rating": null, "review_count": null, "image_main": "https://laz-img-sg.alicdn.com/p/9ba2a4e4105e4c3ba9184da5b951bd91.jpg", "images": [ "https://laz-img-sg.alicdn.com/p/9ba2a4e4105e4c3ba9184da5b951bd91.jpg", "https://laz-img-sg.alicdn.com/p/2c8d6d77b5128f4a35551a3fad55fe55.jpg" ], "options": [ { "name": "flavour", "values": ["Raspberry Ripple", "Durian", "Yam", "Chocolate", "Blueberry Ripple"] } ], "variants": [ { "sku_id": "14715727747", "inner_sku_id": "2470744437_SGAMZ-14715727747", "prop_path": "flavour:Raspberry Ripple", "url": "https://www.lazada.sg/products/...-s14715727747.html" } ], "description_html": "
â€Ķ
", "country": "SG", "language": "en_SG", "scraped_at": "2026-05-14T23:42:11Z" } ```` > **Note on `rating`, `review_count`, `seller_name`**: these load asynchronously on the live Lazada page and aren't in the server-rendered product page. They'll be `null` on every record in the v0.1 release. A v1.0 enrichment pass is planned. *** ### ðŸ’Ą Use cases | Who | What for | | --- | --- | | 🛒 Online sellers | Track competitor prices, stock, and new SKUs daily. | | 📊 Market researchers | Build the Singapore e-commerce dataset they always wanted — by category, by brand, by price band. | | ðŸĪ– ML / AI teams | Train product classifiers, recommendation models, or image embeddings on a real catalog. | | 🔍 Brand monitors | Find counterfeit listings of your products on Lazada. Daily incremental mode keeps the alert list fresh. | | ðŸ’ļ Arbitrage shoppers | Spot Lazada SG ↔ Shopee / Amazon SG price gaps in real time. | *** ### ✅ Tips & tricks - **Daily refresh**: Schedule a daily Apify run with `sitemapMode: recent_30days` and `since: `. Only new/changed products will be fetched — minimal cost. - **Big batches**: For 10k+ products, raise concurrency to 10. Above that, Lazada starts slowing responses without giving you more throughput. - **CSV-flat output**: When downloading as CSV, the nested `options` and `variants` arrays auto-flatten with column prefixes — great for Excel pivots. ### ❓ FAQ **Q: Why isn't `rating`/`review_count` filled in?** Lazada's product page loads those values via a second JavaScript call after the page renders, so they're not in the page HTML. We're working on an opt-in v1.0 enrichment pass. **Q: Can I scrape from search queries like "iphone case"?** Not in v0.1 — the search page also loads results via JavaScript. For now, use sitemap mode or paste category/product URLs directly. Free-text search is on the roadmap. **Q: Does it support other Lazada countries (TH, MY, PH, VN, ID)?** v0.1 is SG-only. The same data shape exists on every Lazada region, so multi-country support is planned for v1. **Q: Will my IP get blocked?** No — the scraper handles all anti-bot bypass internally. You don't need to bring or configure proxies. **Q: How fast is it?** ~5–10 seconds per product at default concurrency. A 1 000-product run usually finishes in 5–10 minutes. *** ### 🛟 Support Open an issue on this actor's *Issues* tab and we'll take a look. PRs welcome from anyone running their own fork. # Actor input Schema ## `startUrls` (type: `array`): Paste any number of Lazada SG product URLs (the long /products/...iXXXX.html links). The scraper visits each one and returns the full product record. Leave empty to use the sitemap mode below. ## `sitemapMode` (type: `string`): If you don't have a URL list, the scraper can pull URLs straight from Lazada's public sitemap. "Recent 30 days" is the recommended starting point — it's the freshest sub-sitemap Lazada publishes and contains products with sales in the last 30 days. ## `maxItems` (type: `integer`): Stop the run after this many successfully scraped products. 0 = unlimited (use carefully with sitemap modes). ## `scrapeDetails` (type: `boolean`): Turn off to return only URLs + product IDs (a cheap discovery / inventory-diff mode). Leave on to fetch the full product page for every URL and return title, price, images, variants, seller, etc. ## `categoryUrls` (type: `array`): Optional. Paste category URLs from Lazada's home page (e.g. https://www.lazada.sg/shop-mobile-phones/) or just type category names (e.g. "Mobiles", "Wireless Earbuds"). The scraper walks Lazada's product sitemap and keeps only products whose URL slug AND/OR breadcrumb match — without ever fetching the irrelevant ones, so proxy bandwidth stays low. Multiple categories work as OR. Combine with Sitemap mode = "Recent 30 days" to harvest a fresh vertical. ## `since` (type: `string`): Optional. When set, sitemap-mode runs skip products whose sitemap is older than this date — useful for incremental refresh runs. Ignored when scraping from the URL list above. Leave blank for a full pull. ## `concurrency` (type: `integer`): Number of product pages to fetch in parallel. 5 is a good balance of speed and politeness; pushing past 10 may trigger temporary rate-limits and hurt throughput. ## Actor input object example ```json { "startUrls": [ "https://www.lazada.sg/products/magnolia-wafer-ice-cream-sandwich-raspberry-ripple-durian-yam-chocolate-blueberry-ripple-i2470744437.html" ], "sitemapMode": "none", "maxItems": 50, "scrapeDetails": true, "categoryUrls": [], "concurrency": 5 } ``` # Actor output Schema ## `listings` (type: `string`): No description ## `listingsCsv` (type: `string`): No description ## `listingsXlsx` (type: `string`): No description ## `consoleView` (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": [ "https://www.lazada.sg/products/magnolia-wafer-ice-cream-sandwich-raspberry-ripple-durian-yam-chocolate-blueberry-ripple-i2470744437.html" ], "maxItems": 50, "categoryUrls": [], "since": "", "concurrency": 5 }; // Run the Actor and wait for it to finish const run = await client.actor("alwaysprimedev/lazada-sg-product-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`ðŸ’ū 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": ["https://www.lazada.sg/products/magnolia-wafer-ice-cream-sandwich-raspberry-ripple-durian-yam-chocolate-blueberry-ripple-i2470744437.html"], "maxItems": 50, "categoryUrls": [], "since": "", "concurrency": 5, } # Run the Actor and wait for it to finish run = client.actor("alwaysprimedev/lazada-sg-product-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("ðŸ’ū 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": [ "https://www.lazada.sg/products/magnolia-wafer-ice-cream-sandwich-raspberry-ripple-durian-yam-chocolate-blueberry-ripple-i2470744437.html" ], "maxItems": 50, "categoryUrls": [], "since": "", "concurrency": 5 }' | apify call alwaysprimedev/lazada-sg-product-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=alwaysprimedev/lazada-sg-product-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Lazada Singapore Product Scraper", "description": "Scrape Lazada Singapore product details from URLs or the public sitemap. Fast, pay-per-result, no setup.", "version": "0.1", "x-build-id": "HMp86OrYfQIRngQMQ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/alwaysprimedev~lazada-sg-product-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-alwaysprimedev-lazada-sg-product-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/alwaysprimedev~lazada-sg-product-scraper/runs": { "post": { "operationId": "runs-sync-alwaysprimedev-lazada-sg-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/alwaysprimedev~lazada-sg-product-scraper/run-sync": { "post": { "operationId": "run-sync-alwaysprimedev-lazada-sg-product-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "startUrls": { "title": "Product URLs", "type": "array", "description": "Paste any number of Lazada SG product URLs (the long /products/...iXXXX.html links). The scraper visits each one and returns the full product record. Leave empty to use the sitemap mode below.", "items": { "type": "string" } }, "sitemapMode": { "title": "Sitemap mode", "enum": [ "none", "recent_30days", "all" ], "type": "string", "description": "If you don't have a URL list, the scraper can pull URLs straight from Lazada's public sitemap. \"Recent 30 days\" is the recommended starting point — it's the freshest sub-sitemap Lazada publishes and contains products with sales in the last 30 days.", "default": "none" }, "maxItems": { "title": "Max items", "minimum": 0, "type": "integer", "description": "Stop the run after this many successfully scraped products. 0 = unlimited (use carefully with sitemap modes)." }, "scrapeDetails": { "title": "Scrape full product details", "type": "boolean", "description": "Turn off to return only URLs + product IDs (a cheap discovery / inventory-diff mode). Leave on to fetch the full product page for every URL and return title, price, images, variants, seller, etc.", "default": true }, "categoryUrls": { "title": "Categories", "type": "array", "description": "Optional. Paste category URLs from Lazada's home page (e.g. https://www.lazada.sg/shop-mobile-phones/) or just type category names (e.g. \"Mobiles\", \"Wireless Earbuds\"). The scraper walks Lazada's product sitemap and keeps only products whose URL slug AND/OR breadcrumb match — without ever fetching the irrelevant ones, so proxy bandwidth stays low. Multiple categories work as OR. Combine with Sitemap mode = \"Recent 30 days\" to harvest a fresh vertical.", "items": { "type": "string" } }, "since": { "title": "Only products updated since (incremental mode)", "type": "string", "description": "Optional. When set, sitemap-mode runs skip products whose sitemap is older than this date — useful for incremental refresh runs. Ignored when scraping from the URL list above. Leave blank for a full pull." }, "concurrency": { "title": "Concurrency", "minimum": 1, "maximum": 25, "type": "integer", "description": "Number of product pages to fetch in parallel. 5 is a good balance of speed and politeness; pushing past 10 may trigger temporary rate-limits and hurt throughput." } } }, "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 } } } } } } } } } } ```