# Lundsandberlys Parser Spider (`getdataforme/lundsandberlys-parser-spider`) Actor The Lundsandberlys Parser Spider extracts rich product data from Lunds & Byerlys, including titles, prices, brands, categories, stock, and imagesβ€”ideal for grocery analytics, retail monitoring, and e-commerce catalog enrichment. - **URL**: https://apify.com/getdataforme/lundsandberlys-parser-spider.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 0 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 --- ## πŸ•·οΈ Lundsandberlys Parser Spider The **Lundsandberlys Parser Spider** is an Apify Actor built to extract **detailed product information** from [Lunds & Byerlys](https://www.lundsandbyerlys.com) β€” a well-known U.S. grocery retailer. It accurately parses product details from individual product URLs, returning rich structured data ideal for analytics, monitoring, and product catalog management. --- ### πŸ“˜ How It Works Provide one or more **product URLs**, and the Actor will scrape detailed data such as the product name, brand, pricing (list/sale), category, availability, and image URL. This Actor is perfect for: * Tracking grocery product details and discounts * Market and competitor price monitoring * Product catalog enrichment for retail analytics --- ### βš™οΈ Input Example ```json { "proxy": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "US" }, "Urls": [ "https://www.lundsandbyerlys.com/sm/pickup/rsid/1007/product/l&b-ready-to-heat-chicken-tenders-id-00262689000000" ] } ```` #### Input Fields | Field | Type | Description | | --------------------- | ------- | --------------------------------------------------------------- | | **proxy** | object | Proxy configuration for accessing product pages safely. | | **useApifyProxy** | boolean | Enables Apify Proxy; recommended to set as `true`. | | **apifyProxyGroups** | array | Recommended: `["RESIDENTIAL"]` for stable scraping performance. | | **apifyProxyCountry** | string | Sets the proxy country (e.g., `"US"`). | | **Urls** | array | List of full product URLs to scrape data from. | *** ### πŸ“€ Output Example The Actor returns structured product data like this: ```json [ { "source_metadata": { "source_id": "www.lundsandbyerlys.com", "source_url": "https://www.lundsandbyerlys.com/sm/pickup/rsid/1007/product/l&b-ready-to-heat-chicken-tenders-id-00262689000000", "extraction_timestamp": "2025-10-19T12:07:51.776720Z", "scraper_version": "1.0.0" }, "product": { "product_id": "00262689000000", "title": "L&B Ready To Heat Chicken Tenders", "description": "Pre-cooked, boneless, skinless deep fried all white meat chicken tenders crafted from hand-cut chicken breast...", "category": "Departments, Deli, Prepared Foods, Entrees", "brand": "Lunds & Byerlys", "sku": "00262689000000" }, "pricing": { "currency": "USD", "list_price": "was $8.99 avg/ea", "sale_price": "$4.49 avg/ea", "discount_percentage": 50.06 }, "availability": { "is_in_stock": true, "stock_quantity": 0 }, "images": "https://storage.googleapis.com/images-lnb-prd-8936dd0.lnb.prd.v8.commerce.mi9cloud.com/product-images/detail/00262689000000.png", "attributes": null, "actor_id": "s9Zm33uTRqZFe1Elc", "run_id": "SoyBVBFrKfAoAUgqK" } ] ``` *** ### 🌟 Features - πŸ›’ Extracts comprehensive product data from Lunds & Byerlys product pages - πŸ’² Captures both **regular** and **sale pricing** including discount percentage - 🏷️ Retrieves product metadata such as category, brand, SKU, and ID - πŸ–ΌοΈ Collects clean image URLs for all product listings - ⏱️ Includes timestamp and versioning for traceable data extractions *** ### πŸ’‘ Use Cases - **Grocery Retail Analytics:** Monitor product listings, sales, and discount trends - **Price Comparison Tools:** Keep tabs on local or national grocery pricing - **E-commerce Enrichment:** Add missing product info to internal databases - **Retail Intelligence:** Understand category-wise product positioning and stock *** ### 🧠 Notes - Residential proxies are **strongly recommended** for stable and accurate scraping. - The scraper supports **multiple URLs** per run. - Output is in **structured JSON** format, easy to integrate into pipelines or APIs. - All timestamps are in **UTC (ISO 8601)** format. *** ### πŸ› οΈ Support For bug reports, feature requests, or custom output needs, please contact: πŸ“§ **** 🌐 > **Email Subject:** `Lundsandberlys Parser Spider - Support Request` *** #### 🧩 Version **Current release:** `v1.0.0` **Maintainer:** GetDataForMe Team **Source:** *** # Actor input Schema ## `Urls` (type: `array`): The urls for the spider. ## `proxy` (type: `object`): Proxy settings for the spider. ## Actor input object example ```json { "Urls": [ "https://www.lundsandbyerlys.com/sm/pickup/rsid/1007/product/l&b-ready-to-heat-chicken-tenders-id-00262689000000" ], "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } ``` # 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 = { "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/lundsandberlys-parser-spider").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "proxy": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], "apifyProxyCountry": "US", } } # Run the Actor and wait for it to finish run = client.actor("getdataforme/lundsandberlys-parser-spider").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "proxy": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } }' | apify call getdataforme/lundsandberlys-parser-spider --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/lundsandberlys-parser-spider", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Lundsandberlys Parser Spider", "description": "The Lundsandberlys Parser Spider extracts rich product data from Lunds & Byerlys, including titles, prices, brands, categories, stock, and imagesβ€”ideal for grocery analytics, retail monitoring, and e-commerce catalog enrichment.", "version": "0.0", "x-build-id": "q8ZqBSdItjDEqZhMf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~lundsandberlys-parser-spider/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-lundsandberlys-parser-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~lundsandberlys-parser-spider/runs": { "post": { "operationId": "runs-sync-getdataforme-lundsandberlys-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~lundsandberlys-parser-spider/run-sync": { "post": { "operationId": "run-sync-getdataforme-lundsandberlys-parser-spider", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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.lundsandbyerlys.com/sm/pickup/rsid/1007/product/l&b-ready-to-heat-chicken-tenders-id-00262689000000" ], "items": { "type": "string" } }, "proxy": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for the spider.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ], "apifyProxyCountry": "US" } } } }, "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 } } } } } } } } } } ```