# Openfoodfacts Profile Scraper (`getdataforme/openfoodfacts-profile-scraper`) Actor Scrape comprehensive food product data from Open Food Facts, including nutritional insights, ingredients, allergens, eco-scores, and images. Perfect for market research, competitive analysis, and academic studies. Delivers structured JSON for easy export and integration.... - **URL**: https://apify.com/getdataforme/openfoodfacts-profile-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Automation, Lead generation, Other - **Stats:** 3 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 ## Openfoodfacts Profile Scraper ### Introduction The Openfoodfacts Profile Scraper is a powerful Apify Actor designed to extract comprehensive product information from Open Food Facts, the world's largest open database of food products. It enables users to scrape detailed profiles including nutritional data, ingredients, allergens, and environmental scores from product pages. This tool is ideal for researchers, businesses, and developers needing structured data for analysis, compliance, or integration into applications. ### Features - **Comprehensive Data Extraction**: Scrapes detailed product profiles including title, barcode, quantity, packaging, brands, categories, origins, and more. - **Nutritional Insights**: Captures nutriscore, nutrient levels, nutrition facts, serving size, and ingredient analysis. - **Environmental and Health Metrics**: Includes eco-score, carbon footprint, nova group, and allergen information. - **Image and Media Support**: Extracts product images (front, nutrition, ingredients, packaging) with URLs and metadata. - **Reliable and Scalable**: Built on CheerioCrawler for fast, efficient scraping with minimal resource usage. - **Structured Output**: Delivers clean JSON data ready for export to CSV, Excel, or databases. - **Open Source Data Source**: Leverages Open Food Facts' community-driven database for accurate, up-to-date information. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|--------|----------|-------------|---------| | startUrls | array | Yes | An array of URLs to start scraping from. Each URL should point to a product page on Open Food Facts. | [{"url": "https://world.openfoodfacts.org/product/6111180000231/levure-patisserie"}] | ### Example Usage #### Input ```json { "startUrls": [ { "url": "https://world.openfoodfacts.org/product/6111180000231/levure-patisserie" } ] } ```` #### Output ```json [ { "title": "levure pâtisserie – – 80 g", "barcode": { "code": "6111180000231", "type": "EAN", "fullText": "Barcode: 6111180000231(EAN / EAN-13)" }, "quantity": "80 g", "packaging": { "materials": [], "text": "" }, "brands": { "list": [], "text": "" }, "categories": { "list": [ "Food additives", "Baking powder or raising agent" ], "text": "Food additives, Baking powder or raising agent" }, "origins": { "list": [], "text": "" }, "manufacturingPlaces": { "list": [], "text": "" }, "traceabilityCode": "N/A", "productUrl": "N/A", "countriesSold": { "list": [ "Morocco" ], "text": "Morocco" }, "nutriscore": { "grade": "Unknown", "status": "Not applicable for the category: Food additives", "missingData": "N/A", "icon": "https://static.openfoodfacts.org/images/attributes/dist/nutriscore-not-applicable-new-en.svg" }, "nutrientLevels": [], "nutritionFacts": [ { "nutrient": "Energy", "per100g": "766 kj(183 kcal)", "perServing": "+50%", "comparison": "N/A" }, { "nutrient": "Fat", "per100g": "0 g", "perServing": "-100%", "comparison": "N/A" }, { "nutrient": "Saturated fat", "per100g": "0 g", "perServing": "-100%", "comparison": "N/A" }, { "nutrient": "Carbohydrates", "per100g": "43.4 g", "perServing": "+74%", "comparison": "N/A" }, { "nutrient": "Sugars", "per100g": "0 g", "perServing": "-100%", "comparison": "N/A" }, { "nutrient": "Fiber", "per100g": "0 g", "perServing": "-100%", "comparison": "N/A" }, { "nutrient": "Proteins", "per100g": "0 g", "perServing": "-100%", "comparison": "N/A" }, { "nutrient": "Salt", "per100g": "0 g", "perServing": "-100%", "comparison": "N/A" }, { "nutrient": "Alcohol", "per100g": "0 % vol", "perServing": "", "comparison": "N/A" }, { "nutrient": "Fruits, vegetables, nuts and rapeseed, walnut and olive oils (estimate from ingredients list analysis)", "per100g": "0 %", "perServing": "", "comparison": "N/A" } ], "servingSize": "N/A", "ingredients": { "text": "French: \n Bicarbonate de sodium, amidon de maïs, pyrophosphate de sodium", "allergens": [ "" ], "estimatedComposition": [ "E500ii: 66.7% (estimate)", "Corn starch: 16.7% (estimate)", "E450i: 16.7% (estimate)" ] }, "allergens": [], "ingredientsAnalysis": { "palm_oil_free": { "title": "Palm oil free", "status": "Palm oil free", "details": "No ingredients containing palm oil detected" }, "vegan": { "title": "Vegan", "status": "Vegan", "details": "No non-vegan ingredients" }, "vegetarian": { "title": "Vegetarian", "status": "Vegetarian", "details": "No non-vegetarian ingredients detected" } }, "novaGroup": { "group": "Ultra-processed foods\n \n \n \n \n Limit ultra-processed foods", "description": "Several studies have found that a lower consumption of ultra-processed foods is associated with a reduced risk of noncommunicable chronic diseases, such as obesity, hypertension and diabetes.", "icon": "https://static.openfoodfacts.org/images/attributes/dist/nova-group-4.svg" }, "ecoScore": { "grade": "C\n \n \n \n \n Average impact of products of the same category: B (Score: 72/100)", "score": "Moderate environmental impact", "description": "⚠️\n ️Select a country in order to include the full impact of transportation." }, "carbonFootprint": { "equivalent": "Equal to driving 1.1 km in a petrol car", "co2Per100g": "211 g CO₂e per 100g of product", "details": "The carbon emission figure comes from ADEME's Agribalyse database, for the category: \n Baking powder or raising agent\n (Source: ADEME Agribalyse Database)" }, "packagingInfo": { "components": [ "Individual bag\n \n \n \n \n - In contact with food" ], "materials": [] }, "transportation": [], "images": [ { "type": "front", "url": "https://images.openfoodfacts.org/images/products/611/118/000/0231/front_fr.117.400.jpg", "alt": "levure pâtisserie - Product - fr", "width": "302", "height": "400" }, { "type": "nutrition", "url": "https://images.openfoodfacts.org/images/products/611/118/000/0231/nutrition_fr.83.400.jpg", "alt": "levure pâtisserie - Nutrition facts - fr", "width": "301", "height": "400" }, { "type": "ingredients", "url": "https://images.openfoodfacts.org/images/products/611/118/000/0231/ingredients_en.9.400.jpg", "alt": "levure pâtisserie - Ingredients", "width": "170", "height": "400" }, { "type": "packaging", "url": "https://images.openfoodfacts.org/images/products/611/118/000/0231/packaging_fr.56.400.jpg", "alt": "levure pâtisserie - Recycling instructions and/or packaging information - fr", "width": "300", "height": "400" } ], "dataSources": [ { "type": "og:url", "content": "https://world.openfoodfacts.org/product/6111180000231/levure-patisserie" }, { "type": "og:image", "content": "https://static.openfoodfacts.org/images/logos/off-logo-vertical-white-social-media-preview.png" }, { "type": "canonical", "content": "https://world.openfoodfacts.org/product/6111180000231/levure-patisserie" }, { "type": "og:image", "content": "https://images.openfoodfacts.org/images/products/611/118/000/0231/front_fr.117.200.jpg" } ], "editHistory": { "addedDate": "N/A", "lastEditDate": "N/A", "editors": [] } } ] ``` ### Use Cases - **Market Research**: Analyze product trends, categories, and nutritional profiles across regions. - **Competitive Intelligence**: Monitor competitors' product offerings, ingredients, and eco-scores. - **Price Monitoring**: Track product details for pricing strategies in retail or e-commerce. - **Content Aggregation**: Build databases for food blogs, apps, or educational platforms. - **Academic Research**: Study nutritional data, allergens, and environmental impacts for studies. - **Business Automation**: Integrate scraped data into supply chain management or compliance systems. ### Installation and Usage 1. Search for "Openfoodfacts Profile Scraper" in the Apify Store 2. Click "Try for free" or "Run" 3. Configure input parameters 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 Actor outputs an array of JSON objects, each representing a scraped product profile. Key fields include basic info (title, barcode, quantity), nutritional data (nutriscore, nutrition facts), ingredients and allergens, environmental metrics (eco-score, carbon footprint), packaging details, and images with URLs. Data is structured for easy parsing and export, with fields like "list" and "text" for categorized information. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** Extract comprehensive food product data from Open Food Facts with ease. Features include nutritional insights, eco-scores, and allergen detection. Perfect for market research, competitive analysis, and academic studies. Run now on Apify! (148 characters) # Actor input Schema ## `startUrls` (type: `array`): URLs to start with. ## Actor input object example ```json { "startUrls": [ { "url": "https://world.openfoodfacts.org/product/6111180000231/levure-patisserie" } ] } ``` # Actor output Schema ## `overview` (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://world.openfoodfacts.org/product/6111180000231/levure-patisserie" } ] }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/openfoodfacts-profile-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://world.openfoodfacts.org/product/6111180000231/levure-patisserie" }] } # Run the Actor and wait for it to finish run = client.actor("getdataforme/openfoodfacts-profile-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://world.openfoodfacts.org/product/6111180000231/levure-patisserie" } ] }' | apify call getdataforme/openfoodfacts-profile-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/openfoodfacts-profile-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Openfoodfacts Profile Scraper", "description": "Scrape comprehensive food product data from Open Food Facts, including nutritional insights, ingredients, allergens, eco-scores, and images. Perfect for market research, competitive analysis, and academic studies. Delivers structured JSON for easy export and integration....", "version": "0.0", "x-build-id": "qBgrBJ02iCZu92bGE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~openfoodfacts-profile-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-openfoodfacts-profile-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~openfoodfacts-profile-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-openfoodfacts-profile-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~openfoodfacts-profile-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-openfoodfacts-profile-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Start URLs", "type": "array", "description": "URLs to start with.", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } } } }, "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 } } } } } } } } } } ```