How to Scrape Open Food Facts Data for CPG Research & Apps

The Challenge of Extracting Global Food and Nutrition Data

Building a food intelligence platform, powering a nutrition app, or monitoring consumer packaged goods (CPG) market trends requires fast access to structured, reliable product data. Retailers, brand managers, and developers often need to analyze thousands of food items across international markets, tracking details like ingredients, allergens, processing levels, and nutritional profiles.

While Open Food Facts hosts a crowd-sourced catalog of over 3 million products worldwide, manually querying or extracting clean, targeted records presents operational hurdles. Requesting massive regional datasets without category narrowing can trigger API throttling, while standard raw database dumps require extensive post-processing to clean up missing or malformed fields.

The Open Food Facts Scraper solves this by providing a targeted, lightweight interface to query, filter, and extract structured grocery data directly into JSON or CSV datasets without requiring proxies, authentication, or session cookies.

Key Features of the Open Food Facts Scraper

The actor interacts directly with the public Open Food Facts JSON API over HTTP. It exposes nine distinct execution modes and built-in filtering parameters to help you isolate exact product subsets:

• Flexible Query Modes: Retrieve products using nine specialized operational modes: search, byBarcodes, byCategory, byBrand, byCountry, byStore, byLabel, byManufacturer, or byUrls.
• Localized API Routing: When querying specific markets (such as country: "united-states" or country: "france"), the scraper automatically directs requests to localized API endpoints (e.g., us.openfoodfacts.org or fr.openfoodfacts.org). This delivers localized catalog data while reducing payload bottlenecks.
• Multi-Dimensional Nutritional Scoring: Extract standardized quality and processing metrics, including Nutri-Score (grades A to E), Eco-Score (environmental impact ratings A to E), and NOVA groups (food processing levels from 1 = unprocessed to 4 = ultra-processed).
• Dietary & Safety Filtering: Exclude specific allergens (like gluten, milk, or peanuts) and enforce required product labels (such as organic, vegan, or fair-trade) directly at fetch time.
• Clean, Omitted Null Outputs: Fields with empty or missing values are automatically omitted from output payloads, preventing unnecessary null clutter in downstream database storage.

Essential Input Parameters Explained

To build custom scraping workflows, you can combine several input schema fields depending on your target criteria:

• mode (required): Defines the primary extraction path. Choose from search, byBarcodes, byCategory, byBrand, byCountry, byStore, byLabel, byManufacturer, or byUrls.
• searchQuery: Free-text string searching product names, brands, or ingredient lists (e.g., chocolate).
• categorySlug: Target food categories using slug formats, such as chocolate-spreads or pastas.
• brandSlug: Specify brand names such as nutella, danone, coca-cola, or kelloggs.
• nutriscore & ecoscore: Multi-select arrays accepting letter grades ("A", "B", "C", "D", "E") to filter products by health or environmental metrics.
• novaGroup: Multi-select array filtering by food processing levels [1, 2, 3, 4].
• excludesAllergens: Array of allergen tags (e.g., ["gluten", "milk"]) to drop unsafe items.
• containsLabels: Array of required product certificates or labels (e.g., ["organic", "vegan"]).
• minProductCompleteness: An integer from 0 to 100 that drops incomplete crowd-sourced records. Setting a higher completeness requirement ensures output records contain rich nutriments and ingredient data.
• maxItems: An integer hard cap on the emitted records (1 to 1000).

For instance, to query organic, vegan chocolate spreads with high nutritional ratings in a single run, configure the input using single-property parameters:

{ "mode": "byCategory", "categorySlug": "chocolate-spreads", "containsLabels": ["organic", "vegan"], "nutriscore": ["A", "B"] }

To extract localized data for a specific retail store, set the retailer and country filters:

{ "mode": "byStore", "storeSlug": "carrefour", "country": "france", "maxItems": 100 }

Extracted Data Output Structure

Every product record returned by the actor delivers clean, standardized keys. Key fields contained in each returned product item include:

• Product Identifiers: Universal barcodes (code canonicalized to EAN-13 / UPC-A format), productName, primaryBrand, brandsTags[], and direct productUrl.
• Categorization & Retail: categoryHierarchy[], mainCategory, stores[], manufacturingPlaces, and countries[].
• Nutritional Panel (Per 100 g): The nested nutriments object contains standardized numerical values for energyKcal, energyKj, fat, saturatedFat, carbohydrates, sugars, addedSugars, fiber, proteins, salt, sodium, cholesterol, iron, calcium, and vitamins vitaminA, vitaminC, and vitaminD.
• Processing & Health Scores: Letter scores for nutriscore and ecoscore, numerical novaGroup classifications (1 to 4), plus additivesTags[] and allergensTags[].
• Dietary Analysis: Raw ingredientsText alongside structured ingredientsAnalysis statuses for palmOilStatus, veganStatus, and vegetarianStatus.
• Media Assets: Direct links in imageUrls for front, ingredients, and nutrition panel photos.

How to Set Up and Run the Open Food Facts Scraper

Follow these four steps to execute your first data extraction on the Apify platform:

1. Open the Actor in Apify Console: Search for openfoodfacts-scraper in the Apify Store and open the Actor task run page.
2. Select Execution Mode and Primary Filters: Set your target mode (such as search, byCategory, or byBarcodes). Enter target text in searchQuery or specify slugs like brandSlug or categorySlug.
3. Refine Health & Dietary Rules: Set minProductCompleteness (e.g., 50 or higher) to drop partial entries. Enforce dietary rules using excludesAllergens or multi-select nutriscore arrays.
4. Start the Run and Export Data: Click Start. Once execution finishes, inspect your dataset directly in the Console or export records via the API in JSON, CSV, Excel, or XML formats.

Practical Use Cases for Open Food Facts Data

1. Nutrition and Fitness App Data Ingestion

Developers building calorie counters or diet tracking applications can ingest barcode-keyed nutritional facts directly into their software. Using mode: "byBarcodes" with lists of EAN/UPC barcodes enables apps to auto-populate per-100 g nutriments, serving sizes, and allergen warnings instantly.

2. CPG Market Research & Competitor Benchmarking

Brand strategists can evaluate competitor portfolios across regional markets. By filtering by brandSlug or manufacturerSlug, researchers can analyze ingredient lists, monitor palm oil status across product ranges, and track how competitor products rank across Nutri-Score and Eco-Score metrics.

3. Retailer Assortment & Sustainability Audits

E-commerce managers and sustainability analysts can examine retailer catalog footprints using mode: "byStore". Combining store filters with ecoscore and packaging tags (packagingTags[]) surfaces insights on packaging recyclability, origin data, and certified sustainable products across major grocery chains.

Frequently Asked Questions

Do I need proxies or authentication to run this actor?

No. The Open Food Facts API is public, free, and open under the Open Database License (ODbL). The actor operates via HTTP requests without requiring proxies, cookies, or API keys. It employs small inter-request delays (≈200 ms) to respect host server guidelines politely.

What is the difference between Nutri-Score, Eco-Score, and NOVA groups?

• Nutri-Score: A front-of-pack nutritional grade (A best to E worst) calculated by balancing positive nutrients (fiber, protein, fruits/vegetables) against negative components (sugars, saturated fats, sodium).
• Eco-Score: An environmental impact rating (A best to E worst) assessing life-cycle impact, packaging recyclability, transport, and certifications.
• NOVA Group: A classification of food processing levels ranging from 1 (unprocessed foods like raw fruits or grains) to 4 (ultra-processed foods like soft drinks, packaged snacks, and ready meals).

Does the scraper track historical grocery prices?

No. Open Food Facts is a catalog of food product attributes, nutritional details, ingredients, and packaging specs. It does not store historical pricing data.

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Ready to try it yourself? Run *Open Food Facts Scraper** on the Apify Store -- no setup required.*