We extract recipe instructions, nutritional profiles, ingredient lists, user ratings, and cooking times from Allrecipes. Delivered as clean JSON, CSV, or Parquet.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Recipe Core Data objects from allrecipes.com. All fields typed and schema-versioned.
"recipe_id": "23600", "title": "World's Best Lasagna", "author": "John Chandler", "prep_time": "30 mins", "cook_time": "2 hrs 30 mins", "rating": 4.8, "review_count": 21453
| # | recipe_id | title | author | url | description | prep_time |
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Complete list of extractable fields for Ingredients objects from allrecipes.com. All fields typed and schema-versioned.
"recipe_id": "23600", "raw_text": "1 pound sweet Italian sausage", "quantity": 1.0, "unit": "pound", "ingredient_name": "sweet Italian sausage", "section": "Meat"
| # | recipe_id | ingredient_id | raw_text | quantity | unit | ingredient_name |
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Complete list of extractable fields for Nutritional Facts objects from allrecipes.com. All fields typed and schema-versioned.
"recipe_id": "23600", "calories": 448, "fat_g": 21.3, "carbohydrates_g": 36.5, "protein_g": 29.7, "sodium_mg": 1400
| # | recipe_id | calories | fat_g | saturated_fat_g | cholesterol_mg | sodium_mg |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Instructions objects from allrecipes.com. All fields typed and schema-versioned.
"recipe_id": "23600", "step_number": 1, "instruction_text": "Gather all ingredients.", "time_mentioned": "None", "temperature_mentioned": "None", "section": "Main"
| # | recipe_id | step_number | instruction_text | image_url | equipment_mentioned | time_mentioned |
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Complete list of extractable fields for Reviews & Ratings objects from allrecipes.com. All fields typed and schema-versioned.
"review_id": "R847291", "recipe_id": "23600", "user_name": "CookingMom99", "star_rating": 5, "helpful_votes": 142, "date_posted": "2023-11-14"
| # | review_id | recipe_id | user_name | user_profile_url | star_rating | review_text |
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Our Allrecipes scraper handles every layer of the platform: ingredient parsing, nutritional profiles, instruction steps, and user reviews, with JavaScript rendering and infinite scroll pagination built in.
Title, description, prep time, cook time, total time, servings, and yield extracted accurately from every recipe page.
Extract raw ingredient strings alongside parsed quantities, units, and core ingredient names.
Capture calories, macronutrients, and micronutrients as structured numeric fields.
Extract ordered instruction text, associated media, and section headers.
Full review text, star ratings, helpful vote counts, and date posted, paginated across all user reviews.
Extract cuisine types, dietary tags, and meal categories to maintain site hierarchy.
Capture author profiles, submission history, and publication dates.
Extract thumbnail URLs, embedded video links, and user-submitted photo galleries.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences.
Brief in. Clean data out.
Provide category URLs, search terms, or specific recipe links. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for allrecipes.com.
Schema validation, null-rate checks, and ingredient parsing accuracy checks before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting culinary data at scale requires handling unstructured text and dynamic rendering. Here is how we stay resilient.
Allrecipes relies heavily on client-side rendering for reviews, dynamic yields, and nutritional panels. We use Playwright to execute JavaScript and wait for network idle states to capture data that static parsers miss.
Ingredient strings are notoriously messy. We parse raw strings like '1 cup finely chopped onion' into structured fields for quantity (1), unit (cup), ingredient (onion), and preparation (finely chopped).
Category pages and review sections use infinite scroll. Our crawlers simulate user scrolling and intercept XHR requests to paginate through thousands of items reliably.
We route requests through residential proxies with realistic browser fingerprints to avoid Datadome and Cloudflare blocks, ensuring high success rates.
Media sites update their layouts constantly. Our selector strategy uses fallback chains and structured data extraction (LD+JSON) to maintain pipeline stability.
Populate databases with macro-tagged recipes, prep times, and dietary classifications.
Map structured ingredient lists to retail SKUs for automated cart building.
Train models on diet trends, macro distributions, and ingredient substitutions.
Track ingredient popularity, flavor pairings, and seasonal cooking trends over time.
Analyse content volume, rating distributions, and user engagement metrics.
Train LLMs on structured instruction steps, ingredient ratios, and user feedback.
"Allrecipes holds the internet's most comprehensive archive of home cooking data, but extracting structured nutritional and ingredient profiles requires purpose-built infrastructure."
Extracting culinary data at scale involves parsing highly variable ingredient strings, navigating infinite scroll pagination, and rendering client-side JavaScript. DataFlirt manages the proxies, the parsers, and the pipelines so your application receives clean, normalised recipe records ready for production.
Everything supported by our allrecipes.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per request with sticky sessions where required to prevent blocks.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About allrecipes.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Allrecipes is generally permissible. DataFlirt targets only public, non-authenticated recipe, ingredient, and review data. We do not extract personal data or circumvent authentication walls.
We use custom parsing logic to break down raw ingredient strings into structured fields: quantity, unit, core ingredient name, and preparation notes.
Yes, we capture all available nutritional data displayed on the recipe page, including calories, macronutrients, and micronutrients.
We configure continuous pipelines at daily or weekly cadences depending on your requirements, ensuring you have the latest recipes and reviews.
Yes, we extract the full review corpus for each recipe, including text, star ratings, helpful votes, and publication dates via infinite scroll pagination.
Our packages start at defined category or keyword lists. For larger extraction requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off dump of dessert recipes or a continuous feed of new dinner ideas, we build and operate the pipeline. Tell us what you need.
