# Foodcom Recipes Ingredients Nutrition Reviews (`velvety_bedbug/foodcom-recipes-ingredients-nutrition-reviews`) Actor Foodcom Recipes Ingredients Nutrition Reviews. Structured data export for lead generation, enrichment, and competitive research. - **URL**: https://apify.com/velvety\_bedbug/foodcom-recipes-ingredients-nutrition-reviews.md - **Developed by:** [Peters Bugs](https://apify.com/velvety_bedbug) (community) - **Categories:** E-commerce, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $5.00 / 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Food.com - Recipes, Ingredients, Nutrition & Reviews ### Overview Scrape Food. com for complete recipe data including ingredients, cooking instructions, nutrition facts, ratings, reviews, and prep/cook times. Recipes, Ingredients, Nutrition & Reviews. ### Features - Search by keywords to find specific results - Filter results by category or type - Export data in JSON, CSV, or Excel formats - Includes ratings and review data - Built-in proxy support for reliable data collection ### Use Cases - **Aggregate** - Aggregate recipes for content platforms and meal planners - **Build** - Build nutrition databases from recipe ingredients - **Analyze** - Analyze food trends and popular ingredients - **Create** - Create recipe recommendation datasets ### Input Parameters | Parameter | Type | Description | Default | |-----------|------|-------------|---------| | `searchQuery` | string | Search for specific recipes (e.g., 'chicken pasta', 'chocolate cake') | `` | | `category` | string | Browse by category | `main-dish` | | `cuisine` | string | Filter by cuisine (e.g., italian, mexican, asian) | `` | | `maxRecipes` | integer | Maximum number of recipes to scrape | `50` | | `includeNutrition` | boolean | Include detailed nutrition information | true | | `includeReviews` | boolean | Include user reviews and comments | true | | `maxReviews` | integer | Maximum reviews to collect per recipe | `10` | | `proxyConfiguration` | object | Proxy configuration for bypassing rate limits and geo-restrictions | `{...}` | ### Output Example Each result contains structured data like this: ```json { "name": "Food.com Sample Item", "rating": 4.5, "reviewCount": 127, "prepTime": "30 minutes", "cookTime": "30 minutes", "servings": 4, "calories": "Sample calories", "url": "https://example.com/item/12345" } ```` ### Pricing This actor uses pay-per-result pricing: - **$0.001** per result - **$1.00** per 1,000 results No monthly fees. You only pay for what you scrape. [Apify Free plan](https://apify.com/pricing) includes $5/month in platform credits. ### How to Run #### Apify Console 1. Go to the [Food.com - Recipes, Ingredients, Nutrition & Reviews](https://apify.com/fortuitous_pirate/foodcom-recipes-ingredients-nutrition-reviews) actor page 2. Configure your input parameters 3. Click **Start** and wait for the results 4. Download data in JSON, CSV, or Excel format #### API ```bash curl -X POST "https://api.apify.com/v2/acts/fortuitous_pirate~foodcom-recipes-ingredients-nutrition-reviews/runs?token=YOUR_API_TOKEN" \ -H "Content-Type: application/json" \ -d '{"maxItems": 10}' ``` #### Python SDK ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run = client.actor("fortuitous_pirate/foodcom-recipes-ingredients-nutrition-reviews").call( run_input={"maxItems": 10} ) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) ``` ### Integration Connect Food.com - Recipes, Ingredients, Nutrition & Reviews with your existing tools and workflows: - **API access** - Programmatic access via [Apify API](https://docs.apify.com/api/v2) - **Webhooks** - Get notified when scraping completes - **Scheduling** - Set up recurring runs on any schedule - **Zapier / Make** - Connect with 5,000+ apps via [Apify integrations](https://apify.com/integrations) - **Python / Node.js SDKs** - Native client libraries for easy integration # Actor input Schema ## `searchQuery` (type: `string`): Search for specific recipes (e.g., 'chicken pasta', 'chocolate cake') ## `category` (type: `string`): Browse by category ## `cuisine` (type: `string`): Filter by cuisine (e.g., italian, mexican, asian) ## `maxRecipes` (type: `integer`): Maximum number of recipes to scrape ## `includeNutrition` (type: `boolean`): Include detailed nutrition information ## `includeReviews` (type: `boolean`): Include user reviews and comments ## `maxReviews` (type: `integer`): Maximum reviews to collect per recipe ## `proxyConfiguration` (type: `object`): Proxy configuration for bypassing rate limits and geo-restrictions ## Actor input object example ```json { "searchQuery": "", "category": "main-dish", "cuisine": "", "maxRecipes": 10, "includeNutrition": true, "includeReviews": false, "maxReviews": 10, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("velvety_bedbug/foodcom-recipes-ingredients-nutrition-reviews").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 = {} # Run the Actor and wait for it to finish run = client.actor("velvety_bedbug/foodcom-recipes-ingredients-nutrition-reviews").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 '{}' | apify call velvety_bedbug/foodcom-recipes-ingredients-nutrition-reviews --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=velvety_bedbug/foodcom-recipes-ingredients-nutrition-reviews", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Foodcom Recipes Ingredients Nutrition Reviews", "description": "Foodcom Recipes Ingredients Nutrition Reviews. Structured data export for lead generation, enrichment, and competitive research.", "version": "1.0", "x-build-id": "bsuZTjbgL1tIp1ooS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/velvety_bedbug~foodcom-recipes-ingredients-nutrition-reviews/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-velvety_bedbug-foodcom-recipes-ingredients-nutrition-reviews", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/velvety_bedbug~foodcom-recipes-ingredients-nutrition-reviews/runs": { "post": { "operationId": "runs-sync-velvety_bedbug-foodcom-recipes-ingredients-nutrition-reviews", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/velvety_bedbug~foodcom-recipes-ingredients-nutrition-reviews/run-sync": { "post": { "operationId": "run-sync-velvety_bedbug-foodcom-recipes-ingredients-nutrition-reviews", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Search for specific recipes (e.g., 'chicken pasta', 'chocolate cake')", "default": "" }, "category": { "title": "Recipe Category", "enum": [ "main-dish", "side-dish", "appetizers", "desserts", "breakfast", "soups", "salads", "beverages", "breads", "sauces" ], "type": "string", "description": "Browse by category", "default": "main-dish" }, "cuisine": { "title": "Cuisine Type", "type": "string", "description": "Filter by cuisine (e.g., italian, mexican, asian)", "default": "" }, "maxRecipes": { "title": "Maximum Recipes", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of recipes to scrape", "default": 10 }, "includeNutrition": { "title": "Include Nutrition Info", "type": "boolean", "description": "Include detailed nutrition information", "default": true }, "includeReviews": { "title": "Include Reviews", "type": "boolean", "description": "Include user reviews and comments", "default": false }, "maxReviews": { "title": "Max Reviews per Recipe", "minimum": 0, "maximum": 50, "type": "integer", "description": "Maximum reviews to collect per recipe", "default": 10 }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy configuration for bypassing rate limits and geo-restrictions", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```