Uber Eats Restaurant & Menu Scraper

Extract restaurant listings and full menus from Uber Eats. Get names, addresses, ratings, review counts, delivery fees, delivery times, price ranges, cuisine types, and detailed menu items with prices and descriptions.

Features

• Multi-location search — Scrape restaurants across multiple cities, zip codes, or addresses in a single run
• Full menu extraction — Get every menu item with name, description, price, and category
• Cuisine filtering — Filter by pizza, sushi, Mexican, Thai, Indian, and 40+ more cuisine types
• Smart extraction — Three-layer extraction (embedded JSON, JSON-LD, HTML DOM) for maximum reliability
• Pay-per-event pricing — Only pay $0.005 per restaurant scraped
• Deduplication — Automatic removal of duplicate restaurants across locations
• Proxy support — Built-in proxy rotation for large-scale scrapes
• Export — JSON, CSV, Excel, XML, or connect via API

Output Schema

Each restaurant record contains:

Field	Type	Description
name	string	Restaurant name
address	string	Full street address
rating	number	Average rating (0-5 scale)
reviewCount	number	Number of ratings/reviews
deliveryFee	string	Delivery fee (e.g., "Free", "$2.49")
deliveryTime	string	Estimated delivery time (e.g., "25-35 min")
priceRange	string	Price level ($, $$, $$$, $$$$)
cuisine	string	Cuisine type(s)
menu	array	Full menu items (when includeMenus is true)
menuItemCount	number	Number of menu items
url	string	Direct link to the restaurant on Uber Eats
imageUrl	string	Restaurant hero/cover image URL
scrapedAt	string	ISO timestamp of when data was scraped

Menu Item Schema

Each item in the menu array contains:

Field	Type	Description
name	string	Item name
description	string	Item description
price	number	Price in USD
category	string	Menu section/category (e.g., "Appetizers", "Entrees")
imageUrl	string	Item image URL (when available)
isPopular	boolean	Whether the item is marked as popular

Example Output

{
  "name": "Joe's Pizza",
  "address": "7 Carmine St, New York, NY 10014",
  "rating": 4.7,
  "reviewCount": 2340,
  "deliveryFee": "$0.99",
  "deliveryTime": "20-30 min",
  "priceRange": "$",
  "cuisine": "Pizza, Italian",
  "menu": [
    {
      "name": "Cheese Pizza (Large)",
      "description": "Classic New York style cheese pizza with mozzarella and tomato sauce",
      "price": 22.00,
      "category": "Whole Pies",
      "imageUrl": null,
      "isPopular": true
    },
    {
      "name": "Pepperoni Slice",
      "description": "Single slice with pepperoni",
      "price": 4.50,
      "category": "Slices",
      "imageUrl": null,
      "isPopular": false
    }
  ],
  "menuItemCount": 24,
  "url": "https://www.ubereats.com/store/joes-pizza/abc123",
  "imageUrl": "https://...",
  "scrapedAt": "2026-03-02T12:00:00.000Z",
  "source": "ubereats"
}

Input Parameters

Parameter	Type	Required	Default	Description
locations	array	Yes	["New York, NY"]	List of city names, addresses, or zip codes to search
cuisine	string	No	(all)	Cuisine filter: pizza, chinese, mexican, sushi, thai, indian, italian, burgers, etc.
maxResults	integer	No	200	Maximum restaurants to scrape (1-5000)
includeMenus	boolean	No	true	Extract full menu with items, prices, and descriptions
proxy	object	No	none	Proxy configuration for avoiding rate limits

Example Input

{
  "locations": ["New York, NY", "Los Angeles, CA", "Chicago, IL"],
  "cuisine": "pizza",
  "maxResults": 100,
  "includeMenus": true,
  "proxy": {
    "useApifyProxy": true
  }
}

Supported Cuisines

Pizza, Chinese, Mexican, Sushi, Japanese, Burgers, Thai, Indian, Italian, Korean, Vietnamese, Mediterranean, American, Seafood, Wings, Breakfast, Desserts, Vegan, Healthy, Sandwiches, Chicken, BBQ, Ramen, Pho, Tacos, Salads, Soup, Coffee, Bakery, Donuts, Ice Cream, Smoothies, Bubble Tea, Caribbean, Greek, Turkish, Ethiopian, Peruvian, Brazilian, French, Halal, Asian, African, Middle Eastern, and more.

Use Cases

• Market research — Analyze restaurant density, pricing, and cuisine distribution by area
• Competitive analysis — Track competitor menus, prices, and ratings over time
• Lead generation — Build restaurant databases for B2B outreach
• Price monitoring — Track menu item prices across restaurants and locations
• Data journalism — Analyze food delivery trends and pricing patterns
• Academic research — Study food accessibility, pricing equity, and delivery coverage

Performance Tips

1. Start small — Test with 1 location and maxResults: 20 before scaling up
2. Use proxies — Enable Apify Proxy for runs over 50 restaurants to avoid rate limits
3. Skip menus for speed — Set includeMenus: false for 3-5x faster scraping when you only need restaurant-level data
4. Cuisine filter — Narrow by cuisine to get more targeted results faster

Pricing

This actor uses pay-per-event pricing:

• $0.005 per restaurant scraped (including menu data when enabled)
• No charge for failed extractions
• Platform compute costs apply separately

Technical Details

• Built with Apify SDK v3 and Crawlee v3 (CheerioCrawler)
• Three-layer extraction strategy for maximum data coverage:
  1. Server-side rendered JSON state (most reliable)
  2. JSON-LD structured data (schema.org)
  3. HTML DOM parsing with multiple selector fallbacks
• Automatic request retries with exponential backoff
• Random user agent rotation and request delays
• Smart deduplication by URL and restaurant name+address

Disclaimer

This actor scrapes publicly available data from Uber Eats. It is intended for personal and research use. Users are responsible for ensuring compliance with Uber Eats' Terms of Service and applicable laws. The actor does not access private data, require authentication, or bypass any access controls.

Support

• Author: Sovereign AI (ricardo.yudi@gmail.com)
• License: MIT
• Issues: Report bugs via the Apify platform or email

Integration — Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("sovereigntaylor/ubereats-scraper").call(run_input={
    "searchTerm": "ubereats",
    "maxResults": 50
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item.get('title', item.get('name', 'N/A'))}")

Integration — JavaScript

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('sovereigntaylor/ubereats-scraper').call({
    searchTerm: 'ubereats',
    maxResults: 50
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(item => console.log(item.title || item.name || 'N/A'));