🚀 Uber Eats Brand Store URLs Scraper

Easily extract every Uber Eats store URL associated with any brand – McDonald's, Starbucks, Subway, you name it – at scale. This Apify Actor turns a list of brand profile links into a ready-to-use JSON feed containing the direct URL of every restaurant in the brand's network. Perfect for local market analysis, competitive research, franchise mapping, and data enrichment.

🧩 Related Actors

• Uber Eats – Brand Listing by Country: Automatically gathers all Uber Eats brand profile URLs for the countries you specify. Perfect if you don’t yet know the exact brand URLs to feed into this Actor.

✨ Key features

• Collects 100 % of live store URLs for one or multiple Uber Eats brand pages.
• Works worldwide – automatically supports country-specific Uber Eats domains.
• Fully asynchronous Python scraper for speed and reliability.
• Clean JSON output ready for Excel, BI tools, databases, or your own API.

🔍 How it works

1. Provide one or more Uber Eats brand profile links as input.
2. The Actor automatically discovers every live restaurant associated with each brand, no matter where it is located.
3. All store URLs and essential metadata are saved to an Apify dataset for easy viewing, download, or API integration.

📝 Input parameters

Field	Type	Description
brand_urls	Array	One or more complete brand profile URLs. Example: ["https://www.ubereats.com/brand/starbucks"]
max_results	Integer (required)	Maximum number of store URLs to return per brand. Default 10. Up to 10 000.

Example JSON:

{
  "brand_urls": [
    "https://www.ubereats.com/brand/mcdonalds"
  ],
  "max_results": 100
}

📤 Output example

[
  {
    "store_url": "https://www.ubereats.com/store/mcdonalds-toronto/abc123",
    "store_slug": "/store/mcdonalds-toronto/abc123",
    "store_name": "McDonald's (Toronto)",
    "parent_url": "https://www.ubereats.com/ca/en/brand/mcdonalds-toronto-on",
    "region_code": "on",
    "city_code": "toronto-on",
    "country_code": "ca",
    "main_task_started": "2025-07-18T05:44:25Z",
    "extraction_date": "2025-07-18",
    "extraction_datetime": "2025-07-18T05:45:02Z",
    "input": {
      "brand_url": "https://www.ubereats.com/brand/mcdonalds"
    }
  }
]

💡 Popular use cases

• Franchise territory mapping & expansion planning
• Competitor density studies in food delivery platforms
• Updating store locator databases
• Market share and promotional activity tracking
• Location-based advertising campaigns

🚀 Quick start

Run in Apify Console

1. Click Run.
2. Paste your input JSON.
3. Watch results stream into the dataset in real time.

Run from code

Python:

from apify_client import ApifyClient

client = ApifyClient("APIFY_API_TOKEN")
run = client.actor("username/ubereats-brands").call({
    "brand_urls": [
        "https://www.ubereats.com/brand/mcdonalds",
        "https://www.ubereats.com/brand/starbucks"
    ],
    "max_results": 1000
})
items = client.dataset(run["defaultDatasetId"]).list_items().items
print(items)

Node.js:

import { Actor } from 'apify';

const run = await Actor.call('username/ubereats-brands', {
  brandUrls: [
    'https://www.ubereats.com/brand/mcdonalds',
    'https://www.ubereats.com/brand/subway'
  ],
  maxResults: 1000
});
const items = await Actor.dataset(run.defaultDatasetId).listItems();
console.log(items.items);

🛠️ Technical details

• Built with Python and the Apify SDK.
• Uses httpx for fast, concurrent HTTP requests.
• Automatic proxy rotation & retry logic emulate genuine users and bypass blocks.
• Handles internationalized URLs (accents, special characters) gracefully.
• Free Apify plan processes the first brand URL with up to 10 results; paid plans unlock full power.

🤝 Support

Need help or custom development? Open an issue in Apify Console – we usually respond within one business day.

---

🔑 SEO keywords

Uber Eats brand scraper, Uber Eats store URLs extractor, UberEats API, restaurant data scraper, food delivery brand analysis, competitor mapping, franchise locations, web scraping tool, Apify Actor, JSON data feed.