LAGIC
Lead Audience Growth Intelligence Computing
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🍴Uber Eats Scraper [] β€” Uber Eats | Lagic

Built For

Get full menus, pricing, reviews, and operational data for any Uber Eats restaurant.

Curated by LagicΒ·Verified working

Configure Agent

Select locale (default is en-US). Applies to both search and URL mode.

Delivery address to search in. (ex: Address, City, Zip Code, etc.) Required when not using urls.

Query to search for. (ex: McDonald's, pizza, etc.) Optional when using address.

Maximum number of restaurants to scrape when using address/query. (0 = all). Not used in Option 2 (URLs): result count = number of input URLs.

List of Uber Eats restaurant/store page URLs to scrape. When provided, address and query are ignored.

Results to deliver

400 credits

This agent actively searches live listings β€” results may vary. You are only charged for what is delivered, up to this number.

Lagic Proxy

Country auto-rotated. Need a specific region? Contact support.

Pricing

4 credits per result
βœ“ 30 free credits on signupβœ“ Refund if 0 resultsβœ“ No card required

Sample Data Preview

Restaurant Profile: Name, phone number, cuisine types, logo and hero image URLs.Full Address: Street address, city, postal code, country, and neighborhood.Geo-Location: Precise latitude and longitude coordinates.Operational Details: Current open/closed status, store hours for each day of the week, and estimated delivery time range.Complete Menu: Every menu item organized by section (e.g., Appetizers, Entrees), including name, description, price, and image URL.Customer Reviews: Both featured and general store reviews with the reviewer's name, rating, review text, and date.
https://...926Value...2026-04-05https://...Sample Text...
https://...909Value...2026-04-05https://...Sample Text...
..................
Exports as:CSVXLSXJSON

Overview

Extract detailed information from Uber Eats by searching any delivery address or providing a list of restaurant URLs. Get complete menus, item prices, customer reviews, and contact details for market research or lead generation.

This tool pulls public data directly from Uber Eats restaurant and store pages, giving you a structured dataset for analysis. It operates in two modes: discovery and targeted extraction. ### Discover Restaurants in Any Area To understand a local food market, provide a delivery address (like "1600 Pennsylvania Ave NW, Washington, DC 20500") and a query (like "pizza" or "sushi"). The tool will find all matching restaurants in that area and extract their complete profiles. This is ideal for identifying all potential competitors, finding sales leads, or analyzing cuisine saturation in a specific neighborhood. ### Extract Data from Specific Restaurants If you already have a list of restaurants you're interested in, you can provide their Uber Eats URLs directly. This bypasses the search function and goes straight to scraping the pages you care about. Use this to monitor specific competitors, gather data for a known list of clients, or enrich your own database of restaurant locations. The output contains everything a market researcher, sales team, or restaurant consultant needs. You get the full menu, broken down by section, with individual item names, descriptions, and prices. You also receive customer reviews, restaurant operating hours, cuisine categories, and precise location data, including latitude and longitude.

Key Capabilities

  • βœ“ Restaurant Profile: Name, phone number, cuisine types, logo and hero image URLs.
  • βœ“ Full Address: Street address, city, postal code, country, and neighborhood.
  • βœ“ Geo-Location: Precise latitude and longitude coordinates.
  • βœ“ Operational Details: Current open/closed status, store hours for each day of the week, and estimated delivery time range.
  • βœ“ Complete Menu: Every menu item organized by section (e.g., Appetizers, Entrees), including name, description, price, and image URL.
  • βœ“ Customer Reviews: Both featured and general store reviews with the reviewer's name, rating, review text, and date.
  • βœ“ Cuisine & Categories: A list of all tags the restaurant is associated with (e.g., "Italian", "Pizza", "Pasta").
  • βœ“ Analyze menu pricing strategies of all Italian restaurants in a specific zip code.
  • βœ“ Generate a lead list of top-rated but expensive restaurants to sell premium ingredients to.
  • βœ“ Monitor competitors' menu updates and customer reviews on a weekly basis.
  • βœ“ Map the density of different cuisine types across a city for a commercial real estate project.
  • βœ“ Enrich an existing list of restaurant names with their full menus, hours, and geo-coordinates.
  • βœ“ Identify underperforming restaurants (low ratings) to pitch marketing and consulting services.
  • βœ“ Power a local food blog by analyzing the most popular menu items based on review mentions.

Field Dictionary

How To Run This Extractor

1

Choose your scraping method: search by area or provide direct URLs.

2

To search an area, enter a delivery address and an optional search query like 'pizza'.

3

To scrape specific stores, paste a list of Uber Eats restaurant URLs into the 'Store URLs' field.

4

Set the maximum number of restaurants you want if you're using the search method.

5

Run the tool to begin the extraction process.

6

Download the collected data as a spreadsheet or JSON file.

Frequently Asked Questions

Do I need technical skills to use this tool?
No. You can start by simply providing a delivery address and a search term like 'burgers'.
In what format can I get the data?
Is it legal to scrape data from Uber Eats?
How many restaurants can I scrape at once?
Can I use this data for my clients?
How is this different from just browsing Uber Eats?
How current is the data?
Can I schedule this to run automatically?
How much will this cost?
Can I scrape restaurants in a specific neighborhood, not just a whole city?
Does this tool get the price of every single menu item?