Swiggy Dineout Restaurants Scraper

🍽️ Find Dineout restaurants by location

Restaurant researchers, local teams, and developers can use Dineout to find public restaurant listings for one location. Save structured rows with names, public pages, locations, cuisines, ratings, cost for two, offers, distance, and public media when those details are published.

Start with one of these focused runs:

• Find public Dineout listings for one location with Dineout Restaurant Listings.
• Compare cost-for-two details across venues with Restaurant Listings With Prices.
• Keep only venues with current dining offers with Restaurants With Offers.
• Narrow a location search by cuisine or dish with Restaurants By Cuisine.
• Browse public restaurant listings in New Delhi with Restaurants In New Delhi.

📍 Dineout restaurant listing data

The default dataset contains structured restaurant listing rows for the selected location. Each row keeps public venue details together so you can review locations, cuisines, prices, ratings, offers, and media in one place when the source publishes them.

▶️ Run a Dineout location search

Run flow

1. Enter one location such as Bangalore.
2. Add one optional cuisine or dish keyword to narrow the search.
3. Turn on Dining offers only to keep venues with a currently published dining offer.
4. Set Maximum listings, or leave it empty to return all available listings until the source is exhausted.
5. Start the run and open the default dataset or its API link.

The Actor uses public listing data and does not require source credentials or private account access. Results depend on what Dineout publishes for the selected location.

⚙️ Input

Input fields

Field	Type	What it does
location	string	One location to search for public Dineout restaurant listings.
keyword	string	Optional cuisine or dish keyword to narrow results for the selected location.
offersOnly	boolean	When true, keep only venues with a currently published dining offer. When false, include all matching venues.
maxItems	integer	Optional cap on eligible public listings. Leave it empty to return all available listings until the source is exhausted.

Successful default input

This example is copied from the public input of a successful hosted default-input run.

{
  "location": "Bangalore",
  "offersOnly": false,
  "maxItems": 20
}

🧾 Output

Dataset link

Field	Type	What it does
dataset	string	Link to the default dataset that contains the restaurant listing rows.

Restaurant listing fields

Field	Type	What it does
name	string	Public restaurant name.
publicPageUrl	string	Public Dineout page URL for the restaurant.
location	object	Public address and place details.
location.city	string	Public city, when published.
location.neighborhood	string	Public neighborhood, when published.
location.address	string	Public street address.
location.coordinates	object	Public geographic coordinates, when published.
location.coordinates.latitude	number	Public latitude.
location.coordinates.longitude	number	Public longitude.
location.distanceFromLocation	object	Public distance from the selected location, when published.
location.distanceFromLocation.value	number	Published distance value.
location.distanceFromLocation.unit	string	Unit for the distance, such as m or km.
cuisines	array of string	Public cuisine labels.
costForTwo	object	Public cost for two, when published.
costForTwo.amount	number	Published cost amount for two people.
costForTwo.currency	string	Currency for the published cost amount.
ratings	object	Public rating context, when published.
ratings.overall	object	Public overall rating and review count.
ratings.overall.score	number	Published overall rating score.
ratings.overall.reviewCount	integer	Published number of overall reviews.
paymentAvailable	boolean	Whether the public listing indicates payment is available, when published.
highlights	array of string	Public highlights shown for the restaurant, when available.
offerHeadings	array of string	Public headings for dining offers shown by the listing, when available.
venueTypes	array of string	Public venue type labels, when available.
serviceAvailability	object	Public service availability signals, when available.
serviceAvailability.dining	boolean	Whether the public listing indicates dining service is available.
media	object	Public image links and source-published image counts, when available.
media.imageUrls	array of string	Public image URLs for the restaurant.
media.imageCount	integer	Image count published by the source.

Example restaurant row

This shortened row is copied from a successful hosted default-input run. The live row had 53 image URLs, so media.imageUrls uses the JSON string "..." as an omission marker. The other shown values are genuine, so this is not a complete row.

{
  "name": "Plan B",
  "publicPageUrl": "https://www.swiggy.com/restaurants/plan-b-ashok-nagar-bangalore-14360/dineout",
  "location": {
    "city": "Bangalore",
    "neighborhood": "Ashok Nagar",
    "address": "Thomas Building, Ashok Nagar, Bangalore",
    "distanceFromLocation": {
      "value": 5.1,
      "unit": "km"
    },
    "coordinates": {
      "latitude": 12.9696394,
      "longitude": 77.60653272
    }
  },
  "cuisines": [
    "American",
    "Italian"
  ],
  "costForTwo": {
    "amount": 1300,
    "currency": "INR"
  },
  "ratings": {
    "overall": {
      "score": 4.1,
      "reviewCount": 5084
    }
  },
  "paymentAvailable": true,
  "highlights": [
    "Alcohol served",
    "Reservation available",
    "Parking available",
    "Valet parking",
    "Smoking area",
    "SwiggyPay accepted",
    "Spicy Potato Wedges",
    "Juicy Lucy",
    "Crispy Bacon",
    "Cheesy Garlic Fries"
  ],
  "offerHeadings": [
    "Flat 50% off",
    "TODAY’S \nDISCOUNT",
    "Get extra ₹100 off using PAYTMUPI",
    "Flat 50% off on Total Bill",
    "Flat 45% off on Total Bill",
    "Flat 40% off on Total Bill",
    "Flat 5% off on Total Bill",
    "Flat 10% off on total bill"
  ],
  "venueTypes": [
    "RESTAURANT_TYPE_PARTICIPATING"
  ],
  "serviceAvailability": {
    "dining": true
  },
  "media": {
    "imageUrls": "...",
    "imageCount": 53
  }
}

💳 Pricing

Per-listing charge

The Actor uses pay-per-event pricing. You pay $0.00445 for each successfully saved public restaurant listing. A run with no saved listing does not trigger this event.

🔌 Integrations

Dataset and API

Open the default dataset from the run or use the Apify dataset API to read the structured rows.

Video walkthrough

❓ FAQ

Can I search more than one location in one run?

No. Each run takes one location. Use separate runs for separate locations.

Can I narrow the search by cuisine or dish?

Yes. Enter one optional keyword to narrow restaurant listings for the selected location.

What does Dining offers only do?

When enabled, the dataset keeps venues with a currently published dining offer. It does not book or pay for a meal.

Will every listing include ratings, distance, and images?

Not always. These fields appear when Dineout publishes them for a listing. Required identity, address, and cuisine fields follow the public schema.

What happens if public Dineout data is unavailable?

Results are not guaranteed for every location. If public access or listing extraction fails, the Actor does not emit partial or diagnostic venue rows.

Do I need a Dineout login or API key?

No. The Actor uses public listing data and does not ask for source credentials or private account access.

📝 Changelog

v0.0 (19-09-2026)

• Initial release.

🆘 Support

For issues, questions, or feature requests, file a ticket and I'll fix or implement it in less than 24h 🫡

🔗 Related Actors

• OpenTable + Resy Scraper: Export public restaurant profiles, ratings, prices, and photos from OpenTable and Resy.
• Google Maps Shared List Scraper: Turn public saved place lists into structured restaurant and venue rows.
• Yelp Business Review Scraper: Collect full-text public Yelp reviews and ratings for restaurant research.
• DoorDash Reviews Scraper: Extract public review previews from DoorDash store pages.
• NYC + Chicago Restaurant Inspections Scraper: Search public restaurant inspections, grades, scores, and violations in New York City or Chicago.

Made with ❤️ by Maxime Dupré