OpenTable Scraper - Restaurants, Reviews & Reservation Counts

OpenTable scraper for restaurant groups, reservation platforms and food market researchers: export OpenTable restaurants and OpenTable reviews as clean dataset rows. Search by cuisine, restaurant name or dish within a city or metro, reuse an existing OpenTable search URL, or supply specific restaurant pages and IDs. Each restaurant and each review becomes its own dataset row, joined by the restaurant's stable place_id.

No OpenTable partner account or API key is needed. The scraper reads the restaurant and review information that OpenTable shows publicly on its search and restaurant pages, on opentable.com, opentable.co.uk and the other supported OpenTable domains.

What the OpenTable scraper returns

Restaurant rows include the name, OpenTable URL, neighbourhood, primary cuisine, price range, overall rating, text review count, recent reservation count, profile photo, address, coordinates and phone number when OpenTable supplies them. When a restaurant profile is fetched, the row can also include its website, additional cuisines, opening hours, dress code, payment options and OpenTable's review summary. Review rows include the overall rating, written text, publication time, dining time, author name, verification status, food, service, ambience and value ratings, and an owner reply when present.

These fields support comparisons between locations, cuisine research and reputation monitoring. Reservation counts reflect the number displayed by OpenTable at collection time. They do not represent a transaction history or a promise of future availability. Save repeated datasets if your analysis requires a series of observations over time.

Use cases for OpenTable data

• Restaurant groups benchmark their own locations against the restaurants around them: rating, review count, price band and the recent reservation count side by side.
• Reservation and hospitality platforms build lead lists of restaurants per city, neighbourhood and cuisine, with phone number, website and OpenTable URL.
• Food market research maps which cuisines and price ranges dominate a metro, and how diners rate them on food, service, ambience and value.
• Reputation monitoring collects new OpenTable reviews since a date, including owner replies, so a team can see which reviews are still unanswered.
• Sentiment and menu analysis uses the review text to find which dishes and service moments guests mention most.

Input

For search, provide a search_term together with metro_id or city. A blank term is allowed when you provide a location. New York City uses metro ID 8, Chicago uses 3 and London uses 72. If both a metro ID and a city are supplied, the metro ID selects the location. Without a metro ID, the city is appended to the term and OpenTable resolves it.

Alternatively, provide search_url, an HTTPS URL for OpenTable's search page. The scraper uses its domain and search filters and starts from the first page. Search URLs can carry location coordinates, a metro ID and a sort order. For specific restaurants, use restaurant_urls, accepting full restaurant URLs, r/slug values, slugs or numeric restaurant IDs expressed as strings. This list takes precedence over search input. Up to 200 entries are accepted, and duplicate URLs are removed.

Set domain to com for the United States or co.uk for the United Kingdom. Other supported domains are Canada, Germany, Ireland, Australia, Mexico, Italy, Spain, the Netherlands, Hong Kong, Singapore and the United Arab Emirates. A full opentable.com style domain also works. Japan is currently outside the supported domain list.

limit sets the maximum number of restaurants, from 1 to 1000, and defaults to 50. To collect reviews, enable include_reviews and set max_reviews, from 1 to 1000 per restaurant, defaulting to 20. Reviews arrive in descending dining-date order. The optional reviews_since field accepts a real calendar date in YYYY-MM-DD format and filters each review using publication time. max_reviews counts only matching reviews. Dining time remains a separate output field and can use the restaurant's local offset.

The restaurant limit also applies to restaurant_urls and counts unique restaurant IDs. An ID and a slug for the same restaurant use one place in the limit. Further input URLs are processed until enough unique restaurants have been returned or the list ends. When the limit is reached, warnings reports how many input URLs remain unprocessed. summary.total_results counts known unique restaurants plus unresolved input URLs, an upper bound until the entire list has been processed. Raise limit to process more input.

Italian restaurants in New York City:

{"search_term":"italian","metro_id":8,"limit":100}

One restaurant with 30 reviews from its most recent dining dates:

{"restaurant_urls":["https://www.opentable.com/r/gran-morsi-new-york"],"include_reviews":true,"max_reviews":30}

Steakhouses in London on the UK domain:

{"search_term":"steak","metro_id":72,"domain":"co.uk","limit":3}

Output

The default dataset contains both place and review rows. Check row_type or _type to distinguish them. Every row includes its source, platform, collection timestamp and stable ID. A place uses the restaurant ID as place_id; its reviews use that same value, so joining restaurants to reviews requires no name matching. Reviews additionally carry review_id, place_name and place_url.

Example OpenTable restaurant row

A real search row from the London steak input above (description and photos shortened out of this example):

{
  "_type": "place", "row_type": "place", "source": "opentable-scraper", "platform": "opentable",
  "fetched_at": "2026-10-05T23:40:22.000Z", "id": "473841", "rid": 473841, "place_id": "473841",
  "name": "Kobe Steakhouse Mayfair",
  "url": "https://www.opentable.co.uk/r/kobe-steakhouse-mayfair-london",
  "address": "16 Hay Hill, London, Greater London W1J 8NY", "street": "16 Hay Hill",
  "city": "London", "state": "Greater London", "postal_code": "W1J 8NY", "country_code": "GB",
  "lat": 51.5091663, "lng": -0.1433412,
  "category": "Steakhouse", "categories": ["Steakhouse"],
  "rating": 4.5, "reviews_count": 71, "phone": "+442036639054", "website": null,
  "neighborhood": "Mayfair", "price_band": 4, "price_range": "£41 and over", "currency_symbol": "£",
  "recent_reservation_count": 17,
  "photo_url": "https://resizer.otstatic.com/v3/photos/92424531-2",
  "phone_formatted": "020 3663 9054", "dining_style": "Casual Dining",
  "top_review": "Amazing service, cosy space, outstanding food! Will definitely be back!",
  "awards": [], "has_takeout": false, "has_outdoor": false, "has_bar": false, "has_private_dining": false,
  "metro_id": 72, "metro_name": "London", "query": "steak", "position": 1, "search_page": 1,
  "domain": "co.uk", "dress_code": null, "hours": null, "payment_options": null,
  "executive_chef": null, "cross_street": null, "review_summary": null
}

Example OpenTable review row

A real review row for Gran Morsi in New York (restaurant_urls: ["151951"], reviews on):

{
  "_type": "review", "row_type": "review", "source": "opentable-scraper", "platform": "opentable",
  "fetched_at": "2026-10-05T23:40:55.000Z",
  "id": "OT-151951-2109864644-170178059617", "review_id": "OT-151951-2109864644-170178059617",
  "place_id": "151951", "place_name": "Gran Morsi",
  "place_url": "https://www.opentable.com/r/gran-morsi-new-york",
  "rating": 5,
  "text": "One of the better Italian restaurants I’ve been to in New York.   The bolognese and carbonara were both excellent.",
  "author_name": "Kirk", "published_at": "2026-09-23T18:54:27Z", "dined_at": "2026-09-22T17:30:00-04:00",
  "likes": 0, "is_verified": true,
  "reply_text": "Thank you for your kind words! We are so happy you enjoyed your visit with us and hope to see you in again soon!",
  "reply_at": "2026-10-01T22:26:38Z",
  "rating_food": 5, "rating_service": 4, "rating_ambience": 5, "rating_value": 4,
  "noise": "MODERATE", "author_metro": "Denver", "author_review_count": 2, "photos": [],
  "position": 3, "domain": "com"
}

Export the dataset as JSON, CSV or Excel using the dataset export controls. For a restaurant comparison, filter to place rows. For sentiment or service analysis, filter to review rows and join by place_id. Missing upstream values remain null. Review titles, review permalinks, review language and author country are not available from this source and are not inferred. The run output record contains delivered counts, warnings and continuation state. The summary record records the search total and fetched page count; the errors record identifies individual URLs that could not be completed.

Coverage and larger collections

OpenTable search results are a selection influenced by relevance and availability. A search for a metro does not enumerate every restaurant in that metro. Several terms and locations can improve coverage, but the scraper cannot return restaurants excluded by the source search. Restaurant and review IDs are used to remove repeated records during a collection. Source totals and ordering may change while a collection is running.

Larger jobs continue automatically in smaller requests. Direct API responses may contain next_cursor; reuse it with the same remaining input settings until it becomes null. Changing filters requires starting a new collection. A partial result has a warning that explains whether continuation remains or source data could not be completed. If a single restaurant URL does not exist, other supplied restaurants can still be returned. A search with no results produces no dataset rows.

How this OpenTable scraper compares

The Store has several OpenTable actors. Most cover one side of the data: restaurant search, reviews only, or live table availability and booking. This actor is built for analysis of restaurants and their reviews together.

	This actor	Typical alternatives
Restaurants and reviews in one run	Yes, joined by place_id	Often separate actors
Recent reservation count per restaurant	Yes	Not always exposed
Food, service, ambience and value ratings per review	Yes	Varies
Owner replies with reply date	Yes	Varies
Reviews since a date	reviews_since	Often only a count
UK and other OpenTable domains	13 domains	Often US only
Booking tables or checking availability	No	Offered by booking actors

Choose a booking or availability actor if you need to reserve tables or watch open time slots. Choose this one for restaurant lists, review exports and market comparisons.

Pricing

The published actor's pricing panel is the source for current rates. The prepared billing model separates a run-start event, delivered restaurant rows and delivered review rows. A restaurant's total review count does not determine how many reviews are billed; the number actually delivered does. Empty searches have no restaurant or review charges, though the run-start event can still apply. There are no separate product credits to manage.

Users without a paid Apify plan receive at most ten dataset rows per run. Restaurant and review rows share this cap. A restaurant with reviews can therefore consume the cap with one restaurant and nine reviews. The collector limits free input volume and charges only for rows delivered after the cap. Paid users use their requested restaurant and review limits. This new listing's event prices are configured during publication.

FAQ

Is this an official OpenTable API?

No. OpenTable's own APIs are meant for restaurants and approved partners that manage reservations. This OpenTable scraper exports the restaurant and review information that is publicly visible on OpenTable, without a partner account, as dataset rows you can download or read through the Apify API.

Does the scraper book tables or change reservations?

It collects publicly displayed restaurant and review information. Booking, cancellation and account operations are outside its functionality. Use the restaurant's own reservation page to make a booking.

Can I collect every restaurant in a city?

A city search returns OpenTable's current selection, which can be smaller than its full restaurant inventory. Use specific restaurant URLs or multiple relevant search terms when you need broader coverage, and deduplicate the combined exports by restaurant ID.

How do I restrict reviews to recent feedback?

Enable reviews and set reviews_since to a calendar date. Each review is checked against its publication date, even after an older publication appears. OpenTable orders reviews by dining date, and reviews can be published months after the visit. Collection continues until max_reviews matching reviews have been returned or the review history ends. A date filter can therefore require reading the entire history. Set max_reviews high enough for the period you want, and use dined_at separately when analysing visit timing.

Does it work for UK restaurants?

Yes. Set domain to co.uk and use a London metro ID (72) or a UK city, or paste a search URL from opentable.co.uk. Prices then come in pounds, as OpenTable shows them.

Why is a field empty or a run partial?

Null means OpenTable did not supply that value on the collected page. A partial result means more continuation work remains or a source page could not be completed. Check the run output, summary and errors alongside the dataset. They help distinguish source gaps, missing restaurants and completed collections without assuming that absent values are zero.