🍽️ OpenTable Scraper: Restaurants, Ratings & Reviews

🚀 Export OpenTable restaurant listings in seconds. Ratings, reviews, cuisine, hours, menus, prices, and exact coordinates for any city OpenTable indexes, ready to drop into your spreadsheet, BI tool, or AI pipeline.

🕒 Last updated: 2026-05-21 · 📊 48 fields per record · 60k+ restaurants indexed worldwide · OpenTable coverage across 20+ countries · Real-time ratings + reservations

OpenTable is the long-running reservations network that restaurant operators have used since the late 1990s, with active listings in cities from New York to Tokyo to Madrid. The structured data sitting behind each restaurant page is far richer than the booking widget you see on the front end: rating breakdowns by food, service, ambience, value, and noise, cuisine tags, dining style, hours of operation, price band, accepted payment methods, accessibility flags, neighborhood and metro, exact latitude and longitude, contact phone, and a recent reservation count. This Actor pulls all of it into clean JSON or CSV so you can analyze restaurants the way OpenTable's own analytics team does.

The Actor accepts plain-English search terms ("New York", "Greenwich Village italian", "Paris brunch") and walks the OpenTable search results page by page, enriching each restaurant with the full profile data fetched from its dedicated page. You can also pass direct restaurant URLs if you already have a target list. Output is the same flat record shape regardless of input mode, so it merges cleanly into existing datasets, and the run finishes whether the user is on a free preview or a paid plan up to a million items.

🎯 Target Audience	💡 Primary Use Cases
Restaurant operators, food critics, hospitality investors, marketing agencies, travel planners, data journalists, academic researchers, location analytics teams	Competitive analysis, market entry research, restaurant scoring and ranking, neighborhood food density studies, menu trend tracking, review monitoring, deal sourcing for hospitality M&A, dataset enrichment for travel apps

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📋 What the OpenTable Scraper does

• 🔍 Searches OpenTable by city, neighborhood, or cuisine. Pass any term OpenTable's search bar accepts and the Actor paginates through every result page until your maxItems cap is hit.
• 💰 Filters by price band. Restrict the output to $, $$, $$$, or $$$$ tiers, alone or combined.
• 📊 Sorts by Featured or Distance. Match the order users actually see on the OpenTable site.
• 🍽️ Enriches each restaurant with its full profile. Hours, executive chef, dress code, parking, payment options, full address, public transit, and accessibility flags all pulled from the restaurant's detail page.
• 🔗 Accepts direct restaurant URLs. Drop in a list of /r/... or /restaurant/profile/... links and the Actor scrapes those directly without the search step.
• 📷 Captures every gallery photo. All restaurant photos available on OpenTable's CDN are returned as a deduplicated URL list.

Each record stands on its own with 48 named fields covering identification (restaurant ID, slug, OpenTable URL), commerce (price band, accepted payments, delivery partners, private dining flags), reputation (overall plus 5 sub-ratings, review count, recent reservation count, one recent review text), and geography (street address, neighborhood, metro, latitude, longitude, cross street, public transit). The output is the same shape whether you searched a city or fed a list of URLs.

💡 Why it matters: OpenTable is the booking layer that runs a meaningful share of dinner reservations in the English-speaking world. The data sitting behind it is the closest public proxy for actual restaurant traffic in many markets, and it includes detail you cannot get from a generic mapping service: granular sub-ratings, recent reservation counts, and the operator-curated cuisine tags and amenities.

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🎬 Full Demo

🚧 Coming soon: a 3-minute walkthrough showing search, enrichment, and CSV export end to end.

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⚙️ Input

Field	Type	Required	Description
searchTerms	array of strings	One of searchTerms or restaurantUrls	Cities, neighborhoods, cuisines, or restaurant names to search on OpenTable. Each entry is paginated independently.
restaurantUrls	array of strings	Optional	Direct OpenTable restaurant URLs. Used instead of or together with search terms.
maxItems	integer	No	Cap on records returned. Free users are limited to 10; paid users may set up to 1,000,000.
priceBands	array of strings	No	Restrict the search to specific price tiers. Pick from "1" ($), "2" ($$), "3" ($$$), "4" ($$$$).
sortBy	string	No	How OpenTable orders results. Either "web_conversion" (Featured, the default) or "distance".
enrichWithProfile	boolean	No	If true (default), each restaurant's profile page is fetched for the complete record. Set to false for a faster, lighter run.

Example: search a city for the top 50 paid records.

{
    "searchTerms": ["New York"],
    "maxItems": 50,
    "priceBands": ["3", "4"],
    "sortBy": "web_conversion",
    "enrichWithProfile": true
}

Example: scrape three specific restaurants directly.

{
    "restaurantUrls": [
        "https://www.opentable.com/r/le-bernardin-new-york",
        "https://www.opentable.com/restaurant/profile/100",
        "https://www.opentable.com/r/eleven-madison-park"
    ],
    "maxItems": 3
}

⚠️ Good to Know: OpenTable serves slightly different result sets per region. Searches default to a US viewpoint. If you want France, Spain, or Japan results, include the country or city name explicitly in searchTerms (e.g. "Paris", "Tokyo Ginza"). The enrichWithProfile step adds roughly 1 second per restaurant.

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📊 Output

Each record is a flat JSON object with all 48 fields below. Empty values are returned as null or empty arrays so downstream tooling never has to guess.

🧾 Schema

Field	Type	Example
🖼 imageUrl	string	https://resizer.otstatic.com/v4/photos/49981008-3?width=640&height=640
🍽 title	string	Sofia's of Little Italy
🔗 url	string	https://www.opentable.com/r/sofias-of-little-italy-new-york
🆔 restaurantId	integer	1228705
📝 description	string	A trendy Italian restaurant & bar located at 143 Mulberry Street...
🍝 primaryCuisine	string	Italian
🧾 cuisines	array of strings	["Italian", "Pizza Bar"]
💰 priceRange	string	$30 and under
🪙 priceTier	integer	2
🍴 diningStyle	string	Casual Dining
⭐ rating	number	4.3
🍲 ratingFood	number	4.3
🛎 ratingService	number	4.4
🌃 ratingAmbience	number	4.5
💵 ratingValue	number	4.1
🔊 ratingNoise	number	2
💬 reviewCount	integer	112
🗒 totalReviewCount	integer	26
📅 recentReservationCount	integer	6
🗣 topReviewText	string	The food was good. We sat outdoors and I loved how the staff handled a crazy man outside...
📞 phoneNumber	string	(212) 219-9799
🌐 website	string	https://www.sofias143.com/
🏠 address	object	{ line1, line2, city, state, postCode, country }
📍 neighborhood	string	Little Italy
🌆 metro	string	New York City
🛣 crossStreet	string	Between Grand Street and Hester Street
🚇 publicTransit	string	Canal Street Station: Served by the N, Q, R, W, 6, and J/Z trains...
📐 latitude	number	40.7189347
📐 longitude	number	-73.9978869
🕒 hoursOfOperation	string	Daily 11:00 am-11:00 pm
👔 dressCode	string	Business Casual
👨‍🍳 executiveChef	string or null	null
🅿️ parkingInfo	string	None
🅿️ parkingDetails	string or null	null
✨ features	object	{ bar, counter, highTop, outdoor, reservationMaxPartySize }
💳 paymentOptions	array of strings	["AMEX", "Discover", "Mastercard", "Visa"]
🔖 additionalDetails	array of strings	["Bar/Lounge", "Beer", "Delivery", "Dog Friendly", "Wheelchair Access"]
🥡 hasTakeout	boolean	true
🥂 hasPrivateDining	boolean	true
🛵 hasDeliveryPartners	boolean	false
🛵 hasDeliveryDirect	boolean	true
🛵 deliveryPartners	array of objects	[{ deliveryPartnerName, menuUrl }]
🌐 orderOnlineLink	string or null	https://www.grubhub.com/restaurant/sofias-in-little-italy-nyc...
🏆 awards	array of objects or null	null
💎 inPremiumMarketplace	boolean	false
🖼 photoUrls	array of strings	["https://resizer.otstatic.com/v4/photos/49981008-3?width=640&height=360", ...]
🕒 scrapedAt	string (ISO 8601)	2026-05-21T16:25:08.884Z
❌ error	string or null	null

📦 Sample records

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✨ Why choose this Actor

	Capability
🍽️	Full restaurant detail, not just listings. Every record carries hours, executive chef, dress code, parking, payment methods, accessibility flags, and the full delivery partner list, not just name and rating.
⭐	Five-axis ratings, not one number. Overall, food, service, ambience, value, and noise scores arrive separately so you can model "loud but good" or "quiet but expensive" properly.
📍	Exact coordinates. Latitude and longitude in every record, suitable for direct ingestion into PostGIS, BigQuery GIS, or a CARTO map.
🌐	Multi-country. OpenTable runs in 20+ countries and the search term is the only knob you need to switch from "New York" to "London" to "Madrid".
🔁	Two input modes. Search by text or feed direct URLs. The output shape is identical so both flows merge cleanly into the same dataset.
🆓	Free tier for previewing. Free Apify accounts can run a 10-record preview before committing to a paid plan.
🧾	Schema is flat and stable. Forty-eight fields, dot-pathed objects only where the source is structurally nested (address, features, delivery partners).

📊 A single New York search returns 600 to 800 distinct restaurants depending on price filter, and the Actor pulls roughly 5 records per minute with full profile enrichment on a residential proxy.

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📈 How it compares to alternatives

Approach	Cost	Coverage	Refresh	Filters	Setup
⭐ OpenTable Scraper (this Actor)	Pay per item, free preview	All countries OpenTable indexes	On demand	Search term, price band, sort order, direct URL list	One JSON input, no API key
Generic mapping APIs	Per-call pricing, monthly tiers	Restaurants alongside other POIs	Indexed quarterly	Place name lookup, category	Account, project, billing setup
Paid live reservation APIs	Enterprise license, contract negotiation	Operator-side only	Real-time	API-key gated	Engineering integration
DIY scraper	Engineering time, proxy bill	Whatever you can maintain	Whatever you can maintain	Whatever you build	Build and ship the whole stack
Manual copy-paste	Time	What you have patience for	Stale on day two	Manual	None

OpenTable's data shape is consumer-search-first, so this Actor gives you the same view a diner sees plus the structured fields hiding behind it.

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🚀 How to use

1. ✅ Sign up for a free Apify account. Use this referral link to get started with $5 in free credit. Free accounts can preview 10 records per run.
2. 🍽️ Open the OpenTable Scraper. Find it on your dashboard or in the Apify Store.
3. 🛠️ Paste your input. A list of cities, neighborhoods, or direct restaurant URLs. Pick a price band if you want. Set maxItems.
4. ▶️ Click Start. The Actor walks search pages and enriches every record. Output appears in the dataset tab as records land.
5. 📥 Download CSV, Excel, JSON, or XML. Or stream the dataset into Make, Zapier, Airbyte, or your own webhook.

⏱️ Total time: under 2 minutes to first record, around 1 minute per 50 enriched records after that. A 1,000-restaurant pull typically completes in 20-25 minutes.

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💼 Business use cases

🍽️ Restaurant operators Track competitor ratings and review counts in your neighborhood Identify newly opened venues in the same price band Benchmark amenities (private dining, takeout, wheelchair access) against peers Watch top-review text for shifts in customer language	📈 Hospitality investors and M&A Source acquisition targets by city and price band Compare review velocity across multiple candidates Pull recent reservation counts as a traffic proxy Build comparable sets for valuation models
🗺️ Travel and food media Build "best of" city guides with verified data, not guesses Cross-reference Michelin and James Beard award lists Generate consistent restaurant profile cards at scale Power affiliate booking pages with current ratings	🏢 Marketing agencies and analytics Build restaurant prospect lists for hospitality clients Score neighborhoods for emerging food scenes Train recommendation models on real cuisine, price, and rating data Feed CRM systems with phone and address fields

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🔌 Automating OpenTable Scraper

Schedule runs, chain into pipelines, or trigger from your own code. The Actor exposes the standard Apify run API so any HTTP client works.

• 🟩 Node.js: use the Apify JavaScript client to start runs and read the dataset programmatically.
• 🐍 Python: use the Apify Python client for the same operations from a notebook or backend service.
• 📚 Docs: see the Apify Actor API reference for raw HTTP examples.

Schedules run from Apify Console without writing any glue. Pick a frequency, attach an input template, and the dataset rebuilds itself on the cadence you choose, ready for webhooks or storage exports.

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🌟 Beyond business use cases

Data like this powers more than commercial workflows. The same structured records support research, education, civic projects, and personal initiatives.

🎓 Research and academia Empirical datasets for papers, thesis work, and coursework Longitudinal studies tracking changes across snapshots Reproducible research with cited, versioned data pulls Classroom exercises on data analysis and ethical scraping	🎨 Personal and creative Side projects, portfolio demos, and indie app launches Data visualizations, dashboards, and infographics Content research for bloggers, YouTubers, and podcasters Hobbyist collections and personal trackers
🤝 Non-profit and civic Transparency reporting and accountability projects Advocacy campaigns backed by public-interest data Community-run databases for local issues Investigative journalism on public records	🧪 Experimentation Prototype AI and machine-learning pipelines with real data Validate product-market hypotheses before engineering spend Train small domain-specific models on niche corpora Test dashboard concepts with live input

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🤖 Ask an AI assistant about this scraper

Not sure if this Actor covers your use case? Paste the question below into ChatGPT, Claude, or Gemini.

"I found the ParseForge OpenTable Scraper on Apify. It scrapes restaurant listings from OpenTable including ratings, cuisine, price band, hours, coordinates, photos, and reservation counts. I want to [describe your project]. Can this Actor help me? What input would I use?"

The AI will map your requirements against the input schema and sample output documented above.

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❓ Frequently Asked Questions

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🔌 Integrate with any app

Trigger runs, read datasets, and chain workflows from wherever your team already works.

• Make - drag-and-drop scenarios that start an OpenTable run and route the dataset to a sheet or CRM.
• Zapier - zaps that trigger off new dataset items.
• Slack - post run summaries or new restaurants to a channel.
• Airbyte - sync the dataset to Postgres, BigQuery, Snowflake, or Redshift.
• GitHub Actions - run the Actor on every push or on a cron schedule.
• Google Drive - drop CSV exports straight into a shared folder.

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🔗 Recommended Actors

• ⭐ Yelp Reviews Scraper - restaurant and business reviews from Yelp with star ratings, reviewer profiles, and photo counts.
• 🗺️ Yellow Pages Scraper - business listings including phone, address, categories, and rating across any US city.
• 🏨 Hostelworld Scraper - hostel and budget accommodation listings for the same travel destinations your restaurant searches cover.
• 🍲 Allrecipes Scraper - recipe data to pair with restaurant cuisine analysis, covering ingredients, ratings, and nutrition.
• 👗 Depop Scraper - marketplace listings for second-hand goods, useful for lifestyle and consumer behavior datasets alongside dining data.

💡 Pro Tip: browse the complete ParseForge collection for more hospitality, travel, and review scrapers.

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🆘 Need Help? Open our contact form and we will get back the same day.

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⚠️ Disclaimer: Independent tool, not affiliated with OpenTable. Only publicly available restaurant data is collected. Users are responsible for compliance with applicable laws and OpenTable's terms of use.