OpenTable Restaurants Scraper

Extract restaurant listings and reservation-focused details from OpenTable in a structured dataset. Collect names, ratings, reviews, cuisine, pricing, address, contact data, and more in one run. Built for market research, local dining analysis, lead generation, and reporting workflows.

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Features

• Rich restaurant records — Collect identity, rating, reviews, pricing, location, contact, and media fields.
• Flexible search inputs — Run by direct search URL or by date, time, party size, and location.
• Controlled result volume — Set exactly how many restaurants to collect with results_wanted.
• Clean structured output — Receive normalized dataset items ready for BI tools and automations.
• Reliable default run setup — Includes QA-friendly defaults for quick successful runs.

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Use Cases

Local Market Research

Track restaurant density, price tiers, and rating distribution across cities and neighborhoods. Use this data to understand positioning and customer demand patterns.

Restaurant Discovery Products

Build searchable dining directories with enriched metadata. Populate cards with cuisine, pricing, ratings, and direct restaurant links.

Competitive Monitoring

Compare restaurants in the same geography by review volume, score, and visibility signals. Identify strong and weak performers in specific dining categories.

Sales Prospecting

Generate outreach lists with restaurant names, addresses, and phone numbers. Segment leads by location, cuisine, and pricing profile.

Reporting and Dashboards

Feed structured records into spreadsheets, BI systems, or internal dashboards. Monitor trends over time by re-running with consistent search inputs.

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Input Parameters

Parameter	Type	Required	Default	Description
start_url	String	No	https://www.opentable.com/s?dateTime=2026-01-20T19%3A00%3A00&covers=2&metroId=4	Full OpenTable search/collection URL. If provided, this is used directly.
date	String	No	2026-01-20	Reservation date in YYYY-MM-DD format. Used when start_url is not provided.
time	String	No	19:00	Reservation time in HH:MM format. Used when start_url is not provided.
covers	Integer	No	2	Party size from 1 to 20. Used when start_url is not provided.
location	String	No	Los Angeles	Location term (city or neighborhood) used for search URL construction.
results_wanted	Integer	No	20	Maximum number of restaurants to store in the dataset.
proxyConfiguration	Object	No	{ "useApifyProxy": false }	Proxy settings for improved stability in your environment.

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Output Data

Each dataset item contains:

Field	Type	Description
restaurant_id	Integer | Null	OpenTable restaurant identifier.
name	String | Null	Restaurant display name.
url	String | Null	Canonical restaurant page URL.
cuisine	String | Null	Primary cuisine label.
price_range	String | Null	Price band text.
rating	Number | Null	Overall rating value.
reviews_count	Integer | Null	Total review count.
neighborhood	String | Null	Neighborhood or local area.
city	String | Null	City name.
state	String | Null	State/region code or label.
postal_code	String | Null	Postal/ZIP code.
address_line1	String | Null	Primary street address.
address_line2	String | Null	Secondary address details.
latitude	Number | Null	Latitude coordinate.
longitude	Number | Null	Longitude coordinate.
phone	String | Null	Raw phone number.
formatted_phone	String | Null	Human-readable phone format.
is_promoted	Boolean	Promotion flag.
has_takeout	Boolean	Takeout availability flag.
dining_style	String | Null	Dining style label.
description	String | Null	Restaurant description text.
booking_slots	Array	Available booking slot values when present.
delivery_partners	Array	Delivery partner labels when available.
image_url	String | Null	Primary restaurant image URL.

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Usage Examples

Basic URL Run

{
  "start_url": "https://www.opentable.com/s?dateTime=2026-01-20T19%3A00%3A00&covers=2&metroId=4",
  "results_wanted": 20
}

Parameter-Based Search

{
  "date": "2026-03-15",
  "time": "19:30",
  "covers": 4,
  "location": "New York",
  "results_wanted": 80
}

Large Collection with Proxy

{
  "start_url": "https://www.opentable.com/s?dateTime=2026-03-15T19%3A00%3A00&covers=2&metroId=8",
  "results_wanted": 300,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

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Sample Output

{
  "restaurant_id": 4485,
  "name": "Zuni Cafe",
  "url": "https://www.opentable.com/zuni-cafe",
  "cuisine": "Modern European",
  "price_range": "$31 to $50",
  "rating": 4.7,
  "reviews_count": 8237,
  "neighborhood": "Civic Center / Hayes Valley / Van Ness",
  "city": "San Francisco",
  "state": "CA",
  "postal_code": "94102",
  "address_line1": "1658 Market Street",
  "address_line2": null,
  "latitude": 37.7736,
  "longitude": -122.421608,
  "phone": "4155522522",
  "formatted_phone": "(415) 552-2522",
  "is_promoted": false,
  "has_takeout": false,
  "dining_style": "Casual Elegant",
  "description": "Zuni Café opened in San Francisco...",
  "booking_slots": [],
  "delivery_partners": [],
  "image_url": "https://resizer.otstatic.com/v4/photos/69629956-2.jpg"
}

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Tips For Best Results

Use Stable Search URLs

• Use complete OpenTable search URLs for reproducible runs.
• Keep date/time/party size consistent when comparing runs.

Start Small, Then Scale

• Test with results_wanted: 20 first.
• Increase limits after validating your workflow.

Handle Data Caps In Planning

• Some search contexts can cap total returned results.
• If you need broader coverage, split by location/date/time windows.

Keep Inputs Clean

• Use valid date/time formats.
• Keep covers between 1 and 20.

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Integrations

• Google Sheets — Share and review restaurant data quickly.
• Airtable — Build searchable restaurant intelligence tables.
• Looker Studio / BI tools — Create dashboards for ratings and pricing trends.
• Webhooks — Send fresh dataset items to downstream systems.
• Zapier / Make — Automate notifications, enrichment, and storage pipelines.

Export formats available from dataset runs include JSON, CSV, Excel, XML, and RSS.

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Frequently Asked Questions

How many restaurants can I collect in one run?

You can request any results_wanted value. Actual collected volume depends on available listings in the selected search context.

Can I run without start_url?

Yes. Provide date, time, covers, and/or location, and the actor will build a search URL automatically.

Why are some fields null?

Some restaurants do not expose every attribute in source listing data. Null values are expected for unavailable fields.

Can I use proxy settings?

Yes. Use proxyConfiguration to match your reliability requirements.

Is this suitable for scheduled monitoring?

Yes. This actor is designed for repeatable runs and can be scheduled in Apify.

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Support

For issues or feature requests, use the Apify Actor issue/support channel for this actor.

Resources

• Apify Documentation
• Apify API Reference
• Apify Scheduling

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Legal Notice

This actor is intended for legitimate data collection use cases. You are responsible for complying with applicable laws, platform terms, and data usage policies in your jurisdiction.