# OpenTable Scraper (`datascrapers/opentable-scraper`) Actor

Scrape OpenTable restaurants by search query (with date/time/party size) or start URLs. Optional listing details and full restaurant reviews.

- **URL**: https://apify.com/datascrapers/opentable-scraper.md
- **Developed by:** [Farhan Ali](https://apify.com/datascrapers) (community)
- **Categories:** Developer tools, E-commerce, Integrations
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.70 / 1,000 restaurants

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

**OpenTable Scraper** creates a structured dataset of restaurant records collected from opentable.com. Each dataset item can include the restaurant name, cuisine, price band, rating, review count, neighborhood, address, coordinates, and contact details, plus full profile fields and reviews when enrichment is enabled. Query the source using search URLs, restaurant profile URLs, or search terms, control the result limit with `maxItems`, and retrieve records through the Apify Dataset API or export them as JSON, CSV, Excel, or XML.

### Dataset at a glance

| Property | Value |
|---|---|
| Source | opentable.com |
| Record unit | One restaurant |
| Input methods | Search result URLs, restaurant profile URLs, or search queries |
| Main identifiers | `restaurantId`, `url` |
| Delivery | Apify Dataset and API |
| Export formats | JSON, CSV, Excel, XML |
| Update model | Fresh records per Actor run |
| Pricing | Pay per event — from $2 per 1,000 restaurants; details and reviews cost extra when enabled |

### Coverage and available records

- **Supported inputs** — OpenTable search result URLs (`https://www.opentable.com/s?...`), restaurant profile URLs (`https://www.opentable.com/r/...`), or free-text search terms resolved through OpenTable autocomplete.
- **Search terms** — `searchQueries` accepts location or cuisine phrases such as `Dresden`, `New York`, or `Italian in Berlin`, and are used only when `startUrls` is empty.
- **Pagination** — Search results are paginated until `maxItems` is reached or the listing is exhausted.
- **Detail enrichment** — `scrapeListingDetails` opens each restaurant profile to collect hours, dress code, amenities, payment options, parking, public transit, chef, and photo galleries.
- **Reviews** — `scrapeReviews` paginates each restaurant's review list, capped by `maxReviews` per restaurant.
- **Conditional fields** — Details and reviews are only collected when enabled; otherwise `detailsFetched` and `reviewsFetched` are `false` and `reviews` is `null`.
- **Not currently collected** — Menu items and live reservation availability beyond the requested date/time snapshot.

### Data dictionary

Core fields present on every restaurant record:

| Field | Type | Nullable | Description | Example |
|---|---:|---|---|---|
| `restaurantId` | number | No | Stable OpenTable restaurant identifier; best deduplication key | `227388` |
| `name` | string | No | Restaurant name | `Peter Pane - Dresden` |
| `url` | string | No | Restaurant profile URL | `https://www.opentable.com/r/...` |
| `cuisine` | string | Yes | Cuisine type | `Burgers` |
| `priceBand` | string | Yes | Price category as shown on OpenTable | `€30 and under` |
| `currencySymbol` | string | Yes | Currency symbol for the price band | `€` |
| `rating` | number | Yes | Average rating (0–5) | `4.5` |
| `reviewCount` | number | Yes | Total reviews shown on OpenTable | `515` |
| `neighborhood` | string | Yes | Neighborhood name | `Old Town` |
| `city` | string | Yes | City | `Dresden` |
| `state` | string | Yes | State or region code | `SN` |
| `postCode` | string | Yes | Postal code | `01067` |
| `address` | string | Yes | Full address line | `Wilsdruffer Straße 14-16, ...` |
| `latitude` / `longitude` | number | Yes | Geographic coordinates | `51.0515654` |
| `phone` | string | Yes | Phone number | `0351 48676840` |
| `diningStyle` | string | Yes | Dining style category | `Casual Dining` |
| `hasTakeout` | boolean | Yes | Whether takeout is offered | `true` |
| `outdoorSeating` | boolean | Yes | Whether outdoor seating is available | `false` |
| `photoUrl` | string | Yes | Primary photo URL | `https://resizer.otstatic.com/...` |
| `photos` | array | Yes | Additional photo URLs | `["https://...", "..."]` |
| `searchQuery` | string | Yes | Search term that produced the record | `Dresden` |
| `detailsFetched` | boolean | No | Whether detail enrichment ran for this restaurant | `false` |
| `reviewsFetched` | boolean | No | Whether reviews were scraped for this restaurant | `false` |
| `reviews` | array | Yes | Review list; populated only when `scrapeReviews` is enabled | `null` |

When `scrapeListingDetails` is enabled, additional conditional fields are collected, including `description`, `dressCode`, `hoursOfOperation`, `amenities`, `paymentOptions`, `parkingDetails`, `publicTransit`, `executiveChef`, `email`, `website`, `facebookUrl`, `orderOnlineLink`, `hasDelivery`, and `country`.

### Example dataset record

```json
{
  "restaurantId": 227388,
  "name": "Peter Pane - Dresden",
  "url": "https://www.opentable.com/r/peter-pane-dresden-am-zwinger-dresden",
  "cuisine": "Burgers",
  "priceBand": "€30 and under",
  "currencySymbol": "€",
  "rating": 4.5,
  "reviewCount": 515,
  "neighborhood": "Old Town",
  "city": "Dresden",
  "state": "SN",
  "postCode": "01067",
  "address": "Wilsdruffer Straße 14-16, Dresden, SN, 01067",
  "latitude": 51.0515654,
  "longitude": 13.7349184,
  "phone": "0351 48676840",
  "diningStyle": "Casual Dining",
  "hasTakeout": true,
  "outdoorSeating": false,
  "photoUrl": "https://resizer.otstatic.com/v3/photos/92048640-2?width=320&height=320&webp=true",
  "searchQuery": "Dresden",
  "detailsFetched": false,
  "reviewsFetched": false,
  "reviews": null
}
```

This record was produced by a `searchQueries` run for `Dresden` with detail and review enrichment disabled.

### Query and input reference

| Input | Type | Required | Default | Accepted values | Description |
|---|---:|---|---|---|---|
| `startUrls` | array | No | — | Search URLs (`/s?...`) or profile URLs (`/r/...`) | Takes priority over `searchQueries` |
| `searchQueries` | array | No | — | Location or cuisine phrases | Used when `startUrls` is empty |
| `date` | string | No | tomorrow | `YYYY-MM-DD` | Reservation date for availability search |
| `time` | string | No | `19:00` | `HH:MM` (24h) | Preferred dining time |
| `partySize` | integer | No | `2` | `1`–`20` | Number of diners |
| `scrapeListingDetails` | boolean | No | `false` | `true` / `false` | Enable profile detail enrichment |
| `scrapeReviews` | boolean | No | `false` | `true` / `false` | Enable per-restaurant review pagination |
| `maxReviews` | integer | No | `0` | `0` = all | Max reviews per restaurant |
| `maxItems` | integer | No | `0` | `0` = unlimited | Max restaurants to scrape |
| `proxyConfiguration` | object | No | Residential | Apify proxy config | Residential proxy recommended (Akamai Bot Manager) |

Minimal request:

```json
{ "searchQueries": ["Dresden"] }
```

Advanced request:

```json
{
  "searchQueries": ["Dresden"],
  "date": "2026-08-20",
  "time": "19:00",
  "partySize": 2,
  "scrapeListingDetails": true,
  "scrapeReviews": true,
  "maxReviews": 10,
  "maxItems": 50,
  "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] }
}
```

### Retrieve the data through the API

1. Start the Actor with a JSON input via the Apify API or the Actor's API tab.
2. Wait for the run to finish, or use the synchronous run endpoint.
3. Retrieve items from the run's default dataset.
4. Paginate or export the dataset in JSON, CSV, Excel, or XML.

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("datascrapers/opentable-scraper").call(
    run_input={"searchQueries": ["Dresden"], "maxItems": 50}
)
items = client.dataset(run["defaultDatasetId"]).list_items().items
```

### Data quality and record handling

- **Completeness** — Core identity and rating fields are present on listing records; `description`, hours, amenities, and photos are conditional on `scrapeListingDetails`.
- **Deduplication** — Use `restaurantId` (preferred) or `url` as the stable external key. The Actor stops after `maxItems` restaurants.
- **Retries** — Failed seeds are retried once with a fresh proxy session; OpenTable's Akamai Bot Manager frequently blocks datacenter IPs, so residential proxies are recommended.
- **Normalization** — Coordinates, rating, and review counts are returned as numbers; availability signals reflect the requested date/time and are a point-in-time snapshot.
- **Raw vs derived** — All fields are taken directly from OpenTable pages; no AI-generated or computed values are added.

### Export and pipeline examples

| Destination | Recommended method | Typical use |
|---|---|---|
| PostgreSQL/Supabase | Dataset API or webhook consumer | Restaurant master table keyed by `restaurantId` |
| Google Sheets | Apify integration | Review of listings and ratings per city |
| S3/cloud storage | Scheduled export or integration | Nightly refresh of restaurant coverage |

### Pricing and cost examples

Billing is pay-per-event. Each event is tiered across Apify plans; prices below are the free-plan rates.

| Event | Charged when | Free-plan rate |
|---|---|---|
| `dataset-item` (Restaurant) | A restaurant is pushed to the dataset | $2 per 1,000 |
| `listing-details` (Detailed Restaurant) | `scrapeListingDetails` completes for a restaurant | $3 per 1,000 |
| `restaurant-reviews` (Reviews) | `scrapeReviews` completes for a restaurant | $2 per 1,000 |

A fixed one-time Actor Start charge of $0.00005 also applies to each run.

| Restaurants | Estimated base cost (listings only) |
|---:|---:|
| 1,000 | $2 |
| 10,000 | $20 |

Estimates depend on the pricing model and the options enabled. Enabling details or reviews adds the corresponding per-event cost.

### Limitations and responsible data use

- Only publicly accessible OpenTable data is collected; the Actor does not bypass login-restricted content.
- Reservation availability is a snapshot at run time and changes frequently.
- Some fields (hours, amenities, photos, reviews) are conditional and may be empty.
- No historical snapshots are stored unless you persist them yourself across scheduled runs.
- You are responsible for complying with OpenTable's terms and applicable privacy and data laws.

### Dataset questions

#### What does one dataset item represent?

One restaurant listing. If `scrapeListingDetails` or `scrapeReviews` is enabled, the same item is enriched with profile details and/or a nested `reviews` array.

#### Which field should I use as a unique identifier?

`restaurantId` is the stable OpenTable identifier; `url` also works as a fallback.

#### Are fields nullable or conditional?

Yes. Enrichment fields (`description`, `hoursOfOperation`, `amenities`, `email`, etc.) are only present when `scrapeListingDetails` is on, and `reviews` is `null` unless `scrapeReviews` is on.

#### Can I retrieve the records as CSV or JSON?

Yes. The default dataset can be exported as JSON, CSV, Excel, or XML from the Apify Console or via the Dataset API.

#### How do I paginate large datasets?

The Actor paginates automatically up to `maxItems`. For very large exports, page through the dataset with the Dataset API `offset` and `limit` parameters.

#### What counts as a billable result?

Each restaurant pushed to the dataset is one `dataset-item` event. Detail enrichment and review extraction are billed separately as `listing-details` and `restaurant-reviews` events.

### Related datasets from Data Scrapers

- [Yelp Scraper](https://apify.com/datascrapers/yelp-scraper) — business listings and reviews for the same restaurant research workflows.
- [TripAdvisor Reviews Scraper](https://apify.com/datascrapers/tripadvisor-reviews-scraper) — hotel and travel reviews that complement restaurant coverage.
- [Clutch.co Company Scraper](https://apify.com/datascrapers/clutch-scraper) — B2B service-provider listings and reviews.

### Data Scrapers support

Need an additional field, record type, or export workflow? Contact Data Scrapers at stardustspotlight@gmail.com. Include a sample source URL, required fields, expected record volume, and preferred delivery format.

# Actor input Schema

## `startUrls` (type: `array`):

OpenTable search URLs (https://www.opentable.com/s?...) or restaurant profile URLs (https://www.opentable.com/r/...). Takes priority when provided.

## `searchQueries` (type: `array`):

Location or restaurant search terms (e.g. Dresden, New York, Italian in Berlin). Used when startUrls are empty. Resolved via OpenTable autocomplete.

## `date` (type: `string`):

Date for availability search (YYYY-MM-DD). Defaults to tomorrow when omitted. Also used when parsing searchQueries.

## `time` (type: `string`):

Preferred dining time in 24h HH:MM format (e.g. 19:00).

## `partySize` (type: `integer`):

Number of diners (covers) for the search.

## `scrapeListingDetails` (type: `boolean`):

When enabled, opens each restaurant profile page and extracts full details (hours, dress code, website, amenities, photos, etc.). Charges a listing-details event per restaurant.

## `scrapeReviews` (type: `boolean`):

When enabled, paginates and extracts reviews for each restaurant. Charges a restaurant-reviews event per restaurant. Nested under the reviews field on each dataset item.

## `maxReviews` (type: `integer`):

Maximum reviews to extract per restaurant when scrapeReviews is enabled (0 = all reviews).

## `maxItems` (type: `integer`):

Maximum number of restaurants to scrape (0 = unlimited).

## `proxyConfiguration` (type: `object`):

Proxy settings. Residential proxies are strongly recommended — OpenTable uses Akamai Bot Manager and blocks most datacenter IPs.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://www.opentable.com/s?dateTime=2026-08-15T19%3A00%3A00&covers=2&latitude=51.052008&longitude=13.741322&areaId=geohash%3Au31f2twp&suggestedSearchName=Dresden&shouldUseLatLongSearch=true&showMap=true"
    }
  ],
  "searchQueries": [
    "Dresden"
  ],
  "time": "19:00",
  "partySize": 2,
  "scrapeListingDetails": false,
  "scrapeReviews": false,
  "maxReviews": 10,
  "maxItems": 10,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# Actor output Schema

## `dataset` (type: `string`):

JSON array of scraped restaurants at {{links.apiDefaultDatasetUrl}}/items

## `runStats` (type: `string`):

Aggregate scrape stats (counts, timestamps)

## `run` (type: `string`):

Apify Console link to inspect this run

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "startUrls": [
        {
            "url": "https://www.opentable.com/s?dateTime=2026-08-15T19%3A00%3A00&covers=2&latitude=51.052008&longitude=13.741322&areaId=geohash%3Au31f2twp&suggestedSearchName=Dresden&shouldUseLatLongSearch=true&showMap=true"
        }
    ],
    "searchQueries": [
        "Dresden"
    ],
    "time": "19:00",
    "scrapeListingDetails": false,
    "scrapeReviews": false,
    "maxReviews": 10,
    "maxItems": 10,
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "RESIDENTIAL"
        ]
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("datascrapers/opentable-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "startUrls": [{ "url": "https://www.opentable.com/s?dateTime=2026-08-15T19%3A00%3A00&covers=2&latitude=51.052008&longitude=13.741322&areaId=geohash%3Au31f2twp&suggestedSearchName=Dresden&shouldUseLatLongSearch=true&showMap=true" }],
    "searchQueries": ["Dresden"],
    "time": "19:00",
    "scrapeListingDetails": False,
    "scrapeReviews": False,
    "maxReviews": 10,
    "maxItems": 10,
    "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"],
    },
}

# Run the Actor and wait for it to finish
run = client.actor("datascrapers/opentable-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "startUrls": [
    {
      "url": "https://www.opentable.com/s?dateTime=2026-08-15T19%3A00%3A00&covers=2&latitude=51.052008&longitude=13.741322&areaId=geohash%3Au31f2twp&suggestedSearchName=Dresden&shouldUseLatLongSearch=true&showMap=true"
    }
  ],
  "searchQueries": [
    "Dresden"
  ],
  "time": "19:00",
  "scrapeListingDetails": false,
  "scrapeReviews": false,
  "maxReviews": 10,
  "maxItems": 10,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}' |
apify call datascrapers/opentable-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,datascrapers/opentable-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/exLVAefpzY0N7sy2D/builds/aJXmeHZKpKUVIcEnQ/openapi.json