Scrape OpenTable restaurants

Search OpenTable for restaurants and extract names, ratings, cuisines, and price ranges from the results.

Prerequisites

• A Browserless API token from your account dashboard

Steps

OpenTable renders its search results with JavaScript and uses bot-detection measures, so a plain HTTP request won't return listing data. The examples below search for Italian restaurants and route through stealth mode with a residential proxy.

Selector stability

OpenTable updates its markup periodically. If selectors stop returning results, inspect the live page with browser DevTools to find the current element and attribute names.

AI Agent

Use the Browserless MCP server to scrape restaurant listings from OpenTable from any MCP-compatible AI agent (Claude Desktop, Cursor, Windsurf, ChatGPT, etc.).

1. Connect the MCP server

Send this prompt to your AI agent to install the Browserless MCP server:

Go to https://github.com/browserless/browserless-mcp/blob/main/install.md
and follow the instructions to install the Browserless MCP server
for my client.

2. Scrape OpenTable

Use browserless_smartscraper. It handles OpenTable's dynamic content and bot protection automatically.

Use the browserless_smartscraper tool to scrape restaurant listings
from https://www.opentable.com/s?term=italian&covers=2
and return the results as markdown

REST API

Send the BQL mutation over HTTP to the stealth endpoint. No browser library or BQL IDE required.

cURL

View Full Code on GitHub

1. Send the request

curl -X POST \
  "https://production-sfo.browserless.io/stealth/bql?token=YOUR_API_TOKEN_HERE&proxy=residential&proxyCountry=us" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "mutation ScrapeOpenTable { goto(url: \"https://www.opentable.com/s?term=italian&covers=2\", waitUntil: networkIdle) { status } waitForSelector(selector: \"[data-test=restaurant-card]\", timeout: 15000) { time } restaurants: mapSelector(selector: \"[data-test=restaurant-card]\") { name: mapSelector(selector: \"h2\") { innerText } rating: mapSelector(selector: \"[data-test=rating-score]\") { innerText } cuisine: mapSelector(selector: \"[data-test=cuisine]\") { innerText } priceRange: mapSelector(selector: \"[data-test=price-range]\") { innerText } bookingsToday: mapSelector(selector: \"[data-test=bookings-today]\") { innerText } } }",
    "variables": {}
  }'

2. Check the output

{
  "data": {
    "goto": { "status": 200 },
    "waitForSelector": { "time": 3412 },
    "restaurants": [
      {
        "name": [{ "innerText": "Carbone" }],
        "rating": [{ "innerText": "4.8" }],
        "cuisine": [{ "innerText": "Italian" }],
        "priceRange": [{ "innerText": "$$$$" }],
        "bookingsToday": [{ "innerText": "Booked 42 times today" }]
      },
      {
        "name": [{ "innerText": "L'Artusi" }],
        "rating": [{ "innerText": "4.7" }],
        "cuisine": [{ "innerText": "Italian" }],
        "priceRange": [{ "innerText": "$$$" }],
        "bookingsToday": [{ "innerText": "Booked 28 times today" }]
      }
    ]
  }
}

JavaScript

View Full Code on GitHub

1. Send the request

const query = `mutation ScrapeOpenTable {
  goto(url: "https://www.opentable.com/s?term=italian&covers=2", waitUntil: networkIdle) {
    status
  }
  waitForSelector(selector: "[data-test=restaurant-card]", timeout: 15000) {
    time
  }
  restaurants: mapSelector(selector: "[data-test=restaurant-card]") {
    name: mapSelector(selector: "h2") { innerText }
    rating: mapSelector(selector: "[data-test=rating-score]") { innerText }
    cuisine: mapSelector(selector: "[data-test=cuisine]") { innerText }
    priceRange: mapSelector(selector: "[data-test=price-range]") { innerText }
    bookingsToday: mapSelector(selector: "[data-test=bookings-today]") { innerText }
  }
}`;

const response = await fetch(
  'https://production-sfo.browserless.io/stealth/bql?token=YOUR_API_TOKEN_HERE&proxy=residential&proxyCountry=us',
  {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ query, variables: {} }),
  }
);

const { data } = await response.json();
console.log(JSON.stringify(data.restaurants, null, 2));

2. Check the output

[
  {
    "name": [{ "innerText": "Carbone" }],
    "rating": [{ "innerText": "4.8" }],
    "cuisine": [{ "innerText": "Italian" }],
    "priceRange": [{ "innerText": "$$$$" }],
    "bookingsToday": [{ "innerText": "Booked 42 times today" }]
  },
  {
    "name": [{ "innerText": "L'Artusi" }],
    "rating": [{ "innerText": "4.7" }],
    "cuisine": [{ "innerText": "Italian" }],
    "priceRange": [{ "innerText": "$$$" }],
    "bookingsToday": [{ "innerText": "Booked 28 times today" }]
  }
]

Python

View Full Code on GitHub

1. Install dependencies

pip install requests

2. Send the request

import requests

query = """
mutation ScrapeOpenTable {
  goto(url: "https://www.opentable.com/s?term=italian&covers=2", waitUntil: networkIdle) {
    status
  }
  waitForSelector(selector: "[data-test=restaurant-card]", timeout: 15000) {
    time
  }
  restaurants: mapSelector(selector: "[data-test=restaurant-card]") {
    name: mapSelector(selector: "h2") { innerText }
    rating: mapSelector(selector: "[data-test=rating-score]") { innerText }
    cuisine: mapSelector(selector: "[data-test=cuisine]") { innerText }
    priceRange: mapSelector(selector: "[data-test=price-range]") { innerText }
    bookingsToday: mapSelector(selector: "[data-test=bookings-today]") { innerText }
  }
}
"""

response = requests.post(
    'https://production-sfo.browserless.io/stealth/bql',
    params={
        'token': 'YOUR_API_TOKEN_HERE',
        'proxy': 'residential',
        'proxyCountry': 'us',
    },
    json={'query': query, 'variables': {}},
)

data = response.json()['data']
for r in data['restaurants']:
    name = r['name'][0]['innerText']
    rating = r['rating'][0]['innerText']
    cuisine = r['cuisine'][0]['innerText']
    price = r['priceRange'][0]['innerText']
    print(f'{name} | {rating} stars | {cuisine} | {price}')

3. Check the output

Carbone | 4.8 stars | Italian | $$$$
L'Artusi | 4.7 stars | Italian | $$$

Java

View Full Code on GitHub

1. Send the request

import java.net.URI;
import java.net.http.*;

String token = "YOUR_API_TOKEN_HERE";
String endpoint = "https://production-sfo.browserless.io/stealth/bql?token=" + token
    + "&proxy=residential&proxyCountry=us";

String query = "mutation ScrapeOpenTable {"
    + " goto(url: \\\"https://www.opentable.com/s?term=italian&covers=2\\\", waitUntil: networkIdle) { status }"
    + " waitForSelector(selector: \\\"[data-test=restaurant-card]\\\", timeout: 15000) { time }"
    + " restaurants: mapSelector(selector: \\\"[data-test=restaurant-card]\\\") {"
    + "   name: mapSelector(selector: \\\"h2\\\") { innerText }"
    + "   rating: mapSelector(selector: \\\"[data-test=rating-score]\\\") { innerText }"
    + "   cuisine: mapSelector(selector: \\\"[data-test=cuisine]\\\") { innerText }"
    + "   priceRange: mapSelector(selector: \\\"[data-test=price-range]\\\") { innerText }"
    + "   bookingsToday: mapSelector(selector: \\\"[data-test=bookings-today]\\\") { innerText }"
    + " }"
    + " }";

String payload = "{\"query\": \"" + query + "\", \"variables\": {}}";

HttpClient client = HttpClient.newHttpClient();
HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create(endpoint))
    .header("Content-Type", "application/json")
    .POST(HttpRequest.BodyPublishers.ofString(payload))
    .build();

HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());

2. Check the output

{
  "data": {
    "goto": { "status": 200 },
    "waitForSelector": { "time": 3412 },
    "restaurants": [
      {
        "name": [{ "innerText": "Carbone" }],
        "rating": [{ "innerText": "4.8" }],
        "cuisine": [{ "innerText": "Italian" }],
        "priceRange": [{ "innerText": "$$$$" }],
        "bookingsToday": [{ "innerText": "Booked 42 times today" }]
      }
    ]
  }
}

C#

View Full Code on GitHub

1. Send the request

using System.Net.Http;
using System.Text;
using System.Text.Json;

string token = "YOUR_API_TOKEN_HERE";
string endpoint = $"https://production-sfo.browserless.io/stealth/bql?token={token}&proxy=residential&proxyCountry=us";

var payload = new
{
    query = @"mutation ScrapeOpenTable {
      goto(url: ""https://www.opentable.com/s?term=italian&covers=2"", waitUntil: networkIdle) { status }
      waitForSelector(selector: ""[data-test=restaurant-card]"", timeout: 15000) { time }
      restaurants: mapSelector(selector: ""[data-test=restaurant-card]"") {
        name: mapSelector(selector: ""h2"") { innerText }
        rating: mapSelector(selector: ""[data-test=rating-score]"") { innerText }
        cuisine: mapSelector(selector: ""[data-test=cuisine]"") { innerText }
        priceRange: mapSelector(selector: ""[data-test=price-range]"") { innerText }
        bookingsToday: mapSelector(selector: ""[data-test=bookings-today]"") { innerText }
      }
    }",
    variables = new { },
};

using (HttpClient httpClient = new HttpClient())
{
    var content = new StringContent(
        JsonSerializer.Serialize(payload), Encoding.UTF8, "application/json");
    var response = await httpClient.PostAsync(endpoint, content);
    string body = await response.Content.ReadAsStringAsync();
    Console.WriteLine(body);
}

2. Check the output

{
  "data": {
    "goto": { "status": 200 },
    "waitForSelector": { "time": 3412 },
    "restaurants": [
      {
        "name": [{ "innerText": "Carbone" }],
        "rating": [{ "innerText": "4.8" }],
        "cuisine": [{ "innerText": "Italian" }],
        "priceRange": [{ "innerText": "$$$$" }],
        "bookingsToday": [{ "innerText": "Booked 42 times today" }]
      }
    ]
  }
}

Frameworks

Connect through stealth mode and a residential proxy so OpenTable sees traffic from a real browser, then extract restaurant data from the rendered page.

Puppeteer

View Full Code on GitHub

1. Install dependencies

npm install puppeteer-core

2. Connect and scrape

import puppeteer from 'puppeteer-core';

const browser = await puppeteer.connect({
  browserWSEndpoint:
    'wss://production-sfo.browserless.io/stealth?token=YOUR_API_TOKEN_HERE&proxy=residential&proxyCountry=us',
});

try {
  const page = await browser.newPage();
  await page.goto('https://www.opentable.com/s?term=italian&covers=2', {
    waitUntil: 'networkidle2',
  });

  await page.waitForSelector('[data-test="restaurant-card"]');

  const restaurants = await page.evaluate(() =>
    Array.from(document.querySelectorAll('[data-test="restaurant-card"]')).map((card) => ({
      name: card.querySelector('h2')?.innerText?.trim() ?? '',
      rating: card.querySelector('[data-test="rating-score"]')?.innerText?.trim() ?? '',
      cuisine: card.querySelector('[data-test="cuisine"]')?.innerText?.trim() ?? '',
      priceRange: card.querySelector('[data-test="price-range"]')?.innerText?.trim() ?? '',
    }))
  );

  console.log(JSON.stringify(restaurants, null, 2));
} finally {
  await browser.close();
}

3. Check the output

Run with node scrape-opentable.mjs. Each object has name, rating, cuisine, and priceRange fields.

[
  {
    "name": "Carbone",
    "rating": "4.8",
    "cuisine": "Italian",
    "priceRange": "$$$$"
  },
  {
    "name": "L'Artusi",
    "rating": "4.7",
    "cuisine": "Italian",
    "priceRange": "$$$"
  }
]

Playwright

View Full Code on GitHub

1. Install dependencies

npm install playwright-core

2. Connect and scrape

import { chromium } from 'playwright-core';

const browser = await chromium.connectOverCDP(
  'wss://production-sfo.browserless.io?token=YOUR_API_TOKEN_HERE&stealth&proxy=residential&proxyCountry=us'
);

try {
  const context = browser.contexts()[0];
  const page = await context.newPage();
  await page.goto('https://www.opentable.com/s?term=italian&covers=2', {
    waitUntil: 'networkidle',
  });

  await page.waitForSelector('[data-test="restaurant-card"]');

  const restaurants = await page.evaluate(() =>
    Array.from(document.querySelectorAll('[data-test="restaurant-card"]')).map((card) => ({
      name: card.querySelector('h2')?.innerText?.trim() ?? '',
      rating: card.querySelector('[data-test="rating-score"]')?.innerText?.trim() ?? '',
      cuisine: card.querySelector('[data-test="cuisine"]')?.innerText?.trim() ?? '',
      priceRange: card.querySelector('[data-test="price-range"]')?.innerText?.trim() ?? '',
    }))
  );

  console.log(JSON.stringify(restaurants, null, 2));
} finally {
  await browser.close();
}

3. Check the output

Run with node scrape-opentable.mjs. Each object has name, rating, cuisine, and priceRange fields.

[
  {
    "name": "Carbone",
    "rating": "4.8",
    "cuisine": "Italian",
    "priceRange": "$$$$"
  },
  {
    "name": "L'Artusi",
    "rating": "4.7",
    "cuisine": "Italian",
    "priceRange": "$$$"
  }
]

BQL

View Full Code on GitHub

1. Write the mutation

Navigate to OpenTable's search results, wait for restaurant cards to render, then extract structured data from each listing. We use /stealth/bql because OpenTable's bot detection blocks plain browser sessions.

mutation ScrapeOpenTable {
  goto(url: "https://www.opentable.com/s?term=italian&covers=2", waitUntil: networkIdle) {
    status
  }
  waitForSelector(selector: "[data-test=restaurant-card]", timeout: 15000) {
    time
  }
  restaurants: mapSelector(selector: "[data-test=restaurant-card]") {
    name: mapSelector(selector: "h2") { innerText }
    rating: mapSelector(selector: "[data-test=rating-score]") { innerText }
    cuisine: mapSelector(selector: "[data-test=cuisine]") { innerText }
    priceRange: mapSelector(selector: "[data-test=price-range]") { innerText }
    bookingsToday: mapSelector(selector: "[data-test=bookings-today]") { innerText }
  }
}

2. Run it

Paste into the BQL IDE and click Run.

3. Check the output

{
  "data": {
    "goto": { "status": 200 },
    "waitForSelector": { "time": 3412 },
    "restaurants": [
      {
        "name": [{ "innerText": "Carbone" }],
        "rating": [{ "innerText": "4.8" }],
        "cuisine": [{ "innerText": "Italian" }],
        "priceRange": [{ "innerText": "$$$$" }],
        "bookingsToday": [{ "innerText": "Booked 42 times today" }]
      },
      {
        "name": [{ "innerText": "L'Artusi" }],
        "rating": [{ "innerText": "4.7" }],
        "cuisine": [{ "innerText": "Italian" }],
        "priceRange": [{ "innerText": "$$$" }],
        "bookingsToday": [{ "innerText": "Booked 28 times today" }]
      }
    ]
  }
}

Next steps

• Scrape Yelp Restaurant Listings -- another restaurant/business listing scrape with stealth mode
• Scrape Glassdoor Job Listings -- stealth-mode scraping against aggressive bot detection
• Solving Cloudflare Challenges -- bypass Cloudflare's interstitial pages before scraping