OpenTable Data API: Extract Structured JSON in 2026

TL;DR: To get structured OpenTable data via API, use a schema-based extraction service like AlterLab. By sending a target URL and a JSON schema to the /v1/extract endpoint, you receive validated, typed JSON containing restaurant details without writing custom HTML parsers.

Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

Why use OpenTable data?

For engineers building modern applications, access to high-quality food and hospitality data is a core requirement. When building data pipelines, there are three primary use cases for structured restaurant data:

1. AI Training & RAG: Feeding up-to-date restaurant metadata (cuisine, location, ratings) into LLMs or Vector Databases to power highly accurate food discovery agents.
2. Market Intelligence: Aggregating cuisine trends and rating distributions across specific geographic regions to identify market gaps.
3. Consumer Aggregators: Building niche discovery tools that combine restaurant information with other data sources like weather or local events.

What data can you extract?

When building a food data API integration, you aren't just looking for raw text; you are looking for specific attributes that can be mapped to a database schema. Because we use LLM-powered extraction, you can target any publicly visible field.

Commonly extracted fields include:

• restaurant_name: The official name of the establishment.
• cuisine: The primary food category (e.g., "Italian", "Sushi").
• rating: The numerical user rating or star count.
• location: Address or neighborhood data.
• price_range: The indicated cost level (e.g., "$$", "$$$").
• delivery_time or availability: Real-time indicators of service.

The extraction approach

Historically, engineers relied on raw HTTP requests followed by heavy BeautifulSoup or Cheerio parsing. This approach is fragile. If a site changes a single <div> class or moves a <span>, your entire pipeline breaks.

A modern data API approach shifts the responsibility of "understanding" the page from your code to the API. Instead of writing selectors like div.restaurant-card > h2, you define a schema. You tell the API: "I need a string called restaurant_name." The engine handles the navigation, anti-bot challenges, and HTML parsing, returning only the clean data you requested.

Quick start with AlterLab Extract API

To begin building your pipeline, you can follow our Getting started guide. Once your environment is set up, you can use the Extract API docs to refine your requests.

Python Implementation

The Python client is the most efficient way to integrate structured extraction into your backend services.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

# Define the exact structure you want the API to return
schema = {
  "type": "object",
  "properties": {
    "restaurant_name": {
      "type": "string",
      "description": "The name of the restaurant"
    },
    "cuisine": {
      "type": "string",
      "description": "The type of food served"
    },
    "rating": {
      "type": "number",
      "description": "The numerical star rating"
    },
    "price_range": {
      "type": "string",
      "description": "The price indicator (e.g. $$)"
    }
  },
  "required": ["restaurant_name", "cuisine"]
}

# The extract method handles the browser rendering and AI extraction
result = client.extract(
    url="https://www.opentable.com/restuarants/example-city",
    schema=schema,
)

print(result.data)

Expected JSON Output:

{
  "restaurant_name": "The Golden Bistro",
  "cuisine": "French",
  "rating": 4.8,
  "price_range": "$$$"
}

cURL Implementation

If you are working in a shell environment or a language without a dedicated SDK, use the standard REST endpoint.

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.opentable.com/example-restaurant",
    "schema": {
      "type": "object",
      "properties": {
        "restaurant_name": {"type": "string"},
        "cuisine": {"type": "string"},
        "rating": {"type": "number"}
      }
    }
  }'

Define your schema

The core strength of the Extract API is the ability to enforce data types. By providing a JSON schema, you ensure that your downstream database (PostgreSQL, MongoDB, etc.) receives predictable data.

If you define rating as a number, the API will attempt to convert "4.5 stars" into 4.5. If you define cuisine as a string, it will strip away any surrounding HTML or noise. This validation happens at the edge, meaning your application logic stays clean.

Handle pagination and scale

When moving from a single URL to a full-scale data pipeline, you need to manage volume and concurrency. For high-volume tasks, do not use synchronous loops. Instead, utilize asynchronous jobs to process multiple pages in parallel.

Batch Processing Example

For large-scale extraction, we recommend using the asynchronous pattern to avoid blocking your main thread.

import alterlab
import asyncio

client = alterlab.Client("YOUR_API_KEY")

async def process_urls(urls):
    tasks = []
    for url in urls:
        # Create asynchronous extraction tasks
        tasks.append(client.extract_async(
            url=url,
            schema={"type": "object", "properties": {"restaurant_name": {"type": "string"}}}
        ))
    
    # Execute all requests concurrently
    results = await asyncio.gather(*tasks)
    return [r.data for r in results]

urls = [
    "https://opentable.com/url1",
    "https://opentable.com/url2",
    "https://opentable.com/url3"
]

# Run the event loop
data = asyncio.run(process_urls(urls))
print(data)

Cost Management and Rate Limiting

When scaling, keep an eye on your AlterLab pricing. Because you pay for what you use, you can estimate costs before running large batches. The API provides an estimation endpoint to help you calculate the cost of an extraction before you commit the request.

Key takeaways

• Avoid brittle parsers: Use schema-based extraction to decouple your code from website HTML changes.
• Enforce types: Use JSON schemas to ensure your data pipeline receives clean, predictable numbers and strings.
• Scale efficiently: Use asyncio for high-volume tasks and monitor your balance through the AlterLab dashboard.
• Target public data: Focus on retrieving publicly available information to maintain compliant data practices.

Hit reply if you have questions.

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