Yelp Data API: Extract Structured JSON in 2026

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

TL;DR

To get structured Yelp data via API, use AlterLab's Extract API: define a JSON schema for the fields you need (e.g., business_name, rating, address), send a POST request to the extract endpoint with the Yelp URL and your schema, and receive validated JSON output. No HTML parsing or selector maintenance required.

Why use Yelp data?

Yelp contains rich, structured local business information valuable for multiple engineering applications:

• Training data for local search AI: Restaurant attributes, service categories, and geographic patterns help build better recommendation models
• Market analytics pipelines: Competitive density analysis, price point correlation, and trend detection across business types
• Lead enrichment for B2B platforms: Verified business details improve sales territory mapping and partnership identification

What data can you extract?

Yelp's public business pages consistently expose these fields through semantic markup:

• business_name: Official display name (e.g., "Joe's Pizza")
• rating: Aggregate score as string (e.g., "4.5") to preserve precision
• address: Full street address with neighborhood context
• phone: Primary contact number in E.164 format where available
• hours: Weekly schedule as structured string (e.g., "Mon-Thu: 11AM-10PM")
• category: Primary and secondary business classifications (e.g., "Pizza, Italian")

These fields appear in predictable locations across Yelp's site structure, making them ideal candidates for schema-based extraction.

The extraction approach

Raw HTTP requests combined with HTML parsing create fragile pipelines for Yelp due to:

• Frequent frontend framework updates breaking CSS selectors
• JavaScript-rendered content requiring headless browser execution
• Anti-bot measures triggering CAPTCHAs or IP blocks during scaling

A data API approach solves these by abstracting the retrieval complexity. AlterLab handles:

• Automatic tier escalation (T1-T5) based on detected bot resistance
• Proxy rotation and session management
• Structured output generation via AI-powered semantic understanding This transforms extraction from a maintenance burden into a reliable API call.

Quick start with AlterLab Extract API

Begin by installing the SDK and making your first extraction request. See the Getting started guide for setup details.

Here's a Python example extracting core business fields from a Yelp page:

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "business_name": {
      "type": "string",
      "description": "The business name field"
    },
    "rating": {
      "type": "string",
      "description": "The rating field"
    },
    "address": {
      "type": "string",
      "description": "The address field"
    },
    "phone": {
      "type": "string",
      "description": "The phone field"
    },
    "hours": {
      "type": "string",
      "description": "The hours field"
    },
    "category": {
      "type": "string",
      "description": "The category field"
The category field"
    }
  }
}

result = client.extract(
    url="https://www.yelp.com/biz/joes-pizza-new-york",
    schema=schema,
)
print(result.data)

For direct HTTP interaction, use this cURL equivalent:

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://www.yelp.com/biz/joes-pizza-new-york",
    "schema": {
      "properties": {
        "business_name": {"type": "string"},
        "rating": {"type": "string"},
        "address": {"type": "string"}
      }
    }
  }'

Define your schema

The Extract API validates output against your JSON Schema definition, ensuring type safety and field presence. Key considerations for Yelp data:

• Use string type for all fields since Yelp presents data as formatted text
• Add description to clarify field semantics for the extraction model
• Specify required array for critical fields (e.g., ["business_name", "rating"])
• Leverage pattern or enum where values follow known formats (e.g., phone numbers)

AlterLab returns strictly typed JSON matching your schema—no need for post-processing validation. This is fundamental to treating AlterLab as a data API rather than a scraper.

Handle pagination and scale

For extracting multiple Yelp listings (e.g., search results or category pages):

1. Batch processing: Send 10-50 URLs per request using the urls array parameter
2. Rate limiting: AlterLab automatically enforces polite crawling; monitor X-RateLimit-Remaining headers
3. Async workflows: Use webhook notifications for large jobs instead of polling
4. Cost optimization: Set min_tier=3 for JavaScript-heavy Yelp pages to avoid unnecessary T1/T2 attempts

See AlterLab pricing for volume tiers—extraction costs scale linearly with successful requests, making high-volume pipelines predictable.

Key takeaways

• Structured Yelp data extraction requires schema definition, not selector maintenance
• AlterLab's Extract API handles anti-bot measures and outputs validated JSON
• Publicly available fields like business_name, rating, and address are reliably accessible
• Always verify compliance with Yelp's robots.txt and Terms of Service
• Treat AlterLab as a data API: define your schema, call the endpoint, use the output