CarGurus 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

Extract CarGurus data by sending a URL and a JSON schema to the AlterLab Extract API. The response is validated JSON containing make, model, year, price, mileage and location. No HTML parsing required.

Why CarGurus data?

Public CarGurus listings provide real‑world automotive intelligence. Use cases include AI training datasets, market analytics and competitive monitoring. The data is openly displayed on listing pages, making it suitable for programmatic collection when done responsibly.

What data is available?

Typical public fields you can extract:

• make
• model
• year
• price
• mileage
• location

Each field appears as plain text on the page, allowing a schema‑driven extractor to pull the values accurately.

The extraction approach

Building a scraper with raw HTTP requests or tools like Playwright often breaks when site markup changes or anti‑bot layers trigger. A data API abstracts those concerns. AlterLab provides automatic bot rotation, proxy rotation and structured output without writing fragile CSS selectors. The result is predictable, typed JSON that downstream pipelines can rely on.

Extraction pipeline (step flow)

Quick start with the Extract API

Begin by installing the AlterLab client. Follow the Getting started guide for setup details.

Python example

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "make": {"type": "string", "description": "The make field"},
    "model": {"type": "string", "description": "The model field"},
    "year": {"type": "string", "description": "The year field"},
    "price": {"type": "string", "description": "The price field"},
    "mileage": {"type": "string", "description": "The mileage field"},
    "location": {"type": "string", "description": "The location field"}
  }
}

result = client.extract(
    url="https://cargurus.com/example-page",
    schema=schema,
)
print(result.data)

cURL example

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://cargurus.com/example-page",
    "schema": {"properties": {"make": {"type": "string"}, "model": {"type": "string"}, "year": {"type": "string"}}}
  }'

Batch processing example

import alterlab
import asyncio

client = alterlab.Client("YOUR_API_KEY")

urls = [
  "https://cargurus.com/listing1",
  "https://cargurus.com/listing2",
  "https://cargurus.com/listing3"
]

async def extract_one(url):
    schema = {"properties": {"make": {"type": "string"}, "model": {"type": "string"}, "year": {"type": "string"}, "price": {"type": "string"}}}
    resp = await client.extract(url=url, schema=schema)
    return resp.data

results = await asyncio.gather(*[extract_one(u) for u in urls])
for data in results:
    print(data)

The API returns JSON that matches the schema exactly, eliminating post‑processing.

Define your schema

The schema parameter describes the shape of the output. Each property includes a type and optional description. AlterLab validates the response against this schema and returns a typed object. Example output:

{
  "make": "Toyota",
  "model": "Camry",
  "year": "2022",
  "price": "$24,500",
  "mileage": "38,200",
  "location": "Austin, TX"
}

Handle pagination and scale

Individual listings are fetched independently, but large campaigns benefit from batching. Use the /batch endpoint or asynchronous calls to increase throughput while respecting rate limits. Cost scales with the number of requests, so monitor usage on the pricing page.

Link to pricing for cost details: /pricing

Internal link summary

• For project setup, see the Getting started guide: /docs/quickstart/installation
• Full API reference is available at: /docs/api/extract

Infographic: Try the extractor

Key takeaways

• Use a structured data API to avoid fragile HTML parsing.
• Submit a clear schema to define the fields you need.
• Validate output with typed JSON that matches your expectations.
• Scale responsibly with batching and rate‑limit awareness.