Get structured JSON from any page

Add extract=auto to a Fetch request and Zenrows returns the page’s data as JSON: names, prices, availability, images, breadcrumbs and more, with no selectors to write or maintain. Reach for it whenever you want fields, not HTML. Uses: Extract (extract=auto, extract_fields), the Extract endpoints and Adaptive Stealth Mode (mode=auto)

Ask your AI agent

With the Zenrows MCP server connected to Claude, Cursor or any MCP client, paste this prompt. Your agent picks the right Zenrows tools and settings on its own.

Use Zenrows to get every product on https://www.scrapingcourse.com/ecommerce/ as JSON
with name, price and URL, plus how many products the store has in total.

Extract a page as JSON

Set extract=auto and pair it with mode=auto, so Zenrows renders JavaScript and switches to premium proxies when the page needs them. The response is JSON with two fields: parsed holds the structured data, html holds the raw page for validation.

import json
import requests

params = {
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": "https://www.scrapingcourse.com/ecommerce/",
    "mode": "auto",
    "extract": "auto",
}

response = requests.get("https://api.zenrows.com/v1/", params=params, timeout=180)
response.raise_for_status()

data = response.json()
print(json.dumps(data["parsed"], indent=2))

const params = new URLSearchParams({
  apikey: "YOUR_ZENROWS_API_KEY",
  url: "https://www.scrapingcourse.com/ecommerce/",
  mode: "auto",
  extract: "auto",
});

const response = await fetch(`https://api.zenrows.com/v1/?${params}`, {
  signal: AbortSignal.timeout(180_000),
});
if (!response.ok) throw new Error(`${response.status}: ${await response.text()}`);

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

curl "https://api.zenrows.com/v1/?apikey=YOUR_ZENROWS_API_KEY&url=https%3A%2F%2Fwww.scrapingcourse.com%2Fecommerce%2F&mode=auto&extract=auto"

zenrows extract https://www.scrapingcourse.com/ecommerce/

The CLI prints parsed directly. Its zenrows extract command sends extract=auto with mode=auto by default, and when a domain isn’t prepared yet it retries the page with autoparse, so you always get JSON back. See the CLI guide.

Example output

The parsed field for the catalog page, trimmed to two products:

{
  "page_context": {
    "current_page": 1,
    "results_per_page": 69,
    "total_pages": 12,
    "total_results": 188
  },
  "products": [
    {
      "availability": "in_stock",
      "currency": "USD",
      "image_url": "https://www.scrapingcourse.com/ecommerce/wp-content/uploads/2024/03/mh09-blue_main.jpg",
      "name": "Abominable Hoodie",
      "price": 69,
      "product_id": "246",
      "product_type": "variable",
      "product_url": "https://www.scrapingcourse.com/ecommerce/product/abominable-hoodie/",
      "sku": "MH09"
    },
    {
      "availability": null,
      "currency": "USD",
      "image_url": "https://www.scrapingcourse.com/ecommerce/wp-content/uploads/2024/03/wj08-gray_main.jpg",
      "name": "Adrienne Trek Jacket",
      "price": 57,
      "product_id": "1864",
      "product_type": "variable",
      "product_url": "https://www.scrapingcourse.com/ecommerce/product/adrienne-trek-jacket/",
      "sku": "WJ08"
    }
  ]
}

A field the page leaves empty comes back as null. Field names follow what each website exposes and stay stable for that website. New fields arrive as extraction grows, so read the fields you use and let the rest pass through.

Keep just the fields you need

Add extract_fields with a comma-separated list. A bare name (products) keeps a whole group, and a dot path (products.price) keeps one field inside a group, on every element of a list. Run one request without it first to read the field names the page offers.

import requests

params = {
    "apikey": "YOUR_ZENROWS_API_KEY",
    "url": "https://www.scrapingcourse.com/ecommerce/",
    "mode": "auto",
    "extract": "auto",
    "extract_fields": "products.name,products.price,products.availability,page_context.total_results",
}

response = requests.get("https://api.zenrows.com/v1/", params=params, timeout=180)
response.raise_for_status()
print(response.json()["parsed"])

const params = new URLSearchParams({
  apikey: "YOUR_ZENROWS_API_KEY",
  url: "https://www.scrapingcourse.com/ecommerce/",
  mode: "auto",
  extract: "auto",
  extract_fields: "products.name,products.price,products.availability,page_context.total_results",
});

const response = await fetch(`https://api.zenrows.com/v1/?${params}`, {
  signal: AbortSignal.timeout(180_000),
});
if (!response.ok) throw new Error(`${response.status}: ${await response.text()}`);

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

curl "https://api.zenrows.com/v1/?apikey=YOUR_ZENROWS_API_KEY&url=https%3A%2F%2Fwww.scrapingcourse.com%2Fecommerce%2F&mode=auto&extract=auto&extract_fields=products.name%2Cproducts.price"

Output (trimmed)

{
  "page_context": { "total_results": 188 },
  "products": [
    { "availability": "in_stock", "name": "Abominable Hoodie", "price": 69 },
    { "availability": null, "name": "Adrienne Trek Jacket", "price": 57 },
    { "availability": null, "name": "Aeon Capri", "price": 48 }
  ]
}

Extract a page from a major store

The same request works on the hundreds of domains Zenrows has prepared, including Amazon, Walmart, eBay, Best Buy, Zillow and Booking.com. This Walmart product page returns pricing, fulfillment, features and media:

cURL

curl "https://api.zenrows.com/v1/?apikey=YOUR_ZENROWS_API_KEY&url=https%3A%2F%2Fwww.walmart.com%2Fip%2FAirPods-Pro-3%2F17835006350&mode=auto&extract=auto"

Output (parsed, trimmed)

{
  "product": {
    "brand": "Apple",
    "name": "Apple AirPods Pro 3",
    "item_id": "17835006350",
    "canonical_url": "https://www.walmart.com/ip/AirPods-Pro-3/17835006350"
  },
  "pricing": {
    "currency": "USD",
    "current_price": 199,
    "original_price": 249,
    "savings_amount": 50,
    "price_qualifier": "Price when purchased online"
  },
  "fulfillment": {
    "pickup_available": true,
    "shipping_available": true,
    "shipping_cost": "Free",
    "shipping_arrival_label": "Arrives tomorrow",
    "sold_by": "Walmart.com"
  },
  "ratings_summary": { "average_rating": 4.4 }
}

Check a domain before you send traffic

GET /v1/extract/domains lists every domain Extract serves right now. It’s free, so check it before you send traffic to a new site:

Python (Requests)

import requests

response = requests.get(
    "https://api.zenrows.com/v1/extract/domains",
    headers={"X-API-Key": "YOUR_ZENROWS_API_KEY"},
    timeout=30,
)
response.raise_for_status()

covered = {entry["domain"]: entry["verticals"] for entry in response.json()["domains"]}
for domain in ["walmart.com", "zillow.com", "example-shop.com"]:
    print(domain, "->", "ready" if domain in covered else "prepare it")

Output

walmart.com -> ready
zillow.com -> ready
example-shop.com -> prepare it

Prepare a new domain

For a domain that isn’t on the list yet, ask Zenrows to prepare it. Send one example URL per kind of page you care about, such as a product page and a search page, then poll until status is ready. Preparation usually takes minutes, and every Zenrows API key can use the domain once it’s ready.

Python (Requests)

import time
import requests

headers = {"X-API-Key": "YOUR_ZENROWS_API_KEY"}
base = "https://api.zenrows.com/v1/extract/prepared-domains"

# 1. Ask for the domain
response = requests.post(
    base,
    headers=headers,
    json={
        "domain": "example-shop.com",
        "example_urls": ["https://www.example-shop.com/product/blue-shirt"],
    },
    timeout=30,
)
print(response.status_code, response.json()["state"])  # 202 received

# 2. Poll until it is ready
while True:
    body = requests.get(f"{base}/example-shop.com", headers=headers, timeout=30).json()
    if body["status"] != "preparing":
        break
    time.sleep(30)

print(body["status"])  # ready, or failed with a failure block that says what to do

Branch on status: preparing, ready or failed. A failed preparation carries failure.code, and the endpoint reference maps each code to the next step.

Use the Zenrows SDKs

Both SDKs have an extract() method that sends extract=auto plus mode=auto for you. Pass extract_fields like any other parameter. When a domain isn’t prepared yet, extract() retries with autoparse; turn that off with fallback_to_autoparse=False (Python) or fallbackToAutoparse: false (Node.js) to get the REQS007 response instead.

# pip install zenrows
from zenrows import ZenRowsClient

client = ZenRowsClient("YOUR_ZENROWS_API_KEY")

response = client.extract(
    "https://www.scrapingcourse.com/ecommerce/",
    params={"extract_fields": "products.name,products.price"},
    timeout=180,
)
response.raise_for_status()
print(response.json()["parsed"])

// npm install zenrows
import { ZenRows } from "zenrows";

const client = new ZenRows("YOUR_ZENROWS_API_KEY");

const response = await client.extract(
  "https://www.scrapingcourse.com/ecommerce/",
  { extract_fields: "products.name,products.price" },
);
if (!response.ok) throw new Error(`${response.status}: ${await response.text()}`);

const { parsed } = await response.json();
console.log(parsed);

Output (trimmed)

{'products': [{'name': 'Abominable Hoodie', 'price': 69}, {'name': 'Adrienne Trek Jacket', 'price': 57}, {'name': 'Aeon Capri', 'price': 48}, ...]}

Customize it

Change	How
The same field names across websites (real estate)	`extract=standard`, see Extraction modes and Extract real estate listings
Content from a specific country	Add `proxy_country=us` (any ISO code), see Proxy country
Data that loads late	Use `js_render=true` with `wait=3000` instead of `mode=auto`, see Combining with other features
Fields you define with selectors	Use `css_extractor`, see Extract data with CSS selectors
Thousands of pages	Put `"extract": "auto"` in a Batch job’s `zenrows_params`, see Scrape a list of URLs

Troubleshooting

403 with REQS007: the domain, or that kind of page on it, isn’t prepared yet. The request isn’t charged. Prepare the domain with that URL in example_urls, then retry once it reads ready.
400 with REQS004 naming extract_fields: none of the names you asked for exist on this page. The error lists the fields the page offers; copy names from there, or omit extract_fields.
422 with no_data_extracted: Extract knows the page type, and the fields came back empty, usually because the page hadn’t finished loading. The request isn’t charged. Retry with js_render=true&wait=3000 (and premium_proxy=true for protected sites), or drop extract_fields to see every field.
A preparation ends failed with unpreparable: the example URLs returned no usable HTML. Send other example URLs from the same site.
The request takes long to return: set your HTTP client timeout to 180 seconds or more so mode=auto has time to escalate.

Extract real estate listings and property data

One schema for Zillow, Redfin and Idealista, from search to every listing.

Extract data with CSS selectors

Pick exact elements with css_extractor, or parse the HTML yourself.

Scrape a list of URLs

Run Extract on thousands of pages as one Batch job.

Turn any web page into LLM-ready markdown

Get the whole page as clean markdown instead of fields.

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Get structured JSON from any page

Ask your AI agent

Extract a page as JSON

Example output

Keep just the fields you need

Extract a page from a major store

Check a domain before you send traffic

Prepare a new domain

Use the Zenrows SDKs

Customize it

Troubleshooting

Extract real estate listings and property data

Extract data with CSS selectors

Scrape a list of URLs

Turn any web page into LLM-ready markdown

​Ask your AI agent

​Extract a page as JSON

​Example output

​Keep just the fields you need

​Extract a page from a major store

​Check a domain before you send traffic

​Prepare a new domain

​Use the Zenrows SDKs

​Customize it

​Troubleshooting

​Related recipes

Extract real estate listings and property data

Extract data with CSS selectors

Scrape a list of URLs

Turn any web page into LLM-ready markdown

Ask your AI agent

Extract a page as JSON

Example output

Keep just the fields you need

Extract a page from a major store

Check a domain before you send traffic

Prepare a new domain

Use the Zenrows SDKs

Customize it

Troubleshooting

Related recipes