DivParser is an intelligent extraction platform for developers, analysts, and researchers. Use the interactive dashboard to scrape and explore data visually, or hit the REST API to integrate extraction directly into your pipeline.
To Start
URL or raw markup
Export formats
Dashboard + API keys
Drop DivParser into your stack as an extraction layer — or use the dashboard to collect data without writing a line of code.
Use DivParser as the extraction step inside your existing pipeline. POST a URL or raw HTML, define your schema, get structured JSON back — no custom scraper to maintain.
Schedule recurring scrapes on competitor pages, listing sites, or marketplaces. Structured results land directly in your database or dashboard on your cadence.
Extract structured data from academic sources, public directories, or news archives. Export to JSON, CSV, or Excel and feed it straight into your analysis workflow.
Pull product names, prices, images, and metadata from any e-commerce page. Handles pagination automatically — give it a URL range and it scrapes every page.
Real estate, job boards, classifieds — any site with repeating structured listings. Define once what to extract and DivParser handles the rest across hundreds of pages.
Already have HTML from a dataset, archive, or another scraper? POST it directly to the parse endpoint and extract structured data without touching the live site again.
Submit a URL and a schema, get structured JSON back in seconds. No browser automation scripts to write, no headless browser to configure.
Data comes back exactly as you defined it — consistent field names, correct types, no stray columns. Drop it straight into your pipeline or spreadsheet.
Describe what to extract in natural language, or use Nestlang for strict typed schemas. Either way, no CSS selectors or XPath expressions needed.
Three steps from URL to structured data.
Simply paste the URL you want to extract data from. No proxies, selectors, or technical setup required.
Write a natural‑language prompt or a Nestlang schema describing the fields you need. No selectors, no XPath.
Download perfectly structured CSV or Excel files in seconds. Review the data and import it right into your workflow.
See how easy it is to extract different types of data using DivParser.
{
"name": "Dyson V15 Detect",
"price": "$749.00",
"rating": 4.8,
"image": "https://amazon.com/v15-hero.jpg"
}{
"address": "123 Silicon Valley Way",
"price": "$1,250,000",
"stats": {
"beds": 3,
"baths": 2.5
},
"agent": "jane.doe@realestate.com"
}{
"title": "The Future of Agentic AI",
"author": "Alex Rivera",
"date": "2026-04-15",
"tags": [
"AI",
"Agents",
"Future"
]
}{
"company": "Acme Corp",
"location": "San Francisco, CA",
"links": {
"linkedin": "linkedin.com/acme"
}
}We are in early access, this are things we don't do well (yet)
Toggle on pagination, enter your first URL, last URL, and number of pages. DivParser auto-detects the pattern, generates all intermediate URLs, and scrapes them automatically.
Just toggle pagination ON. Enter your starting URL, ending URL, and how many pages you need. No complex configuration—DivParser handles the rest.
DivParser analyzes your first and last URLs, detects the pattern, and automatically generates every URL in between. No manual URL listing required.
AI ANALYZING URL STRUCTURE...
DYNAMIC PARAMETER DETECTED: "page"
Say goodbye to messy, inconsistent AI outputs. DivParser runs on Nestlang, guaranteeing that every extraction perfectly matches your required schema with strict type validation.
Most AI tools hallucinate or format data randomly. Nestlang ensures if you ask for a number, you get a number. If a field is missing, it handles it predictably. Structure is enforced at the core level.
Define your schema using Nestlang's natural language syntax. Whether you need basic lists or deeply nested relational data, the engine rigorously enforces the output type with human-readable definitions.
products: the top 10 products (array)
-name: Product name (string)
-price: Product price (number)
-in_stock: Whether the product is in stock (boolean){
"products": [
{
"name": "Wireless Headphones",
"price": 99.99,
"in_stock": true
},
...
]
}You don't need DivParser to fetch the page. If you already have HTML — from a file, a dataset, another scraper, or your own crawler — hand it directly to DivParser and get back structured JSON.
Already running your own crawler or browser automation? Drop DivParser in as the extraction step. POST the HTML, define your schema, get structured JSON back — no need to rewrite your pipeline.
Working with saved HTML exports, cached pages, or files handed to you by a client? Upload the markup and extract exactly what you need — tables, lists, nested data — in one call.
Processing large HTML datasets from archives, research corpora, or bulk exports? Run DivParser as a batch extraction layer over your existing data without touching the source sites.
Example — POST /v1/parse
curl -X POST "https://api.divparser.com/v1/parse" \
-H "Authorization: Bearer YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"html": "<html>..your content..</html>",
"schema": "Extract product name, price, and stock status",
"name": "product-batch-01"
}'{
"id": "scr_parse001",
"status": "COMPLETED",
"results": [
{
"data": [
{ "name": "Widget Pro", "price": "$49.99", "stock": "In Stock" },
{ "name": "Widget Lite", "price": "$19.99", "stock": "Low Stock" }
]
}
]
}Works with any HTML source — files, scrapers, archives, exports
FREE
Testers and Validators
$0
/ month
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Individuals & Hobbyists
$10.99
/ month
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Freelancers & Power Users
$29.99
/ month
Everything you need to know about DivParser