Turn Any URL Into Structured Data
Point the API at any webpage. Get structured JSON back. AI-powered extraction handles the parsing — no CSS selectors, no DOM traversal, no maintenance when sites change. Plus 16+ specialized parsers for major platforms.
Every Custom Scraper Is a Liability
The traditional approach to web data extraction: inspect the DOM, write CSS selectors, extract the data, deploy, wait for it to break, fix it, repeat. Every website you target becomes a separate maintenance burden. Selectors that worked last Tuesday break when the site ships a design update.
At scale, this becomes untenable. A team scraping 20 websites maintains 20 separate extraction configurations, each with its own failure modes and update cadence. The engineering cost of keeping scrapers alive often exceeds the value of the data they collect.
The alternative — manually copying data from websites — doesn't scale past a few dozen records. Between custom scrapers that break and manual processes that don't scale, most teams are stuck.
Two Core Endpoints Plus 16+ Specialized Parsers
Web Parser
The universal endpoint. Send any URL, get structured content back. The parser extracts the page's main content, cleans HTML artifacts, and returns structured text with metadata.
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
url |
string | Yes | Any public URL |
extract_links |
boolean | No | Include extracted links |
extract_images |
boolean | No | Include image URLs |
timeout |
integer | No | Timeout in seconds (20–1500) |
Response Example
{
"url": "https://example.com/blog/data-infrastructure-guide",
"title": "The Complete Guide to Data Infrastructure",
"content": "Data infrastructure is the foundation layer...",
"author": "Jane Doe",
"published_date": "2026-03-05",
"meta_description": "Learn how to build modern data infrastructure...",
"links": [
{"text": "Apache Kafka", "url": "https://kafka.apache.org"},
{"text": "data warehouse guide", "url": "/guides/warehouse"}
],
"images": [
{"src": "https://example.com/images/architecture.png", "alt": "Architecture diagram"}
],
"word_count": 3420
}
Sitemap Extraction
Get all URLs from a website's sitemap. Useful for discovering all pages on a site before extraction, building site inventories, or monitoring for new content.
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
url |
string | Yes | Website URL (finds sitemap automatically) or sitemap URL |
Response: List of URLs with last modified dates and change frequency.
AI-Powered Specialized Parsers
For popular platforms that have complex page structures, Anysite provides specialized AI parsers that extract structured data with platform-specific field names.
| Parser | Platform | What It Extracts |
|---|---|---|
/api/ai-parser/github | GitHub | Repos, READMEs, issues, PRs |
/api/ai-parser/amazon | Amazon | Products, prices, reviews, ratings |
/api/ai-parser/glassdoor | Glassdoor | Company reviews, salaries, interviews |
/api/ai-parser/g2 | G2 | Software reviews, ratings, comparisons |
/api/ai-parser/trustpilot | Trustpilot | Business reviews, ratings |
/api/ai-parser/capterra | Capterra | Software reviews, pricing |
/api/ai-parser/producthunt | Product Hunt | Product launches, upvotes |
/api/ai-parser/crunchbase | Crunchbase | Company data, funding rounds |
/api/ai-parser/angellist | AngelList | Startup data, jobs |
/api/ai-parser/pinterest | Pins, boards, profiles | |
/api/ai-parser/hackernews | Hacker News | Posts, comments, scores |
/api/ai-parser/builtwith | BuiltWith | Technology stacks |
/api/ai-parser/applyboard | ApplyBoard | Program data |
/api/ai-parser/wikileaks | WikiLeaks | Document data |
/api/ai-parser/trustmrr | TrustMRR | MRR data |
| More added continuously | ||
Code Examples
import requests API_KEY = "YOUR_API_KEY" BASE = "https://api.anysite.io" headers = {"access-token": API_KEY} # Parse any URL page = requests.post( f"{BASE}/api/webparser/parse", headers=headers, json={ "url": "https://example.com/blog/data-infrastructure-guide", "extract_links": True, "extract_images": True } ).json() print(f"Title: {page['title']}") print(f"Word count: {page['word_count']}") print(f"Links found: {len(page.get('links', []))}") print(f"\nContent preview: {page['content'][:500]}")
import requests API_KEY = "YOUR_API_KEY" BASE = "https://api.anysite.io" headers = {"access-token": API_KEY} # Step 1: Get all URLs from sitemap sitemap = requests.post( f"{BASE}/api/webparser/sitemap", headers=headers, json={"url": "https://example.com"} ).json() print(f"Found {len(sitemap['urls'])} pages") # Step 2: Extract content from each page pages = [] for url_entry in sitemap["urls"][:100]: # First 100 pages page = requests.post( f"{BASE}/api/webparser/parse", headers=headers, json={"url": url_entry["url"]} ).json() pages.append(page) print(f" Extracted: {page['title']}")
import requests API_KEY = "YOUR_API_KEY" BASE = "https://api.anysite.io" headers = {"access-token": API_KEY} # Extract Glassdoor company reviews reviews = requests.post( f"{BASE}/api/ai-parser/glassdoor", headers=headers, json={"url": "https://glassdoor.com/Reviews/TechCorp-Reviews-E12345.htm"} ).json() # Extract G2 software reviews g2_data = requests.post( f"{BASE}/api/ai-parser/g2", headers=headers, json={"url": "https://g2.com/products/techcorp/reviews"} ).json() # Extract Amazon product data product = requests.post( f"{BASE}/api/ai-parser/amazon", headers=headers, json={"url": "https://amazon.com/dp/B0XXXXXXX"} ).json()
# Parse any URL curl -X POST "https://api.anysite.io/api/webparser/parse" \ -H "access-token: YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"url": "https://example.com/blog/post", "extract_links": true}'
# Get sitemap curl -X POST "https://api.anysite.io/api/webparser/sitemap" \ -H "access-token: YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{"url": "https://example.com"}'
# Parse any URL anysite api /api/webparser/parse url="https://example.com/blog/post" # Extract with links and images anysite api /api/webparser/parse \ url="https://example.com/pricing" \ extract_links=true extract_images=true # Get sitemap URLs anysite api /api/webparser/sitemap url="https://example.com" # Batch: parse multiple URLs anysite api /api/webparser/parse --from-file urls.txt \ --input-key url --parallel 5 --format csv # AI parser anysite api /api/ai-parser/glassdoor \ url="https://glassdoor.com/Reviews/TechCorp-Reviews-E12345.htm"
name: site-crawler sources: sitemap: endpoint: /api/webparser/sitemap input: url: "https://competitor.com" pages: endpoint: /api/webparser/parse depends_on: sitemap input: url: ${sitemap.url} extract_links: true parallel: 5 on_error: skip storage: format: parquet path: ./data/site-crawl
Use Cases
Competitor Website Monitoring
Problem
Tracking changes on competitor websites — pricing updates, new feature launches, messaging changes, new blog content — requires manually checking sites or building custom scrapers for each competitor.
Solution
Crawl competitor sitemaps to discover all pages. Extract content from key pages (pricing, features, about, blog). Run on a schedule and use the CLI's diff capability to highlight changes between runs.
Result
Automated competitive monitoring. Get alerts when competitors change their pricing page, launch new features, or shift their messaging. No custom scrapers to maintain.
Lead Enrichment from Company Websites
Problem
Your CRM has company URLs but you need structured data: what the company does, their product offerings, team size signals, technology indicators. Manually reading each company's website doesn't scale.
Solution
Parse company homepages, about pages, and product pages. Extract structured content, team descriptions, and technology mentions. Combine with LinkedIn company data for a complete picture.
Result
CRM records enriched with current website data. Know what each target company does, how they position themselves, and what technology they use.
Content Aggregation and Research
Problem
Researchers, analysts, and content teams need to read and synthesize information from dozens or hundreds of web sources. Manually visiting each source, copying text, and organizing it is tedious and error-prone.
Solution
Build a URL list of relevant sources (industry blogs, documentation sites, news articles). Batch-parse all pages. Store structured content for analysis, summarization, or knowledge base construction.
Result
A structured content library built from the web. Feed into LLM analysis for summarization, topic extraction, or trend identification.
Review Aggregation Across Platforms
Problem
Understanding public perception of a product means checking Glassdoor, G2, Trustpilot, Capterra, Amazon reviews, and more. Each platform has a different structure, and none provides a unified API.
Solution
Use the specialized AI parsers to extract reviews from each platform. Aggregate into a single dataset. Analyze sentiment, recurring themes, and rating distributions across sources.
Result
A unified review dashboard covering all major platforms. Compare sentiment across Glassdoor (employee), G2 (user), and Trustpilot (customer) to get the complete picture.
How Anysite Compares
| Feature | Anysite | Firecrawl | Jina Reader | Apify | ScrapingBee |
|---|---|---|---|---|---|
| Any URL parsing | AI-powered extraction | LLM-powered | Markdown conversion | Actor per site | Proxy + render |
| Specialized parsers | 16+ platforms | None | None | 1,800+ actors | None |
| Sitemap extraction | Built-in endpoint | Via crawl | Not available | Actor | Not available |
| Output format | Structured JSON | Markdown/JSON | Markdown | Varies by actor | HTML/JSON |
| Social platforms | LinkedIn, Instagram, Twitter, Reddit, YouTube | Not available | Not available | Separate actors each | Not available |
| Pricing | 1 credit/page ($0.003) | $0.004/page | $0.002/page | $0.004+/page | $0.005/page |
| Pipeline support | YAML + batch CLI | API only | API only | Actor scheduling | API only |
| MCP integration | Native | None | None | None | None |
Endpoint Pricing
Pay only for the data you pull. Credits are shared across all Anysite endpoints.
| Endpoint | Credit Cost |
|---|---|
| Web parser (any URL) | 1 credit |
| Sitemap extraction | 1 credit |
| AI parsers (per URL) | 1 credit |
Cost Examples
| Use Case | Monthly Volume | Credits | Recommended Plan |
|---|---|---|---|
| Monitor 10 competitor pages (daily) | ~300 pages | ~300 | Starter ($49/mo) |
| Crawl 5 websites (weekly, 100 pages each) | ~2,000 pages | ~2,000 | Starter ($49/mo) |
| Review aggregation (100 URLs) | 100 pages | 100 | Starter ($49/mo) |
| Content research (500 articles) | 500 pages | 500 | Starter ($49/mo) |
| Full site audit (sitemap + all pages) | sitemap + pages | 1 + page count | Starter ($49/mo) |
At 1 credit per page ($0.003 on the Starter plan), web parsing is extremely cost-efficient. Crawling and extracting an entire 1,000-page website costs approximately $3.30.
Frequently Asked Questions
Related Endpoints
Start Extracting Data from Any Website
7-day free trial with 1,000 credits. Any URL to structured JSON. Plus 16+ specialized parsers. No selectors, no maintenance.