Extract clean page content, metadata, and structured text from any URL so agents can read the live web without brittle scraping.
Crawl response preview
/web/crawlExtract the article body, title, author, publish date, and outbound links from this URL.
Response contract
One URL in, full page content out — clean, structured, and AI-ready
from desearch_py import Desearch
desearch = Desearch(api_key='your-api-key')
result = await desearch.web_crawl(
url='https://en.wikipedia.org/wiki/Artificial_intelligence',
format='html'
)Decentralized crawl infrastructure with clean output — built for AI agents and data pipelines
| Feature | RECOMMENDEDDesearchDecentralized | Other ProvidersCentralized |
|---|---|---|
| Architecture | Decentralized(Multiple Providers) | Centralized |
| Setup Time | < 5 minutes | Hours |
| Cost | $0.50/1000 pages | $1.00+/1000 pages |
| Rate Limit | 1000+ req/sec | Limited |
| Full Page Text | Yes(Clean, stripped output) | Raw HTML only |
| Structured Data | Yes(JSON with metadata) | Varies |
| JavaScript Rendering | Yes(Dynamic pages supported) | Limited |
| Open-source | No |
Trusted by AI innovators worldwide
Built for teams that need fresh web, social, crawl, and cited AI context in production workflows.



From AI agent knowledge bases to competitive intelligence, Desearch Web Crawl API powers content-driven applications at scale
Feed live web content into AI agents and RAG pipelines for grounded, up-to-date responses
Extract and monitor competitor pricing pages, product updates, and announcements automatically
Pull clean article text, author metadata, and publication dates from news and blog pages
Crawl landing pages, extract on-page content, and monitor changes over time
Build automated pipelines that ingest, clean, and structure web content at scale
Aggregate structured content from multiple sources for research, reports, and analysis
Everything you need to know about Desearch Web Crawl API
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Totally agree with this, i just connected mine to Bittensor $TAO subnet 22 @desearch_ai with an API end point for X data to help me with subnet analysis and the results are getting where they should be. This could be used by anyone in crypto doing reporting and research and is an incredible opportunity based on pricing.

SN22 is becoming an intelligence layer for agents. And behind agents… come predictions, then portfolio management. Revenue + aligned incentives. The market probably hasn't fully priced this yet.

The forecasting environments just got richer. Starting this Thursday, miners can choose to take on the cost of news retrieval via @desearch_ai The cost limit will move from $0.02 to $0.1 - making our environments ready for heavy forecasting tasks. #SN6 #TAO

// Desearch integration complete crustty now has full social sentiment guidance and its integrated (weighted) in his subnet valuations. All thanks to Bittensor SN22 @desearch_ai Desearch vs. native X API below. I gave him both and asked to research @HermesSubnet, no bias.
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