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WebLens PDF Extraction API
x402
WebLens PDF Extraction API is a paid API for AI agents from api.weblens.dev, paid per call via x402, $0.004/call, status unknown (last checked 2026-10-02).
Extracts full text, page-by-page content, and metadata from a PDF document hosted at a given URL
Price / call$0.004
Success—
StatusUnknown
How this service works
# WebLens - Web Intelligence API, pay per call Scrape, crawl, map and extract the web. No account, no API key, no monthly minimum — you pay for the calls you make and nothing else. ## Pricing Page fetching starts at **$0.002**, whole-site crawling at **$0.0015/page**, and sitemap discovery at **$0.004**. Comparable services bill $0.007-0.008 per request, or reach a lower per-page rate only on a $99/month commitment. WebLens has no commitment to reach. ## Payment Protocol All paid endpoints use the [x402 protocol](https://x402.org) for HTTP-native micropayments (USDC on Base). ## Cache Discount Cached responses are **70% cheaper** than fresh fetches.
Output
A JSON object containing the PDF URL, an array of page objects (each with pageNumber and content), the full concatenated text, document metadata (title, author, page count), a unique request ID, and an ISO timestamp of when the extraction occurred.
Example use cases
Research paper content extraction
“Pull the full text and metadata from this academic paper PDF: https://arxiv.org/pdf/2301.00001.pdf — I need the title, author, page count, and the content of each page.”
Legal document text retrieval
“Read this contract PDF at https://example.com/contracts/agreement.pdf and extract all the text so I can search through it.”
Financial report data extraction
“Extract everything from this annual report PDF at https://company.com/reports/2024-annual.pdf — I need the full text and which page each section appears on.”
Technical details
Endpoint
POSThttps://api.weblens.dev/pdf?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to programmatically read the contents of a PDF hosted at a public URL, especially when you also need per-page structure and document metadata (title, author, page count). Prefer it over generic web scraping endpoints when the target is specifically a PDF file. The caching discount makes it cost-effective for repeated access to the same document.
Side effects
- URL is not reachable or returns a non-200 status — extraction fails with an error
- URL does not point to a valid PDF — parsing error returned
- PDF is password-protected or DRM-restricted — content cannot be extracted
- Very large PDFs may time out or return partial results
- Payment failure via x402 protocol blocks the request
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| url | string | URL of the PDF document |
| pages | array | Specific page numbers to extract (omit for all pages) |
Output
Schema · Response
{
"type": "json",
"example": {
"url": "https://example.com/document.pdf",
"pages": [
{
"content": "Page 1 text content...",
"pageNumber": 1
}
],
"fullText": "Page 1 text content...",
"metadata": {
"title": "Sample Document",
"author": "John Doe",
"pageCount": 10
},
"requestId": "req_pdf123",
"extractedAt": "2026-01-26T12:00:00.000Z"
}
}Tags
x402
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