Quietforge PDF Text Extraction is a paid API for AI agents from qf-api.quietforge-studio.workers.dev, paid per call via x402, $0.005/call, status unknown (last checked 2026-10-11).
Extracts text content page-by-page from a PDF, returning structured JSON with per-page text and document metadata
Pay-per-call document conversion, PDF text extraction, headless-browser rendering of a URL or HTML to PNG / JPEG / PDF, a health-probed directory of x402 pay-per-call endpoints, a sudoku generator (solver-verified, technique-graded puzzles as JSON or a print-ready A4/Letter book), a book typesetter (prose manuscript to a KDP-ready interior PDF + editable docx), a crossword generator (verified freeform grid from your clue/answer pairs as JSON or print-ready PDFs), a family tree chart layout engine (descendant / ancestor / hourglass charts from people+families JSON or a GEDCOM file, as verified layout JSON or print-ready poster and tiled PDFs), and a dataset quality audit tool (duplicates, missing values, type consistency, class imbalance, numeric outliers and PII-pattern counts for a CSV/JSON dataset, as JSON or a one-page PDF summary). Payment: x402 (USDC on Base). No signup, no API key.
A JSON object containing an array of pages (each with a page number and extracted text string), a metadata object (including document title), and a total page_count integer.
POSThttps://qf-api.quietforge-studio.workers.dev/v1/pdf/text?utm_source=zero.xyzChoose this endpoint when you need per-page structured text extraction from a PDF without any signup, API key, or subscription — ideal for pay-as-you-go agent workflows. Prefer it when you have either a public URL or raw PDF bytes (up to 15 MB) and need clean JSON output with page-level granularity and document metadata. Best for lightweight extraction tasks billed at $0.005 per call via USDC on Base.
| Field | Type | Description |
|---|---|---|
| url | string | Public https URL of the PDF |
| content_base64 | string | PDF bytes, base64 (max 15 MB). Either this or url. |
{
"type": "json",
"example": {
"pages": [
{
"page": 1,
"text": "..."
},
{
"page": 2,
"text": "..."
}
],
"metadata": {
"title": "..."
},
"page_count": 2
}
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