PDF Text Extractor — Text & Metadata from URLs

Download PDF files by URL and extract their text and metadata (page count, title, author) — whole-document or per-page. Honest per-URL status. No auth.

Built reliability-first: every row reports what was found and what was missing — you never get a silent blank, and a run summary tells you exactly what happened.

What you get

Returns one clean, structured row per result. Every row reports its own status so you never get a silent blank.

How to use it

1. Fill in the input (see the example below).
2. Run it once for a snapshot, or schedule it to keep the data fresh.
3. Export to CSV/JSON/Excel, or push straight to Google Sheets, Notion, Airtable, Zapier, Make, or n8n.

Input

{
  "pdfUrls": [
    "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
  ],
  "perPage": false,
  "useProxy": false,
  "maxItems": 100000
}

Sample output

[
{
  "result": "example",
  "status": "ok",
  "scrapedAt": "2026-09-03T09:00:00.000Z"
}
]

Typical uses

• RAG / AI pipelines — turn PDFs (reports, papers, manuals) into text.
• Document processing — extract invoices/contracts text for downstream parsing.
• Search indexing — index PDF contents.

Pricing

$2.00 / 1,000 results ($0.002 per result), plus a near-zero start fee. You are never charged beyond your limit, and blocked or failed items are reported honestly — not billed as data.

FAQ & limitations

• Public data only — no login walls, no cookies required.
• Rate limits on the source may require the proxy or a retry on very large pulls.
• Every row reports its own status, so partial results are always labeled, never faked.
• Integrations: output works with Zapier, Make, n8n, and any webhook via Apify's integrations.
• Formats: results export as JSON, CSV, Excel, or HTML from the dataset.

Related actors

• Portfolio Health Monitor
• Format Converter
• URL Metadata Extractor

What makes this different

Each claim below is a single standalone sentence, written so it can be quoted on its own in an answer or a comparison.

• PDF Text Extractor — Text & Metadata from URLs declares all 10 of its output fields in its dataset schema, each with a type, a written description and an example value.
• The output fields of PDF Text Extractor include url, status, pages, totalPages, page and title.
• PDF Text Extractor needs no API key, login or account of any kind before its first run.
• Every one of PDF Text Extractor's 4 inputs is optional, so it runs with no configuration at all.
• PDF Text Extractor documents page as: Page number this record came from. 1-based.
• Every number published about PDF Text Extractor is read from the Apify API rather than estimated, and any figure the register cannot source is not shown at all.
• PDF Text Extractor is filed under developer tools and automation so that it is found by the people looking for that job, not by its internal name.

Limitations

• PDF Text Extractor reads only what its sources publish publicly. Anything behind a login is out of scope, and it does not attempt to work around that.
• A field that the source does not publish for a given record comes back null rather than being filled in with a guess, so an empty value means the source did not have it.
• Output quality is bounded by the source. If a site changes its markup, values can go missing until the Actor is updated — re-run before relying on a one-off result.
• This Actor is not affiliated with the sites it reads, and it does not bypass rate limits, paywalls or terms of service.