# PDF to Markdown & JSON Extractor for LLMs (`f0rty7even/pdf-extractor`) Actor

Turn any PDF URL into clean, LLM-ready Markdown and structured JSON. Extracts text + tables + document metadata for RAG, agents, and fine-tuning. No AGPL components.

- **URL**: https://apify.com/f0rty7even/pdf-extractor.md
- **Developed by:** [Michael Yousrie](https://apify.com/f0rty7even) (community)
- **Categories:** AI, Developer tools
- **Features:** Limited permissions
- **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 1,000 page-parseds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## PDF to Markdown & JSON Extractor — LLM-Ready

**Turn any PDF into clean, LLM-ready Markdown and structured JSON.** Point this **PDF to JSON** / **PDF to Markdown** converter at one or many PDF URLs and get back tidy Markdown (text **plus tables**), plain text, and document metadata — one structured record per PDF. Built for feeding **LLMs, RAG pipelines, AI agents, and fine-tuning datasets** clean document content instead of raw, messy PDF bytes.

No headless browser, no external services — just fast, reliable extraction.

### What it does

- **PDF to Markdown** — converts a PDF's text into clean Markdown ready to drop into an LLM prompt or vector database.
- **Table extraction** — detects tables and renders them as Markdown **pipe tables**, so structure survives the conversion.
- **Metadata** — title, author, subject, keywords, producer, and creation/modification dates from the PDF's own info dictionary.
- **Per-document or per-page output** — one record per PDF by default, or one record per page (`splitPages`) for easy RAG chunking.
- **Page ranges & caps** — extract only the pages you need (`pageRange`) and bound cost with `maxPagesPerPdf`.
- **Structured output** — exportable to JSON, JSONL, CSV, or Excel, or via the Apify API.

### Use cases

- Build a **RAG knowledge base** from reports, papers, manuals, or contracts.
- Feed **LLM agents** clean document text instead of raw PDF.
- Assemble **fine-tuning / training datasets** from public PDFs.
- Convert **research papers, invoices, or datasheets** into structured, queryable data.

### Input

| Field | Description |
|---|---|
| `startUrls` | Direct links to the PDF files to extract. |
| `outputFormat` | `markdown` (LLM-ready) or `text`. |
| `extractTables` | Detect tables and render them as Markdown pipe tables. |
| `splitPages` | Output one item per page instead of one per document. |
| `pageRange` | Pages to extract, e.g. `1-10` or `3` (empty = all). |
| `maxPagesPerPdf` | Hard cap on pages parsed per document (main cost lever). |
| `maxFileSizeMb` | Skip PDFs larger than this, without charging. |

### Output

Each PDF becomes one dataset item (or one per page with `splitPages`):

```json
{
  "url": "https://arxiv.org/pdf/1706.03762",
  "title": "Attention Is All You Need",
  "content": "## Page 1\n\nClean markdown of the page text...\n\n| Layer | Complexity |\n| --- | --- |\n| Self-Attention | O(n²·d) |",
  "format": "markdown",
  "wordCount": 8123,
  "pageCount": 15,
  "totalPages": 15,
  "metadata": {
    "author": "Vaswani et al.",
    "creationDate": "D:20170606",
    "producer": "pdfTeX",
    "subject": null,
    "keywords": null
  }
}
```

### Pricing

Pay-per-result: you're charged **per page successfully parsed** — no monthly fee, and no charge for PDFs that fail, are password-protected, are too large, or have no extractable text.

### Notes

- Works on **text-based PDFs** (papers, reports, docs, invoices, datasheets). **Scanned / image-only PDFs** have no text layer; they're detected and skipped free. **OCR is on the roadmap** (future toggle).
- Password-protected and corrupt PDFs are reported as error records (not charged).
- Processes **public PDF URLs**; it does not log in or bypass access controls.

### Licensing

Built entirely on permissively licensed libraries — **pdfplumber (MIT)**, **pdfminer.six (MIT)**, **pypdfium2 (BSD/Apache)**, and **Pillow**. **No AGPL components.**

### FAQ

**Does it keep tables?** Yes — with `extractTables` on and Markdown output, detected tables are rendered as Markdown pipe tables beneath the page text.

**Can I extract just some pages?** Yes — set `pageRange` (e.g. `1-5`) and/or `maxPagesPerPdf`.

**What about scanned PDFs?** They have no text layer, so v1 skips them for free (no charge). OCR support is planned.

**What formats can I export?** JSON, JSONL, CSV, or Excel, or via the Apify API.

# Actor input Schema

## `startUrls` (type: `array`):

Direct links to PDF files to extract. Each becomes one dataset item (or one item per page if 'One item per page' is on).

## `outputFormat` (type: `string`):

Content format returned per PDF.

## `extractTables` (type: `boolean`):

Detect tables and render them as Markdown pipe tables (Markdown format only).

## `splitPages` (type: `boolean`):

Output one dataset item per page instead of one per document. Useful for chunking into a vector DB.

## `pageRange` (type: `string`):

Pages to extract, e.g. "1-10" or "3". Leave empty for all pages.

## `maxPagesPerPdf` (type: `integer`):

Hard cap on pages parsed per document (main cost lever).

## `maxFileSizeMb` (type: `integer`):

Skip PDFs larger than this, without charging.

## Actor input object example

```json
{
  "startUrls": [
    {
      "url": "https://arxiv.org/pdf/1706.03762"
    }
  ],
  "outputFormat": "markdown",
  "extractTables": true,
  "splitPages": false,
  "maxPagesPerPdf": 100,
  "maxFileSizeMb": 50
}
```

# Actor output Schema

## `extractedPdfs` (type: `string`):

Clean content and metadata for every successfully parsed PDF. Export as JSON, JSONL, CSV, or Excel.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "startUrls": [
        {
            "url": "https://arxiv.org/pdf/1706.03762"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("f0rty7even/pdf-extractor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "startUrls": [{ "url": "https://arxiv.org/pdf/1706.03762" }] }

# Run the Actor and wait for it to finish
run = client.actor("f0rty7even/pdf-extractor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "startUrls": [
    {
      "url": "https://arxiv.org/pdf/1706.03762"
    }
  ]
}' |
apify call f0rty7even/pdf-extractor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,f0rty7even/pdf-extractor"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/qzwEsQX03YVPWszAF/builds/QWtDhFxssTurxNA62/openapi.json