# PDF to Markdown & JSON for LLMs/RAG (`wmora/pdf-to-markdown`) Actor

Deterministic, non-AI extraction of native-text PDFs into per-page Markdown and structured JSON (headings, paragraphs, tables) for LLM ingestion.

- **URL**: https://apify.com/wmora/pdf-to-markdown.md
- **Developed by:** [Will M](https://apify.com/wmora) (community)
- **Categories:**
- **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $2.00 / 1,000 page extracteds

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?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## PDF to Markdown and JSON

This Actor takes a PDF that already has a text layer and gives you back Markdown and structured JSON, page by page. No OCR, no LLM calls. It reads the text that is already in the file and lays it out.

It's built for the case in which you have a report, an invoice or a paper, and you want its text in a format you can index or hand to a model.

### What this is for

Reports, invoices, contracts, papers, anything born digital. If you can select the text in a PDF viewer, this works on it. If your PDF is a scan, meaning a picture of a page with no text layer, you get an empty page back. There is no OCR here.

### Input

```json
{
  "pdfUrl": "https://example.com/report.pdf"
}
```

or, to send the file directly instead of a URL:

```json
{
  "pdfBase64": "JVBERi0xLjQKJ..."
}
```

Provide exactly one of `pdfUrl` or `pdfBase64`. PDFs up to 25 MB.

### Output

One dataset item per run:

```json
{
  "pageCount": 2,
  "markdown": "# Q3 Report\n\nRevenue grew...\n\n# Appendix\n\n| Region | Revenue |\n| --- | --- |\n| EU | $4.2M |",
  "pages": [
    {
      "pageNumber": 1,
      "markdown": "# Q3 Report\n\nRevenue grew...",
      "headings": [{ "level": 1, "text": "Q3 Report" }],
      "tables": []
    },
    {
      "pageNumber": 2,
      "markdown": "# Appendix\n\n| Region | Revenue |\n| --- | --- |\n| EU | $4.2M |",
      "headings": [{ "level": 1, "text": "Appendix" }],
      "tables": [
        {
          "rows": [
            ["Region", "Revenue"],
            ["EU", "$4.2M"]
          ]
        }
      ]
    }
  ]
}
```

### Pricing

You pay per event:

| Event            | Price  | When it's charged                        |
| ---------------- | ------ | ---------------------------------------- |
| `pdf-processed`  | $0.01  | Once per PDF that is successfully opened |
| `page-extracted` | $0.002 | Per page extracted                       |

So a 5-page PDF costs $0.02 and a 1-page PDF costs $0.012. Trying it out costs cents.

### What it does well

Text extraction is the main job and what the pricing is built around. You get the prose of the document, per page and as one blob, which is what you want for RAG ingestion, search indexing, filling a context window, or simple PDF parsing.

Headings are detected from relative font size, so the Markdown keeps the document's structure instead of coming out as one flat wall of text.

Tables work, including borderless ones. That is the common case in reports, invoices and papers, where there are no ruled lines and the columns are just aligned text. Detection looks for column positions that repeat across a run of lines instead of requiring every row to look identical, so a row with a missing or extra cell still gets picked up, headers included. You get each table twice: as a Markdown pipe table inside the page text, and as JSON rows.

### Limitations

- No images or figures. Only the text layer comes out.
- Math formulas come out as their plain characters. The text layer does not say which characters are a formula and which are prose.
- No OCR. Scanned or image-only PDFs return empty pages.
- Highly complex tables can degrade.
- 25 MB per PDF.
- Encrypted or password-protected PDFs are not supported.

# Actor input Schema

## `pdfUrl` (type: `string`):

Publicly reachable URL of the PDF to convert. Provide this or pdfBase64.

## `pdfBase64` (type: `string`):

Base64-encoded PDF bytes. Provide this or pdfUrl.

## Actor input object example

```json
{}
```

# Actor output Schema

## `results` (type: `string`):

Dataset items: pageCount (number), markdown (full document as Markdown), pages (per-page structured JSON).

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("wmora/pdf-to-markdown").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("wmora/pdf-to-markdown").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 '{}' |
apify call wmora/pdf-to-markdown --silent --output-dataset

```

## MCP server setup

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

```

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/DkeELTUOPR0tAJk1K/builds/7bkO0AfwONDeVUFiY/openapi.json