# PDF Parser API (`george.the.developer/pdf-parser-api`) Actor

Instant API that parses any PDF from a URL — extracts full text, page count, metadata (title, author, dates), and PDF version. Returns structured JSON. Perfect for document processing pipelines and AI agents.

- **URL**: https://apify.com/george.the.developer/pdf-parser-api.md
- **Developed by:** [George Kioko](https://apify.com/george.the.developer) (community)
- **Categories:** Developer tools
- **Stats:** 5 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $4.00 / 1,000 pdf-parseds

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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 Parser API - Extract Text & Metadata from PDF Files

A fast, reliable **PDF parser API** that extracts text content, metadata, page count, and word count from any publicly accessible PDF file. Simply provide a PDF URL and get back structured JSON with the full text and document properties -- perfect for **RAG pipelines**, document processing, and AI training data preparation.

Built as an always-on Standby API on [Apify](https://apify.com), it responds instantly with no cold starts, no queues, and no SDK required.

### Key Features

- **Full text extraction** -- get every word from any PDF, ready for indexing or NLP
- **Rich metadata** -- title, author, subject, creator, producer, creation/modification dates
- **Page & word counts** -- instant document statistics without downloading the file yourself
- **PDF version detection** -- know exactly what PDF spec the document uses
- **GET and POST endpoints** -- use query parameters or JSON body, your choice
- **CORS enabled** -- call directly from browser-based apps
- **Magic-byte validation** -- rejects non-PDF files before wasting parse time
- **Password-protected detection** -- returns a clear error instead of crashing
- **Streaming size guard** -- enforces the 50 MB limit even when Content-Length is missing

### How It Works

```mermaid
flowchart LR
    A["Client\n(curl / Python / JS)"] -->|HTTP GET or POST\nwith PDF URL| B["PDF Parser API\n(Apify Standby)"]
    B -->|Download PDF| C["Remote PDF\nServer"]
    C -->|PDF binary| B
    B -->|pdf-parse\nprocessing| D["Extracted Data"]
    D -->|JSON response| A

    style A fill:#e8f4fd,stroke:#2196F3
    style B fill:#fff3e0,stroke:#FF9800
    style C fill:#f3e5f5,stroke:#9C27B0
    style D fill:#e8f5e9,stroke:#4CAF50
```

### Endpoints

| Method | Path | Description |
|--------|------|-------------|
| `GET` | `/parse?url=<pdf_url>` | Parse a PDF by passing the URL as a query parameter |
| `POST` | `/parse` | Parse a PDF by sending `{"url": "<pdf_url>"}` as JSON body |
| `GET` | `/health` | Health check -- returns `{"status": "ok"}` |
| `GET` | `/` | Service info with usage instructions |

### Input

#### GET request

Pass the PDF URL as a query parameter:

```
GET /parse?url=https://example.com/document.pdf
```

#### POST request

Send a JSON body with the `url` field:

```json
{
  "url": "https://example.com/document.pdf"
}
```

### Output

A successful response returns structured JSON:

```json
{
  "success": true,
  "pages": 12,
  "text": "Full extracted text content of the PDF document...",
  "metadata": {
    "title": "Annual Report 2025",
    "author": "Jane Smith",
    "subject": "Financial Summary",
    "creator": "Microsoft Word",
    "producer": "macOS Quartz PDFContext",
    "creationDate": "D:20250115102030Z",
    "modDate": "D:20250120083000Z"
  },
  "pdfVersion": "1.7",
  "textLength": 48320,
  "wordCount": 7841,
  "processingTimeMs": 342
}
```

#### Error response

```json
{
  "success": false,
  "error": "PDF is password-protected and cannot be parsed."
}
```

### How to Use

#### Using curl (GET)

```bash
curl "https://pdf-parser-api.apify.actor/parse?url=https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
```

#### Using curl (POST)

```bash
curl -X POST "https://pdf-parser-api.apify.actor/parse" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"}'
```

#### Health check

```bash
curl "https://pdf-parser-api.apify.actor/health"
```

### Integration Examples

#### Python

```python
import requests

response = requests.get(
    "https://pdf-parser-api.apify.actor/parse",
    params={"url": "https://example.com/report.pdf"}
)
data = response.json()

print(f"Pages: {data['pages']}")
print(f"Words: {data['wordCount']}")
print(f"Title: {data['metadata']['title']}")
print(f"Text preview: {data['text'][:500]}")
```

#### Node.js

```javascript
const response = await fetch("https://apify.com/service/https://pdf-parser-api.apify.actor/parse", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    url: "https://example.com/report.pdf",
  }),
});

const data = await response.json();
console.log(`Pages: ${data.pages}`);
console.log(`Words: ${data.wordCount}`);
console.log(`Text preview: ${data.text.slice(0, 500)}`);
```

#### RAG Pipeline (Python + LangChain)

```python
import requests
from langchain.text_splitter import RecursiveCharacterTextSplitter

## Extract text from PDF
resp = requests.get(
    "https://pdf-parser-api.apify.actor/parse",
    params={"url": "https://example.com/knowledge-base.pdf"}
)
pdf_data = resp.json()

## Chunk for vector store
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_text(pdf_data["text"])

## Each chunk is ready for embedding and indexing
print(f"Split {pdf_data['wordCount']} words into {len(chunks)} chunks")
```

### Use Cases

- **RAG pipelines** -- extract text from PDFs and chunk it for vector databases (Pinecone, Weaviate, Chroma)
- **Document processing** -- batch-process invoices, contracts, and reports into structured data
- **AI training data** -- convert PDF corpora into clean text for fine-tuning language models
- **Legal & compliance** -- parse regulatory filings, court documents, and compliance reports at scale
- **Academic research** -- extract text from research papers for citation analysis or literature reviews
- **Content migration** -- pull text from legacy PDF archives into modern CMS platforms
- **Search indexing** -- feed PDF content into Elasticsearch, Algolia, or Meilisearch

### Pricing

| Event | Cost |
|-------|------|
| PDF parsed successfully | **$0.004** per PDF |

You only pay when a PDF is successfully parsed. Failed requests (invalid URL, timeout, password-protected files) are not charged.

### Limitations

| Constraint | Limit |
|------------|-------|
| Maximum file size | **50 MB** |
| Download timeout | **60 seconds** |
| Request body size | **1 MB** (for POST requests) |
| Scanned PDFs | **No OCR** -- only digitally created PDFs with embedded text are supported |
| Password-protected PDFs | **Not supported** -- returns a clear error message |
| Protocols | **HTTP and HTTPS only** -- no local file paths or FTP |

### FAQ

#### Does this API support scanned PDFs or images inside PDFs?

No. This API extracts embedded text from digitally created PDFs. If a PDF was created by scanning paper documents and contains only images, the extracted text will be empty or minimal. For scanned PDFs, you would need an OCR service as a preprocessing step.

#### What happens if the PDF is too large or the download times out?

The API enforces a 50 MB file size limit and a 60-second download timeout. If either limit is exceeded, you will receive a clear error response with the appropriate HTTP status code (413 for size, 408 for timeout). You are not charged for failed requests.

#### Can I parse PDFs that require authentication or are behind a login?

The API fetches PDFs from the URL you provide using a standard HTTP request. If the PDF requires cookies, authentication headers, or is behind a login wall, the download will likely fail. The PDF must be publicly accessible or accessible via a direct URL with any required tokens embedded in the query string.

#### What metadata fields are extracted?

The API extracts seven metadata fields when available: **title**, **author**, **subject**, **creator** (the application that created the document), **producer** (the PDF library used), **creation date**, and **modification date**. Not all PDFs contain all metadata fields -- missing fields are returned as `null`.

***

Built by [George The Developer](https://apify.com/george.the.developer) on Apify.

# Actor input Schema

## `url` (type: `string`):

Direct URL to a PDF file to parse.

## Actor input object example

```json
{
  "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
}
```

# 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 = {
    "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
};

// Run the Actor and wait for it to finish
const run = await client.actor("george.the.developer/pdf-parser-api").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 = { "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf" }

# Run the Actor and wait for it to finish
run = client.actor("george.the.developer/pdf-parser-api").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 '{
  "url": "https://www.w3.org/WAI/ER/tests/xhtml/testfiles/resources/pdf/dummy.pdf"
}' |
apify call george.the.developer/pdf-parser-api --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,george.the.developer/pdf-parser-api"
        }
    }
}

```

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/wOH9032V09jZhCHlZ/builds/ZevdWt8Q72QWPuvDI/openapi.json