Can I Use Firecrawl to Scrape PDF Files?

Yes, Firecrawl can scrape PDF files and extract their content in markdown or structured format. Firecrawl's /scrape endpoint supports PDF documents through its document parsing capabilities, making it easy to extract text, tables, and other structured data from PDF files without requiring separate PDF parsing libraries.

How Firecrawl Handles PDF Files

Firecrawl automatically detects and processes PDF files when you provide a URL pointing to a PDF document. The service handles the heavy lifting of PDF parsing, text extraction, and formatting conversion behind the scenes. This eliminates the need for managing complex PDF parsing libraries like PyPDF2, pdfplumber, or pdf.js in your application.

When Firecrawl processes a PDF, it:

• Downloads and parses the PDF document
• Extracts text content while preserving structure
• Converts tables and formatted data into markdown
• Returns clean, structured output ready for further processing

Basic PDF Scraping with Firecrawl

Python Example

Here's how to scrape a PDF file using Firecrawl's Python SDK:

from firecrawl import FirecrawlApp

# Initialize Firecrawl
app = FirecrawlApp(api_key='your_api_key_here')

# Scrape a PDF file
pdf_url = 'https://example.com/document.pdf'
result = app.scrape_url(pdf_url)

# Access the extracted content
print(result['markdown'])  # PDF content in markdown format
print(result['metadata'])  # Document metadata

JavaScript/Node.js Example

For JavaScript developers, here's the equivalent implementation:

import FirecrawlApp from '@mendable/firecrawl-js';

// Initialize Firecrawl
const app = new FirecrawlApp({ apiKey: 'your_api_key_here' });

// Scrape a PDF file
const pdfUrl = 'https://example.com/document.pdf';

async function scrapePDF() {
  try {
    const result = await app.scrapeUrl(pdfUrl);

    // Access the extracted content
    console.log(result.markdown);  // PDF content in markdown format
    console.log(result.metadata);  // Document metadata
  } catch (error) {
    console.error('Error scraping PDF:', error);
  }
}

scrapePDF();

Advanced PDF Scraping Options

Extracting Specific Data with Actions

Firecrawl supports custom actions for more precise data extraction from PDFs:

from firecrawl import FirecrawlApp

app = FirecrawlApp(api_key='your_api_key_here')

# Scrape with specific extraction schema
result = app.scrape_url(
    'https://example.com/financial-report.pdf',
    params={
        'formats': ['markdown', 'html'],
        'onlyMainContent': True
    }
)

# Access different formats
markdown_content = result['markdown']
html_content = result['html']

Using LLM Extract for Structured Data

One of Firecrawl's most powerful features is its ability to extract structured data from PDFs using LLM-based extraction:

from firecrawl import FirecrawlApp

app = FirecrawlApp(api_key='your_api_key_here')

# Define extraction schema
schema = {
    'type': 'object',
    'properties': {
        'title': {'type': 'string'},
        'author': {'type': 'string'},
        'publication_date': {'type': 'string'},
        'summary': {'type': 'string'},
        'key_findings': {
            'type': 'array',
            'items': {'type': 'string'}
        }
    }
}

# Extract structured data from PDF
result = app.scrape_url(
    'https://example.com/research-paper.pdf',
    params={
        'formats': ['extract'],
        'extract': {
            'schema': schema
        }
    }
)

print(result['extract'])  # Structured JSON data

JavaScript Structured Extraction

import FirecrawlApp from '@mendable/firecrawl-js';

const app = new FirecrawlApp({ apiKey: 'your_api_key_here' });

// Define extraction schema
const schema = {
  type: 'object',
  properties: {
    title: { type: 'string' },
    author: { type: 'string' },
    sections: {
      type: 'array',
      items: {
        type: 'object',
        properties: {
          heading: { type: 'string' },
          content: { type: 'string' }
        }
      }
    }
  }
};

async function extractPDFData() {
  const result = await app.scrapeUrl(
    'https://example.com/document.pdf',
    {
      formats: ['extract'],
      extract: { schema }
    }
  );

  console.log(result.extract);  // Structured JSON data
}

extractPDFData();

Handling Password-Protected PDFs

For password-protected PDF files, you'll need to ensure the PDF is accessible via a public URL or handle authentication separately. Firecrawl doesn't directly support password-protected PDFs, but you can:

1. Use a proxy service to unlock the PDF first
2. Store unlocked PDFs in a temporary accessible location
3. Use authentication handling techniques if the PDF is behind a login wall

Batch Processing Multiple PDFs

When you need to scrape multiple PDF files, Firecrawl's batch processing capabilities can help:

from firecrawl import FirecrawlApp
import concurrent.futures

app = FirecrawlApp(api_key='your_api_key_here')

pdf_urls = [
    'https://example.com/doc1.pdf',
    'https://example.com/doc2.pdf',
    'https://example.com/doc3.pdf',
]

def scrape_pdf(url):
    try:
        result = app.scrape_url(url)
        return {
            'url': url,
            'content': result['markdown'],
            'success': True
        }
    except Exception as e:
        return {
            'url': url,
            'error': str(e),
            'success': False
        }

# Process PDFs in parallel
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(scrape_pdf, pdf_urls))

# Process results
for result in results:
    if result['success']:
        print(f"Successfully scraped: {result['url']}")
    else:
        print(f"Failed to scrape {result['url']}: {result['error']}")

Best Practices for PDF Scraping with Firecrawl

1. Handle Timeouts Appropriately

Large PDF files may take longer to process. Set appropriate timeout values:

from firecrawl import FirecrawlApp

app = FirecrawlApp(api_key='your_api_key_here')

result = app.scrape_url(
    'https://example.com/large-document.pdf',
    params={
        'timeout': 60000  # 60 seconds
    }
)

Similar to handling timeouts in Puppeteer, it's important to set reasonable timeout values based on document size.

2. Validate Output Format

Always validate the extracted content before processing:

def validate_pdf_content(result):
    if 'markdown' not in result:
        raise ValueError("No markdown content extracted")

    if len(result['markdown'].strip()) == 0:
        raise ValueError("Extracted content is empty")

    return True

result = app.scrape_url('https://example.com/doc.pdf')
if validate_pdf_content(result):
    # Process content
    process_content(result['markdown'])

3. Cache Results for Efficiency

PDF processing can be resource-intensive. Implement caching to avoid redundant API calls:

import hashlib
import json
from pathlib import Path

def get_cache_key(url):
    return hashlib.md5(url.encode()).hexdigest()

def scrape_with_cache(url, cache_dir='./pdf_cache'):
    cache_path = Path(cache_dir)
    cache_path.mkdir(exist_ok=True)

    cache_file = cache_path / f"{get_cache_key(url)}.json"

    # Check cache
    if cache_file.exists():
        with open(cache_file, 'r') as f:
            return json.load(f)

    # Scrape and cache
    result = app.scrape_url(url)
    with open(cache_file, 'w') as f:
        json.dump(result, f)

    return result

4. Error Handling and Retries

Implement robust error handling for production use:

async function scrapePDFWithRetry(url, maxRetries = 3) {
  let lastError;

  for (let attempt = 1; attempt <= maxRetries; attempt++) {
    try {
      const result = await app.scrapeUrl(url);
      return result;
    } catch (error) {
      lastError = error;
      console.log(`Attempt ${attempt} failed:`, error.message);

      if (attempt < maxRetries) {
        // Exponential backoff
        await new Promise(resolve =>
          setTimeout(resolve, Math.pow(2, attempt) * 1000)
        );
      }
    }
  }

  throw new Error(`Failed after ${maxRetries} attempts: ${lastError.message}`);
}

Limitations and Considerations

File Size Limits

Firecrawl has reasonable file size limits for PDF processing. Very large PDFs (>100MB) may encounter timeout or processing limits. Consider:

• Splitting large PDFs before processing
• Increasing timeout values
• Processing pages individually if possible

Image-Based PDFs

PDFs containing scanned images (non-searchable text) may have limited extraction capabilities. Firecrawl works best with text-based PDFs. For image-based PDFs, you might need to:

• Use OCR preprocessing
• Combine Firecrawl with dedicated OCR services
• Extract images and process them separately

Complex Layouts

PDFs with complex multi-column layouts, embedded forms, or heavy formatting may not preserve exact visual structure in markdown output. The content will be extracted, but layout information might be simplified.

Comparing Firecrawl to Traditional PDF Libraries

Traditional Approach (PyPDF2)

import PyPDF2

with open('document.pdf', 'rb') as file:
    reader = PyPDF2.PdfReader(file)
    text = ''
    for page in reader.pages:
        text += page.extract_text()

Firecrawl Approach

from firecrawl import FirecrawlApp

app = FirecrawlApp(api_key='your_api_key_here')
result = app.scrape_url('https://example.com/document.pdf')
text = result['markdown']

Advantages of Firecrawl: - No need to manage PDF files locally - Better table extraction - Automatic formatting to markdown - Cloud-based processing (no local resources) - Handles complex PDF structures better

Use Cases for PDF Scraping with Firecrawl

1. Research Paper Analysis: Extract titles, abstracts, and citations from academic papers
2. Financial Report Processing: Parse quarterly reports and extract key metrics
3. Invoice Data Extraction: Pull structured data from PDF invoices
4. Legal Document Review: Extract clauses and terms from contracts
5. Resume Parsing: Extract candidate information from PDF resumes

Alternative Solutions

While Firecrawl excels at PDF scraping, consider these alternatives for specific use cases:

• WebScraping.AI: Offers similar PDF extraction with additional AI-powered data extraction
• DocumentAI services: Specialized services for form extraction and OCR
• Apache Tika: Open-source option for various document formats
• Custom solutions: Using Puppeteer for file downloads combined with PDF.js

Conclusion

Firecrawl provides a robust, developer-friendly solution for scraping PDF files without the complexity of managing PDF parsing libraries. Its LLM-based extraction capabilities make it particularly powerful for extracting structured data from documents. While it has some limitations with very large files and image-based PDFs, it's an excellent choice for most PDF scraping use cases.

Whether you're building a document processing pipeline, analyzing research papers, or extracting data from business documents, Firecrawl's PDF scraping capabilities can significantly simplify your workflow while delivering reliable results.