Keep your dependency documentation lean, current, and AI-ready. This toolkit helps you extract clean, focused documentation from any framework or library website, perfect for both human readers and LLM consumption.

In today's fast-paced development environment, you need:

• 📚 Quick access to dependency documentation without the bloat
• 🤖 Documentation in a format that's ready for RAG systems and LLMs
• 🎯 Focused content without navigation elements, ads, or irrelevant sections
• ⚡ Fast, efficient way to keep documentation up-to-date
• 🧹 Clean Markdown output for easy integration with documentation tools

Traditional web scraping often gives you everything - including navigation menus, footers, ads, and other noise. This toolkit is specifically designed to extract only what matters: the actual documentation content.

1. Clean Documentation Output

   • Markdown format for content-focused documentation
   • JSON format for structured menu data
   • Perfect for documentation sites, wikis, and knowledge bases
   • Ideal format for LLM training and RAG systems

2. Smart Content Extraction

   • Automatically identifies main content areas
   • Strips away navigation, ads, and irrelevant sections
   • Preserves code blocks and technical formatting
   • Maintains proper Markdown structure

3. Flexible Crawling Strategies

   • Single page for quick reference docs
   • Multi-page for comprehensive library documentation
   • Sitemap-based for complete framework coverage
   • Menu-based for structured documentation hierarchies

4. LLM and RAG Ready

   • Clean Markdown text suitable for embeddings
   • Preserved code blocks for technical accuracy
   • Structured menu data in JSON format
   • Consistent formatting for reliable processing

A comprehensive Python toolkit for scraping documentation websites using different crawling strategies. Built using the Crawl4AI library for efficient web crawling.

• 🚀 Multiple crawling strategies
• 📑 Automatic nested menu expansion
• 🔄 Handles dynamic content and lazy-loaded elements
• 🎯 Configurable selectors
• 📝 Clean Markdown output for documentation
• 📊 JSON output for menu structure
• 🎨 Colorful terminal feedback
• 🔍 Smart URL processing
• ⚡ Asynchronous execution

1. Single URL Crawler (single_url_crawler.py)

   • Extracts content from a single documentation page
   • Outputs clean Markdown format
   • Perfect for targeted content extraction
   • Configurable content selectors

2. Multi URL Crawler (multi_url_crawler.py)

   • Processes multiple URLs in parallel
   • Generates individual Markdown files per page
   • Efficient batch processing
   • Shared browser session for better performance

3. Sitemap Crawler (sitemap_crawler.py)

   • Automatically discovers and crawls sitemap.xml
   • Creates Markdown files for each page
   • Supports recursive sitemap parsing
   • Handles gzipped sitemaps

4. Menu Crawler (menu_crawler.py)

   • Extracts all menu links from documentation
   • Outputs structured JSON format
   • Handles nested and dynamic menus
   • Smart menu expansion

• Python 3.7+
• Virtual Environment (recommended)

1. Clone the repository:

git clone https://github.com/felores/crawl4ai_docs_scraper.git
cd crawl4ai_docs_scraper

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

python single_url_crawler.py https://docs.example.com/page

Arguments:

• URL: Target documentation URL (required, first argument)

Note: Use quotes only if your URL contains special characters or spaces.

Output format (Markdown):

# Page Title

## Section 1
Content with preserved formatting, including:
- Lists
- Links
- Tables

### Code Examples
```python
def example():
    return "Code blocks are preserved"

# Using a text file with URLs
python multi_url_crawler.py urls.txt

# Using JSON output from menu crawler
python multi_url_crawler.py menu_links.json

# Using custom output prefix
python multi_url_crawler.py menu_links.json --output-prefix custom_name

Arguments:

• URLs file: Path to file containing URLs (required, first argument)
  • Can be .txt with one URL per line
  • Or .json from menu crawler output
• --output-prefix: Custom prefix for output markdown file (optional)

Note: Use quotes only if your file path contains spaces.

Output filename format:

• Without --output-prefix: domain_path_docs_content_timestamp.md (e.g., cloudflare_agents_docs_content_20240323_223656.md)
• With --output-prefix: custom_prefix_docs_content_timestamp.md (e.g., custom_name_docs_content_20240323_223656.md)

The crawler accepts two types of input files:

1. Text file with one URL per line:

https://docs.example.com/page1
https://docs.example.com/page2
https://docs.example.com/page3

2. JSON file (compatible with menu crawler output):

{
    "menu_links": [
        "https://docs.example.com/page1",
        "https://docs.example.com/page2"
    ]
}

python sitemap_crawler.py https://docs.example.com/sitemap.xml

Options:

• --max-depth: Maximum sitemap recursion depth (optional)
• --patterns: URL patterns to include (optional)

python menu_crawler.py https://docs.example.com

Options:

• --selectors: Custom menu selectors (optional)

The menu crawler now saves its output to the input_files directory, making it ready for immediate use with the multi-url crawler. The output JSON has this format:

{
    "start_url": "https://docs.example.com/",
    "total_links_found": 42,
    "menu_links": [
        "https://docs.example.com/page1",
        "https://docs.example.com/page2"
    ]
}

After running the menu crawler, you'll get a command to run the multi-url crawler with the generated file.

crawl4ai_docs_scraper/
├── input_files/           # Input files for URL processing
│   ├── urls.txt          # Text file with URLs
│   └── menu_links.json   # JSON output from menu crawler
├── scraped_docs/         # Output directory for markdown files
│   └── docs_timestamp.md # Generated documentation
├── multi_url_crawler.py
├── menu_crawler.py
└── requirements.txt

All crawlers include comprehensive error handling with colored terminal output:

• 🟢 Green: Success messages
• 🔵 Cyan: Processing status
• 🟡 Yellow: Warnings
• 🔴 Red: Error messages

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.

This project uses Crawl4AI for web data extraction.

• Built with Crawl4AI
• Uses termcolor for colorful terminal output