An intelligent web crawler that uses LLM (Large Language Models) to enhance content extraction, normalize titles, extract tags, and generate Obsidian-compatible markdown vaults.

• Smart URL Management: BFS-based crawling with depth control and domain filtering
• Robust Error Handling: Automatic retries with exponential backoff
• Rate Limiting: Configurable request delays to respect server resources
• Content Filtering: Skip binary files, media, and non-content paths

• Priority-Based Frontier Queue: Intelligent URL prioritization for efficient crawling
• Entity Extraction Storage: Track people, organizations, locations, and concepts
• LLM Operation Logging: Monitor token usage, performance, and success rates
• PageRank Computation: Identify important pages based on link analysis
• Crawl Job Tracking: Session management with comprehensive statistics

• Title Normalization: Improve page titles using AI
• Tag Extraction: Automatic tagging based on content analysis
• Entity Recognition: Extract named entities from pages
• Content Summarization: Generate concise summaries

• Wiki-style Links: Automatic internal linking between pages
• Frontmatter Metadata: Title, URL, tags, timestamps, word count
• Backlink Support: Track which pages link to each document
• Clean Markdown: Properly formatted content with preserved structure

• Python 3.8+
• SQLite 3
• Ollama (for LLM features) or compatible API endpoint
• Required Python packages (see requirements.txt)

# Clone and setup
git clone https://github.com/DiatomicAI/python-ai-crawler-scraper.git
cd python-ai-crawler-scraper
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

# Configure
cp .env.example .env
# Edit .env with your settings

# Run a test crawl
python main.py --seeds https://docs.python.org/3/tutorial/ --max-pages 10 --max-depth 2

python main.py --seeds https://example.com --max-pages 50 --max-depth 2

python main.py --seeds https://example.com --skip-llm --max-pages 100

python main.py --resume

python main.py --seeds https://site1.com https://site2.com --max-pages 100

Real-world crawl on Python documentation:

✅ Crawled 10 pages successfully
📊 Extracted 615 links (580 internal, 35 external)  
📄 Generated 10 Obsidian markdown files
💾 Database: 3.2MB with full content and metadata

• pages - Crawled pages with content and metadata
• links - Page relationships (src → dst)
• entities - Extracted named entities
• frontier - Priority queue for URL crawling
• crawl_jobs - Session tracking
• llm_operations_log - LLM usage metrics
• fetch_log - HTTP request history

python-ai-crawler-scraper/
├── main.py                   # Main orchestration
├── crawler.py                # Core crawler engine
├── database_enhanced.py      # Enhanced database with frontier
├── llm_normalizer.py        # LLM integration
├── content_processor.py     # HTML to Markdown
├── obsidian_writer.py       # Vault generation
├── test_enhanced_crawler.py # Test suite
└── requirements.txt         # Dependencies

Edit .env:

# Crawl Settings
SEED_URLS=https://example.com
MAX_DEPTH=2
MAX_PAGES=100
REQUEST_DELAY=1.0

# LLM Settings
OLLAMA_BASE_URL=http://localhost:11434
LLM_MODEL=llama3.1:8b

Run the comprehensive test suite:

python test_enhanced_crawler.py

Tests include:

• ✅ Frontier queue operations
• ✅ Entity extraction and storage
• ✅ LLM operation logging
• ✅ PageRank computation
• ✅ Enhanced statistics

Contributions welcome! Please fork and submit PRs.

MIT License - see LICENSE file.

Built with BeautifulSoup4, Requests, Ollama, and inspired by Obsidian.

Diatonic AI | @DiatomicAI