Implement a privacy-first, provider-agnostic telemetry system to help improve Crawl4AI stability
through anonymous crash reporting. The system is designed with user privacy as the top priority,
collecting only exception information without any PII, URLs, or crawled content.
Architecture & Design:
- Provider-agnostic architecture with base TelemetryProvider interface
- Sentry as the initial provider implementation with easy extensibility
- Separate handling for sync and async code paths
- Environment-aware behavior (CLI, Docker, Jupyter/Colab)
Key Features:
- Opt-in by default for CLI/library usage with interactive consent prompt
- Opt-out by default for Docker/API server (enabled unless CRAWL4AI_TELEMETRY=0)
- Jupyter/Colab support with widget-based consent (fallback to code snippets)
- Persistent consent storage in ~/.crawl4ai/config.json
- Optional email collection for critical issue follow-up
CLI Integration:
- `crwl telemetry enable [--email <email>] [--once]` - Enable telemetry
- `crwl telemetry disable` - Disable telemetry
- `crwl telemetry status` - Check current status
Python API:
- Decorators: @telemetry_decorator, @async_telemetry_decorator
- Context managers: telemetry_context(), async_telemetry_context()
- Manual capture: capture_exception(exc, context)
- Control: telemetry.enable(), telemetry.disable(), telemetry.status()
Privacy Safeguards:
- No URL collection
- No request/response data
- No authentication tokens or cookies
- No crawled content
- Automatic sanitization of sensitive fields
- Local consent storage only
Testing:
- Comprehensive test suite with 15 test cases
- Coverage for all environments and consent flows
- Mock providers for testing without external dependencies
Documentation:
- Detailed documentation in docs/md_v2/core/telemetry.md
- Added to mkdocs navigation under Core section
- Privacy commitment and FAQ included
- Examples for all usage patterns
Installation:
- Optional dependency: pip install crawl4ai[telemetry]
- Graceful degradation if sentry-sdk not installed
- Added to pyproject.toml optional dependencies
- Docker requirements updated
Integration Points:
- AsyncWebCrawler: Automatic exception capture in arun() and aprocess_html()
- Docker server: Automatic initialization with environment control
- Global exception handler for uncaught exceptions (CLI only)
This implementation provides valuable error insights to improve Crawl4AI while maintaining
complete transparency and user control over data collection.
- Add comprehensive v0.7.4 release blog post with LLMTableExtraction feature highlight
- Update blog index to feature v0.7.4 as latest release
- Update README.md to showcase v0.7.4 features alongside v0.7.3
- Accurately describe dispatcher fix as bug fix rather than major enhancement
- Include practical code examples for new LLMTableExtraction capabilities
BREAKING CHANGE: Table extraction now uses Strategy Design Pattern
This epic commit introduces a game-changing approach to table extraction in Crawl4AI:
✨ NEW FEATURES:
- LLMTableExtraction: AI-powered extraction for complex HTML tables with rowspan/colspan
- Smart Chunking: Automatically splits massive tables into optimal chunks at row boundaries
- Parallel Processing: Processes multiple chunks simultaneously for blazing-fast extraction
- Intelligent Merging: Seamlessly combines chunk results into complete tables
- Header Preservation: Each chunk maintains context with original headers
- Auto-retry Logic: Built-in resilience with configurable retry attempts
🏗️ ARCHITECTURE:
- Strategy Design Pattern for pluggable table extraction strategies
- ThreadPoolExecutor for concurrent chunk processing
- Token-based chunking with configurable thresholds
- Handles tables without headers gracefully
⚡ PERFORMANCE:
- Process 1000+ row tables without timeout
- Parallel processing with up to 5 concurrent chunks
- Smart token estimation prevents LLM context overflow
- Optimized for providers like Groq for massive tables
🔧 CONFIGURATION:
- enable_chunking: Auto-handle large tables (default: True)
- chunk_token_threshold: When to split (default: 3000 tokens)
- min_rows_per_chunk: Meaningful chunk sizes (default: 10)
- max_parallel_chunks: Concurrent processing (default: 5)
📚 BACKWARD COMPATIBILITY:
- Existing code continues to work unchanged
- DefaultTableExtraction remains the default strategy
- Progressive enhancement approach
This is the future of web table extraction - handling everything from simple tables to massive, complex data grids with merged cells and nested structures. The chunking is completely transparent to users while providing unprecedented scalability.
- fix handling of special keys in Windows msvcrt implementation
- Guard against UnicodeDecodeError from multi-byte key sequences
- Filter out non-printable characters and control sequences
- Add error handling to prevent coroutine crashes
- Add unit test to verify keyboard input handling
Key changes:
- Safe UTF-8 decoding with try/except for special keys
- Skip non-printable and multi-byte character sequences
- Add broad exception handling in keyboard listener
Test runs on Windows only due to msvcrt dependency.
This commit introduces platform-specific handling for the 'q' key press to quit the browser profiler, ensuring compatibility with both Windows and Unix-like systems. It also adds a check to see if the browser process has already exited, terminating the input listener if so.
- Implemented `msvcrt` for Windows to capture keyboard input without requiring a newline.
- Retained `termios`, `tty`, and `select` for Unix-like systems.
- Added a check for browser process termination to gracefully exit the input listener.
- Updated logger messages to use colored output for better user experience.
BREAKING CHANGE: Table extraction now uses Strategy Design Pattern
This epic commit introduces a game-changing approach to table extraction in Crawl4AI:
✨ NEW FEATURES:
- LLMTableExtraction: AI-powered extraction for complex HTML tables with rowspan/colspan
- Smart Chunking: Automatically splits massive tables into optimal chunks at row boundaries
- Parallel Processing: Processes multiple chunks simultaneously for blazing-fast extraction
- Intelligent Merging: Seamlessly combines chunk results into complete tables
- Header Preservation: Each chunk maintains context with original headers
- Auto-retry Logic: Built-in resilience with configurable retry attempts
🏗️ ARCHITECTURE:
- Strategy Design Pattern for pluggable table extraction strategies
- ThreadPoolExecutor for concurrent chunk processing
- Token-based chunking with configurable thresholds
- Handles tables without headers gracefully
⚡ PERFORMANCE:
- Process 1000+ row tables without timeout
- Parallel processing with up to 5 concurrent chunks
- Smart token estimation prevents LLM context overflow
- Optimized for providers like Groq for massive tables
🔧 CONFIGURATION:
- enable_chunking: Auto-handle large tables (default: True)
- chunk_token_threshold: When to split (default: 3000 tokens)
- min_rows_per_chunk: Meaningful chunk sizes (default: 10)
- max_parallel_chunks: Concurrent processing (default: 5)
📚 BACKWARD COMPATIBILITY:
- Existing code continues to work unchanged
- DefaultTableExtraction remains the default strategy
- Progressive enhancement approach
This is the future of web table extraction - handling everything from simple tables to massive, complex data grids with merged cells and nested structures. The chunking is completely transparent to users while providing unprecedented scalability.
The MemoryAdaptiveDispatcher was processing tasks sequentially despite
max_session_permit > 1 due to fetching only one task per event loop iteration.
This particularly affected raw:// URLs which complete in microseconds.
Changes:
- Replace single task fetch with greedy slot filling using get_nowait()
- Fill all available slots (up to max_session_permit) immediately
- Break on empty queue instead of waiting with timeout
This ensures proper parallelization for all task types, especially
ultra-fast operations like raw HTML processing.
- Add raw HTML URL validation alongside http/https checks
- Fix URL preprocessing logic to handle raw: and raw:// prefixes
- Update error message and add comprehensive test cases
- Remove deprecated API token authentication from all Docker examples
- Fix async job endpoints: /crawl -> /crawl/job for submission, /task/{id} -> /crawl/job/{id} for polling
- Fix sync endpoint: /crawl_sync -> /crawl (synchronous)
- Remove non-existent /crawl_direct endpoint
- Update request format to use new structure with browser_config and crawler_config
- Fix response handling for both async and sync calls
- Update extraction strategy format to use proper nested structure
- Add Ollama connectivity check before running tests
- Update test schemas and selectors for current website structures
This makes the Docker examples work out-of-the-box with the current API structure.
- Introduced a new section in SPONSORS.md to recognize the first 50 sponsors as Founding Sponsors.
- Updated README-first.md to include comprehensive project details, features, installation instructions, and advanced usage examples.
- Highlighted the recent version 0.7.0 release with new features and improvements.
- Added a sponsorship program with tiered benefits and a mission statement to promote data democratization.
- Add FUNDING.yml to enable sponsor button
- Add sponsor section to README with tier overview
- Create SPONSORS.md for sponsor recognition
- Set up 4 tiers: Believer, Builder, Growing Team, Data Infrastructure Partner
- Extract base href from <head><base> tag using XPath in _process_element method
- Use base URL as the primary URL for link normalization when present
- Add error handling with logging for malformed or problematic base tags
- Maintain backward compatibility when no base tag is present
- Add test to verify the functionality of the base tag extraction.
- Update Table-to-DataFrame Extraction example in README.md
- Replace old method of accessing tables via result.media directly with result.tables in the documentation
- Remove tables section from links & media page.
- Add tables section to crawler result page.
- Support LLM_PROVIDER env var to override default provider (openai/gpt-4o-mini)
- Add optional 'provider' parameter to API endpoints for per-request overrides
- Implement provider validation to ensure API keys exist
- Update documentation and examples with new configuration options
Closes the need to hardcode providers in config.yml
