- Generate OneShot js code geenrator
- Introduced a new C4A-Script tutorial example for login flow using Blockly.
- Updated index.html to include Blockly theme and event editor modal for script editing.
- Created a test HTML file for testing Blockly integration.
- Added comprehensive C4A-Script API reference documentation covering commands, syntax, and examples.
- Developed core documentation for C4A-Script, detailing its features, commands, and real-world examples.
- Updated mkdocs.yml to include new C4A-Script documentation in navigation.
This commit introduces a comprehensive set of new scripts and examples to enhance the scripting capabilities of the crawl4ai project. The changes include the addition of several Python scripts for compiling and executing scripts, as well as a variety of example scripts demonstrating different functionalities such as login flows, data extraction, and multi-step workflows. Additionally, detailed documentation has been created to guide users on how to utilize these new features effectively.
The following significant modifications were made:
- Added core scripting files: , , and .
- Created a new documentation file to provide an overview of the new features.
- Introduced multiple example scripts in the directory to showcase various use cases.
- Updated and to integrate the new functionalities.
- Added font assets for improved documentation presentation.
These changes significantly expand the functionality of the crawl4ai project, allowing users to create more complex and varied scripts with ease.
This commit introduces significant updates to the LinkedIn data discovery documentation by adding two new Jupyter notebooks that provide detailed insights into data discovery processes. The previous workshop notebook has been removed to streamline the content and avoid redundancy. Additionally, the URL seeder documentation has been expanded with a new tutorial and several enhancements to existing scripts, improving usability and clarity.
The changes include:
- Added and for comprehensive LinkedIn data discovery.
- Removed to eliminate outdated content.
- Updated to reflect new data visualization requirements.
- Introduced and to facilitate easier access to URL seeding techniques.
- Enhanced existing Python scripts and markdown files in the URL seeder section for better documentation and examples.
These changes aim to improve the overall documentation quality and user experience for developers working with LinkedIn data and URL seeding techniques.
- Implemented a comprehensive research pipeline using URLSeeder.
- Steps include user query input, optional LLM enhancement, URL discovery and ranking, content crawling, and synthesis generation.
- Introduced caching mechanism for enhanced query results and crawled content.
- Configurable settings for testing and production modes.
- Output results in JSON and Markdown formats with detailed research insights and citations.
This commit introduces AsyncUrlSeeder, a high-performance URL discovery system that enables intelligent crawling at scale by pre-discovering and filtering URLs before crawling.
## Core Features
### AsyncUrlSeeder Component
- Discovers URLs from multiple sources:
- Sitemaps (including nested and gzipped)
- Common Crawl index
- Combined sources for maximum coverage
- Extracts page metadata without full crawling:
- Title, description, keywords
- Open Graph and Twitter Card tags
- JSON-LD structured data
- Language and charset information
- BM25 relevance scoring for intelligent filtering:
- Query-based URL discovery
- Configurable score thresholds
- Automatic ranking by relevance
- Performance optimizations:
- Async/concurrent processing with configurable workers
- Rate limiting (hits per second)
- Automatic caching with TTL
- Streaming results for large datasets
### SeedingConfig
- Comprehensive configuration for URL seeding:
- Source selection (sitemap, cc, or both)
- URL pattern filtering with wildcards
- Live URL validation options
- Metadata extraction controls
- BM25 scoring parameters
- Concurrency and rate limiting
### Integration with AsyncWebCrawler
- Seamless pipeline: discover → filter → crawl
- Direct compatibility with arun_many()
- Significant resource savings by pre-filtering URLs
## Documentation
- Comprehensive guide comparing URL seeding vs deep crawling
- Complete API reference with parameter tables
- Practical examples showing all features
- Performance benchmarks and best practices
- Integration patterns with AsyncWebCrawler
## Examples
- url_seeder_demo.py: Interactive Rich-based demo with:
- Basic discovery
- Cache management
- Live validation
- BM25 scoring
- Multi-domain discovery
- Complete pipeline integration
- url_seeder_quick_demo.py: Screenshot-friendly examples:
- Pattern-based filtering
- Metadata exploration
- Smart search with BM25
## Testing
- Comprehensive test suite (test_async_url_seeder_bm25.py)
- Coverage of all major features
- Edge cases and error handling
- Performance and consistency tests
## Implementation Details
- Built on httpx with HTTP/2 support
- Optional dependencies: lxml, brotli, rank_bm25
- Cache management in ~/.crawl4ai/seeder_cache/
- Logger integration with AsyncLoggerBase
- Proper error handling and retry logic
## Bug Fixes
- Fixed logger color compatibility (lightblack → bright_black)
- Corrected URL extraction from seeder results for arun_many()
- Updated all examples and documentation with proper usage
This feature enables users to crawl smarter, not harder, by discovering
and analyzing URLs before committing resources to crawling them.
Add session_id feature to allow reusing browser pages across multiple crawls.
Add support for view-source: protocol in URL handling.
Fix browser config reference and string formatting issues.
Update examples to demonstrate new session management features.
BREAKING CHANGE: Browser page handling now persists when using session_id
Add new RegexExtractionStrategy for fast, zero-LLM extraction of common data types:
- Built-in patterns for emails, URLs, phones, dates, and more
- Support for custom regex patterns
- LLM-assisted pattern generation utility
- Optimized HTML preprocessing with fit_html field
- Enhanced network response body capture
Breaking changes: None
Implements new asynchronous endpoints for handling long-running crawl and LLM tasks:
- POST /crawl/job and GET /crawl/job/{task_id} for crawl operations
- POST /llm/job and GET /llm/job/{task_id} for LLM operations
- Added Redis-based task management with configurable TTL
- Moved schema definitions to dedicated schemas.py
- Added example polling client demo_docker_polling.py
This change allows clients to handle long-running operations asynchronously through a polling pattern rather than holding connections open.
Modify BrowserConfig to respect explicit headless parameter setting instead of forcing True. Update version to 0.6.2 and clean up code formatting in examples.
BREAKING CHANGE: BrowserConfig no longer defaults to headless=True when explicitly set to False
- Add tables field to CrawlResult model while maintaining backward compatibility
- Update async_webcrawler.py to extract tables from media and pass to tables field
- Update crypto_analysis_example.py to use the new tables field
- Add /config/dump examples to demo_docker_api.py
- Bump version to 0.6.1
- Update Docker base image to Python 3.12-slim-bookworm
- Bump version from 0.6.0rc1 to 0.6.0
- Update documentation to reflect release version changes
- Fix license specification in pyproject.toml and setup.py
- Clean up code formatting in demo_docker_api.py
BREAKING CHANGE: Base Python version upgraded from 3.10 to 3.12
Add support for controlling browser geolocation, locale and timezone settings:
- New GeolocationConfig class for managing GPS coordinates
- Add locale and timezone_id parameters to CrawlerRunConfig
- Update browser context creation to handle location settings
- Add example script for geolocation usage
- Update documentation with location-based identity features
This enables more precise control over browser identity and location reporting.
Improve the Docker API demo script with better error handling, more detailed output,
and enhanced visualization:
- Add detailed error messages and stack traces for debugging
- Implement better status code handling and display
- Enhance JSON output formatting with monokai theme and word wrap
- Add depth information display for deep crawls
- Improve proxy usage reporting
- Fix port number inconsistency
No breaking changes.
Add a new example script demonstrating Docker API usage with extensive features:
- Basic crawling with single/multi URL support
- Markdown generation with various filters
- Parameter demonstrations (CSS, JS, screenshots, SSL, proxies)
- Extraction strategies using CSS and LLM
- Deep crawling capabilities with streaming
- Integration examples with proxy rotation and SSL certificate fetching
Also includes minor formatting improvements in async_webcrawler.py
Adds a new content_source parameter to MarkdownGenerationStrategy that allows
selecting which HTML content to use for markdown generation:
- cleaned_html (default): uses post-processed HTML
- raw_html: uses original webpage HTML
- fit_html: uses preprocessed HTML for schema extraction
Changes include:
- Added content_source parameter to MarkdownGenerationStrategy
- Updated AsyncWebCrawler to handle HTML source selection
- Added examples and tests for the new feature
- Updated documentation with new parameter details
BREAKING CHANGE: Renamed cleaned_html parameter to input_html in generate_markdown()
method signature to better reflect its generalized purpose
Moved ProxyConfig class from proxy_strategy.py to async_configs.py for better organization.
Improved LLM token handling with new PROVIDER_MODELS_PREFIXES.
Added test cases for deep crawling and proxy rotation.
Removed docker_config from BrowserConfig as it's handled separately.
BREAKING CHANGE: ProxyConfig import path changed from crawl4ai.proxy_strategy to crawl4ai
Implement comprehensive network request and console message capturing functionality:
- Add capture_network_requests and capture_console_messages config parameters
- Add network_requests and console_messages fields to models
- Implement Playwright event listeners to capture requests, responses, and console output
- Create detailed documentation and examples
- Add comprehensive tests
This feature enables deep visibility into web page activity for debugging,
security analysis, performance profiling, and API discovery in web applications.
Reorganize browser strategy code into separate modules for better maintainability and separation of concerns. Improve Docker implementation with:
- Add Alpine and Debian-based Dockerfiles for better container options
- Enhance Docker registry to share configuration with BuiltinBrowserStrategy
- Add CPU and memory limits to container configuration
- Improve error handling and logging
- Update documentation and examples
BREAKING CHANGE: DockerConfig, DockerRegistry, and DockerUtils have been moved to new locations and their APIs have been updated.
Adds a new browser management system with strategy pattern implementation:
- Introduces BrowserManager class with strategy pattern support
- Adds PlaywrightBrowserStrategy, CDPBrowserStrategy, and BuiltinBrowserStrategy
- Implements BrowserProfileManager for profile management
- Adds PagePoolConfig for browser page pooling
- Includes comprehensive test suite for all browser strategies
BREAKING CHANGE: Browser management has been moved to browser/ module. Direct usage of browser_manager.py and browser_profiler.py is deprecated.
Implements a persistent browser management system that allows running a single shared browser instance
that can be reused across multiple crawler sessions. Key changes include:
- Added browser_mode config option with 'builtin', 'dedicated', and 'custom' modes
- Implemented builtin browser management in BrowserProfiler
- Added CLI commands for managing builtin browser (start, stop, status, restart, view)
- Modified browser process handling to support detached processes
- Added automatic builtin browser setup during package installation
BREAKING CHANGE: The browser_mode config option changes how browser instances are managed
Add special handling for single URL requests in Docker API to use arun() instead of arun_many()
Add new example script demonstrating performance differences between sequential and parallel crawling
Update cache mode from aggressive to bypass in examples and tests
Remove unused dependencies (zstandard, msgpack)
BREAKING CHANGE: Changed default cache_mode from aggressive to bypass in examples
Implements a comprehensive monitoring and visualization system for tracking web crawler operations in real-time. The system includes:
- Terminal-based dashboard with rich UI for displaying task statuses
- Memory pressure monitoring and adaptive dispatch control
- Queue statistics and performance metrics tracking
- Detailed task progress visualization
- Stress testing framework for memory management
This addition helps operators track crawler performance and manage memory usage more effectively.
Adds new target_elements parameter to CrawlerRunConfig that allows more flexible content selection than css_selector. This enables focusing markdown generation and data extraction on specific elements while still processing the entire page for links and media.
Key changes:
- Added target_elements list parameter to CrawlerRunConfig
- Modified WebScrapingStrategy and LXMLWebScrapingStrategy to handle target_elements
- Updated documentation with examples and comparison between css_selector and target_elements
- Fixed table extraction in content_scraping_strategy.py
BREAKING CHANGE: Table extraction logic has been modified to better handle thead/tbody structures
Add comprehensive table detection and extraction functionality to the web scraping system:
- Implement intelligent table detection algorithm with scoring system
- Add table extraction with support for headers, rows, captions
- Update models to include tables in Media class
- Add table_score_threshold configuration option
- Add documentation and examples for table extraction
- Include crypto analysis example demonstrating table usage
This change enables users to extract structured data from HTML tables while intelligently filtering out layout tables.
Moves ProxyConfig from configs/ directory into proxy_strategy.py to improve code organization and reduce fragmentation. Updates all imports and type hints to reflect the new location.
Key changes:
- Moved ProxyConfig class from configs/proxy_config.py to proxy_strategy.py
- Updated type hints in async_configs.py to support ProxyConfig
- Fixed proxy configuration handling in browser_manager.py
- Updated documentation and examples to use new import path
BREAKING CHANGE: ProxyConfig import path has changed from crawl4ai.configs to crawl4ai.proxy_strategy
Add new features to enhance browser automation and HTML extraction:
- Add CDP browser launch capability with customizable ports and profiles
- Implement JsonLxmlExtractionStrategy for faster HTML parsing
- Add CLI command 'crwl cdp' for launching standalone CDP browsers
- Support connecting to external CDP browsers via URL
- Optimize selector caching and context-sensitive queries
BREAKING CHANGE: LLMConfig import path changed from crawl4ai.types to crawl4ai
Rename LlmConfig to LLMConfig across the codebase to follow consistent naming conventions.
Update all imports and usages to use the new name.
Update documentation and examples to reflect the change.
BREAKING CHANGE: LlmConfig has been renamed to LLMConfig. Users need to update their imports and usage.
