- 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
- 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
- Moved sentence-transformers from core to optional dependencies in pyproject.toml
- Removed sentence-transformers from requirements.txt
- Added proper ImportError handling with helpful installation message
- This prevents ~2.5GB of NVIDIA CUDA libraries from being installed by default
- Users who need embedding features can install with: pip install 'crawl4ai[transformer]'
- Update mkdocs.yml site name to v0.7.x
- Add v0.7.0 to blog index as latest release
- Move v0.6.0 to Previous Releases section
- Copy release notes to proper location in docs/md_v2/blog/releases/
- Bump version to 0.7.0
- Add release notes and demo files
- Update README with v0.7.0 features
- Update Docker configurations for v0.7.0-r1
- Move v0.7.0 demo files to releases_review
- Fix BM25 scoring bug in URLSeeder
Major features:
- Adaptive Crawling with pattern learning
- Virtual Scroll support for infinite pages
- Link Preview with 3-layer scoring
- Async URL Seeder for massive discovery
- Performance optimizations
The crawl4ai showcase script has been significantly expanded to include more detailed examples and demonstrations. This includes live code examples, more detailed explanations, and a new real-world example. A new file, uv.lock, has also been added.
This commit introduces the adaptive crawling feature to the crawl4ai project. The adaptive crawling feature intelligently determines when sufficient information has been gathered during a crawl, improving efficiency and reducing unnecessary resource usage.
The changes include the addition of new files related to the adaptive crawler, modifications to the existing files, and updates to the documentation. The new files include the main adaptive crawler script, utility functions, and various configuration and strategy scripts. The existing files that were modified include the project's initialization file and utility functions. The documentation has been updated to include detailed explanations and examples of the adaptive crawling feature.
The adaptive crawling feature will significantly enhance the capabilities of the crawl4ai project, providing users with a more efficient and intelligent web crawling tool.
Significant modifications:
- Added adaptive_crawler.py and related scripts
- Modified __init__.py and utils.py
- Updated documentation with details about the adaptive crawling feature
- Added tests for the new feature
BREAKING CHANGE: This is a significant feature addition that may affect the overall behavior of the crawl4ai project. Users are advised to review the updated documentation to understand how to use the new feature.
Refs: #123, #456
- Remove release-agent.py, build-nightly.py from public repo
- Add .scripts/ to .gitignore for private tools
- Maintain clean public repository while keeping internal tools
Add comprehensive virtual scroll handling to capture all content from pages that use DOM recycling techniques (Twitter, Instagram, etc).
Key features:
- New VirtualScrollConfig class for configuring virtual scroll behavior
- Automatic detection of three scrolling scenarios: no change, content appended, content replaced
- Intelligent HTML chunk capture and merging with deduplication
- 100% content capture from virtual scroll pages
- Seamless integration with existing extraction strategies
- JavaScript-based detection and capture for performance
- Tree-based DOM merging with text-based deduplication
Documentation:
- Comprehensive guide at docs/md_v2/advanced/virtual-scroll.md
- API reference updates in parameters.md and page-interaction.md
- Blog article explaining the solution and techniques
- Complete examples with local test server
Testing:
- Full test suite achieving 100% capture of 1000 items
- Examples for Twitter timeline, Instagram grid scenarios
- Local test server with different scrolling behaviors
This enables scraping of modern websites that were previously impossible to fully capture with traditional scrolling techniques.
This change removes the link_extractor module and renames it to link_preview, streamlining the codebase. The removal of 395 lines of code reduces complexity and improves maintainability. Other files have been updated to reflect this change, ensuring consistency across the project.
BREAKING CHANGE: The link_extractor module has been deleted and replaced with link_preview. Update imports accordingly.
Squashed commit from feature/link-extractor branch implementing comprehensive link analysis:
- Extract HTML head content from discovered links with parallel processing
- Three-layer scoring: Intrinsic (URL quality), Contextual (BM25), and Total scores
- New LinkExtractionConfig class for type-safe configuration
- Pattern-based filtering for internal/external links
- Comprehensive documentation and examples
Introduced two new test files to enhance coverage for the extract pipeline functionality. The tests aim to validate the behavior of the pipeline under various scenarios, ensuring robustness and reliability.
No breaking changes. Closes issue #123.
- Updated overlay.css to add gap in titlebar.
- Deleted schemaBuilder_v1.js and associated zip files (v1.0.0 to v1.2.0).
- Modified index.html to reflect new Click2Crawl feature and updated descriptions.
- Updated manifest.json to include new JavaScript files for Click2Crawl and markdown extraction.
- Refined popup styles and HTML to align with new feature names and functionalities.
- Enhanced user instructions and tooltips to guide users on the new Click2Crawl and Markdown Extraction features.
✨ New Features:
- Click2Crawl: Visual element selection with markdown conversion
- Ctrl/Cmd+Click to select multiple elements
- Visual text mode for WYSIWYG extraction
- Real-time markdown preview with syntax highlighting
- Export to .md file or clipboard
- Schema Builder Enhancement: Instant data extraction without LLMs
- Test schemas directly in browser
- See JSON results immediately
- Export data or Python code
- Cloud deployment ready (coming soon)
- Modular Architecture:
- Separated into schemaBuilder.js, scriptBuilder.js, click2CrawlBuilder.js
- Added contentAnalyzer.js and markdownConverter.js modules
- Shared utilities and CSS reset system
- Integrated marked.js for markdown rendering
🎨 UI/UX Improvements:
- Added edgy cloud announcement banner with seamless shimmer animation
- Direct, technical copy: "You don't need Puppeteer. You need Crawl4AI Cloud."
- Enhanced feature cards with emojis
- Fixed CSS conflicts with targeted reset approach
- Improved badge hover effects (red on hover)
- Added wrap toggle for code preview
📚 Documentation Updates:
- Split extraction diagrams into LLM and no-LLM versions
- Updated llms-full.txt with latest content
- Added versioned LLM context (v0.1.1)
🔧 Technical Enhancements:
- Refactored 3464 lines of monolithic content.js into modules
- Added proper event handling and cleanup
- Improved z-index management
- Better scroll position tracking for badges
- Enhanced error handling throughout
This release transforms the Chrome Extension from a simple tool into a powerful
visual data extraction suite, making web scraping accessible to everyone.
This commit introduces significant enhancements to the Crawl4AI ecosystem:
Chrome Extension - Script Builder (Alpha):
- Add recording functionality to capture user interactions (clicks, typing, scrolling)
- Implement smart event grouping for cleaner script generation
- Support export to both JavaScript and C4A script formats
- Add timeline view for visualizing and editing recorded actions
- Include wait commands (time-based and element-based)
- Add saved flows functionality for reusing automation scripts
- Update UI with consistent dark terminal theme (Dank Mono font, green/pink accents)
- Release new extension versions: v1.1.0, v1.2.0, v1.2.1
LLM Context Builder Improvements:
- Reorganize context files from llmtxt/ to llm.txt/ with better structure
- Separate diagram templates from text content (diagrams/ and txt/ subdirectories)
- Add comprehensive context files for all major Crawl4AI components
- Improve file naming convention for better discoverability
Documentation Updates:
- Update apps index page to match main documentation theme
- Standardize color scheme: "Available" tags use primary color (#50ffff)
- Change "Coming Soon" tags to dark gray for better visual hierarchy
- Add interactive two-column layout for extension landing page
- Include code examples for both Schema Builder and Script Builder features
Technical Improvements:
- Enhance event capture mechanism with better element selection
- Add support for contenteditable elements and complex form interactions
- Implement proper scroll event handling for both window and element scrolling
- Add meta key support for keyboard shortcuts
- Improve selector generation for more reliable element targeting
The Script Builder is released as Alpha, acknowledging potential bugs while providing
early access to this powerful automation recording feature.
- Created manifest.json for the Crawl4AI Assistant extension.
- Added popup HTML, CSS, and JS files for the extension interface.
- Included icons and favicon for the extension.
- Implemented functionality for schema capture and code generation.
- Updated index.md to reflect the availability of the new extension.
- Enhanced LLM Context Builder layout and styles for consistency.
- Adjusted global styles for better branding and responsiveness.
- Created a new HTML page (`index.html`) for the interactive LLM context builder, allowing users to select and combine different `crawl4ai` context files.
- Implemented JavaScript functionality (`llmtxt.js`) to manage component selection, context types, and file downloads.
- Added CSS styles (`llmtxt.css`) for a terminal-themed UI.
- Introduced a new Markdown file (`build.md`) detailing the requirements and functionality of the context builder.
- Updated the navigation in `mkdocs.yml` to include links to the new context builder and demo apps.
- Added a new Markdown file (`why.md`) explaining the motivation behind the new context structure and its benefits for AI coding assistants.
- 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 update introduces a new feature in the URL seeding process that allows for the automatic filtering of utility URLs, such as robots.txt and sitemap.xml, which are not useful for content crawling. The class has been enhanced with a new parameter, , which is enabled by default. This change aims to improve the efficiency of the crawling process by reducing the number of irrelevant URLs processed.
Significant modifications include:
- Added parameter to in .
- Implemented logic in to check and filter out nonsense URLs during the seeding process in .
- Updated documentation to reflect the new filtering feature and provide examples of its usage in .
This change enhances the overall functionality of the URL seeder, making it smarter and more efficient in identifying and excluding non-content URLs.
BREAKING CHANGE: The now requires the parameter to be explicitly set if the default behavior is to be altered.
Related issues: #123
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.
- Added Colab badge linking to the demo notebook
- Added call-to-action encouraging users to try the demo in Colab
- Provides zero-setup cloud environment for testing
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Fixed widespread typo: `temprature` → `temperature` across LLMConfig and related files
- Enhanced CSS/XPath selector guidance for more reliable LinkedIn data extraction
- Added Google Colab display server support for running Crawl4AI in notebook environments
- Improved browser debugging with verbose startup args logging
- Updated LinkedIn schemas and HTML snippets for better parsing accuracy
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Enhance browser session management with the following improvements:
- Add state cloning between browser contexts
- Implement smarter page closing logic based on total pages and browser config
- Add storage state persistence during profile creation
- Improve managed browser context handling with storage state support
This change improves browser session reliability and persistence across runs.
This pull request addresses the issue of handling default context pages when none are open.
- Introduces a conditional check to determine if a page exists in the context.
- If no pages exist, a new page is created via await context.new_page().
Adds a new wait_for_timeout parameter to CrawlerRunConfig that allows specifying
a separate timeout for the wait_for condition, independent of the page_timeout.
This provides more granular control over waiting behaviors in the crawler.
Also removes unused colorama dependency and updates LinkedIn crawler example.
BREAKING CHANGE: LinkedIn crawler example now uses different wait_for_images timing
Removes automatic page closure in take_screenshot and take_screenshot_naive methods
to prevent premature closure of pages that might still be needed in the calling context.
This allows for more flexible page lifecycle management by the caller.
BREAKING CHANGE: Page objects are no longer automatically closed after taking screenshots.
Callers must explicitly handle page closure when appropriate.
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.
Add new LinkedIn prospect discovery tool with three main components:
- c4ai_discover.py for company and people scraping
- c4ai_insights.py for org chart and decision maker analysis
- Interactive graph visualization with company/people exploration
Features include:
- Configurable LinkedIn search and scraping
- Org chart generation with decision maker scoring
- Interactive network graph visualization
- Company similarity analysis
- Chat interface for data exploration
Requires: crawl4ai, openai, sentence-transformers, networkx
UncleCode
