- Updated ProxyConfig.from_string to support multiple proxy formats, including URLs with credentials.
- Deprecated the 'proxy' parameter in BrowserConfig, replacing it with 'proxy_config' for better flexibility.
- Added warnings for deprecated usage and clarified behavior when both parameters are provided.
- Updated documentation and tests to reflect changes in proxy configuration handling.
This commit introduces several significant enhancements to the Crawl4AI Docker deployment:
1. Add MCP Protocol Support:
- Implement WebSocket and SSE transport layers for MCP server communication
- Create mcp_bridge.py to expose existing API endpoints via MCP protocol
- Add comprehensive tests for both socket and SSE transport methods
2. Enhance Docker Server Capabilities:
- Add PDF generation endpoint with file saving functionality
- Add screenshot capture endpoint with configurable wait time
- Implement JavaScript execution endpoint for dynamic page interaction
- Add intelligent file path handling for saving generated assets
3. Improve Search and Context Functionality:
- Implement syntax-aware code function chunking using AST parsing
- Add BM25-based intelligent document search with relevance scoring
- Create separate code and documentation context endpoints
- Enhance response format with structured results and scores
4. Rename and Fix File Organization:
- Fix typo in test_docker_config_gen.py filename
- Update import statements and dependencies
- Add FileResponse for context endpoints
This enhancement significantly improves the machine-to-machine communication
capabilities of Crawl4AI, making it more suitable for integration with LLM agents
and other automated systems.
The CHANGELOG update has been applied successfully, highlighting the key features and improvements made in this release. The commit message provides a detailed explanation of all the
changes, which will be helpful for tracking the project's evolution.
Replace crawler_manager.py with simpler crawler_pool.py implementation:
- Add global page semaphore for hard concurrency cap
- Implement browser pool with idle cleanup
- Add playground UI for testing and stress testing
- Update API handlers to use pooled crawlers
- Enhance logging levels and symbols
BREAKING CHANGE: Removes CrawlerManager class in favor of simpler pool-based approach
Adds a new CrawlerManager class to handle browser instance pooling and failover:
- Implements auto-scaling based on system resources
- Adds primary/backup crawler management
- Integrates memory monitoring and throttling
- Adds streaming support with memory tracking
- Updates API endpoints to use pooled crawlers
BREAKING CHANGE: API endpoints now require CrawlerManager initialization
Add comprehensive stress testing solution for SDK using arun_many and dispatcher system:
- Create test_stress_sdk.py for running high volume crawl tests
- Add run_benchmark.py for orchestrating tests with predefined configs
- Implement benchmark_report.py for generating performance reports
- Add memory tracking and local test site generation
- Support both streaming and batch processing modes
- Add detailed documentation in README.md
The framework enables testing SDK performance, concurrency handling,
and memory behavior under high-volume scenarios.
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.
