docs: add v0.7.4 release blog post and update documentation
- 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
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UncleCode
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# 🚀 Crawl4AI v0.7.4: The Intelligent Table Extraction & Performance Update
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*August 17, 2025 • 6 min read*
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---
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Today I'm releasing Crawl4AI v0.7.4—the Intelligent Table Extraction & Performance Update. This release introduces revolutionary LLM-powered table extraction with intelligent chunking, significant performance improvements for concurrent crawling, enhanced browser management, and critical stability fixes that make Crawl4AI more robust for production workloads.
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## 🎯 What's New at a Glance
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- **🚀 LLMTableExtraction**: Revolutionary table extraction with intelligent chunking for massive tables
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- **⚡ Enhanced Concurrency**: True concurrency improvements for fast-completing tasks in batch operations
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- **🧹 Memory Management Refactor**: Streamlined memory utilities and better resource management
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- **🔧 Browser Manager Fixes**: Resolved race conditions in concurrent page creation
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- **⌨️ Cross-Platform Browser Profiler**: Improved keyboard handling and quit mechanisms
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- **🔗 Advanced URL Processing**: Better handling of raw URLs and base tag link resolution
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- **🛡️ Enhanced Proxy Support**: Flexible proxy configuration with dict and string formats
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- **🐳 Docker Improvements**: Better API handling and raw HTML support
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## 🚀 LLMTableExtraction: Revolutionary Table Processing
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**The Problem:** Complex tables with rowspan, colspan, nested structures, or massive datasets that traditional HTML parsing can't handle effectively. Large tables that exceed token limits crash extraction processes.
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**My Solution:** I developed LLMTableExtraction—an intelligent table extraction strategy that uses Large Language Models with automatic chunking to handle tables of any size and complexity.
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### Technical Implementation
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```python
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from crawl4ai import (
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AsyncWebCrawler,
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CrawlerRunConfig,
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LLMConfig,
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LLMTableExtraction,
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CacheMode
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)
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# Configure LLM for table extraction
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llm_config = LLMConfig(
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provider="openai/gpt-4.1-mini",
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api_token="env:OPENAI_API_KEY",
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temperature=0.1, # Low temperature for consistency
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max_tokens=32000
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)
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# Create intelligent table extraction strategy
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table_strategy = LLMTableExtraction(
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llm_config=llm_config,
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verbose=True,
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max_tries=2,
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enable_chunking=True, # Handle massive tables
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chunk_token_threshold=5000, # Smart chunking threshold
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overlap_threshold=100, # Maintain context between chunks
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extraction_type="structured" # Get structured data output
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)
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# Apply to crawler configuration
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config = CrawlerRunConfig(
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table_extraction_strategy=table_strategy,
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cache_mode=CacheMode.BYPASS
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)
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async with AsyncWebCrawler() as crawler:
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# Extract complex tables with intelligence
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result = await crawler.arun(
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"https://en.wikipedia.org/wiki/List_of_countries_by_GDP",
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config=config
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)
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# Access extracted tables directly
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for i, table in enumerate(result.tables):
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print(f"Table {i}: {len(table['data'])} rows × {len(table['headers'])} columns")
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# Convert to pandas DataFrame instantly
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import pandas as pd
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df = pd.DataFrame(table['data'], columns=table['headers'])
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print(df.head())
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```
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**Intelligent Chunking for Massive Tables:**
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```python
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# Handle tables that exceed token limits
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large_table_strategy = LLMTableExtraction(
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llm_config=llm_config,
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enable_chunking=True,
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chunk_token_threshold=3000, # Conservative threshold
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overlap_threshold=150, # Preserve context
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max_concurrent_chunks=3, # Parallel processing
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merge_strategy="intelligent" # Smart chunk merging
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)
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# Process Wikipedia comparison tables, financial reports, etc.
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config = CrawlerRunConfig(
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table_extraction_strategy=large_table_strategy,
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# Target specific table containers
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css_selector="div.wikitable, table.sortable",
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delay_before_return_html=2.0
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)
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result = await crawler.arun(
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"https://en.wikipedia.org/wiki/Comparison_of_operating_systems",
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config=config
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)
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# Tables are automatically chunked, processed, and merged
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print(f"Extracted {len(result.tables)} complex tables")
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for table in result.tables:
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print(f"Merged table: {len(table['data'])} total rows")
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```
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**Advanced Features:**
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- **Intelligent Chunking**: Automatically splits massive tables while preserving structure
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- **Context Preservation**: Overlapping chunks maintain column relationships
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- **Parallel Processing**: Concurrent chunk processing for speed
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- **Smart Merging**: Reconstructs complete tables from processed chunks
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- **Complex Structure Support**: Handles rowspan, colspan, nested tables
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- **Metadata Extraction**: Captures table context, captions, and relationships
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**Expected Real-World Impact:**
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- **Financial Analysis**: Extract complex earnings tables and financial statements
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- **Research & Academia**: Process large datasets from Wikipedia, research papers
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- **E-commerce**: Handle product comparison tables with complex layouts
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- **Government Data**: Extract census data, statistical tables from official sources
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- **Competitive Intelligence**: Process competitor pricing and feature tables
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## ⚡ Enhanced Concurrency: True Performance Gains
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**The Problem:** The `arun_many()` method wasn't achieving true concurrency for fast-completing tasks, leading to sequential processing bottlenecks in batch operations.
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**My Solution:** I implemented true concurrency improvements in the dispatcher that enable genuine parallel processing for fast-completing tasks.
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### Performance Optimization
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```python
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# Before v0.7.4: Sequential-like behavior for fast tasks
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# After v0.7.4: True concurrency
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async with AsyncWebCrawler() as crawler:
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# These will now run with true concurrency
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urls = [
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"https://httpbin.org/delay/1",
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"https://httpbin.org/delay/1",
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"https://httpbin.org/delay/1",
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"https://httpbin.org/delay/1"
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]
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# Processes in truly parallel fashion
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results = await crawler.arun_many(urls)
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# Performance improvement: ~4x faster for fast-completing tasks
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print(f"Processed {len(results)} URLs with true concurrency")
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```
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**Expected Real-World Impact:**
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- **API Crawling**: 3-4x faster processing of REST endpoints and API documentation
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- **Batch URL Processing**: Significant speedup for large URL lists
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- **Monitoring Systems**: Faster health checks and status page monitoring
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- **Data Aggregation**: Improved performance for real-time data collection
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## 🧹 Memory Management Refactor: Cleaner Architecture
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**The Problem:** Memory utilities were scattered and difficult to maintain, with potential import conflicts and unclear organization.
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**My Solution:** I consolidated all memory-related utilities into the main `utils.py` module, creating a cleaner, more maintainable architecture.
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### Improved Memory Handling
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```python
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# All memory utilities now consolidated
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from crawl4ai.utils import get_true_memory_usage_percent, MemoryMonitor
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# Enhanced memory monitoring
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monitor = MemoryMonitor()
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monitor.start_monitoring()
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async with AsyncWebCrawler() as crawler:
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# Memory-efficient batch processing
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results = await crawler.arun_many(large_url_list)
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# Get accurate memory metrics
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memory_usage = get_true_memory_usage_percent()
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memory_report = monitor.get_report()
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print(f"Memory efficiency: {memory_report['efficiency']:.1f}%")
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print(f"Peak usage: {memory_report['peak_mb']:.1f} MB")
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```
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**Expected Real-World Impact:**
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- **Production Stability**: More reliable memory tracking and management
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- **Code Maintainability**: Cleaner architecture for easier debugging
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- **Import Clarity**: Resolved potential conflicts and import issues
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- **Developer Experience**: Simpler API for memory monitoring
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## 🔧 Critical Stability Fixes
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### Browser Manager Race Condition Resolution
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**The Problem:** Concurrent page creation in persistent browser contexts caused "Target page/context closed" errors during high-concurrency operations.
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**My Solution:** Implemented thread-safe page creation with proper locking mechanisms.
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```python
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# Fixed: Safe concurrent page creation
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browser_config = BrowserConfig(
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browser_type="chromium",
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use_persistent_context=True, # Now thread-safe
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max_concurrent_sessions=10 # Safely handle concurrent requests
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)
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async with AsyncWebCrawler(config=browser_config) as crawler:
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# These concurrent operations are now stable
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tasks = [crawler.arun(url) for url in url_list]
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results = await asyncio.gather(*tasks) # No more race conditions
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```
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### Enhanced Browser Profiler
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**The Problem:** Inconsistent keyboard handling across platforms and unreliable quit mechanisms.
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**My Solution:** Cross-platform keyboard listeners with improved quit handling.
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### Advanced URL Processing
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**The Problem:** Raw URL formats (`raw://` and `raw:`) weren't properly handled, and base tag link resolution was incomplete.
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**My Solution:** Enhanced URL preprocessing and base tag support.
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```python
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# Now properly handles all URL formats
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urls = [
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"https://example.com",
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"raw://static-html-content",
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"raw:file://local-file.html"
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]
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# Base tag links are now correctly resolved
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config = CrawlerRunConfig(
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include_links=True, # Links properly resolved with base tags
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resolve_absolute_urls=True
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)
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```
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## 🛡️ Enhanced Proxy Configuration
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**The Problem:** Proxy configuration only accepted specific formats, limiting flexibility.
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**My Solution:** Enhanced ProxyConfig to support both dictionary and string formats.
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```python
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# Multiple proxy configuration formats now supported
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from crawl4ai import BrowserConfig, ProxyConfig
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# String format
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proxy_config = ProxyConfig("http://proxy.example.com:8080")
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# Dictionary format
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proxy_config = ProxyConfig({
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"server": "http://proxy.example.com:8080",
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"username": "user",
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"password": "pass"
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})
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# Use with crawler
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browser_config = BrowserConfig(proxy_config=proxy_config)
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async with AsyncWebCrawler(config=browser_config) as crawler:
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result = await crawler.arun("https://httpbin.org/ip")
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```
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## 🐳 Docker & Infrastructure Improvements
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This release includes several Docker and infrastructure improvements:
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- **Better API Token Handling**: Improved Docker example scripts with correct endpoints
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- **Raw HTML Support**: Enhanced Docker API to handle raw HTML content properly
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- **Documentation Updates**: Comprehensive Docker deployment examples
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- **Test Coverage**: Expanded test suite with better coverage
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## 📚 Documentation & Examples
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Enhanced documentation includes:
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- **LLM Table Extraction Guide**: Comprehensive examples and best practices
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- **Migration Documentation**: Updated patterns for new table extraction methods
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- **Docker Deployment**: Complete deployment guide with examples
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- **Performance Optimization**: Guidelines for concurrent crawling
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## 🙏 Acknowledgments
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Thanks to our contributors and community for feedback, bug reports, and feature requests that made this release possible.
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## 📚 Resources
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- [Full Documentation](https://docs.crawl4ai.com)
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- [GitHub Repository](https://github.com/unclecode/crawl4ai)
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- [Discord Community](https://discord.gg/crawl4ai)
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- [LLM Table Extraction Examples](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/llm_table_extraction_example.py)
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---
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|
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*Crawl4AI v0.7.4 delivers intelligent table extraction and significant performance improvements. The new LLMTableExtraction strategy handles complex tables that were previously impossible to process, while concurrency improvements make batch operations 3-4x faster. Try the intelligent table extraction—it's a game changer for data extraction workflows!*
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**Happy Crawling! 🕷️**
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*- The Crawl4AI Team*
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@@ -20,136 +20,22 @@ Ever wondered why your AI coding assistant struggles with your library despite c
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|
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## Latest Release
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|
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### [Crawl4AI v0.7.3 – The Multi-Config Intelligence Update](releases/0.7.3.md)
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*August 6, 2025*
|
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### [Crawl4AI v0.7.4 – The Intelligent Table Extraction & Performance Update](../blog/release-v0.7.4.md)
|
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*August 17, 2025*
|
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|
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Crawl4AI v0.7.3 brings smarter URL-specific configurations, flexible Docker deployments, and critical stability improvements. Configure different crawling strategies for different URL patterns in a single batch—perfect for mixed content sites with docs, blogs, and APIs.
|
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Crawl4AI v0.7.4 introduces revolutionary LLM-powered table extraction with intelligent chunking, performance improvements for concurrent crawling, enhanced browser management, and critical stability fixes that make Crawl4AI more robust for production workloads.
|
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|
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Key highlights:
|
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- **Multi-URL Configurations**: Different strategies for different URL patterns in one crawl
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- **Flexible Docker LLM Providers**: Configure providers via environment variables
|
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- **Bug Fixes**: Critical stability improvements for production deployments
|
||||
- **Documentation Updates**: Clearer examples and improved API documentation
|
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- **🚀 LLMTableExtraction**: Revolutionary table extraction with intelligent chunking for massive tables
|
||||
- **⚡ Dispatcher Bug Fix**: Fixed sequential processing issue in arun_many for fast-completing tasks
|
||||
- **🧹 Memory Management Refactor**: Streamlined memory utilities and better resource management
|
||||
- **🔧 Browser Manager Fixes**: Resolved race conditions in concurrent page creation
|
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- **🔗 Advanced URL Processing**: Better handling of raw URLs and base tag link resolution
|
||||
|
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[Read full release notes →](releases/0.7.3.md)
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[Read full release notes →](../blog/release-v0.7.4.md)
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|
||||
---
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## Previous Releases
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### [Crawl4AI v0.7.0 – The Adaptive Intelligence Update](releases/0.7.0.md)
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*January 28, 2025*
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Introduced groundbreaking intelligence features including Adaptive Crawling, Virtual Scroll support, intelligent Link Preview, and the Async URL Seeder for massive URL discovery.
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|
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[Read release notes →](releases/0.7.0.md)
|
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|
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### [Crawl4AI v0.6.0 – World-Aware Crawling, Pre-Warmed Browsers, and the MCP API](releases/0.6.0.md)
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*December 23, 2024*
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|
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Crawl4AI v0.6.0 brought major architectural upgrades including world-aware crawling (set geolocation, locale, and timezone), real-time traffic capture, and a memory-efficient crawler pool with pre-warmed pages.
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The Docker server now exposes a full-featured MCP socket + SSE interface, supports streaming, and comes with a new Playground UI. Plus, table extraction is now native, and the new stress-test framework supports crawling 1,000+ URLs.
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Other key changes:
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* Native support for `result.media["tables"]` to export DataFrames
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* Full network + console logs and MHTML snapshot per crawl
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* Browser pooling and pre-warming for faster cold starts
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* New streaming endpoints via MCP API and Playground
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* Robots.txt support, proxy rotation, and improved session handling
|
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* Deprecated old markdown names, legacy modules cleaned up
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||||
* Massive repo cleanup: ~36K insertions, ~5K deletions across 121 files
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||||
|
||||
[Read full release notes →](releases/0.6.0.md)
|
||||
|
||||
---
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||||
|
||||
### [Crawl4AI v0.5.0: Deep Crawling, Scalability, and a New CLI!](releases/0.5.0.md)
|
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|
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My dear friends and crawlers, there you go, this is the release of Crawl4AI v0.5.0! This release brings a wealth of new features, performance improvements, and a more streamlined developer experience. Here's a breakdown of what's new:
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||||
|
||||
**Major New Features:**
|
||||
|
||||
* **Deep Crawling:** Explore entire websites with configurable strategies (BFS, DFS, Best-First). Define custom filters and URL scoring for targeted crawls.
|
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* **Memory-Adaptive Dispatcher:** Handle large-scale crawls with ease! Our new dispatcher dynamically adjusts concurrency based on available memory and includes built-in rate limiting.
|
||||
* **Multiple Crawler Strategies:** Choose between the full-featured Playwright browser-based crawler or a new, *much* faster HTTP-only crawler for simpler tasks.
|
||||
* **Docker Deployment:** Deploy Crawl4AI as a scalable, self-contained service with built-in API endpoints and optional JWT authentication.
|
||||
* **Command-Line Interface (CLI):** Interact with Crawl4AI directly from your terminal. Crawl, configure, and extract data with simple commands.
|
||||
* **LLM Configuration (`LLMConfig`):** A new, unified way to configure LLM providers (OpenAI, Anthropic, Ollama, etc.) for extraction, filtering, and schema generation. Simplifies API key management and switching between models.
|
||||
|
||||
**Minor Updates & Improvements:**
|
||||
|
||||
* **LXML Scraping Mode:** Faster HTML parsing with `LXMLWebScrapingStrategy`.
|
||||
* **Proxy Rotation:** Added `ProxyRotationStrategy` with a `RoundRobinProxyStrategy` implementation.
|
||||
* **PDF Processing:** Extract text, images, and metadata from PDF files.
|
||||
* **URL Redirection Tracking:** Automatically follows and records redirects.
|
||||
* **Robots.txt Compliance:** Optionally respect website crawling rules.
|
||||
* **LLM-Powered Schema Generation:** Automatically create extraction schemas using an LLM.
|
||||
* **`LLMContentFilter`:** Generate high-quality, focused markdown using an LLM.
|
||||
* **Improved Error Handling & Stability:** Numerous bug fixes and performance enhancements.
|
||||
* **Enhanced Documentation:** Updated guides and examples.
|
||||
|
||||
**Breaking Changes & Migration:**
|
||||
|
||||
This release includes several breaking changes to improve the library's structure and consistency. Here's what you need to know:
|
||||
|
||||
* **`arun_many()` Behavior:** Now uses the `MemoryAdaptiveDispatcher` by default. The return type depends on the `stream` parameter in `CrawlerRunConfig`. Adjust code that relied on unbounded concurrency.
|
||||
* **`max_depth` Location:** Moved to `CrawlerRunConfig` and now controls *crawl depth*.
|
||||
* **Deep Crawling Imports:** Import `DeepCrawlStrategy` and related classes from `crawl4ai.deep_crawling`.
|
||||
* **`BrowserContext` API:** Updated; the old `get_context` method is deprecated.
|
||||
* **Optional Model Fields:** Many data model fields are now optional. Handle potential `None` values.
|
||||
* **`ScrapingMode` Enum:** Replaced with strategy pattern (`WebScrapingStrategy`, `LXMLWebScrapingStrategy`).
|
||||
* **`content_filter` Parameter:** Removed from `CrawlerRunConfig`. Use extraction strategies or markdown generators with filters.
|
||||
* **Removed Functionality:** The synchronous `WebCrawler`, the old CLI, and docs management tools have been removed.
|
||||
* **Docker:** Significant changes to deployment. See the [Docker documentation](../deploy/docker/README.md).
|
||||
* **`ssl_certificate.json`:** This file has been removed.
|
||||
* **Config**: FastFilterChain has been replaced with FilterChain
|
||||
* **Deep-Crawl**: DeepCrawlStrategy.arun now returns Union[CrawlResultT, List[CrawlResultT], AsyncGenerator[CrawlResultT, None]]
|
||||
* **Proxy**: Removed synchronous WebCrawler support and related rate limiting configurations
|
||||
* **LLM Parameters:** Use the new `LLMConfig` object instead of passing `provider`, `api_token`, `base_url`, and `api_base` directly to `LLMExtractionStrategy` and `LLMContentFilter`.
|
||||
|
||||
**In short:** Update imports, adjust `arun_many()` usage, check for optional fields, and review the Docker deployment guide.
|
||||
|
||||
## License Change
|
||||
|
||||
Crawl4AI v0.5.0 updates the license to Apache 2.0 *with a required attribution clause*. This means you are free to use, modify, and distribute Crawl4AI (even commercially), but you *must* clearly attribute the project in any public use or distribution. See the updated `LICENSE` file for the full legal text and specific requirements.
|
||||
|
||||
**Get Started:**
|
||||
|
||||
* **Installation:** `pip install "crawl4ai[all]"` (or use the Docker image)
|
||||
* **Documentation:** [https://docs.crawl4ai.com](https://docs.crawl4ai.com)
|
||||
* **GitHub:** [https://github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
|
||||
|
||||
I'm very excited to see what you build with Crawl4AI v0.5.0!
|
||||
|
||||
---
|
||||
|
||||
### [0.4.2 - Configurable Crawlers, Session Management, and Smarter Screenshots](releases/0.4.2.md)
|
||||
*December 12, 2024*
|
||||
|
||||
The 0.4.2 update brings massive improvements to configuration, making crawlers and browsers easier to manage with dedicated objects. You can now import/export local storage for seamless session management. Plus, long-page screenshots are faster and cleaner, and full-page PDF exports are now possible. Check out all the new features to make your crawling experience even smoother.
|
||||
|
||||
[Read full release notes →](releases/0.4.2.md)
|
||||
|
||||
---
|
||||
|
||||
### [0.4.1 - Smarter Crawling with Lazy-Load Handling, Text-Only Mode, and More](releases/0.4.1.md)
|
||||
*December 8, 2024*
|
||||
|
||||
This release brings major improvements to handling lazy-loaded images, a blazing-fast Text-Only Mode, full-page scanning for infinite scrolls, dynamic viewport adjustments, and session reuse for efficient crawling. If you're looking to improve speed, reliability, or handle dynamic content with ease, this update has you covered.
|
||||
|
||||
[Read full release notes →](releases/0.4.1.md)
|
||||
|
||||
---
|
||||
|
||||
### [0.4.0 - Major Content Filtering Update](releases/0.4.0.md)
|
||||
*December 1, 2024*
|
||||
|
||||
Introduced significant improvements to content filtering, multi-threaded environment handling, and user-agent generation. This release features the new PruningContentFilter, enhanced thread safety, and improved test coverage.
|
||||
|
||||
[Read full release notes →](releases/0.4.0.md)
|
||||
|
||||
## Project History
|
||||
|
||||
Curious about how Crawl4AI has evolved? Check out our [complete changelog](https://github.com/unclecode/crawl4ai/blob/main/CHANGELOG.md) for a detailed history of all versions and updates.
|
||||
|
||||
Reference in New Issue
Block a user