Release v0.8.0: Crash Recovery, Prefetch Mode & Security Fixes (#1712)
* Fix: Use correct URL variable for raw HTML extraction (#1116) - Prevents full HTML content from being passed as URL to extraction strategies - Added unit tests to verify raw HTML and regular URL processing Fix: Wrong URL variable used for extraction of raw html * Fix #1181: Preserve whitespace in code blocks during HTML scraping The remove_empty_elements_fast() method was removing whitespace-only span elements inside <pre> and <code> tags, causing import statements like "import torch" to become "importtorch". Now skips elements inside code blocks where whitespace is significant. * Refactor Pydantic model configuration to use ConfigDict for arbitrary types * Fix EmbeddingStrategy: Uncomment response handling for the variations and clean up mock data. ref #1621 * Fix: permission issues with .cache/url_seeder and other runtime cache dirs. ref #1638 * fix: ensure BrowserConfig.to_dict serializes proxy_config * feat: make LLM backoff configurable end-to-end - extend LLMConfig with backoff delay/attempt/factor fields and thread them through LLMExtractionStrategy, LLMContentFilter, table extraction, and Docker API handlers - expose the backoff parameter knobs on perform_completion_with_backoff/aperform_completion_with_backoff and document them in the md_v2 guides * reproduced AttributeError from #1642 * pass timeout parameter to docker client request * added missing deep crawling objects to init * generalized query in ContentRelevanceFilter to be a str or list * import modules from enhanceable deserialization * parameterized tests * Fix: capture current page URL to reflect JavaScript navigation and add test for delayed redirects. ref #1268 * refactor: replace PyPDF2 with pypdf across the codebase. ref #1412 * Add browser_context_id and target_id parameters to BrowserConfig Enable Crawl4AI to connect to pre-created CDP browser contexts, which is essential for cloud browser services that pre-create isolated contexts. Changes: - Add browser_context_id and target_id parameters to BrowserConfig - Update from_kwargs() and to_dict() methods - Modify BrowserManager.start() to use existing context when provided - Add _get_page_by_target_id() helper method - Update get_page() to handle pre-existing targets - Add test for browser_context_id functionality This enables cloud services to: 1. Create isolated CDP contexts before Crawl4AI connects 2. Pass context/target IDs to BrowserConfig 3. Have Crawl4AI reuse existing contexts instead of creating new ones * Add cdp_cleanup_on_close flag to prevent memory leaks in cloud/server scenarios * Fix: add cdp_cleanup_on_close to from_kwargs * Fix: find context by target_id for concurrent CDP connections * Fix: use target_id to find correct page in get_page * Fix: use CDP to find context by browserContextId for concurrent sessions * Revert context matching attempts - Playwright cannot see CDP-created contexts * Add create_isolated_context flag for concurrent CDP crawls When True, forces creation of a new browser context instead of reusing the default context. Essential for concurrent crawls on the same browser to prevent navigation conflicts. * Add context caching to create_isolated_context branch Uses contexts_by_config cache (same as non-CDP mode) to reuse contexts for multiple URLs with same config. Still creates new page per crawl for navigation isolation. Benefits batch/deep crawls. * Add init_scripts support to BrowserConfig for pre-page-load JS injection This adds the ability to inject JavaScript that runs before any page loads, useful for stealth evasions (canvas/audio fingerprinting, userAgentData). - Add init_scripts parameter to BrowserConfig (list of JS strings) - Apply init_scripts in setup_context() via context.add_init_script() - Update from_kwargs() and to_dict() for serialization * Fix CDP connection handling: support WS URLs and proper cleanup Changes to browser_manager.py: 1. _verify_cdp_ready(): Support multiple URL formats - WebSocket URLs (ws://, wss://): Skip HTTP verification, Playwright handles directly - HTTP URLs with query params: Properly parse with urlparse to preserve query string - Fixes issue where naive f"{cdp_url}/json/version" broke WS URLs and query params 2. close(): Proper cleanup when cdp_cleanup_on_close=True - Close all sessions (pages) - Close all contexts - Call browser.close() to disconnect (doesn't terminate browser, just releases connection) - Wait 1 second for CDP connection to fully release - Stop Playwright instance to prevent memory leaks This enables: - Connecting to specific browsers via WS URL - Reusing the same browser with multiple sequential connections - No user wait needed between connections (internal 1s delay handles it) Added tests/browser/test_cdp_cleanup_reuse.py with comprehensive tests. * Update gitignore * Some debugging for caching * Add _generate_screenshot_from_html for raw: and file:// URLs Implements the missing method that was being called but never defined. Now raw: and file:// URLs can generate screenshots by: 1. Loading HTML into a browser page via page.set_content() 2. Taking screenshot using existing take_screenshot() method 3. Cleaning up the page afterward This enables cached HTML to be rendered with screenshots in crawl4ai-cloud. * Add PDF and MHTML support for raw: and file:// URLs - Replace _generate_screenshot_from_html with _generate_media_from_html - New method handles screenshot, PDF, and MHTML in one browser session - Update raw: and file:// URL handlers to use new method - Enables cached HTML to generate all media types * Add crash recovery for deep crawl strategies Add optional resume_state and on_state_change parameters to all deep crawl strategies (BFS, DFS, Best-First) for cloud deployment crash recovery. Features: - resume_state: Pass saved state to resume from checkpoint - on_state_change: Async callback fired after each URL for real-time state persistence to external storage (Redis, DB, etc.) - export_state(): Get last captured state manually - Zero overhead when features are disabled (None defaults) State includes visited URLs, pending queue/stack, depths, and pages_crawled count. All state is JSON-serializable. * Fix: HTTP strategy raw: URL parsing truncates at # character The AsyncHTTPCrawlerStrategy.crawl() method used urlparse() to extract content from raw: URLs. This caused HTML with CSS color codes like #eee to be truncated because # is treated as a URL fragment delimiter. Before: raw:body{background:#eee} -> parsed.path = 'body{background:' After: raw:body{background:#eee} -> raw_content = 'body{background:#eee' Fix: Strip the raw: or raw:// prefix directly instead of using urlparse, matching how the browser strategy handles it. * Add base_url parameter to CrawlerRunConfig for raw HTML processing When processing raw: HTML (e.g., from cache), the URL parameter is meaningless for markdown link resolution. This adds a base_url parameter that can be set explicitly to provide proper URL resolution context. Changes: - Add base_url parameter to CrawlerRunConfig.__init__ - Add base_url to CrawlerRunConfig.from_kwargs - Update aprocess_html to use base_url for markdown generation Usage: config = CrawlerRunConfig(base_url='https://example.com') result = await crawler.arun(url='raw:{html}', config=config) * Add prefetch mode for two-phase deep crawling - Add `prefetch` parameter to CrawlerRunConfig - Add `quick_extract_links()` function for fast link extraction - Add short-circuit in aprocess_html() for prefetch mode - Add 42 tests (unit, integration, regression) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com> * Updates on proxy rotation and proxy configuration * Add proxy support to HTTP crawler strategy * Add browser pipeline support for raw:/file:// URLs - Add process_in_browser parameter to CrawlerRunConfig - Route raw:/file:// URLs through _crawl_web() when browser operations needed - Use page.set_content() instead of goto() for local content - Fix cookie handling for non-HTTP URLs in browser_manager - Auto-detect browser requirements: js_code, wait_for, screenshot, etc. - Maintain fast path for raw:/file:// without browser params Fixes #310 * Add smart TTL cache for sitemap URL seeder - Add cache_ttl_hours and validate_sitemap_lastmod params to SeedingConfig - New JSON cache format with metadata (version, created_at, lastmod, url_count) - Cache validation by TTL expiry and sitemap lastmod comparison - Auto-migration from old .jsonl to new .json format - Fixes bug where incomplete cache was used indefinitely * Update URL seeder docs with smart TTL cache parameters - Add cache_ttl_hours and validate_sitemap_lastmod to parameter table - Document smart TTL cache validation with examples - Add cache-related troubleshooting entries - Update key features summary * Add MEMORY.md to gitignore * Docs: Add multi-sample schema generation section Add documentation explaining how to pass multiple HTML samples to generate_schema() for stable selectors that work across pages with varying DOM structures. Includes: - Problem explanation (fragile nth-child selectors) - Solution with code example - Key points for multi-sample queries - Comparison table of fragile vs stable selectors * Fix critical RCE and LFI vulnerabilities in Docker API deployment Security fixes for vulnerabilities reported by ProjectDiscovery: 1. Remote Code Execution via Hooks (CVE pending) - Remove __import__ from allowed_builtins in hook_manager.py - Prevents arbitrary module imports (os, subprocess, etc.) - Hooks now disabled by default via CRAWL4AI_HOOKS_ENABLED env var 2. Local File Inclusion via file:// URLs (CVE pending) - Add URL scheme validation to /execute_js, /screenshot, /pdf, /html - Block file://, javascript:, data: and other dangerous schemes - Only allow http://, https://, and raw: (where appropriate) 3. Security hardening - Add CRAWL4AI_HOOKS_ENABLED=false as default (opt-in for hooks) - Add security warning comments in config.yml - Add validate_url_scheme() helper for consistent validation Testing: - Add unit tests (test_security_fixes.py) - 16 tests - Add integration tests (run_security_tests.py) for live server Affected endpoints: - POST /crawl (hooks disabled by default) - POST /crawl/stream (hooks disabled by default) - POST /execute_js (URL validation added) - POST /screenshot (URL validation added) - POST /pdf (URL validation added) - POST /html (URL validation added) Breaking changes: - Hooks require CRAWL4AI_HOOKS_ENABLED=true to function - file:// URLs no longer work on API endpoints (use library directly) * Enhance authentication flow by implementing JWT token retrieval and adding authorization headers to API requests * Add release notes for v0.7.9, detailing breaking changes, security fixes, new features, bug fixes, and documentation updates * Add release notes for v0.8.0, detailing breaking changes, security fixes, new features, bug fixes, and documentation updates Documentation for v0.8.0 release: - SECURITY.md: Security policy and vulnerability reporting guidelines - RELEASE_NOTES_v0.8.0.md: Comprehensive release notes - migration/v0.8.0-upgrade-guide.md: Step-by-step migration guide - security/GHSA-DRAFT-RCE-LFI.md: GitHub security advisory drafts - CHANGELOG.md: Updated with v0.8.0 changes Breaking changes documented: - Docker API hooks disabled by default (CRAWL4AI_HOOKS_ENABLED) - file:// URLs blocked on Docker API endpoints Security fixes credited to Neo by ProjectDiscovery * Add examples for deep crawl crash recovery and prefetch mode in documentation * Release v0.8.0: The v0.8.0 Update - Updated version to 0.8.0 - Added comprehensive demo and release notes - Updated all documentation * Update security researcher acknowledgment with a hyperlink for Neo by ProjectDiscovery * Add async agenerate_schema method for schema generation - Extract prompt building to shared _build_schema_prompt() method - Add agenerate_schema() async version using aperform_completion_with_backoff - Refactor generate_schema() to use shared prompt builder - Fixes Gemini/Vertex AI compatibility in async contexts (FastAPI) * Fix: Enable litellm.drop_params for O-series/GPT-5 model compatibility O-series (o1, o3) and GPT-5 models only support temperature=1. Setting litellm.drop_params=True auto-drops unsupported parameters instead of throwing UnsupportedParamsError. Fixes temperature=0.01 error for these models in LLM extraction. --------- Co-authored-by: rbushria <rbushri@gmail.com> Co-authored-by: AHMET YILMAZ <tawfik@kidocode.com> Co-authored-by: Soham Kukreti <kukretisoham@gmail.com> Co-authored-by: Chris Murphy <chris.murphy@klaviyo.com> Co-authored-by: unclecode <unclecode@kidocode.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Nasrin
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docs/examples/deep_crawl_crash_recovery.py
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docs/examples/deep_crawl_crash_recovery.py
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#!/usr/bin/env python3
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"""
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Deep Crawl Crash Recovery Example
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This example demonstrates how to implement crash recovery for long-running
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deep crawls. The feature is useful for:
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- Cloud deployments with spot/preemptible instances
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- Long-running crawls that may be interrupted
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- Distributed crawling with state coordination
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Key concepts:
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- `on_state_change`: Callback fired after each URL is processed
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- `resume_state`: Pass saved state to continue from a checkpoint
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- `export_state()`: Get the last captured state manually
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Works with all strategies: BFSDeepCrawlStrategy, DFSDeepCrawlStrategy,
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BestFirstCrawlingStrategy
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"""
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import asyncio
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import json
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import os
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from pathlib import Path
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from typing import Dict, Any, List
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from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
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from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
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# File to store crawl state (in production, use Redis/database)
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STATE_FILE = Path("crawl_state.json")
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async def save_state_to_file(state: Dict[str, Any]) -> None:
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"""
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Callback to save state after each URL is processed.
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In production, you might save to:
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- Redis: await redis.set("crawl_state", json.dumps(state))
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- Database: await db.execute("UPDATE crawls SET state = ?", json.dumps(state))
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- S3: await s3.put_object(Bucket="crawls", Key="state.json", Body=json.dumps(state))
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"""
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with open(STATE_FILE, "w") as f:
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json.dump(state, f, indent=2)
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print(f" [State saved] Pages: {state['pages_crawled']}, Pending: {len(state['pending'])}")
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def load_state_from_file() -> Dict[str, Any] | None:
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"""Load previously saved state, if it exists."""
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if STATE_FILE.exists():
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with open(STATE_FILE, "r") as f:
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return json.load(f)
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return None
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async def example_basic_state_persistence():
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"""
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Example 1: Basic state persistence with file storage.
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The on_state_change callback is called after each URL is processed,
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allowing you to save progress in real-time.
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"""
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print("\n" + "=" * 60)
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print("Example 1: Basic State Persistence")
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print("=" * 60)
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# Clean up any previous state
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if STATE_FILE.exists():
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STATE_FILE.unlink()
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strategy = BFSDeepCrawlStrategy(
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max_depth=2,
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max_pages=5,
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on_state_change=save_state_to_file, # Save after each URL
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)
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config = CrawlerRunConfig(
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deep_crawl_strategy=strategy,
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verbose=False,
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)
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print("\nStarting crawl with state persistence...")
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async with AsyncWebCrawler(verbose=False) as crawler:
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results = await crawler.arun("https://books.toscrape.com", config=config)
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# Show final state
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if STATE_FILE.exists():
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with open(STATE_FILE, "r") as f:
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final_state = json.load(f)
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print(f"\nFinal state saved to {STATE_FILE}:")
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print(f" - Strategy: {final_state['strategy_type']}")
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print(f" - Pages crawled: {final_state['pages_crawled']}")
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print(f" - URLs visited: {len(final_state['visited'])}")
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print(f" - URLs pending: {len(final_state['pending'])}")
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print(f"\nCrawled {len(results)} pages total")
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async def example_crash_and_resume():
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"""
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Example 2: Simulate a crash and resume from checkpoint.
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This demonstrates the full crash recovery workflow:
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1. Start crawling with state persistence
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2. "Crash" after N pages
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3. Resume from saved state
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4. Verify no duplicate work
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"""
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print("\n" + "=" * 60)
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print("Example 2: Crash and Resume")
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print("=" * 60)
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# Clean up any previous state
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if STATE_FILE.exists():
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STATE_FILE.unlink()
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crash_after = 3
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crawled_urls_phase1: List[str] = []
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async def save_and_maybe_crash(state: Dict[str, Any]) -> None:
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"""Save state, then simulate crash after N pages."""
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# Always save state first
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await save_state_to_file(state)
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crawled_urls_phase1.clear()
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crawled_urls_phase1.extend(state["visited"])
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# Simulate crash after reaching threshold
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if state["pages_crawled"] >= crash_after:
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raise Exception("Simulated crash! (This is intentional)")
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# Phase 1: Start crawl that will "crash"
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print(f"\n--- Phase 1: Crawl until 'crash' after {crash_after} pages ---")
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strategy1 = BFSDeepCrawlStrategy(
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max_depth=2,
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max_pages=10,
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on_state_change=save_and_maybe_crash,
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)
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config = CrawlerRunConfig(
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deep_crawl_strategy=strategy1,
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verbose=False,
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)
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try:
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async with AsyncWebCrawler(verbose=False) as crawler:
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await crawler.arun("https://books.toscrape.com", config=config)
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except Exception as e:
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print(f"\n Crash occurred: {e}")
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print(f" URLs crawled before crash: {len(crawled_urls_phase1)}")
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# Phase 2: Resume from checkpoint
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print("\n--- Phase 2: Resume from checkpoint ---")
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saved_state = load_state_from_file()
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if not saved_state:
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print(" ERROR: No saved state found!")
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return
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print(f" Loaded state: {saved_state['pages_crawled']} pages, {len(saved_state['pending'])} pending")
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crawled_urls_phase2: List[str] = []
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async def track_resumed_crawl(state: Dict[str, Any]) -> None:
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"""Track new URLs crawled in phase 2."""
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await save_state_to_file(state)
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new_urls = set(state["visited"]) - set(saved_state["visited"])
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for url in new_urls:
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if url not in crawled_urls_phase2:
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crawled_urls_phase2.append(url)
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strategy2 = BFSDeepCrawlStrategy(
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max_depth=2,
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max_pages=10,
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resume_state=saved_state, # Resume from checkpoint!
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on_state_change=track_resumed_crawl,
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)
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config2 = CrawlerRunConfig(
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deep_crawl_strategy=strategy2,
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verbose=False,
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)
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async with AsyncWebCrawler(verbose=False) as crawler:
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results = await crawler.arun("https://books.toscrape.com", config=config2)
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# Verify no duplicates
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already_crawled = set(saved_state["visited"])
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duplicates = set(crawled_urls_phase2) & already_crawled
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print(f"\n--- Results ---")
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print(f" Phase 1 URLs: {len(crawled_urls_phase1)}")
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print(f" Phase 2 new URLs: {len(crawled_urls_phase2)}")
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print(f" Duplicate crawls: {len(duplicates)} (should be 0)")
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print(f" Total results: {len(results)}")
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if len(duplicates) == 0:
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print("\n SUCCESS: No duplicate work after resume!")
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else:
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print(f"\n WARNING: Found duplicates: {duplicates}")
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async def example_export_state():
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"""
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Example 3: Manual state export using export_state().
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If you don't need real-time persistence, you can export
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the state manually after the crawl completes.
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"""
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print("\n" + "=" * 60)
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print("Example 3: Manual State Export")
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print("=" * 60)
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strategy = BFSDeepCrawlStrategy(
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max_depth=1,
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max_pages=3,
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# No callback - state is still tracked internally
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)
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config = CrawlerRunConfig(
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deep_crawl_strategy=strategy,
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verbose=False,
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)
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print("\nCrawling without callback...")
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async with AsyncWebCrawler(verbose=False) as crawler:
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results = await crawler.arun("https://books.toscrape.com", config=config)
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# Export state after crawl completes
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# Note: This only works if on_state_change was set during crawl
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# For this example, we'd need to set on_state_change to get state
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print(f"\nCrawled {len(results)} pages")
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print("(For manual export, set on_state_change to capture state)")
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async def example_state_structure():
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"""
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Example 4: Understanding the state structure.
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Shows the complete state dictionary that gets saved.
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"""
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print("\n" + "=" * 60)
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print("Example 4: State Structure")
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print("=" * 60)
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captured_state = None
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async def capture_state(state: Dict[str, Any]) -> None:
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nonlocal captured_state
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captured_state = state
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strategy = BFSDeepCrawlStrategy(
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max_depth=1,
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max_pages=2,
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on_state_change=capture_state,
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)
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config = CrawlerRunConfig(
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deep_crawl_strategy=strategy,
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verbose=False,
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)
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async with AsyncWebCrawler(verbose=False) as crawler:
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await crawler.arun("https://books.toscrape.com", config=config)
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if captured_state:
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print("\nState structure:")
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print(json.dumps(captured_state, indent=2, default=str)[:1000] + "...")
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print("\n\nKey fields:")
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print(f" strategy_type: '{captured_state['strategy_type']}'")
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print(f" visited: List of {len(captured_state['visited'])} URLs")
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print(f" pending: List of {len(captured_state['pending'])} queued items")
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print(f" depths: Dict mapping URL -> depth level")
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print(f" pages_crawled: {captured_state['pages_crawled']}")
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async def main():
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"""Run all examples."""
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print("=" * 60)
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print("Deep Crawl Crash Recovery Examples")
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print("=" * 60)
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await example_basic_state_persistence()
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await example_crash_and_resume()
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await example_state_structure()
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# # Cleanup
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# if STATE_FILE.exists():
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# STATE_FILE.unlink()
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# print(f"\n[Cleaned up {STATE_FILE}]")
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if __name__ == "__main__":
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asyncio.run(main())
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Block a user