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157 Commits

Author SHA1 Message Date
ntohidi
a5354f267a Merge branch 'develop' into release/v0.8.0 2026-01-16 11:34:24 +01:00
unclecode
6090629ee0 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.
2026-01-16 09:56:38 +00:00
unclecode
a00da6557b 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)
2026-01-16 07:05:48 +00:00
ntohidi
177e298af0 Update security researcher acknowledgment with a hyperlink for Neo by ProjectDiscovery 2026-01-14 14:19:23 +01:00
ntohidi
f09146c435 Release v0.8.0: The v0.8.0 Update
- Updated version to 0.8.0
- Added comprehensive demo and release notes
- Updated all documentation
2026-01-14 13:46:42 +01:00
ntohidi
315eae9e6f Add examples for deep crawl crash recovery and prefetch mode in documentation 2026-01-14 12:58:44 +01:00
unclecode
530cde351f 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
2026-01-12 13:45:42 +00:00
ntohidi
122b4fe3f0 Add release notes for v0.7.9, detailing breaking changes, security fixes, new features, bug fixes, and documentation updates 2026-01-12 13:46:39 +01:00
ntohidi
acfab80dd4 Enhance authentication flow by implementing JWT token retrieval and adding authorization headers to API requests 2026-01-12 13:46:32 +01:00
unclecode
f24396c23e 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)
2026-01-12 04:14:37 +00:00
unclecode
6b2dca76c3 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
2026-01-04 12:50:08 +00:00
unclecode
0d3f9e65b0 Add MEMORY.md to gitignore 2025-12-30 03:04:30 +00:00
unclecode
db61ab8559 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
2025-12-30 03:03:41 +00:00
unclecode
3d78001c30 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
2025-12-30 01:59:09 +00:00
unclecode
2550f3d2d5 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
2025-12-27 12:32:42 +00:00
unclecode
a43256b27a Add proxy support to HTTP crawler strategy 2025-12-26 13:17:28 +00:00
unclecode
9e7f5aa44b Updates on proxy rotation and proxy configuration 2025-12-26 12:45:57 +00:00
unclecode
fde4e9f0c6 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>
2025-12-25 01:55:08 +00:00
unclecode
3937efcf0b 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)
2025-12-24 06:05:55 +00:00
unclecode
624e34164d 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.
2025-12-24 04:31:57 +00:00
unclecode
31ebf37252 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.
2025-12-22 14:51:10 +00:00
unclecode
67e03d64b8 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
2025-12-22 01:24:51 +00:00
unclecode
444cb14f82 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.
2025-12-22 01:10:20 +00:00
unclecode
48426f73f0 Some debugging for caching 2025-12-21 04:48:03 +00:00
unclecode
f6b29a8f9f Update gitignore 2025-12-21 04:48:03 +00:00
unclecode
02acad1dc6 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.
2025-12-18 22:04:52 +08:00
unclecode
d10ca38599 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
2025-12-14 01:58:11 +00:00
unclecode
ecedb6113e 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.
2025-12-13 08:58:21 +00:00
unclecode
55eb968a8d 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.
2025-12-13 08:29:05 +00:00
unclecode
6185d3cb32 Revert context matching attempts - Playwright cannot see CDP-created contexts 2025-12-13 07:57:29 +00:00
unclecode
8014805c17 Fix: use CDP to find context by browserContextId for concurrent sessions 2025-12-13 07:02:23 +00:00
unclecode
c1e485e0b0 Fix: use target_id to find correct page in get_page 2025-12-13 06:51:54 +00:00
unclecode
b2e4a1f2e3 Fix: find context by target_id for concurrent CDP connections 2025-12-13 06:41:13 +00:00
unclecode
d22825eea4 Fix: add cdp_cleanup_on_close to from_kwargs 2025-12-13 06:33:26 +00:00
unclecode
66941a59e8 Add cdp_cleanup_on_close flag to prevent memory leaks in cloud/server scenarios 2025-12-13 06:25:25 +00:00
unclecode
8ae908bede 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
2025-12-13 02:42:48 +00:00
ntohidi
306ddcbf3d Merge branch 'main' into develop 2025-12-11 11:18:30 +01:00
Nasrin
a87e8c1c9e Release/v0.7.8 (#1662)
* 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

* announcement: add application form for cloud API closed beta

* Release v0.7.8: Stability & Bug Fix Release

- Updated version to 0.7.8
- Introduced focused stability release addressing 11 community-reported bugs.
- Key fixes include Docker API improvements, LLM extraction enhancements, URL handling corrections, and dependency updates.
- Added detailed release notes for v0.7.8 in the blog and created a dedicated verification script to ensure all fixes are functioning as intended.
- Updated documentation to reflect recent changes and improvements.

* docs: add section for Crawl4AI Cloud API closed beta with application link

* fix: add disk cleanup step to Docker workflow

---------

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: Aravind Karnam <aravind.karanam@gmail.com>
2025-12-11 11:04:52 +01:00
UncleCode
835e3c56fe Add disk cleanup step in Docker release workflow
Added a step to free up disk space before the build process.
2025-12-11 09:49:27 +01:00
Nasrin
5a8fb57795 Merge pull request #1648 from christopher-w-murphy/fix/content-relevance-filter
[Fix]: Docker server does not decode ContentRelevanceFilter
2025-12-03 18:36:07 +08:00
ntohidi
df4d87ed78 refactor: replace PyPDF2 with pypdf across the codebase. ref #1412 2025-12-03 10:59:18 +01:00
Nasrin
f32cfc6db0 Merge pull request #1645 from unclecode/fix/configurable-backoff
Make LLM backoff configurable end-to-end
2025-12-02 21:07:49 +08:00
Nasrin
d06c39e8ab Merge pull request #1641 from unclecode/fix/serialize-proxy-config
Fix BrowserConfig proxy_config serialization
2025-12-02 21:06:02 +08:00
ntohidi
afc31e144a Merge branch 'develop' of https://github.com/unclecode/crawl4ai into develop 2025-12-02 13:01:11 +01:00
ntohidi
07ccf13be6 Fix: capture current page URL to reflect JavaScript navigation and add test for delayed redirects. ref #1268 2025-12-02 13:00:54 +01:00
Aravind
3a07c5962c Sponsors/new (#1643) 2025-12-02 00:49:39 +01:00
Chris Murphy
6893094f58 parameterized tests 2025-12-01 16:19:19 -05:00
Chris Murphy
3a8f8298d3 import modules from enhanceable deserialization 2025-12-01 16:18:59 -05:00
Chris Murphy
e95e8e1a97 generalized query in ContentRelevanceFilter to be a str or list 2025-12-01 16:16:31 -05:00
Chris Murphy
eb76df2c0d added missing deep crawling objects to init 2025-12-01 16:15:58 -05:00
Chris Murphy
6ec6bc4d8a pass timeout parameter to docker client request 2025-12-01 16:15:27 -05:00
Chris Murphy
33a3cc3933 reproduced AttributeError from #1642 2025-12-01 11:31:07 -05:00
Soham Kukreti
7a133e22cc 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
2025-11-28 18:50:04 +05:30
Nasrin
dcb77c94bf Merge pull request #1623 from unclecode/fix/deprecated_pydantic
Refactor Pydantic model configuration to use ConfigDict for arbitrary…
2025-11-27 20:05:42 +08:00
Soham Kukreti
a0c5f0f79a fix: ensure BrowserConfig.to_dict serializes proxy_config 2025-11-26 17:44:06 +05:30
ntohidi
b36c6daa5c Fix: permission issues with .cache/url_seeder and other runtime cache dirs. ref #1638 2025-11-25 11:51:59 +01:00
Nasrin
94c8a833bf Merge pull request #1447 from rbushri/fix/wrong_url_raw
Fix: Wrong URL variable used for extraction of raw html
2025-11-25 17:49:44 +08:00
ntohidi
84bfea8bd1 Fix EmbeddingStrategy: Uncomment response handling for the variations and clean up mock data. ref #1621 2025-11-25 10:46:00 +01:00
Aravind
0024c82cdc Sponsors/new (#1637) 2025-11-24 13:29:33 +01:00
Rachel Bushrian
7771ed3894 Merge branch 'develop' into fix/wrong_url_raw 2025-11-24 13:54:07 +02:00
AHMET YILMAZ
eca04b0368 Refactor Pydantic model configuration to use ConfigDict for arbitrary types 2025-11-18 15:40:17 +08:00
ntohidi
c2c4d42be4 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.
2025-11-17 12:21:23 +01:00
Aravind
f68e7531e3 Sponsors/scrapeless (#1619) 2025-11-17 07:44:52 +01:00
UncleCode
cb637fb5c4 Merge pull request #1613 from unclecode/release/v0.7.7 2025-11-16 12:26:54 +01:00
ntohidi
6244f56f36 Release v0.7.7
- Updated version to 0.7.7
- Added comprehensive demo and release notes
- Updated all documentation
2025-11-14 10:23:31 +01:00
ntohidi
2c973b1183 Merge branch 'develop' into release/v0.7.7 2025-11-13 14:54:05 +01:00
Nasrin
f3146de969 Merge pull request #1609 from unclecode/fix/update-config-documentation
Update browser and crawler run config documentation to match async_configs.py implementation
2025-11-13 21:52:53 +08:00
Soham Kukreti
d6b6d11a2d docs: update browser and crawler run config documentation to match async_configs.py implementation
Updated browser-crawler-config.md and parameters.md to ensure complete
accuracy with the actual BrowserConfig and CrawlerRunConfig implementations.

Changes:
- Removed non-existent parameters from documentation:
  * enable_rate_limiting, rate_limit_config (never implemented)
  * memory_threshold_percent, check_interval, max_session_permit (internal to AsyncDispatcher)
  * display_mode (doesn't exist)

- Added missing BrowserConfig parameters (14 total):
  * browser_mode, use_managed_browser, cdp_url, debugging_port, host
  * viewport, chrome_channel, channel
  * accept_downloads, downloads_path, storage_state, sleep_on_close
  * user_agent_mode, user_agent_generator_config, enable_stealth

- Added missing CrawlerRunConfig parameters (29 total):
  * chunking_strategy, keep_attrs, parser_type, scraping_strategy
  * proxy_config, proxy_rotation_strategy
  * locale, timezone_id, geolocation, fetch_ssl_certificate
  * shared_data, wait_for_timeout
  * c4a_script, max_scroll_steps
  * exclude_all_images, table_score_threshold, table_extraction
  * exclude_internal_links, score_links
  * capture_network_requests, capture_console_messages
  * method, stream, url, user_agent, user_agent_mode, user_agent_generator_config
  * deep_crawl_strategy, link_preview_config, url_matcher, match_mode, experimental

- Marked deprecated cache parameters (bypass_cache, disable_cache, no_cache_read, no_cache_write)
- Reorganized parameters into logical sections (Content Processing, Browser Location & Identity,
  Caching & Session, Page Navigation & Timing, Page Interaction, Media Handling, Link/Domain
  Handling, Debug & Logging, Connection & HTTP, Virtual Scroll, URL Matching, Advanced Features)
- Ensured all parameter descriptions match source code docstrings
- Added proper default values from __init__ signatures
2025-11-13 14:54:16 +05:30
ntohidi
b58579548c Bump version to 0.7.7 for stable release 2025-11-13 09:52:18 +01:00
Nasrin
466be69e72 Merge pull request #1607 from unclecode/fix/dfs_deep_crawling
Fix/dfs deep crawling
2025-11-13 16:43:47 +08:00
AHMET YILMAZ
ceade853c3 Enhance DFSDeepCrawlStrategy documentation for clarity and detail 2025-11-13 16:39:08 +08:00
ntohidi
998c809e08 Rename folder name for NSTProxy integration examples for crawl4ai 2025-11-13 09:36:39 +01:00
ntohidi
d0fb53540d Update proxy-security documentation 2025-11-13 09:23:44 +01:00
Nasrin
8116b15b63 Merge pull request #1596 from unclecode/docs-proxy-security
#1591 enhance proxy configuration with security, SSL analysis, and rotation examples
2025-11-13 16:22:28 +08:00
AHMET YILMAZ
fe353c4e27 Refactor proxy configuration documentation for clarity and consistency 2025-11-13 11:20:24 +08:00
ntohidi
89cc29fe44 Merge branch 'fix/docker' into develop 2025-11-12 17:06:31 +01:00
Nasrin
cdcb8836b7 Merge pull request #1605 from Nstproxy/feat/nstproxy
feat: Add Nstproxy Proxies
2025-11-12 23:56:14 +08:00
Nasrin
b207ae2848 Merge pull request #1528 from unclecode/fix/managed-browser-cdp-timing
Add CDP endpoint verification with exponential backoff for managed browsers
2025-11-12 23:53:57 +08:00
Nasrin
be00fc3a42 Merge pull request #1598 from unclecode/fix/sitemap_seeder
#1559 :Add tests for sitemap parsing and URL normalization in AsyncUr…
2025-11-12 18:09:34 +08:00
Nasrin
124ac583bb Merge pull request #1599 from unclecode/docs-llm-strategies-update
#1551 : Fix casing and variable name consistency for LLMConfig in doc…
2025-11-12 17:54:26 +08:00
AHMET YILMAZ
1bd3de6a47 #1510 : Add DFS deep crawler demonstration script and enhance DFS strategy with seen URL tracking 2025-11-12 17:44:43 +08:00
nstproxy
80452166c8 feat: Add Nstproxy Proxies 2025-11-12 16:25:39 +08:00
UncleCode
a99cd37c0e Merge pull request #1597 from unclecode/sponsors/capsolver 2025-11-11 14:50:44 +08:00
AHMET YILMAZ
2e8f8c9b49 #1551 : Fix casing and variable name consistency for LLMConfig in documentation 2025-11-10 15:38:14 +08:00
AHMET YILMAZ
80745bceb9 #1559 :Add tests for sitemap parsing and URL normalization in AsyncUrlSeeder 2025-11-10 14:15:54 +08:00
Aravind Karnam
4bee230c37 docs: Add a tip for captcha solving usecases using a third party integration 2025-11-10 11:20:48 +05:30
Aravind
006e29f308 Merge pull request #1589 from capsolver/main
Add some examples of using capsolver to solve captcha
2025-11-10 10:45:16 +05:30
AHMET YILMAZ
263ac890fd #1591
: Enhance proxy configuration documentation with security features, SSL analysis, and improved examples
2025-11-10 11:42:07 +08:00
Nasrin
d56b0eb9a9 Merge pull request #1495 from unclecode/fix/viewport_in_managed_browser
feat(ManagedBrowser): add viewport size configuration for browser launch
2025-11-06 18:42:45 +08:00
Nasrin
66175e132b Merge pull request #1590 from unclecode/fix/async-llm-extraction-arunMany
This commit resolves issue #1055 where LLM extraction was blocking async
2025-11-06 18:40:42 +08:00
ntohidi
a30548a98f This commit resolves issue #1055 where LLM extraction was blocking async
execution, causing URLs to be processed sequentially instead of in parallel.

  Changes:
  - Added aperform_completion_with_backoff() using litellm.acompletion for async LLM calls
  - Implemented arun() method in ExtractionStrategy base class with thread pool fallback
  - Created async arun() and aextract() methods in LLMExtractionStrategy using asyncio.gather
  - Updated AsyncWebCrawler.arun() to detect and use arun() when available
  - Added comprehensive test suite to verify parallel execution

  Impact:
  - LLM extraction now runs truly in parallel across multiple URLs
  - Significant performance improvement for multi-URL crawls with LLM strategies
  - Backward compatible - existing extraction strategies continue to work
  - No breaking changes to public API

  Technical details:
  - Uses litellm.acompletion for non-blocking LLM calls
  - Leverages asyncio.gather for concurrent chunk processing
  - Maintains backward compatibility via asyncio.to_thread fallback
  - Works seamlessly with MemoryAdaptiveDispatcher and other dispatchers
2025-11-06 11:22:45 +01:00
CapSolver
2ae9899eac Clarify CapSolver integration instructions
Updated text for clarity and capitalization.
2025-11-06 15:49:30 +08:00
CapSolver
57aeb70f00 Add CapSolver Captcha Solver 2025-11-06 15:37:31 +08:00
Nasrin
2c918155aa Merge pull request #1529 from unclecode/fix/remove_overlay_elements
Fix remove_overlay_elements functionality by calling injected JS function.
2025-11-06 00:10:32 +08:00
Nasrin
854694ef33 Merge pull request #1537 from unclecode/fix/docker-compose-llm-env
fix(docker): Remove environment variable overrides in docker-compose.yml
2025-11-06 00:07:51 +08:00
Nasrin
6534ece026 Merge pull request #1532 from unclecode/fix/update-documentation
Standardize C4A-Script tutorial, add CLI identity-based crawling, and add sponsorship CTA
2025-11-05 23:37:05 +08:00
Nasrin
89e28d4eee Merge pull request #1558 from unclecode/claude/fix-update-pyopenssl-security-011CUPexU25DkNvoxfu5ZrnB
Claude/fix update pyopenssl security 011 cu pex u25 dk nvoxfu5 zrn b
2025-10-28 17:09:11 +08:00
ntohidi
c0f1865287 feat(api): update marketplace version and build date in root endpoint response 2025-10-26 11:35:39 +01:00
ntohidi
46ef1116c4 fix(app-detail): enhance tab functionality, hide documentation and support tabs in marketplace 2025-10-26 11:21:29 +01:00
Nasrin
4df83893ac Merge pull request #1560 from unclecode/fix/marketplace
Fix/marketplace
2025-10-23 22:17:06 +08:00
ntohidi
13e116610d fix(marketplace): improve app detail page content rendering and UX
Fixed multiple issues with app detail page content display and formatting
2025-10-23 16:12:30 +02:00
Claude
613097d121 test: add verification tests for pyOpenSSL security update
- Add lightweight security test to verify version requirements
- Add comprehensive integration test for crawl4ai functionality
- Tests verify pyOpenSSL >= 25.3.0 and cryptography >= 45.0.7
- All tests passing: security vulnerability is resolved

Related to #1545

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-23 06:57:25 +00:00
Claude
44ef0682b0 fix: update pyOpenSSL to >=25.3.0 to address security vulnerability
- Updates pyOpenSSL from >=24.3.0 to >=25.3.0
- This resolves CVE affecting cryptography package versions >=37.0.0 & <43.0.1
- pyOpenSSL 25.3.0 requires cryptography>=45.0.7, which is above the vulnerable range
- Fixes issue #1545

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-23 06:51:25 +00:00
Nasrin
40173eeb73 Update Docker hooks and Webhook documents (#1557)
* fix(docker-api): migrate to modern datetime library API

Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>

* Fix examples in README.md

* feat(docker): add user-provided hooks support to Docker API

Implements comprehensive hooks functionality allowing users to provide custom Python
functions as strings that execute at specific points in the crawling pipeline.

Key Features:
- Support for all 8 crawl4ai hook points:
  • on_browser_created: Initialize browser settings
  • on_page_context_created: Configure page context
  • before_goto: Pre-navigation setup
  • after_goto: Post-navigation processing
  • on_user_agent_updated: User agent modification handling
  • on_execution_started: Crawl execution initialization
  • before_retrieve_html: Pre-extraction processing
  • before_return_html: Final HTML processing

Implementation Details:
- Created UserHookManager for validation, compilation, and safe execution
- Added IsolatedHookWrapper for error isolation and timeout protection
- AST-based validation ensures code structure correctness
- Sandboxed execution with restricted builtins for security
- Configurable timeout (1-120 seconds) prevents infinite loops
- Comprehensive error handling ensures hooks don't crash main process
- Execution tracking with detailed statistics and logging

API Changes:
- Added HookConfig schema with code and timeout fields
- Extended CrawlRequest with optional hooks parameter
- Added /hooks/info endpoint for hook discovery
- Updated /crawl and /crawl/stream endpoints to support hooks

Safety Features:
- Malformed hooks return clear validation errors
- Hook errors are isolated and reported without stopping crawl
- Execution statistics track success/failure/timeout rates
- All hook results are JSON-serializable

Testing:
- Comprehensive test suite covering all 8 hooks
- Error handling and timeout scenarios validated
- Authentication, performance, and content extraction examples
- 100% success rate in production testing

Documentation:
- Added extensive hooks section to docker-deployment.md
- Security warnings about user-provided code risks
- Real-world examples using httpbin.org, GitHub, BBC
- Best practices and troubleshooting guide

ref #1377

* fix(deep-crawl): BestFirst priority inversion; remove pre-scoring truncation. ref #1253

  Use negative scores in PQ to visit high-score URLs first and drop link cap prior to scoring; add test for ordering.

* docs: Update URL seeding examples to use proper async context managers
- Wrap all AsyncUrlSeeder usage with async context managers
- Update URL seeding adventure example to use "sitemap+cc" source, focus on course posts, and add stream=True parameter to fix runtime error

* fix(crawler): Removed the incorrect reference in browser_config variable #1310

* docs: update Docker instructions to use the latest release tag

* fix(docker): Fix LLM API key handling for multi-provider support

Previously, the system incorrectly used OPENAI_API_KEY for all LLM providers
due to a hardcoded api_key_env fallback in config.yml. This caused authentication
errors when using non-OpenAI providers like Gemini.

Changes:
- Remove api_key_env from config.yml to let litellm handle provider-specific env vars
- Simplify get_llm_api_key() to return None, allowing litellm to auto-detect keys
- Update validate_llm_provider() to trust litellm's built-in key detection
- Update documentation to reflect the new automatic key handling

The fix leverages litellm's existing capability to automatically find the correct
environment variable for each provider (OPENAI_API_KEY, GEMINI_API_TOKEN, etc.)
without manual configuration.

ref #1291

* docs: update adaptive crawler docs and cache defaults; remove deprecated examples (#1330)
- Replace BaseStrategy with CrawlStrategy in custom strategy examples (DomainSpecificStrategy, HybridStrategy)
- Remove “Custom Link Scoring” and “Caching Strategy” sections no longer aligned with current library
- Revise memory pruning example to use adaptive.get_relevant_content and index-based retention of top 500 docs
- Correct Quickstart note: default cache mode is CacheMode.BYPASS; instruct enabling with CacheMode.ENABLED

* fix(utils): Improve URL normalization by avoiding quote/unquote to preserve '+' signs. ref #1332

* feat: Add comprehensive website to API example with frontend

This commit adds a complete, web scraping API example that demonstrates how to get structured data from any website and use it like an API using the crawl4ai library with a minimalist frontend interface.

Core Functionality
- AI-powered web scraping with plain English queries
- Dual scraping approaches: Schema-based (faster) and LLM-based (flexible)
- Intelligent schema caching for improved performance
- Custom LLM model support with API key management
- Automatic duplicate request prevention

Modern Frontend Interface
- Minimalist black-and-white design inspired by modern web apps
- Responsive layout with smooth animations and transitions
- Three main pages: Scrape Data, Models Management, API Request History
- Real-time results display with JSON formatting
- Copy-to-clipboard functionality for extracted data
- Toast notifications for user feedback
- Auto-scroll to results when scraping starts

Model Management System
- Web-based model configuration interface
- Support for any LLM provider (OpenAI, Gemini, Anthropic, etc.)
- Simplified configuration requiring only provider and API token
- Add, list, and delete model configurations
- Secure storage of API keys in local JSON files

API Request History
- Automatic saving of all API requests and responses
- Display of request history with URL, query, and cURL commands
- Duplicate prevention (same URL + query combinations)
- Request deletion functionality
- Clean, simplified display focusing on essential information

Technical Implementation

Backend (FastAPI)
- RESTful API with comprehensive endpoints
- Pydantic models for request/response validation
- Async web scraping with crawl4ai library
- Error handling with detailed error messages
- File-based storage for models and request history

Frontend (Vanilla JS/CSS/HTML)
- No framework dependencies - pure HTML, CSS, JavaScript
- Modern CSS Grid and Flexbox layouts
- Custom dropdown styling with SVG arrows
- Responsive design for mobile and desktop
- Smooth scrolling and animations

Core Library Integration
- WebScraperAgent class for orchestration
- ModelConfig class for LLM configuration management
- Schema generation and caching system
- LLM extraction strategy support
- Browser configuration with headless mode

* fix(dependencies): add cssselect to project dependencies

Fixes bug reported in issue #1405
[Bug]: Excluded selector (excluded_selector) doesn't work

This commit reintroduces the cssselect library which was removed by PR (https://github.com/unclecode/crawl4ai/pull/1368) and merged via (437395e490).

Integration tested against 0.7.4 Docker container. Reintroducing cssselector package eliminated errors seen in logs and excluded_selector functionality was restored.

Refs: #1405

* fix(docker): resolve filter serialization and JSON encoding errors in deep crawl strategy (ref #1419)

  - Fix URLPatternFilter serialization by preventing private __slots__ from being serialized as constructor params
  - Add public attributes to URLPatternFilter to store original constructor parameters for proper serialization
  - Handle property descriptors in CrawlResult.model_dump() to prevent JSON serialization errors
  - Ensure filter chains work correctly with Docker client and REST API

  The issue occurred because:
  1. Private implementation details (_simple_suffixes, etc.) were being serialized and passed as constructor arguments during deserialization
  2. Property descriptors were being included in the serialized output, causing "Object of type property is not JSON serializable" errors

  Changes:
  - async_configs.py: Comment out __slots__ serialization logic (lines 100-109)
  - filters.py: Add patterns, use_glob, reverse to URLPatternFilter __slots__ and store as public attributes
  - models.py: Convert property descriptors to strings in model_dump() instead of including them directly

* fix(logger): ensure logger is a Logger instance in crawling strategies. ref #1437

* feat(docker): Add temperature and base_url parameters for LLM configuration. ref #1035

  Implement hierarchical configuration for LLM parameters with support for:
  - Temperature control (0.0-2.0) to adjust response creativity
  - Custom base_url for proxy servers and alternative endpoints
  - 4-tier priority: request params > provider env > global env > defaults

  Add helper functions in utils.py, update API schemas and handlers,
  support environment variables (LLM_TEMPERATURE, OPENAI_TEMPERATURE, etc.),
  and provide comprehensive documentation with examples.

* feat(docker): improve docker error handling
- Return comprehensive error messages along with status codes for api internal errors.
- Fix fit_html property serialization issue in both /crawl and /crawl/stream endpoints
- Add sanitization to ensure fit_html is always JSON-serializable (string or None)
- Add comprehensive error handling test suite.

* #1375 : refactor(proxy) Deprecate 'proxy' parameter in BrowserConfig and enhance proxy string parsing

- 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.

* Remove deprecated test for 'proxy' parameter in BrowserConfig and update .gitignore to include test_scripts directory.

* feat: add preserve_https_for_internal_links flag to maintain HTTPS during crawling. Ref #1410

Added a new `preserve_https_for_internal_links` configuration flag that preserves the original HTTPS scheme for same-domain links even when the server redirects to HTTP.

* feat: update documentation for preserve_https_for_internal_links. ref #1410

* fix: drop Python 3.9 support and require Python >=3.10.
The library no longer supports Python 3.9 and so it was important to drop all references to python 3.9.
Following changes have been made:
- pyproject.toml: set requires-python to ">=3.10"; remove 3.9 classifier
- setup.py: set python_requires to ">=3.10"; remove 3.9 classifier
- docs: update Python version mentions
  - deploy/docker/c4ai-doc-context.md: options -> 3.10, 3.11, 3.12, 3.13

* issue #1329 refactor(crawler): move unwanted properties to CrawlerRunConfig class

* fix(auth): fixed Docker JWT authentication. ref #1442

* remove: delete unused yoyo snapshot subproject

* fix: raise error on last attempt failure in perform_completion_with_backoff. ref #989

* Commit without API

* fix: update option labels in request builder for clarity

* fix: allow custom LLM providers for adaptive crawler embedding config. ref: #1291

  - Change embedding_llm_config from Dict to Union[LLMConfig, Dict] for type safety
  - Add backward-compatible conversion property _embedding_llm_config_dict
  - Replace all hardcoded OpenAI embedding configs with configurable options
  - Fix LLMConfig object attribute access in query expansion logic
  - Add comprehensive example demonstrating multiple provider configurations
  - Update documentation with both LLMConfig object and dictionary usage patterns

  Users can now specify any LLM provider for query expansion in embedding strategy:
  - New: embedding_llm_config=LLMConfig(provider='anthropic/claude-3', api_token='key')
  - Old: embedding_llm_config={'provider': 'openai/gpt-4', 'api_token': 'key'} (still works)

* refactor(BrowserConfig): change deprecation warning for 'proxy' parameter to UserWarning

* feat(StealthAdapter): fix stealth features for Playwright integration. ref #1481

* #1505 fix(api): update config handling to only set base config if not provided by user

* fix(docker-deployment): replace console.log with print for metadata extraction

* Release v0.7.5: The Update

- Updated version to 0.7.5
- Added comprehensive demo and release notes
- Updated documentation

* refactor(release): remove memory management section for cleaner documentation. ref #1443

* feat(docs): add brand book and page copy functionality

- Add comprehensive brand book with color system, typography, components
- Add page copy dropdown with markdown copy/view functionality
- Update mkdocs.yml with new assets and branding navigation
- Use terminal-style ASCII icons and condensed menu design

* Update gitignore add local scripts folder

* fix: remove this import as it causes python to treat "json" as a variable in the except block

* fix: always return a list, even if we catch an exception

* feat(marketplace): Add Crawl4AI marketplace with secure configuration

- Implement marketplace frontend and admin dashboard
- Add FastAPI backend with environment-based configuration
- Use .env file for secrets management
- Include data generation scripts
- Add proper CORS configuration
- Remove hardcoded password from admin login
- Update gitignore for security

* fix(marketplace): Update URLs to use /marketplace path and relative API endpoints

- Change API_BASE to relative '/api' for production
- Move marketplace to /marketplace instead of /marketplace/frontend
- Update MkDocs navigation
- Fix logo path in marketplace index

* fix(docs): hide copy menu on non-markdown pages

* feat(marketplace): add sponsor logo uploads

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>

* feat(docs): add chatgpt quick link to page actions

* fix(marketplace): align admin api with backend endpoints

* fix(marketplace): isolate api under marketplace prefix

* fix(marketplace): resolve app detail page routing and styling issues

- Fixed JavaScript errors from missing HTML elements (install-code, usage-code, integration-code)
- Added missing CSS classes for tabs, overview layout, sidebar, and integration content
- Fixed tab navigation to display horizontally in single line
- Added proper padding to tab content sections (removed from container, added to content)
- Fixed tab selector from .nav-tab to .tab-btn to match HTML structure
- Added sidebar styling with stats grid and metadata display
- Improved responsive design with mobile-friendly tab scrolling
- Fixed code block positioning for copy buttons
- Removed margin from first headings to prevent extra spacing
- Added null checks for DOM elements in JavaScript to prevent errors

These changes resolve the routing issue where clicking on apps caused page redirects,
and fix the broken layout where CSS was not properly applied to the app detail page.

* fix(marketplace): prevent hero image overflow and secondary card stretching

- Fixed hero image to 200px height with min/max constraints
- Added object-fit: cover to hero-image img elements
- Changed secondary-featured align-items from stretch to flex-start
- Fixed secondary-card height to 118px (no flex: 1 stretching)
- Updated responsive grid layouts for wider screens
- Added flex: 1 to hero-content for better content distribution

These changes ensure a rigid, predictable layout that prevents:
1. Large images from pushing text content down
2. Single secondary cards from stretching to fill entire height

* feat: Add hooks utility for function-based hooks with Docker client integration. ref #1377

   Add hooks_to_string() utility function that converts Python function objects
   to string representations for the Docker API, enabling developers to write hooks
   as regular Python functions instead of strings.

   Core Changes:
   - New hooks_to_string() utility in crawl4ai/utils.py using inspect.getsource()
   - Docker client now accepts both function objects and strings for hooks
   - Automatic detection and conversion in Crawl4aiDockerClient._prepare_request()
   - New hooks and hooks_timeout parameters in client.crawl() method

   Documentation:
   - Docker client examples with function-based hooks (docs/examples/docker_client_hooks_example.py)
   - Updated main Docker deployment guide with comprehensive hooks section
   - Added unit tests for hooks utility (tests/docker/test_hooks_utility.py)

* feat: Add hooks utility for function-based hooks with Docker client integration. ref #1377

   Add hooks_to_string() utility function that converts Python function objects
   to string representations for the Docker API, enabling developers to write hooks
   as regular Python functions instead of strings.

   Core Changes:
   - New hooks_to_string() utility in crawl4ai/utils.py using inspect.getsource()
   - Docker client now accepts both function objects and strings for hooks
   - Automatic detection and conversion in Crawl4aiDockerClient._prepare_request()
   - New hooks and hooks_timeout parameters in client.crawl() method

   Documentation:
   - Docker client examples with function-based hooks (docs/examples/docker_client_hooks_example.py)
   - Updated main Docker deployment guide with comprehensive hooks section
   - Added unit tests for hooks utility (tests/docker/test_hooks_utility.py)

* fix(docs): clarify Docker Hooks System with function-based API in README

* docs: Add demonstration files for v0.7.5 release, showcasing the new Docker Hooks System and all other features.

* docs: Update 0.7.5 video walkthrough

* docs: add complete SDK reference documentation

Add comprehensive single-page SDK reference combining:
- Installation & Setup
- Quick Start
- Core API (AsyncWebCrawler, arun, arun_many, CrawlResult)
- Configuration (BrowserConfig, CrawlerConfig, Parameters)
- Crawling Patterns
- Content Processing (Markdown, Fit Markdown, Selection, Interaction, Link & Media)
- Extraction Strategies (LLM and No-LLM)
- Advanced Features (Session Management, Hooks & Auth)

Generated using scripts/generate_sdk_docs.py in ultra-dense mode
optimized for AI assistant consumption.

Stats: 23K words, 185 code blocks, 220KB

* feat: add AI assistant skill package for Crawl4AI

- Create comprehensive skill package for AI coding assistants
- Include complete SDK reference (23K words, v0.7.4)
- Add three extraction scripts (basic, batch, pipeline)
- Implement version tracking in skill and scripts
- Add prominent download section on homepage
- Place skill in docs/assets for web distribution

The skill enables AI assistants like Claude, Cursor, and Windsurf
to effectively use Crawl4AI with optimized workflows for markdown
generation and data extraction.

* fix: remove non-existent wiki link and clarify skill usage instructions

* fix: update Crawl4AI skill with corrected parameters and examples

- Fixed CrawlerConfig → CrawlerRunConfig throughout
- Fixed parameter names (timeout → page_timeout, store_html removed)
- Fixed schema format (selector → baseSelector)
- Corrected proxy configuration (in BrowserConfig, not CrawlerRunConfig)
- Fixed fit_markdown usage with content filters
- Added comprehensive references to docs/examples/ directory
- Created safe packaging script to avoid root directory pollution
- All scripts tested and verified working

* fix: thoroughly verify and fix all Crawl4AI skill examples

- Cross-checked every section against actual docs
- Fixed BM25ContentFilter parameters (user_query, bm25_threshold)
- Removed incorrect wait_for selector from basic example
- Added comprehensive test suite (4 test files)
- All examples now tested and verified working
- Tests validate: basic crawling, markdown generation, data extraction, advanced patterns
- Package size: 76.6 KB (includes tests for future validation)

* feat(ci): split release pipeline and add Docker caching

- Split release.yml into PyPI/GitHub release and Docker workflows
- Add GitHub Actions cache for Docker builds (10-15x faster rebuilds)
- Implement dual-trigger for docker-release.yml (auto + manual)
- Add comprehensive workflow documentation in .github/workflows/docs/
- Backup original workflow as release.yml.backup

* feat: add webhook notifications for crawl job completion

Implements webhook support for the crawl job API to eliminate polling requirements.

Changes:
- Added WebhookConfig and WebhookPayload schemas to schemas.py
- Created webhook.py with WebhookDeliveryService class
- Integrated webhook notifications in api.py handle_crawl_job
- Updated job.py CrawlJobPayload to accept webhook_config
- Added webhook configuration section to config.yml
- Included comprehensive usage examples in WEBHOOK_EXAMPLES.md

Features:
- Webhook notifications on job completion (success/failure)
- Configurable data inclusion in webhook payload
- Custom webhook headers support
- Global default webhook URL configuration
- Exponential backoff retry logic (5 attempts: 1s, 2s, 4s, 8s, 16s)
- 30-second timeout per webhook call

Usage:
POST /crawl/job with optional webhook_config:
- webhook_url: URL to receive notifications
- webhook_data_in_payload: include full results (default: false)
- webhook_headers: custom headers for authentication

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: add webhook documentation to Docker README

Added comprehensive webhook section to README.md including:
- Overview of asynchronous job queue with webhooks
- Benefits and use cases
- Quick start examples
- Webhook authentication
- Global webhook configuration
- Job status polling alternative

Updated table of contents and summary to include webhook feature.
Maintains consistent tone and style with rest of README.

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: add webhook example for Docker deployment

Added docker_webhook_example.py demonstrating:
- Submitting crawl jobs with webhook configuration
- Flask-based webhook receiver implementation
- Three usage patterns:
  1. Webhook notification only (fetch data separately)
  2. Webhook with full data in payload
  3. Traditional polling approach for comparison

Includes comprehensive comments explaining:
- Webhook payload structure
- Authentication headers setup
- Error handling
- Production deployment tips

Example is fully functional and ready to run with Flask installed.

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* test: add webhook implementation validation tests

Added comprehensive test suite to validate webhook implementation:
- Module import verification
- WebhookDeliveryService initialization
- Pydantic model validation (WebhookConfig)
- Payload construction logic
- Exponential backoff calculation
- API integration checks

All tests pass (6/6), confirming implementation is correct.

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* test: add comprehensive webhook feature test script

Added end-to-end test script that automates webhook feature testing:

Script Features (test_webhook_feature.sh):
- Automatic branch switching and dependency installation
- Redis and server startup/shutdown management
- Webhook receiver implementation
- Integration test for webhook notifications
- Comprehensive cleanup and error handling
- Returns to original branch after completion

Test Flow:
1. Fetch and checkout webhook feature branch
2. Activate venv and install dependencies
3. Start Redis and Crawl4AI server
4. Submit crawl job with webhook config
5. Verify webhook delivery and payload
6. Clean up all processes and return to original branch

Documentation:
- WEBHOOK_TEST_README.md with usage instructions
- Troubleshooting guide
- Exit codes and safety features

Usage: ./tests/test_webhook_feature.sh

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* fix: properly serialize Pydantic HttpUrl in webhook config

Use model_dump(mode='json') instead of deprecated dict() method to ensure
Pydantic special types (HttpUrl, UUID, etc.) are properly serialized to
JSON-compatible native Python types.

This fixes webhook delivery failures caused by HttpUrl objects remaining
as Pydantic types in the webhook_config dict, which caused JSON
serialization errors and httpx request failures.

Also update mcp requirement to >=1.18.0 for compatibility.

* feat: add webhook support for /llm/job endpoint

Add comprehensive webhook notification support for the /llm/job endpoint,
following the same pattern as the existing /crawl/job implementation.

Changes:
- Add webhook_config field to LlmJobPayload model (job.py)
- Implement webhook notifications in process_llm_extraction() with 4
  notification points: success, provider validation failure, extraction
  failure, and general exceptions (api.py)
- Store webhook_config in Redis task data for job tracking
- Initialize WebhookDeliveryService with exponential backoff retry logic
Documentation:
- Add Example 6 to WEBHOOK_EXAMPLES.md showing LLM extraction with webhooks
- Update Flask webhook handler to support both crawl and llm_extraction tasks
- Add TypeScript client examples for LLM jobs
- Add comprehensive examples to docker_webhook_example.py with schema support
- Clarify data structure differences between webhook and API responses

Testing:
- Add test_llm_webhook_feature.py with 7 validation tests (all passing)
- Verify pattern consistency with /crawl/job implementation
- Add implementation guide (WEBHOOK_LLM_JOB_IMPLEMENTATION.md)

* fix: remove duplicate comma in webhook_config parameter

* fix: update Crawl4AI Docker container port from 11234 to 11235

* docs: enhance README and docker-deployment documentation with Job Queue and Webhook API details

* docs: update docker_hooks_examples.py with comprehensive examples and improved structure

---------

Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: Nezar Ali <abu5sohaib@gmail.com>
Co-authored-by: Soham Kukreti <kukretisoham@gmail.com>
Co-authored-by: James T. Wood <jamesthomaswood@gmail.com>
Co-authored-by: AHMET YILMAZ <tawfik@kidocode.com>
Co-authored-by: nafeqq-1306 <nafiquee@yahoo.com>
Co-authored-by: unclecode <unclecode@kidocode.com>
Co-authored-by: Martin Sjöborg <martin.sjoborg@quartr.se>
Co-authored-by: Martin Sjöborg <martin@sjoborg.org>
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
2025-10-22 22:34:19 +08:00
ntohidi
b74524fdfb docs: update docker_hooks_examples.py with comprehensive examples and improved structure 2025-10-22 16:29:19 +02:00
ntohidi
bcac486921 docs: enhance README and docker-deployment documentation with Job Queue and Webhook API details 2025-10-22 16:19:30 +02:00
ntohidi
6aef5a120f Merge branch 'main' into develop 2025-10-22 15:53:54 +02:00
Nasrin
7cac008c10 Release/v0.7.6 (#1556)
* fix(docker-api): migrate to modern datetime library API

Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>

* Fix examples in README.md

* feat(docker): add user-provided hooks support to Docker API

Implements comprehensive hooks functionality allowing users to provide custom Python
functions as strings that execute at specific points in the crawling pipeline.

Key Features:
- Support for all 8 crawl4ai hook points:
  • on_browser_created: Initialize browser settings
  • on_page_context_created: Configure page context
  • before_goto: Pre-navigation setup
  • after_goto: Post-navigation processing
  • on_user_agent_updated: User agent modification handling
  • on_execution_started: Crawl execution initialization
  • before_retrieve_html: Pre-extraction processing
  • before_return_html: Final HTML processing

Implementation Details:
- Created UserHookManager for validation, compilation, and safe execution
- Added IsolatedHookWrapper for error isolation and timeout protection
- AST-based validation ensures code structure correctness
- Sandboxed execution with restricted builtins for security
- Configurable timeout (1-120 seconds) prevents infinite loops
- Comprehensive error handling ensures hooks don't crash main process
- Execution tracking with detailed statistics and logging

API Changes:
- Added HookConfig schema with code and timeout fields
- Extended CrawlRequest with optional hooks parameter
- Added /hooks/info endpoint for hook discovery
- Updated /crawl and /crawl/stream endpoints to support hooks

Safety Features:
- Malformed hooks return clear validation errors
- Hook errors are isolated and reported without stopping crawl
- Execution statistics track success/failure/timeout rates
- All hook results are JSON-serializable

Testing:
- Comprehensive test suite covering all 8 hooks
- Error handling and timeout scenarios validated
- Authentication, performance, and content extraction examples
- 100% success rate in production testing

Documentation:
- Added extensive hooks section to docker-deployment.md
- Security warnings about user-provided code risks
- Real-world examples using httpbin.org, GitHub, BBC
- Best practices and troubleshooting guide

ref #1377

* fix(deep-crawl): BestFirst priority inversion; remove pre-scoring truncation. ref #1253

  Use negative scores in PQ to visit high-score URLs first and drop link cap prior to scoring; add test for ordering.

* docs: Update URL seeding examples to use proper async context managers
- Wrap all AsyncUrlSeeder usage with async context managers
- Update URL seeding adventure example to use "sitemap+cc" source, focus on course posts, and add stream=True parameter to fix runtime error

* fix(crawler): Removed the incorrect reference in browser_config variable #1310

* docs: update Docker instructions to use the latest release tag

* fix(docker): Fix LLM API key handling for multi-provider support

Previously, the system incorrectly used OPENAI_API_KEY for all LLM providers
due to a hardcoded api_key_env fallback in config.yml. This caused authentication
errors when using non-OpenAI providers like Gemini.

Changes:
- Remove api_key_env from config.yml to let litellm handle provider-specific env vars
- Simplify get_llm_api_key() to return None, allowing litellm to auto-detect keys
- Update validate_llm_provider() to trust litellm's built-in key detection
- Update documentation to reflect the new automatic key handling

The fix leverages litellm's existing capability to automatically find the correct
environment variable for each provider (OPENAI_API_KEY, GEMINI_API_TOKEN, etc.)
without manual configuration.

ref #1291

* docs: update adaptive crawler docs and cache defaults; remove deprecated examples (#1330)
- Replace BaseStrategy with CrawlStrategy in custom strategy examples (DomainSpecificStrategy, HybridStrategy)
- Remove “Custom Link Scoring” and “Caching Strategy” sections no longer aligned with current library
- Revise memory pruning example to use adaptive.get_relevant_content and index-based retention of top 500 docs
- Correct Quickstart note: default cache mode is CacheMode.BYPASS; instruct enabling with CacheMode.ENABLED

* fix(utils): Improve URL normalization by avoiding quote/unquote to preserve '+' signs. ref #1332

* feat: Add comprehensive website to API example with frontend

This commit adds a complete, web scraping API example that demonstrates how to get structured data from any website and use it like an API using the crawl4ai library with a minimalist frontend interface.

Core Functionality
- AI-powered web scraping with plain English queries
- Dual scraping approaches: Schema-based (faster) and LLM-based (flexible)
- Intelligent schema caching for improved performance
- Custom LLM model support with API key management
- Automatic duplicate request prevention

Modern Frontend Interface
- Minimalist black-and-white design inspired by modern web apps
- Responsive layout with smooth animations and transitions
- Three main pages: Scrape Data, Models Management, API Request History
- Real-time results display with JSON formatting
- Copy-to-clipboard functionality for extracted data
- Toast notifications for user feedback
- Auto-scroll to results when scraping starts

Model Management System
- Web-based model configuration interface
- Support for any LLM provider (OpenAI, Gemini, Anthropic, etc.)
- Simplified configuration requiring only provider and API token
- Add, list, and delete model configurations
- Secure storage of API keys in local JSON files

API Request History
- Automatic saving of all API requests and responses
- Display of request history with URL, query, and cURL commands
- Duplicate prevention (same URL + query combinations)
- Request deletion functionality
- Clean, simplified display focusing on essential information

Technical Implementation

Backend (FastAPI)
- RESTful API with comprehensive endpoints
- Pydantic models for request/response validation
- Async web scraping with crawl4ai library
- Error handling with detailed error messages
- File-based storage for models and request history

Frontend (Vanilla JS/CSS/HTML)
- No framework dependencies - pure HTML, CSS, JavaScript
- Modern CSS Grid and Flexbox layouts
- Custom dropdown styling with SVG arrows
- Responsive design for mobile and desktop
- Smooth scrolling and animations

Core Library Integration
- WebScraperAgent class for orchestration
- ModelConfig class for LLM configuration management
- Schema generation and caching system
- LLM extraction strategy support
- Browser configuration with headless mode

* fix(dependencies): add cssselect to project dependencies

Fixes bug reported in issue #1405
[Bug]: Excluded selector (excluded_selector) doesn't work

This commit reintroduces the cssselect library which was removed by PR (https://github.com/unclecode/crawl4ai/pull/1368) and merged via (437395e490).

Integration tested against 0.7.4 Docker container. Reintroducing cssselector package eliminated errors seen in logs and excluded_selector functionality was restored.

Refs: #1405

* fix(docker): resolve filter serialization and JSON encoding errors in deep crawl strategy (ref #1419)

  - Fix URLPatternFilter serialization by preventing private __slots__ from being serialized as constructor params
  - Add public attributes to URLPatternFilter to store original constructor parameters for proper serialization
  - Handle property descriptors in CrawlResult.model_dump() to prevent JSON serialization errors
  - Ensure filter chains work correctly with Docker client and REST API

  The issue occurred because:
  1. Private implementation details (_simple_suffixes, etc.) were being serialized and passed as constructor arguments during deserialization
  2. Property descriptors were being included in the serialized output, causing "Object of type property is not JSON serializable" errors

  Changes:
  - async_configs.py: Comment out __slots__ serialization logic (lines 100-109)
  - filters.py: Add patterns, use_glob, reverse to URLPatternFilter __slots__ and store as public attributes
  - models.py: Convert property descriptors to strings in model_dump() instead of including them directly

* fix(logger): ensure logger is a Logger instance in crawling strategies. ref #1437

* feat(docker): Add temperature and base_url parameters for LLM configuration. ref #1035

  Implement hierarchical configuration for LLM parameters with support for:
  - Temperature control (0.0-2.0) to adjust response creativity
  - Custom base_url for proxy servers and alternative endpoints
  - 4-tier priority: request params > provider env > global env > defaults

  Add helper functions in utils.py, update API schemas and handlers,
  support environment variables (LLM_TEMPERATURE, OPENAI_TEMPERATURE, etc.),
  and provide comprehensive documentation with examples.

* feat(docker): improve docker error handling
- Return comprehensive error messages along with status codes for api internal errors.
- Fix fit_html property serialization issue in both /crawl and /crawl/stream endpoints
- Add sanitization to ensure fit_html is always JSON-serializable (string or None)
- Add comprehensive error handling test suite.

* #1375 : refactor(proxy) Deprecate 'proxy' parameter in BrowserConfig and enhance proxy string parsing

- 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.

* Remove deprecated test for 'proxy' parameter in BrowserConfig and update .gitignore to include test_scripts directory.

* feat: add preserve_https_for_internal_links flag to maintain HTTPS during crawling. Ref #1410

Added a new `preserve_https_for_internal_links` configuration flag that preserves the original HTTPS scheme for same-domain links even when the server redirects to HTTP.

* feat: update documentation for preserve_https_for_internal_links. ref #1410

* fix: drop Python 3.9 support and require Python >=3.10.
The library no longer supports Python 3.9 and so it was important to drop all references to python 3.9.
Following changes have been made:
- pyproject.toml: set requires-python to ">=3.10"; remove 3.9 classifier
- setup.py: set python_requires to ">=3.10"; remove 3.9 classifier
- docs: update Python version mentions
  - deploy/docker/c4ai-doc-context.md: options -> 3.10, 3.11, 3.12, 3.13

* issue #1329 refactor(crawler): move unwanted properties to CrawlerRunConfig class

* fix(auth): fixed Docker JWT authentication. ref #1442

* remove: delete unused yoyo snapshot subproject

* fix: raise error on last attempt failure in perform_completion_with_backoff. ref #989

* Commit without API

* fix: update option labels in request builder for clarity

* fix: allow custom LLM providers for adaptive crawler embedding config. ref: #1291

  - Change embedding_llm_config from Dict to Union[LLMConfig, Dict] for type safety
  - Add backward-compatible conversion property _embedding_llm_config_dict
  - Replace all hardcoded OpenAI embedding configs with configurable options
  - Fix LLMConfig object attribute access in query expansion logic
  - Add comprehensive example demonstrating multiple provider configurations
  - Update documentation with both LLMConfig object and dictionary usage patterns

  Users can now specify any LLM provider for query expansion in embedding strategy:
  - New: embedding_llm_config=LLMConfig(provider='anthropic/claude-3', api_token='key')
  - Old: embedding_llm_config={'provider': 'openai/gpt-4', 'api_token': 'key'} (still works)

* refactor(BrowserConfig): change deprecation warning for 'proxy' parameter to UserWarning

* feat(StealthAdapter): fix stealth features for Playwright integration. ref #1481

* #1505 fix(api): update config handling to only set base config if not provided by user

* fix(docker-deployment): replace console.log with print for metadata extraction

* Release v0.7.5: The Update

- Updated version to 0.7.5
- Added comprehensive demo and release notes
- Updated documentation

* refactor(release): remove memory management section for cleaner documentation. ref #1443

* feat(docs): add brand book and page copy functionality

- Add comprehensive brand book with color system, typography, components
- Add page copy dropdown with markdown copy/view functionality
- Update mkdocs.yml with new assets and branding navigation
- Use terminal-style ASCII icons and condensed menu design

* Update gitignore add local scripts folder

* fix: remove this import as it causes python to treat "json" as a variable in the except block

* fix: always return a list, even if we catch an exception

* feat(marketplace): Add Crawl4AI marketplace with secure configuration

- Implement marketplace frontend and admin dashboard
- Add FastAPI backend with environment-based configuration
- Use .env file for secrets management
- Include data generation scripts
- Add proper CORS configuration
- Remove hardcoded password from admin login
- Update gitignore for security

* fix(marketplace): Update URLs to use /marketplace path and relative API endpoints

- Change API_BASE to relative '/api' for production
- Move marketplace to /marketplace instead of /marketplace/frontend
- Update MkDocs navigation
- Fix logo path in marketplace index

* fix(docs): hide copy menu on non-markdown pages

* feat(marketplace): add sponsor logo uploads

Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>

* feat(docs): add chatgpt quick link to page actions

* fix(marketplace): align admin api with backend endpoints

* fix(marketplace): isolate api under marketplace prefix

* fix(marketplace): resolve app detail page routing and styling issues

- Fixed JavaScript errors from missing HTML elements (install-code, usage-code, integration-code)
- Added missing CSS classes for tabs, overview layout, sidebar, and integration content
- Fixed tab navigation to display horizontally in single line
- Added proper padding to tab content sections (removed from container, added to content)
- Fixed tab selector from .nav-tab to .tab-btn to match HTML structure
- Added sidebar styling with stats grid and metadata display
- Improved responsive design with mobile-friendly tab scrolling
- Fixed code block positioning for copy buttons
- Removed margin from first headings to prevent extra spacing
- Added null checks for DOM elements in JavaScript to prevent errors

These changes resolve the routing issue where clicking on apps caused page redirects,
and fix the broken layout where CSS was not properly applied to the app detail page.

* fix(marketplace): prevent hero image overflow and secondary card stretching

- Fixed hero image to 200px height with min/max constraints
- Added object-fit: cover to hero-image img elements
- Changed secondary-featured align-items from stretch to flex-start
- Fixed secondary-card height to 118px (no flex: 1 stretching)
- Updated responsive grid layouts for wider screens
- Added flex: 1 to hero-content for better content distribution

These changes ensure a rigid, predictable layout that prevents:
1. Large images from pushing text content down
2. Single secondary cards from stretching to fill entire height

* feat: Add hooks utility for function-based hooks with Docker client integration. ref #1377

   Add hooks_to_string() utility function that converts Python function objects
   to string representations for the Docker API, enabling developers to write hooks
   as regular Python functions instead of strings.

   Core Changes:
   - New hooks_to_string() utility in crawl4ai/utils.py using inspect.getsource()
   - Docker client now accepts both function objects and strings for hooks
   - Automatic detection and conversion in Crawl4aiDockerClient._prepare_request()
   - New hooks and hooks_timeout parameters in client.crawl() method

   Documentation:
   - Docker client examples with function-based hooks (docs/examples/docker_client_hooks_example.py)
   - Updated main Docker deployment guide with comprehensive hooks section
   - Added unit tests for hooks utility (tests/docker/test_hooks_utility.py)

* feat: Add hooks utility for function-based hooks with Docker client integration. ref #1377

   Add hooks_to_string() utility function that converts Python function objects
   to string representations for the Docker API, enabling developers to write hooks
   as regular Python functions instead of strings.

   Core Changes:
   - New hooks_to_string() utility in crawl4ai/utils.py using inspect.getsource()
   - Docker client now accepts both function objects and strings for hooks
   - Automatic detection and conversion in Crawl4aiDockerClient._prepare_request()
   - New hooks and hooks_timeout parameters in client.crawl() method

   Documentation:
   - Docker client examples with function-based hooks (docs/examples/docker_client_hooks_example.py)
   - Updated main Docker deployment guide with comprehensive hooks section
   - Added unit tests for hooks utility (tests/docker/test_hooks_utility.py)

* fix(docs): clarify Docker Hooks System with function-based API in README

* docs: Add demonstration files for v0.7.5 release, showcasing the new Docker Hooks System and all other features.

* docs: Update 0.7.5 video walkthrough

* docs: add complete SDK reference documentation

Add comprehensive single-page SDK reference combining:
- Installation & Setup
- Quick Start
- Core API (AsyncWebCrawler, arun, arun_many, CrawlResult)
- Configuration (BrowserConfig, CrawlerConfig, Parameters)
- Crawling Patterns
- Content Processing (Markdown, Fit Markdown, Selection, Interaction, Link & Media)
- Extraction Strategies (LLM and No-LLM)
- Advanced Features (Session Management, Hooks & Auth)

Generated using scripts/generate_sdk_docs.py in ultra-dense mode
optimized for AI assistant consumption.

Stats: 23K words, 185 code blocks, 220KB

* feat: add AI assistant skill package for Crawl4AI

- Create comprehensive skill package for AI coding assistants
- Include complete SDK reference (23K words, v0.7.4)
- Add three extraction scripts (basic, batch, pipeline)
- Implement version tracking in skill and scripts
- Add prominent download section on homepage
- Place skill in docs/assets for web distribution

The skill enables AI assistants like Claude, Cursor, and Windsurf
to effectively use Crawl4AI with optimized workflows for markdown
generation and data extraction.

* fix: remove non-existent wiki link and clarify skill usage instructions

* fix: update Crawl4AI skill with corrected parameters and examples

- Fixed CrawlerConfig → CrawlerRunConfig throughout
- Fixed parameter names (timeout → page_timeout, store_html removed)
- Fixed schema format (selector → baseSelector)
- Corrected proxy configuration (in BrowserConfig, not CrawlerRunConfig)
- Fixed fit_markdown usage with content filters
- Added comprehensive references to docs/examples/ directory
- Created safe packaging script to avoid root directory pollution
- All scripts tested and verified working

* fix: thoroughly verify and fix all Crawl4AI skill examples

- Cross-checked every section against actual docs
- Fixed BM25ContentFilter parameters (user_query, bm25_threshold)
- Removed incorrect wait_for selector from basic example
- Added comprehensive test suite (4 test files)
- All examples now tested and verified working
- Tests validate: basic crawling, markdown generation, data extraction, advanced patterns
- Package size: 76.6 KB (includes tests for future validation)

* feat(ci): split release pipeline and add Docker caching

- Split release.yml into PyPI/GitHub release and Docker workflows
- Add GitHub Actions cache for Docker builds (10-15x faster rebuilds)
- Implement dual-trigger for docker-release.yml (auto + manual)
- Add comprehensive workflow documentation in .github/workflows/docs/
- Backup original workflow as release.yml.backup

* feat: add webhook notifications for crawl job completion

Implements webhook support for the crawl job API to eliminate polling requirements.

Changes:
- Added WebhookConfig and WebhookPayload schemas to schemas.py
- Created webhook.py with WebhookDeliveryService class
- Integrated webhook notifications in api.py handle_crawl_job
- Updated job.py CrawlJobPayload to accept webhook_config
- Added webhook configuration section to config.yml
- Included comprehensive usage examples in WEBHOOK_EXAMPLES.md

Features:
- Webhook notifications on job completion (success/failure)
- Configurable data inclusion in webhook payload
- Custom webhook headers support
- Global default webhook URL configuration
- Exponential backoff retry logic (5 attempts: 1s, 2s, 4s, 8s, 16s)
- 30-second timeout per webhook call

Usage:
POST /crawl/job with optional webhook_config:
- webhook_url: URL to receive notifications
- webhook_data_in_payload: include full results (default: false)
- webhook_headers: custom headers for authentication

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: add webhook documentation to Docker README

Added comprehensive webhook section to README.md including:
- Overview of asynchronous job queue with webhooks
- Benefits and use cases
- Quick start examples
- Webhook authentication
- Global webhook configuration
- Job status polling alternative

Updated table of contents and summary to include webhook feature.
Maintains consistent tone and style with rest of README.

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* docs: add webhook example for Docker deployment

Added docker_webhook_example.py demonstrating:
- Submitting crawl jobs with webhook configuration
- Flask-based webhook receiver implementation
- Three usage patterns:
  1. Webhook notification only (fetch data separately)
  2. Webhook with full data in payload
  3. Traditional polling approach for comparison

Includes comprehensive comments explaining:
- Webhook payload structure
- Authentication headers setup
- Error handling
- Production deployment tips

Example is fully functional and ready to run with Flask installed.

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* test: add webhook implementation validation tests

Added comprehensive test suite to validate webhook implementation:
- Module import verification
- WebhookDeliveryService initialization
- Pydantic model validation (WebhookConfig)
- Payload construction logic
- Exponential backoff calculation
- API integration checks

All tests pass (6/6), confirming implementation is correct.

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* test: add comprehensive webhook feature test script

Added end-to-end test script that automates webhook feature testing:

Script Features (test_webhook_feature.sh):
- Automatic branch switching and dependency installation
- Redis and server startup/shutdown management
- Webhook receiver implementation
- Integration test for webhook notifications
- Comprehensive cleanup and error handling
- Returns to original branch after completion

Test Flow:
1. Fetch and checkout webhook feature branch
2. Activate venv and install dependencies
3. Start Redis and Crawl4AI server
4. Submit crawl job with webhook config
5. Verify webhook delivery and payload
6. Clean up all processes and return to original branch

Documentation:
- WEBHOOK_TEST_README.md with usage instructions
- Troubleshooting guide
- Exit codes and safety features

Usage: ./tests/test_webhook_feature.sh

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>

* fix: properly serialize Pydantic HttpUrl in webhook config

Use model_dump(mode='json') instead of deprecated dict() method to ensure
Pydantic special types (HttpUrl, UUID, etc.) are properly serialized to
JSON-compatible native Python types.

This fixes webhook delivery failures caused by HttpUrl objects remaining
as Pydantic types in the webhook_config dict, which caused JSON
serialization errors and httpx request failures.

Also update mcp requirement to >=1.18.0 for compatibility.

* feat: add webhook support for /llm/job endpoint

Add comprehensive webhook notification support for the /llm/job endpoint,
following the same pattern as the existing /crawl/job implementation.

Changes:
- Add webhook_config field to LlmJobPayload model (job.py)
- Implement webhook notifications in process_llm_extraction() with 4
  notification points: success, provider validation failure, extraction
  failure, and general exceptions (api.py)
- Store webhook_config in Redis task data for job tracking
- Initialize WebhookDeliveryService with exponential backoff retry logic
Documentation:
- Add Example 6 to WEBHOOK_EXAMPLES.md showing LLM extraction with webhooks
- Update Flask webhook handler to support both crawl and llm_extraction tasks
- Add TypeScript client examples for LLM jobs
- Add comprehensive examples to docker_webhook_example.py with schema support
- Clarify data structure differences between webhook and API responses

Testing:
- Add test_llm_webhook_feature.py with 7 validation tests (all passing)
- Verify pattern consistency with /crawl/job implementation
- Add implementation guide (WEBHOOK_LLM_JOB_IMPLEMENTATION.md)

* fix: remove duplicate comma in webhook_config parameter

* fix: update Crawl4AI Docker container port from 11234 to 11235

* Release v0.7.6: The 0.7.6 Update

- Updated version to 0.7.6
- Added comprehensive demo and release notes
- Updated all documentation
- Update the veriosn in Dockerfile to 0.7.6

---------

Signed-off-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: Emmanuel Ferdman <emmanuelferdman@gmail.com>
Co-authored-by: Nezar Ali <abu5sohaib@gmail.com>
Co-authored-by: Soham Kukreti <kukretisoham@gmail.com>
Co-authored-by: James T. Wood <jamesthomaswood@gmail.com>
Co-authored-by: AHMET YILMAZ <tawfik@kidocode.com>
Co-authored-by: nafeqq-1306 <nafiquee@yahoo.com>
Co-authored-by: unclecode <unclecode@kidocode.com>
Co-authored-by: Martin Sjöborg <martin.sjoborg@quartr.se>
Co-authored-by: Martin Sjöborg <martin@sjoborg.org>
Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
2025-10-22 20:41:06 +08:00
ntohidi
7e8fb3a8f3 Merge branch 'release/v0.7.5' into develop 2025-10-22 13:16:16 +02:00
ntohidi
3efb59fb9a fix: update Crawl4AI Docker container port from 11234 to 11235 2025-10-22 13:14:11 +02:00
ntohidi
c7b7475b92 fix: remove duplicate comma in webhook_config parameter 2025-10-22 13:12:42 +02:00
ntohidi
b71d624168 Merge branch 'implement-webhook-crawl-feature-011CULZY1Jy8N5MUkZqXkRVp' into develop 2025-10-22 13:12:25 +02:00
ntohidi
d670dcde0a feat: add webhook support for /llm/job endpoint
Add comprehensive webhook notification support for the /llm/job endpoint,
following the same pattern as the existing /crawl/job implementation.

Changes:
- Add webhook_config field to LlmJobPayload model (job.py)
- Implement webhook notifications in process_llm_extraction() with 4
  notification points: success, provider validation failure, extraction
  failure, and general exceptions (api.py)
- Store webhook_config in Redis task data for job tracking
- Initialize WebhookDeliveryService with exponential backoff retry logic
Documentation:
- Add Example 6 to WEBHOOK_EXAMPLES.md showing LLM extraction with webhooks
- Update Flask webhook handler to support both crawl and llm_extraction tasks
- Add TypeScript client examples for LLM jobs
- Add comprehensive examples to docker_webhook_example.py with schema support
- Clarify data structure differences between webhook and API responses

Testing:
- Add test_llm_webhook_feature.py with 7 validation tests (all passing)
- Verify pattern consistency with /crawl/job implementation
- Add implementation guide (WEBHOOK_LLM_JOB_IMPLEMENTATION.md)
2025-10-22 13:03:09 +02:00
unclecode
f8606f6865 fix: properly serialize Pydantic HttpUrl in webhook config
Use model_dump(mode='json') instead of deprecated dict() method to ensure
Pydantic special types (HttpUrl, UUID, etc.) are properly serialized to
JSON-compatible native Python types.

This fixes webhook delivery failures caused by HttpUrl objects remaining
as Pydantic types in the webhook_config dict, which caused JSON
serialization errors and httpx request failures.

Also update mcp requirement to >=1.18.0 for compatibility.
2025-10-22 15:50:25 +08:00
Claude
52da8d72bc test: add comprehensive webhook feature test script
Added end-to-end test script that automates webhook feature testing:

Script Features (test_webhook_feature.sh):
- Automatic branch switching and dependency installation
- Redis and server startup/shutdown management
- Webhook receiver implementation
- Integration test for webhook notifications
- Comprehensive cleanup and error handling
- Returns to original branch after completion

Test Flow:
1. Fetch and checkout webhook feature branch
2. Activate venv and install dependencies
3. Start Redis and Crawl4AI server
4. Submit crawl job with webhook config
5. Verify webhook delivery and payload
6. Clean up all processes and return to original branch

Documentation:
- WEBHOOK_TEST_README.md with usage instructions
- Troubleshooting guide
- Exit codes and safety features

Usage: ./tests/test_webhook_feature.sh

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-22 00:35:07 +00:00
Claude
8b7e67566e test: add webhook implementation validation tests
Added comprehensive test suite to validate webhook implementation:
- Module import verification
- WebhookDeliveryService initialization
- Pydantic model validation (WebhookConfig)
- Payload construction logic
- Exponential backoff calculation
- API integration checks

All tests pass (6/6), confirming implementation is correct.

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-22 00:25:35 +00:00
Claude
7388baa205 docs: add webhook example for Docker deployment
Added docker_webhook_example.py demonstrating:
- Submitting crawl jobs with webhook configuration
- Flask-based webhook receiver implementation
- Three usage patterns:
  1. Webhook notification only (fetch data separately)
  2. Webhook with full data in payload
  3. Traditional polling approach for comparison

Includes comprehensive comments explaining:
- Webhook payload structure
- Authentication headers setup
- Error handling
- Production deployment tips

Example is fully functional and ready to run with Flask installed.

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-21 16:38:53 +00:00
Claude
897bc3a493 docs: add webhook documentation to Docker README
Added comprehensive webhook section to README.md including:
- Overview of asynchronous job queue with webhooks
- Benefits and use cases
- Quick start examples
- Webhook authentication
- Global webhook configuration
- Job status polling alternative

Updated table of contents and summary to include webhook feature.
Maintains consistent tone and style with rest of README.

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-21 16:21:07 +00:00
Claude
8a37710313 feat: add webhook notifications for crawl job completion
Implements webhook support for the crawl job API to eliminate polling requirements.

Changes:
- Added WebhookConfig and WebhookPayload schemas to schemas.py
- Created webhook.py with WebhookDeliveryService class
- Integrated webhook notifications in api.py handle_crawl_job
- Updated job.py CrawlJobPayload to accept webhook_config
- Added webhook configuration section to config.yml
- Included comprehensive usage examples in WEBHOOK_EXAMPLES.md

Features:
- Webhook notifications on job completion (success/failure)
- Configurable data inclusion in webhook payload
- Custom webhook headers support
- Global default webhook URL configuration
- Exponential backoff retry logic (5 attempts: 1s, 2s, 4s, 8s, 16s)
- 30-second timeout per webhook call

Usage:
POST /crawl/job with optional webhook_config:
- webhook_url: URL to receive notifications
- webhook_data_in_payload: include full results (default: false)
- webhook_headers: custom headers for authentication

Generated with Claude Code https://claude.com/claude-code

Co-Authored-By: Claude <noreply@anthropic.com>
2025-10-21 16:17:40 +00:00
ntohidi
97c92c4f62 fix(marketplace): replace hardcoded app detail content with database-driven fields.
The app detail page was displaying hardcoded/templated content instead of
using actual data from the database. This prevented admins from controlling
the content shown in Overview, Integration, and Documentation tabs.
2025-10-21 15:39:04 +02:00
ntohidi
f6a02c4358 Merge branch 'develop' into release/v0.7.5 2025-10-21 09:25:29 +02:00
unclecode
6d1a398419 feat(ci): split release pipeline and add Docker caching
- Split release.yml into PyPI/GitHub release and Docker workflows
- Add GitHub Actions cache for Docker builds (10-15x faster rebuilds)
- Implement dual-trigger for docker-release.yml (auto + manual)
- Add comprehensive workflow documentation in .github/workflows/docs/
- Backup original workflow as release.yml.backup
2025-10-21 10:53:12 +08:00
unclecode
c107617920 fix: thoroughly verify and fix all Crawl4AI skill examples
- Cross-checked every section against actual docs
- Fixed BM25ContentFilter parameters (user_query, bm25_threshold)
- Removed incorrect wait_for selector from basic example
- Added comprehensive test suite (4 test files)
- All examples now tested and verified working
- Tests validate: basic crawling, markdown generation, data extraction, advanced patterns
- Package size: 76.6 KB (includes tests for future validation)
2025-10-19 17:08:04 +08:00
unclecode
69d0ef89dd fix: update Crawl4AI skill with corrected parameters and examples
- Fixed CrawlerConfig → CrawlerRunConfig throughout
- Fixed parameter names (timeout → page_timeout, store_html removed)
- Fixed schema format (selector → baseSelector)
- Corrected proxy configuration (in BrowserConfig, not CrawlerRunConfig)
- Fixed fit_markdown usage with content filters
- Added comprehensive references to docs/examples/ directory
- Created safe packaging script to avoid root directory pollution
- All scripts tested and verified working
2025-10-19 16:16:20 +08:00
unclecode
1bf85bcb1a fix: remove non-existent wiki link and clarify skill usage instructions 2025-10-19 13:19:14 +08:00
unclecode
749232ba1a feat: add AI assistant skill package for Crawl4AI
- Create comprehensive skill package for AI coding assistants
- Include complete SDK reference (23K words, v0.7.4)
- Add three extraction scripts (basic, batch, pipeline)
- Implement version tracking in skill and scripts
- Add prominent download section on homepage
- Place skill in docs/assets for web distribution

The skill enables AI assistants like Claude, Cursor, and Windsurf
to effectively use Crawl4AI with optimized workflows for markdown
generation and data extraction.
2025-10-19 13:19:14 +08:00
unclecode
c7288dd2f1 docs: add complete SDK reference documentation
Add comprehensive single-page SDK reference combining:
- Installation & Setup
- Quick Start
- Core API (AsyncWebCrawler, arun, arun_many, CrawlResult)
- Configuration (BrowserConfig, CrawlerConfig, Parameters)
- Crawling Patterns
- Content Processing (Markdown, Fit Markdown, Selection, Interaction, Link & Media)
- Extraction Strategies (LLM and No-LLM)
- Advanced Features (Session Management, Hooks & Auth)

Generated using scripts/generate_sdk_docs.py in ultra-dense mode
optimized for AI assistant consumption.

Stats: 23K words, 185 code blocks, 220KB
2025-10-19 13:19:14 +08:00
UncleCode
fdbcddbf1a Merge pull request #1546 from unclecode/sponsors 2025-10-17 18:07:16 +08:00
Aravind Karnam
564d437d97 docs: fix order of star history and Current sponsors 2025-10-17 15:31:29 +05:30
Aravind Karnam
9cd06ea7eb docs: fix order of star history and Current sponsors 2025-10-17 15:30:02 +05:30
ntohidi
c91b235cb7 docs: Update 0.7.5 video walkthrough 2025-10-14 13:49:57 +08:00
Aravind Karnam
eb257c2ba3 docs: fixed sponsorship link 2025-10-13 17:47:42 +05:30
Aravind Karnam
8d364a0731 docs: Adjust background of sponsor logo to compensate for light themes 2025-10-13 17:45:10 +05:30
Aravind Karnam
6aff0e55aa docs: Adjust background of sponsor logo to compensate for light themes 2025-10-13 17:42:29 +05:30
Aravind Karnam
38a0742708 docs: Adjust background of sponsor logo to compensate for light themes 2025-10-13 17:41:19 +05:30
Aravind Karnam
a720a3a9fe docs: Adjust background of sponsor logo to compensate for light themes 2025-10-13 17:32:34 +05:30
Aravind Karnam
017144c2dd docs: Adjust background of sponsor logo to compensate for light themes 2025-10-13 17:30:22 +05:30
Aravind Karnam
32887ea40d docs: Adjust background of sponsor logo to compensate for light themes 2025-10-13 17:13:52 +05:30
Aravind Karnam
eea41bf1ca docs: Add a slight background to compensate light theme on github docs 2025-10-13 17:00:24 +05:30
Aravind Karnam
21c302f439 docs: Add Current sponsors section in README file 2025-10-13 16:45:16 +05:30
ntohidi
8fc1747225 docs: Add demonstration files for v0.7.5 release, showcasing the new Docker Hooks System and all other features. 2025-10-13 13:59:34 +08:00
ntohidi
aadab30c3d fix(docs): clarify Docker Hooks System with function-based API in README 2025-10-13 13:08:47 +08:00
ntohidi
4a04b8506a feat: Add hooks utility for function-based hooks with Docker client integration. ref #1377
Add hooks_to_string() utility function that converts Python function objects
   to string representations for the Docker API, enabling developers to write hooks
   as regular Python functions instead of strings.

   Core Changes:
   - New hooks_to_string() utility in crawl4ai/utils.py using inspect.getsource()
   - Docker client now accepts both function objects and strings for hooks
   - Automatic detection and conversion in Crawl4aiDockerClient._prepare_request()
   - New hooks and hooks_timeout parameters in client.crawl() method

   Documentation:
   - Docker client examples with function-based hooks (docs/examples/docker_client_hooks_example.py)
   - Updated main Docker deployment guide with comprehensive hooks section
   - Added unit tests for hooks utility (tests/docker/test_hooks_utility.py)
2025-10-13 12:53:33 +08:00
ntohidi
7dadb65b80 Merge branch 'develop' into release/v0.7.5 2025-10-13 12:34:45 +08:00
ntohidi
a3f057e19f feat: Add hooks utility for function-based hooks with Docker client integration. ref #1377
Add hooks_to_string() utility function that converts Python function objects
   to string representations for the Docker API, enabling developers to write hooks
   as regular Python functions instead of strings.

   Core Changes:
   - New hooks_to_string() utility in crawl4ai/utils.py using inspect.getsource()
   - Docker client now accepts both function objects and strings for hooks
   - Automatic detection and conversion in Crawl4aiDockerClient._prepare_request()
   - New hooks and hooks_timeout parameters in client.crawl() method

   Documentation:
   - Docker client examples with function-based hooks (docs/examples/docker_client_hooks_example.py)
   - Updated main Docker deployment guide with comprehensive hooks section
   - Added unit tests for hooks utility (tests/docker/test_hooks_utility.py)
2025-10-13 12:34:08 +08:00
ntohidi
611d48f93b Merge branch 'develop' into release/v0.7.5 2025-10-09 12:53:39 +08:00
ntohidi
936397ee0e Merge branch 'develop' of https://github.com/unclecode/crawl4ai into develop 2025-10-09 12:53:15 +08:00
Soham Kukreti
46e1a67f61 fix(docker): Remove environment variable overrides in docker-compose.yml (#1411)
The docker-compose.yml had an `environment:` section with variable
substitutions (${VAR:-}) that was overriding values from .llm.env with
empty strings.

- Commented out the `environment:` section to prevent overwrites
- Added clear warning comment explaining the override behavior
- .llm.env values now load directly into container without interference
2025-10-06 14:41:22 +05:30
Soham Kukreti
7dfe528d43 fix(docs): standardize C4A-Script tutorial, add CLI identity-based crawling, and add sponsorship CTA
- Switch installs to pip install -r requirements.txt (tutorial and app docs)
- Update local run steps to python server.py and http://localhost:8000
- Set default PORT to 8000; update port-in-use commands and alt port 8001
- Replace unsupported :contains() example with accessible attribute selector
- Update example URLs in tutorial servers to 127.0.0.1:8000
- Add “Identity-based crawling” section with crwl profiles CLI workflow and code usage
- Replace legacy-docs note with sponsorship message in docs/md_v2/index.md
- Minor copy and consistency fixes across pages
2025-10-03 22:00:46 +05:30
Nasrin
9900f63f97 Merge pull request #1531 from unclecode/develop
Marketplace and brand book changes
2025-10-03 13:24:51 +08:00
ntohidi
9292b265fc Merge branch 'develop' of https://github.com/unclecode/crawl4ai into develop 2025-10-03 12:57:23 +08:00
ntohidi
70af81d9d7 refactor(release): remove memory management section for cleaner documentation. ref #1443 2025-09-30 11:54:21 +08:00
Soham Kukreti
2dc6588573 fix: remove_overlay_elements functionality by calling injected JS function. ref: #1396
- Fix critical bug where overlay removal JS function was injected but never called
  - Change remove_overlay_elements() to properly execute the injected async function
  - Wrap JS execution in async to handle the async overlay removal logic
  - Add test_remove_overlay_elements() test case to verify functionality works
  - Ensure overlay elements (cookie banners, popups, modals) are actually removed

  The remove_overlay_elements feature now works as intended:
  - Before: Function definition injected but never executed (silent failure)
  - After: Function injected and called, successfully removing overlay elements
2025-09-29 20:40:08 +05:30
Soham Kukreti
34c0996ee4 fix: Add CDP endpoint verification with exponential backoff for managed browsers (#1445)
browser_manager:
- Add CDP endpoint verification with retry logic and exponential backoff
- Call verification before connecting to CDP in `start()` method
- Graceful handling of timing issues during browser startup

test_cdp_strategy:
- Fix cookie persistence test by adding storage state management
- Fix session management test to work with managed browser architecture
- Add comprehensive CDP timing tests covering:
  - Fast startup scenarios
  - Delayed browser startup simulation
  - Exponential backoff behavior validation
  - Concurrent browser connections
  - Stress testing with multiple successive startups
  - Retry count verification

Impact:
- Eliminates browser startup failures due to CDP timing issues
- Provides robust fallback with automatic retries
- Maintains fast startup when CDP is immediately available
- Comprehensive test coverage ensures reliability

Resolves CDP connection timing issues in managed browser mode.
2025-09-29 19:31:09 +05:30
ntohidi
361499d291 Release v0.7.5: The Update
- Updated version to 0.7.5
- Added comprehensive demo and release notes
- Updated documentation
2025-09-29 18:05:26 +08:00
AHMET YILMAZ
e3467c08f6 #1490 feat(ManagedBrowser): add viewport size configuration for browser launch 2025-09-17 17:40:38 +08:00
rbushria
edd0b576b1 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
2025-09-01 23:15:56 +03:00
149 changed files with 35692 additions and 3495 deletions

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@@ -0,0 +1,100 @@
name: Docker Release
on:
release:
types: [published]
push:
tags:
- 'docker-rebuild-v*' # Allow manual Docker rebuilds via tags
jobs:
docker:
runs-on: ubuntu-latest
steps:
- name: Free up disk space
run: |
echo "=== Disk space before cleanup ==="
df -h
# Remove unnecessary tools and libraries (frees ~25GB)
sudo rm -rf /usr/share/dotnet
sudo rm -rf /usr/local/lib/android
sudo rm -rf /opt/ghc
sudo rm -rf /opt/hostedtoolcache/CodeQL
sudo rm -rf /usr/local/share/boost
sudo rm -rf /usr/share/swift
# Clean apt cache
sudo apt-get clean
echo "=== Disk space after cleanup ==="
df -h
- name: Checkout code
uses: actions/checkout@v4
- name: Extract version from release or tag
id: get_version
run: |
if [ "${{ github.event_name }}" == "release" ]; then
# Triggered by release event
VERSION="${{ github.event.release.tag_name }}"
VERSION=${VERSION#v} # Remove 'v' prefix
else
# Triggered by docker-rebuild-v* tag
VERSION=${GITHUB_REF#refs/tags/docker-rebuild-v}
fi
echo "VERSION=$VERSION" >> $GITHUB_OUTPUT
echo "Building Docker images for version: $VERSION"
- name: Extract major and minor versions
id: versions
run: |
VERSION=${{ steps.get_version.outputs.VERSION }}
MAJOR=$(echo $VERSION | cut -d. -f1)
MINOR=$(echo $VERSION | cut -d. -f1-2)
echo "MAJOR=$MAJOR" >> $GITHUB_OUTPUT
echo "MINOR=$MINOR" >> $GITHUB_OUTPUT
echo "Semantic versions - Major: $MAJOR, Minor: $MINOR"
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Log in to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_TOKEN }}
- name: Build and push Docker images
uses: docker/build-push-action@v5
with:
context: .
push: true
tags: |
unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}
unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}
unclecode/crawl4ai:latest
platforms: linux/amd64,linux/arm64
cache-from: type=gha
cache-to: type=gha,mode=max
- name: Summary
run: |
echo "## 🐳 Docker Release Complete!" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### Published Images" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:latest\`" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### Platforms" >> $GITHUB_STEP_SUMMARY
echo "- linux/amd64" >> $GITHUB_STEP_SUMMARY
echo "- linux/arm64" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 🚀 Pull Command" >> $GITHUB_STEP_SUMMARY
echo "\`\`\`bash" >> $GITHUB_STEP_SUMMARY
echo "docker pull unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
echo "\`\`\`" >> $GITHUB_STEP_SUMMARY

917
.github/workflows/docs/ARCHITECTURE.md vendored Normal file
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@@ -0,0 +1,917 @@
# Workflow Architecture Documentation
## Overview
This document describes the technical architecture of the split release pipeline for Crawl4AI.
---
## Architecture Diagram
```
┌─────────────────────────────────────────────────────────────────┐
│ Developer │
│ │ │
│ ▼ │
│ git tag v1.2.3 │
│ git push --tags │
└──────────────────────────────┬──────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ GitHub Repository │
│ │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ Tag Event: v1.2.3 │ │
│ └────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ release.yml (Release Pipeline) │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 1. Extract Version │ │ │
│ │ │ v1.2.3 → 1.2.3 │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 2. Validate Version │ │ │
│ │ │ Tag == __version__.py │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 3. Build Python Package │ │ │
│ │ │ - Source dist (.tar.gz) │ │ │
│ │ │ - Wheel (.whl) │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 4. Upload to PyPI │ │ │
│ │ │ - Authenticate with token │ │ │
│ │ │ - Upload dist/* │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 5. Create GitHub Release │ │ │
│ │ │ - Tag: v1.2.3 │ │ │
│ │ │ - Body: Install instructions │ │ │
│ │ │ - Status: Published │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ └────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ Release Event: published (v1.2.3) │ │
│ └────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────┐ │
│ │ docker-release.yml (Docker Pipeline) │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 1. Extract Version from Release │ │ │
│ │ │ github.event.release.tag_name → 1.2.3 │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 2. Parse Semantic Versions │ │ │
│ │ │ 1.2.3 → Major: 1, Minor: 1.2 │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 3. Setup Multi-Arch Build │ │ │
│ │ │ - Docker Buildx │ │ │
│ │ │ - QEMU emulation │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 4. Authenticate Docker Hub │ │ │
│ │ │ - Username: DOCKER_USERNAME │ │ │
│ │ │ - Token: DOCKER_TOKEN │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 5. Build Multi-Arch Images │ │ │
│ │ │ ┌────────────────┬────────────────┐ │ │ │
│ │ │ │ linux/amd64 │ linux/arm64 │ │ │ │
│ │ │ └────────────────┴────────────────┘ │ │ │
│ │ │ Cache: GitHub Actions (type=gha) │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ │ ┌──────────────────────────────────────────────┐ │ │
│ │ │ 6. Push to Docker Hub │ │ │
│ │ │ Tags: │ │ │
│ │ │ - unclecode/crawl4ai:1.2.3 │ │ │
│ │ │ - unclecode/crawl4ai:1.2 │ │ │
│ │ │ - unclecode/crawl4ai:1 │ │ │
│ │ │ - unclecode/crawl4ai:latest │ │ │
│ │ └──────────────────────────────────────────────┘ │ │
│ └────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ External Services │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ PyPI │ │ Docker Hub │ │ GitHub │ │
│ │ │ │ │ │ │ │
│ │ crawl4ai │ │ unclecode/ │ │ Releases │ │
│ │ 1.2.3 │ │ crawl4ai │ │ v1.2.3 │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────────────┘
```
---
## Component Details
### 1. Release Pipeline (release.yml)
#### Purpose
Fast publication of Python package and GitHub release.
#### Input
- **Trigger**: Git tag matching `v*` (excluding `test-v*`)
- **Example**: `v1.2.3`
#### Processing Stages
##### Stage 1: Version Extraction
```bash
Input: refs/tags/v1.2.3
Output: VERSION=1.2.3
```
**Implementation**:
```bash
TAG_VERSION=${GITHUB_REF#refs/tags/v} # Remove 'refs/tags/v' prefix
echo "VERSION=$TAG_VERSION" >> $GITHUB_OUTPUT
```
##### Stage 2: Version Validation
```bash
Input: TAG_VERSION=1.2.3
Check: crawl4ai/__version__.py contains __version__ = "1.2.3"
Output: Pass/Fail
```
**Implementation**:
```bash
PACKAGE_VERSION=$(python -c "from crawl4ai.__version__ import __version__; print(__version__)")
if [ "$TAG_VERSION" != "$PACKAGE_VERSION" ]; then
exit 1
fi
```
##### Stage 3: Package Build
```bash
Input: Source code + pyproject.toml
Output: dist/crawl4ai-1.2.3.tar.gz
dist/crawl4ai-1.2.3-py3-none-any.whl
```
**Implementation**:
```bash
python -m build
# Uses build backend defined in pyproject.toml
```
##### Stage 4: PyPI Upload
```bash
Input: dist/*.{tar.gz,whl}
Auth: PYPI_TOKEN
Output: Package published to PyPI
```
**Implementation**:
```bash
twine upload dist/*
# Environment:
# TWINE_USERNAME: __token__
# TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
```
##### Stage 5: GitHub Release Creation
```bash
Input: Tag: v1.2.3
Body: Markdown content
Output: Published GitHub release
```
**Implementation**:
```yaml
uses: softprops/action-gh-release@v2
with:
tag_name: v1.2.3
name: Release v1.2.3
body: |
Installation instructions and changelog
draft: false
prerelease: false
```
#### Output
- **PyPI Package**: https://pypi.org/project/crawl4ai/1.2.3/
- **GitHub Release**: Published release on repository
- **Event**: `release.published` (triggers Docker workflow)
#### Timeline
```
0:00 - Tag pushed
0:01 - Checkout + Python setup
0:02 - Version validation
0:03 - Package build
0:04 - PyPI upload starts
0:06 - PyPI upload complete
0:07 - GitHub release created
0:08 - Workflow complete
```
---
### 2. Docker Release Pipeline (docker-release.yml)
#### Purpose
Build and publish multi-architecture Docker images.
#### Inputs
##### Input 1: Release Event (Automatic)
```yaml
Event: release.published
Data: github.event.release.tag_name = "v1.2.3"
```
##### Input 2: Docker Rebuild Tag (Manual)
```yaml
Tag: docker-rebuild-v1.2.3
```
#### Processing Stages
##### Stage 1: Version Detection
```bash
# From release event:
VERSION = github.event.release.tag_name.strip("v")
# Result: "1.2.3"
# From rebuild tag:
VERSION = GITHUB_REF.replace("refs/tags/docker-rebuild-v", "")
# Result: "1.2.3"
```
##### Stage 2: Semantic Version Parsing
```bash
Input: VERSION=1.2.3
Output: MAJOR=1
MINOR=1.2
PATCH=3 (implicit)
```
**Implementation**:
```bash
MAJOR=$(echo $VERSION | cut -d. -f1) # Extract first component
MINOR=$(echo $VERSION | cut -d. -f1-2) # Extract first two components
```
##### Stage 3: Multi-Architecture Setup
```yaml
Setup:
- Docker Buildx (multi-platform builder)
- QEMU (ARM emulation on x86)
Platforms:
- linux/amd64 (x86_64)
- linux/arm64 (aarch64)
```
**Architecture**:
```
GitHub Runner (linux/amd64)
├─ Buildx Builder
│ ├─ Native: Build linux/amd64 image
│ └─ QEMU: Emulate ARM to build linux/arm64 image
└─ Generate manifest list (points to both images)
```
##### Stage 4: Docker Hub Authentication
```bash
Input: DOCKER_USERNAME
DOCKER_TOKEN
Output: Authenticated Docker client
```
##### Stage 5: Build with Cache
```yaml
Cache Configuration:
cache-from: type=gha # Read from GitHub Actions cache
cache-to: type=gha,mode=max # Write all layers
Cache Key Components:
- Workflow file path
- Branch name
- Architecture (amd64/arm64)
```
**Cache Hierarchy**:
```
Cache Entry: main/docker-release.yml/linux-amd64
├─ Layer: sha256:abc123... (FROM python:3.12)
├─ Layer: sha256:def456... (RUN apt-get update)
├─ Layer: sha256:ghi789... (COPY requirements.txt)
├─ Layer: sha256:jkl012... (RUN pip install)
└─ Layer: sha256:mno345... (COPY . /app)
Cache Hit/Miss Logic:
- If layer input unchanged → cache hit → skip build
- If layer input changed → cache miss → rebuild + all subsequent layers
```
##### Stage 6: Tag Generation
```bash
Input: VERSION=1.2.3, MAJOR=1, MINOR=1.2
Output Tags:
- unclecode/crawl4ai:1.2.3 (exact version)
- unclecode/crawl4ai:1.2 (minor version)
- unclecode/crawl4ai:1 (major version)
- unclecode/crawl4ai:latest (latest stable)
```
**Tag Strategy**:
- All tags point to same image SHA
- Users can pin to desired stability level
- Pushing new version updates `1`, `1.2`, and `latest` automatically
##### Stage 7: Push to Registry
```bash
For each tag:
For each platform (amd64, arm64):
Push image to Docker Hub
Create manifest list:
Manifest: unclecode/crawl4ai:1.2.3
├─ linux/amd64: sha256:abc...
└─ linux/arm64: sha256:def...
Docker CLI automatically selects correct platform on pull
```
#### Output
- **Docker Images**: 4 tags × 2 platforms = 8 image variants + 4 manifests
- **Docker Hub**: https://hub.docker.com/r/unclecode/crawl4ai/tags
#### Timeline
**Cold Cache (First Build)**:
```
0:00 - Release event received
0:01 - Checkout + Buildx setup
0:02 - Docker Hub auth
0:03 - Start build (amd64)
0:08 - Complete amd64 build
0:09 - Start build (arm64)
0:14 - Complete arm64 build
0:15 - Generate manifests
0:16 - Push all tags
0:17 - Workflow complete
```
**Warm Cache (Code Change Only)**:
```
0:00 - Release event received
0:01 - Checkout + Buildx setup
0:02 - Docker Hub auth
0:03 - Start build (amd64) - cache hit for layers 1-4
0:04 - Complete amd64 build (only layer 5 rebuilt)
0:05 - Start build (arm64) - cache hit for layers 1-4
0:06 - Complete arm64 build (only layer 5 rebuilt)
0:07 - Generate manifests
0:08 - Push all tags
0:09 - Workflow complete
```
---
## Data Flow
### Version Information Flow
```
Developer
crawl4ai/__version__.py
__version__ = "1.2.3"
├─► Git Tag
│ v1.2.3
│ │
│ ▼
│ release.yml
│ │
│ ├─► Validation
│ │ ✓ Match
│ │
│ ├─► PyPI Package
│ │ crawl4ai==1.2.3
│ │
│ └─► GitHub Release
│ v1.2.3
│ │
│ ▼
│ docker-release.yml
│ │
│ └─► Docker Tags
│ 1.2.3, 1.2, 1, latest
└─► Package Metadata
pyproject.toml
version = "1.2.3"
```
### Secrets Flow
```
GitHub Secrets (Encrypted at Rest)
├─► PYPI_TOKEN
│ │
│ ▼
│ release.yml
│ │
│ ▼
│ TWINE_PASSWORD env var (masked in logs)
│ │
│ ▼
│ PyPI API (HTTPS)
├─► DOCKER_USERNAME
│ │
│ ▼
│ docker-release.yml
│ │
│ ▼
│ docker/login-action (masked in logs)
│ │
│ ▼
│ Docker Hub API (HTTPS)
└─► DOCKER_TOKEN
docker-release.yml
docker/login-action (masked in logs)
Docker Hub API (HTTPS)
```
### Artifact Flow
```
Source Code
├─► release.yml
│ │
│ ▼
│ python -m build
│ │
│ ├─► crawl4ai-1.2.3.tar.gz
│ │ │
│ │ ▼
│ │ PyPI Storage
│ │ │
│ │ ▼
│ │ pip install crawl4ai
│ │
│ └─► crawl4ai-1.2.3-py3-none-any.whl
│ │
│ ▼
│ PyPI Storage
│ │
│ ▼
│ pip install crawl4ai
└─► docker-release.yml
docker build
├─► Image: linux/amd64
│ │
│ └─► Docker Hub
│ unclecode/crawl4ai:1.2.3-amd64
└─► Image: linux/arm64
└─► Docker Hub
unclecode/crawl4ai:1.2.3-arm64
```
---
## State Machines
### Release Pipeline State Machine
```
┌─────────┐
│ START │
└────┬────┘
┌──────────────┐
│ Extract │
│ Version │
└──────┬───────┘
┌──────────────┐ ┌─────────┐
│ Validate │─────►│ FAILED │
│ Version │ No │ (Exit 1)│
└──────┬───────┘ └─────────┘
│ Yes
┌──────────────┐
│ Build │
│ Package │
└──────┬───────┘
┌──────────────┐ ┌─────────┐
│ Upload │─────►│ FAILED │
│ to PyPI │ Error│ (Exit 1)│
└──────┬───────┘ └─────────┘
│ Success
┌──────────────┐
│ Create │
│ GH Release │
└──────┬───────┘
┌──────────────┐
│ SUCCESS │
│ (Emit Event) │
└──────────────┘
```
### Docker Pipeline State Machine
```
┌─────────┐
│ START │
│ (Event) │
└────┬────┘
┌──────────────┐
│ Detect │
│ Version │
│ Source │
└──────┬───────┘
┌──────────────┐
│ Parse │
│ Semantic │
│ Versions │
└──────┬───────┘
┌──────────────┐ ┌─────────┐
│ Authenticate │─────►│ FAILED │
│ Docker Hub │ Error│ (Exit 1)│
└──────┬───────┘ └─────────┘
│ Success
┌──────────────┐
│ Build │
│ amd64 │
└──────┬───────┘
┌──────────────┐ ┌─────────┐
│ Build │─────►│ FAILED │
│ arm64 │ Error│ (Exit 1)│
└──────┬───────┘ └─────────┘
│ Success
┌──────────────┐
│ Push All │
│ Tags │
└──────┬───────┘
┌──────────────┐
│ SUCCESS │
└──────────────┘
```
---
## Security Architecture
### Threat Model
#### Threats Mitigated
1. **Secret Exposure**
- Mitigation: GitHub Actions secret masking
- Evidence: Secrets never appear in logs
2. **Unauthorized Package Upload**
- Mitigation: Scoped PyPI tokens
- Evidence: Token limited to `crawl4ai` project
3. **Man-in-the-Middle**
- Mitigation: HTTPS for all API calls
- Evidence: PyPI, Docker Hub, GitHub all use TLS
4. **Supply Chain Tampering**
- Mitigation: Immutable artifacts, content checksums
- Evidence: PyPI stores SHA256, Docker uses content-addressable storage
#### Trust Boundaries
```
┌─────────────────────────────────────────┐
│ Trusted Zone │
│ ┌────────────────────────────────┐ │
│ │ GitHub Actions Runner │ │
│ │ - Ephemeral VM │ │
│ │ - Isolated environment │ │
│ │ - Access to secrets │ │
│ └────────────────────────────────┘ │
│ │ │
│ │ HTTPS (TLS 1.2+) │
│ ▼ │
└─────────────────────────────────────────┘
┌────────────┼────────────┐
│ │ │
▼ ▼ ▼
┌────────┐ ┌─────────┐ ┌──────────┐
│ PyPI │ │ Docker │ │ GitHub │
│ API │ │ Hub │ │ API │
└────────┘ └─────────┘ └──────────┘
External External External
Service Service Service
```
### Secret Management
#### Secret Lifecycle
```
Creation (Developer)
├─► PyPI: Create API token (scoped to project)
├─► Docker Hub: Create access token (read/write)
Storage (GitHub)
├─► Encrypted at rest (AES-256)
├─► Access controlled (repo-scoped)
Usage (Workflow)
├─► Injected as env vars
├─► Masked in logs (GitHub redacts on output)
├─► Never persisted to disk (in-memory only)
Transmission (API Call)
├─► HTTPS only
├─► TLS 1.2+ with strong ciphers
Rotation (Manual)
└─► Regenerate on PyPI/Docker Hub
Update GitHub secret
```
---
## Performance Characteristics
### Release Pipeline Performance
| Metric | Value | Notes |
|--------|-------|-------|
| Cold start | ~2-3 min | First run on new runner |
| Warm start | ~2-3 min | Minimal caching benefit |
| PyPI upload | ~30-60 sec | Network-bound |
| Package build | ~30 sec | CPU-bound |
| Parallelization | None | Sequential by design |
### Docker Pipeline Performance
| Metric | Cold Cache | Warm Cache (code) | Warm Cache (deps) |
|--------|-----------|-------------------|-------------------|
| Total time | 10-15 min | 1-2 min | 3-5 min |
| amd64 build | 5-7 min | 30-60 sec | 1-2 min |
| arm64 build | 5-7 min | 30-60 sec | 1-2 min |
| Push time | 1-2 min | 30 sec | 30 sec |
| Cache hit rate | 0% | 85% | 60% |
### Cache Performance Model
```python
def estimate_build_time(changes):
base_time = 60 # seconds (setup + push)
if "Dockerfile" in changes:
return base_time + (10 * 60) # Full rebuild: ~11 min
elif "requirements.txt" in changes:
return base_time + (3 * 60) # Deps rebuild: ~4 min
elif any(f.endswith(".py") for f in changes):
return base_time + 60 # Code only: ~2 min
else:
return base_time # No changes: ~1 min
```
---
## Scalability Considerations
### Current Limits
| Resource | Limit | Impact |
|----------|-------|--------|
| Workflow concurrency | 20 (default) | Max 20 releases in parallel |
| Artifact storage | 500 MB/artifact | PyPI packages small (<10 MB) |
| Cache storage | 10 GB/repo | Docker layers fit comfortably |
| Workflow run time | 6 hours | Plenty of headroom |
### Scaling Strategies
#### Horizontal Scaling (Multiple Repos)
```
crawl4ai (main)
├─ release.yml
└─ docker-release.yml
crawl4ai-plugins (separate)
├─ release.yml
└─ docker-release.yml
Each repo has independent:
- Secrets
- Cache (10 GB each)
- Concurrency limits (20 each)
```
#### Vertical Scaling (Larger Runners)
```yaml
jobs:
docker:
runs-on: ubuntu-latest-8-cores # GitHub-hosted larger runner
# 4x faster builds for CPU-bound layers
```
---
## Disaster Recovery
### Failure Scenarios
#### Scenario 1: Release Pipeline Fails
**Failure Point**: PyPI upload fails (network error)
**State**:
- ✓ Version validated
- ✓ Package built
- ✗ PyPI upload
- ✗ GitHub release
**Recovery**:
```bash
# Manual upload
twine upload dist/*
# Retry workflow (re-run from GitHub Actions UI)
```
**Prevention**: Add retry logic to PyPI upload
#### Scenario 2: Docker Pipeline Fails
**Failure Point**: ARM build fails (dependency issue)
**State**:
- ✓ PyPI published
- ✓ GitHub release created
- ✓ amd64 image built
- ✗ arm64 image build
**Recovery**:
```bash
# Fix Dockerfile
git commit -am "fix: ARM build dependency"
# Trigger rebuild
git tag docker-rebuild-v1.2.3
git push origin docker-rebuild-v1.2.3
```
**Impact**: PyPI package available, only Docker ARM users affected
#### Scenario 3: Partial Release
**Failure Point**: GitHub release creation fails
**State**:
- ✓ PyPI published
- ✗ GitHub release
- ✗ Docker images
**Recovery**:
```bash
# Create release manually
gh release create v1.2.3 \
--title "Release v1.2.3" \
--notes "..."
# This triggers docker-release.yml automatically
```
---
## Monitoring and Observability
### Metrics to Track
#### Release Pipeline
- Success rate (target: >99%)
- Duration (target: <3 min)
- PyPI upload time (target: <60 sec)
#### Docker Pipeline
- Success rate (target: >95%)
- Duration (target: <15 min cold, <2 min warm)
- Cache hit rate (target: >80% for code changes)
### Alerting
**Critical Alerts**:
- Release pipeline failure (blocks release)
- PyPI authentication failure (expired token)
**Warning Alerts**:
- Docker build >15 min (performance degradation)
- Cache hit rate <50% (cache issue)
### Logging
**GitHub Actions Logs**:
- Retention: 90 days
- Downloadable: Yes
- Searchable: Limited
**Recommended External Logging**:
```yaml
- name: Send logs to external service
if: failure()
run: |
curl -X POST https://logs.example.com/api/v1/logs \
-H "Content-Type: application/json" \
-d "{\"workflow\": \"${{ github.workflow }}\", \"status\": \"failed\"}"
```
---
## Future Enhancements
### Planned Improvements
1. **Automated Changelog Generation**
- Use conventional commits
- Generate CHANGELOG.md automatically
2. **Pre-release Testing**
- Test builds on `test-v*` tags
- Upload to TestPyPI
3. **Notification System**
- Slack/Discord notifications on release
- Email on failure
4. **Performance Optimization**
- Parallel Docker builds (amd64 + arm64 simultaneously)
- Persistent runners for better caching
5. **Enhanced Validation**
- Smoke tests after PyPI upload
- Container security scanning
---
## References
- [GitHub Actions Architecture](https://docs.github.com/en/actions/learn-github-actions/understanding-github-actions)
- [Docker Build Cache](https://docs.docker.com/build/cache/)
- [PyPI API Documentation](https://warehouse.pypa.io/api-reference/)
---
**Last Updated**: 2025-01-21
**Version**: 2.0

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@@ -0,0 +1,287 @@
# Workflow Quick Reference
## Quick Commands
### Standard Release
```bash
# 1. Update version
vim crawl4ai/__version__.py # Set to "1.2.3"
# 2. Commit and tag
git add crawl4ai/__version__.py
git commit -m "chore: bump version to 1.2.3"
git tag v1.2.3
git push origin main
git push origin v1.2.3
# 3. Monitor
# - PyPI: ~2-3 minutes
# - Docker: ~1-15 minutes
```
### Docker Rebuild Only
```bash
git tag docker-rebuild-v1.2.3
git push origin docker-rebuild-v1.2.3
```
### Delete Tag (Undo Release)
```bash
# Local
git tag -d v1.2.3
# Remote
git push --delete origin v1.2.3
# GitHub Release
gh release delete v1.2.3
```
---
## Workflow Triggers
### release.yml
| Event | Pattern | Example |
|-------|---------|---------|
| Tag push | `v*` | `v1.2.3` |
| Excludes | `test-v*` | `test-v1.2.3` |
### docker-release.yml
| Event | Pattern | Example |
|-------|---------|---------|
| Release published | `release.published` | Automatic |
| Tag push | `docker-rebuild-v*` | `docker-rebuild-v1.2.3` |
---
## Environment Variables
### release.yml
| Variable | Source | Example |
|----------|--------|---------|
| `VERSION` | Git tag | `1.2.3` |
| `TWINE_USERNAME` | Static | `__token__` |
| `TWINE_PASSWORD` | Secret | `pypi-Ag...` |
| `GITHUB_TOKEN` | Auto | `ghp_...` |
### docker-release.yml
| Variable | Source | Example |
|----------|--------|---------|
| `VERSION` | Release/Tag | `1.2.3` |
| `MAJOR` | Computed | `1` |
| `MINOR` | Computed | `1.2` |
| `DOCKER_USERNAME` | Secret | `unclecode` |
| `DOCKER_TOKEN` | Secret | `dckr_pat_...` |
---
## Docker Tags Generated
| Version | Tags Created |
|---------|-------------|
| v1.0.0 | `1.0.0`, `1.0`, `1`, `latest` |
| v1.1.0 | `1.1.0`, `1.1`, `1`, `latest` |
| v1.2.3 | `1.2.3`, `1.2`, `1`, `latest` |
| v2.0.0 | `2.0.0`, `2.0`, `2`, `latest` |
---
## Workflow Outputs
### release.yml
| Output | Location | Time |
|--------|----------|------|
| PyPI Package | https://pypi.org/project/crawl4ai/ | ~2-3 min |
| GitHub Release | Repository → Releases | ~2-3 min |
| Workflow Summary | Actions → Run → Summary | Immediate |
### docker-release.yml
| Output | Location | Time |
|--------|----------|------|
| Docker Images | https://hub.docker.com/r/unclecode/crawl4ai | ~1-15 min |
| Workflow Summary | Actions → Run → Summary | Immediate |
---
## Common Issues
| Issue | Solution |
|-------|----------|
| Version mismatch | Update `crawl4ai/__version__.py` to match tag |
| PyPI 403 Forbidden | Check `PYPI_TOKEN` secret |
| PyPI 400 File exists | Version already published, increment version |
| Docker auth failed | Regenerate `DOCKER_TOKEN` |
| Docker build timeout | Check Dockerfile, review build logs |
| Cache not working | First build on branch always cold |
---
## Secrets Checklist
- [ ] `PYPI_TOKEN` - PyPI API token (project or account scope)
- [ ] `DOCKER_USERNAME` - Docker Hub username
- [ ] `DOCKER_TOKEN` - Docker Hub access token (read/write)
- [ ] `GITHUB_TOKEN` - Auto-provided (no action needed)
---
## Workflow Dependencies
### release.yml Dependencies
```yaml
Python: 3.12
Actions:
- actions/checkout@v4
- actions/setup-python@v5
- softprops/action-gh-release@v2
PyPI Packages:
- build
- twine
```
### docker-release.yml Dependencies
```yaml
Actions:
- actions/checkout@v4
- docker/setup-buildx-action@v3
- docker/login-action@v3
- docker/build-push-action@v5
Docker:
- Buildx
- QEMU (for multi-arch)
```
---
## Cache Information
### Type
- GitHub Actions Cache (`type=gha`)
### Storage
- **Limit**: 10GB per repository
- **Retention**: 7 days for unused entries
- **Cleanup**: Automatic LRU eviction
### Performance
| Scenario | Cache Hit | Build Time |
|----------|-----------|------------|
| First build | 0% | 10-15 min |
| Code change only | 85% | 1-2 min |
| Dependency update | 60% | 3-5 min |
| No changes | 100% | 30-60 sec |
---
## Build Platforms
| Platform | Architecture | Devices |
|----------|--------------|---------|
| linux/amd64 | x86_64 | Intel/AMD servers, AWS EC2, GCP |
| linux/arm64 | aarch64 | Apple Silicon, AWS Graviton, Raspberry Pi |
---
## Version Validation
### Pre-Tag Checklist
```bash
# Check current version
python -c "from crawl4ai.__version__ import __version__; print(__version__)"
# Verify it matches intended tag
# If tag is v1.2.3, version should be "1.2.3"
```
### Post-Release Verification
```bash
# PyPI
pip install crawl4ai==1.2.3
python -c "import crawl4ai; print(crawl4ai.__version__)"
# Docker
docker pull unclecode/crawl4ai:1.2.3
docker run unclecode/crawl4ai:1.2.3 python -c "import crawl4ai; print(crawl4ai.__version__)"
```
---
## Monitoring URLs
| Service | URL |
|---------|-----|
| GitHub Actions | `https://github.com/{owner}/{repo}/actions` |
| PyPI Project | `https://pypi.org/project/crawl4ai/` |
| Docker Hub | `https://hub.docker.com/r/unclecode/crawl4ai` |
| GitHub Releases | `https://github.com/{owner}/{repo}/releases` |
---
## Rollback Strategy
### PyPI (Cannot Delete)
```bash
# Increment patch version
git tag v1.2.4
git push origin v1.2.4
```
### Docker (Can Overwrite)
```bash
# Rebuild with fix
git tag docker-rebuild-v1.2.3
git push origin docker-rebuild-v1.2.3
```
### GitHub Release
```bash
# Delete release
gh release delete v1.2.3
# Delete tag
git push --delete origin v1.2.3
```
---
## Status Badge Markdown
```markdown
[![Release Pipeline](https://github.com/{owner}/{repo}/actions/workflows/release.yml/badge.svg)](https://github.com/{owner}/{repo}/actions/workflows/release.yml)
[![Docker Release](https://github.com/{owner}/{repo}/actions/workflows/docker-release.yml/badge.svg)](https://github.com/{owner}/{repo}/actions/workflows/docker-release.yml)
```
---
## Timeline Example
```
0:00 - Push tag v1.2.3
0:01 - release.yml starts
0:02 - Version validation passes
0:03 - Package built
0:04 - PyPI upload starts
0:06 - PyPI upload complete ✓
0:07 - GitHub release created ✓
0:08 - release.yml complete
0:08 - docker-release.yml triggered
0:10 - Docker build starts
0:12 - amd64 image built (cache hit)
0:14 - arm64 image built (cache hit)
0:15 - Images pushed to Docker Hub ✓
0:16 - docker-release.yml complete
Total: ~16 minutes
Critical path (PyPI + GitHub): ~8 minutes
```
---
## Contact
For workflow issues:
1. Check Actions tab for logs
2. Review this reference
3. See [README.md](./README.md) for detailed docs

View File

@@ -10,53 +10,53 @@ jobs:
runs-on: ubuntu-latest
permissions:
contents: write # Required for creating releases
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.12'
- name: Extract version from tag
id: get_version
run: |
TAG_VERSION=${GITHUB_REF#refs/tags/v}
echo "VERSION=$TAG_VERSION" >> $GITHUB_OUTPUT
echo "Releasing version: $TAG_VERSION"
- name: Install package dependencies
run: |
pip install -e .
- name: Check version consistency
run: |
TAG_VERSION=${{ steps.get_version.outputs.VERSION }}
PACKAGE_VERSION=$(python -c "from crawl4ai.__version__ import __version__; print(__version__)")
echo "Tag version: $TAG_VERSION"
echo "Package version: $PACKAGE_VERSION"
if [ "$TAG_VERSION" != "$PACKAGE_VERSION" ]; then
echo "❌ Version mismatch! Tag: $TAG_VERSION, Package: $PACKAGE_VERSION"
echo "Please update crawl4ai/__version__.py to match the tag version"
exit 1
fi
echo "✅ Version check passed: $TAG_VERSION"
- name: Install build dependencies
run: |
python -m pip install --upgrade pip
pip install build twine
- name: Build package
run: python -m build
- name: Check package
run: twine check dist/*
- name: Upload to PyPI
env:
TWINE_USERNAME: __token__
@@ -65,37 +65,7 @@ jobs:
echo "📦 Uploading to PyPI..."
twine upload dist/*
echo "✅ Package uploaded to https://pypi.org/project/crawl4ai/"
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Log in to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_TOKEN }}
- name: Extract major and minor versions
id: versions
run: |
VERSION=${{ steps.get_version.outputs.VERSION }}
MAJOR=$(echo $VERSION | cut -d. -f1)
MINOR=$(echo $VERSION | cut -d. -f1-2)
echo "MAJOR=$MAJOR" >> $GITHUB_OUTPUT
echo "MINOR=$MINOR" >> $GITHUB_OUTPUT
- name: Build and push Docker images
uses: docker/build-push-action@v5
with:
context: .
push: true
tags: |
unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}
unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}
unclecode/crawl4ai:latest
platforms: linux/amd64,linux/arm64
- name: Create GitHub Release
uses: softprops/action-gh-release@v2
with:
@@ -103,26 +73,29 @@ jobs:
name: Release v${{ steps.get_version.outputs.VERSION }}
body: |
## 🎉 Crawl4AI v${{ steps.get_version.outputs.VERSION }} Released!
### 📦 Installation
**PyPI:**
```bash
pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}
```
**Docker:**
```bash
docker pull unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
docker pull unclecode/crawl4ai:latest
```
**Note:** Docker images are being built and will be available shortly.
Check the [Docker Release workflow](https://github.com/${{ github.repository }}/actions/workflows/docker-release.yml) for build status.
### 📝 What's Changed
See [CHANGELOG.md](https://github.com/${{ github.repository }}/blob/main/CHANGELOG.md) for details.
draft: false
prerelease: false
token: ${{ secrets.GITHUB_TOKEN }}
- name: Summary
run: |
echo "## 🚀 Release Complete!" >> $GITHUB_STEP_SUMMARY
@@ -132,11 +105,9 @@ jobs:
echo "- URL: https://pypi.org/project/crawl4ai/" >> $GITHUB_STEP_SUMMARY
echo "- Install: \`pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 🐳 Docker Images" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:latest\`" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 📋 GitHub Release" >> $GITHUB_STEP_SUMMARY
echo "https://github.com/${{ github.repository }}/releases/tag/v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
echo "- https://github.com/${{ github.repository }}/releases/tag/v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 🐳 Docker Images" >> $GITHUB_STEP_SUMMARY
echo "Docker images are being built in a separate workflow." >> $GITHUB_STEP_SUMMARY
echo "Check: https://github.com/${{ github.repository }}/actions/workflows/docker-release.yml" >> $GITHUB_STEP_SUMMARY

142
.github/workflows/release.yml.backup vendored Normal file
View File

@@ -0,0 +1,142 @@
name: Release Pipeline
on:
push:
tags:
- 'v*'
- '!test-v*' # Exclude test tags
jobs:
release:
runs-on: ubuntu-latest
permissions:
contents: write # Required for creating releases
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.12'
- name: Extract version from tag
id: get_version
run: |
TAG_VERSION=${GITHUB_REF#refs/tags/v}
echo "VERSION=$TAG_VERSION" >> $GITHUB_OUTPUT
echo "Releasing version: $TAG_VERSION"
- name: Install package dependencies
run: |
pip install -e .
- name: Check version consistency
run: |
TAG_VERSION=${{ steps.get_version.outputs.VERSION }}
PACKAGE_VERSION=$(python -c "from crawl4ai.__version__ import __version__; print(__version__)")
echo "Tag version: $TAG_VERSION"
echo "Package version: $PACKAGE_VERSION"
if [ "$TAG_VERSION" != "$PACKAGE_VERSION" ]; then
echo "❌ Version mismatch! Tag: $TAG_VERSION, Package: $PACKAGE_VERSION"
echo "Please update crawl4ai/__version__.py to match the tag version"
exit 1
fi
echo "✅ Version check passed: $TAG_VERSION"
- name: Install build dependencies
run: |
python -m pip install --upgrade pip
pip install build twine
- name: Build package
run: python -m build
- name: Check package
run: twine check dist/*
- name: Upload to PyPI
env:
TWINE_USERNAME: __token__
TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
run: |
echo "📦 Uploading to PyPI..."
twine upload dist/*
echo "✅ Package uploaded to https://pypi.org/project/crawl4ai/"
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v3
- name: Log in to Docker Hub
uses: docker/login-action@v3
with:
username: ${{ secrets.DOCKER_USERNAME }}
password: ${{ secrets.DOCKER_TOKEN }}
- name: Extract major and minor versions
id: versions
run: |
VERSION=${{ steps.get_version.outputs.VERSION }}
MAJOR=$(echo $VERSION | cut -d. -f1)
MINOR=$(echo $VERSION | cut -d. -f1-2)
echo "MAJOR=$MAJOR" >> $GITHUB_OUTPUT
echo "MINOR=$MINOR" >> $GITHUB_OUTPUT
- name: Build and push Docker images
uses: docker/build-push-action@v5
with:
context: .
push: true
tags: |
unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}
unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}
unclecode/crawl4ai:latest
platforms: linux/amd64,linux/arm64
- name: Create GitHub Release
uses: softprops/action-gh-release@v2
with:
tag_name: v${{ steps.get_version.outputs.VERSION }}
name: Release v${{ steps.get_version.outputs.VERSION }}
body: |
## 🎉 Crawl4AI v${{ steps.get_version.outputs.VERSION }} Released!
### 📦 Installation
**PyPI:**
```bash
pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}
```
**Docker:**
```bash
docker pull unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}
docker pull unclecode/crawl4ai:latest
```
### 📝 What's Changed
See [CHANGELOG.md](https://github.com/${{ github.repository }}/blob/main/CHANGELOG.md) for details.
draft: false
prerelease: false
token: ${{ secrets.GITHUB_TOKEN }}
- name: Summary
run: |
echo "## 🚀 Release Complete!" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 📦 PyPI Package" >> $GITHUB_STEP_SUMMARY
echo "- Version: ${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY
echo "- URL: https://pypi.org/project/crawl4ai/" >> $GITHUB_STEP_SUMMARY
echo "- Install: \`pip install crawl4ai==${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 🐳 Docker Images" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.get_version.outputs.VERSION }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MINOR }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:${{ steps.versions.outputs.MAJOR }}\`" >> $GITHUB_STEP_SUMMARY
echo "- \`unclecode/crawl4ai:latest\`" >> $GITHUB_STEP_SUMMARY
echo "" >> $GITHUB_STEP_SUMMARY
echo "### 📋 GitHub Release" >> $GITHUB_STEP_SUMMARY
echo "https://github.com/${{ github.repository }}/releases/tag/v${{ steps.get_version.outputs.VERSION }}" >> $GITHUB_STEP_SUMMARY

4
.gitignore vendored
View File

@@ -266,6 +266,9 @@ continue_config.json
.llm.env
.private/
.claude/
.context/
CLAUDE_MONITOR.md
CLAUDE.md
@@ -293,3 +296,4 @@ scripts/
*.db
*.rdb
*.ldb
MEMORY.md

View File

@@ -5,6 +5,46 @@ All notable changes to Crawl4AI will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.8.0] - 2026-01-12
### Security
- **🔒 CRITICAL: Remote Code Execution Fix**: Removed `__import__` from hook allowed builtins
- Prevents arbitrary module imports in user-provided hook code
- Hooks now disabled by default via `CRAWL4AI_HOOKS_ENABLED` environment variable
- Credit: Neo by ProjectDiscovery
- **🔒 HIGH: Local File Inclusion Fix**: Added URL scheme validation to Docker API endpoints
- Blocks `file://`, `javascript:`, `data:` URLs on `/execute_js`, `/screenshot`, `/pdf`, `/html`
- Only allows `http://`, `https://`, and `raw:` URLs
- Credit: Neo by ProjectDiscovery
### Breaking Changes
- **Docker API: Hooks disabled by default**: Set `CRAWL4AI_HOOKS_ENABLED=true` to enable
- **Docker API: file:// URLs blocked**: Use Python library directly for local file processing
### Added
- **🚀 init_scripts for BrowserConfig**: Pre-page-load JavaScript injection for stealth evasions
- **🔄 CDP Connection Improvements**: WebSocket URL support, proper cleanup, browser reuse
- **💾 Crash Recovery for Deep Crawl**: `resume_state` and `on_state_change` for BFS/DFS/Best-First strategies
- **📄 PDF/MHTML for raw:/file:// URLs**: Generate PDFs and MHTML from cached HTML content
- **📸 Screenshots for raw:/file:// URLs**: Render cached HTML and capture screenshots
- **🔗 base_url Parameter**: Proper URL resolution for raw: HTML processing
- **⚡ Prefetch Mode**: Two-phase deep crawling with fast link extraction
- **🔀 Enhanced Proxy Support**: Improved proxy rotation and sticky sessions
- **🌐 HTTP Strategy Proxy Support**: Non-browser crawler now supports proxies
- **🖥️ Browser Pipeline for raw:/file://**: New `process_in_browser` parameter
- **📋 Smart TTL Cache for Sitemap Seeder**: `cache_ttl_hours` and `validate_sitemap_lastmod` parameters
- **📚 Security Documentation**: Added SECURITY.md with vulnerability reporting guidelines
### Fixed
- **raw: URL Parsing**: Fixed truncation at `#` character (CSS color codes like `#eee`)
- **Caching System**: Various improvements to cache validation and persistence
### Documentation
- Multi-sample schema generation section
- URL seeder smart TTL cache parameters
- v0.8.0 migration guide
- Security policy and disclosure process
## [Unreleased]
### Added

View File

@@ -1,7 +1,7 @@
FROM python:3.12-slim-bookworm AS build
# C4ai version
ARG C4AI_VER=0.7.0-r1
ARG C4AI_VER=0.8.0
ENV C4AI_VERSION=$C4AI_VER
LABEL c4ai.version=$C4AI_VER
@@ -167,6 +167,11 @@ RUN mkdir -p /home/appuser/.cache/ms-playwright \
RUN crawl4ai-doctor
# Ensure all cache directories belong to appuser
# This fixes permission issues with .cache/url_seeder and other runtime cache dirs
RUN mkdir -p /home/appuser/.cache \
&& chown -R appuser:appuser /home/appuser/.cache
# Copy application code
COPY deploy/docker/* ${APP_HOME}/

251
README.md
View File

@@ -12,6 +12,16 @@
[![Downloads](https://static.pepy.tech/badge/crawl4ai/month)](https://pepy.tech/project/crawl4ai)
[![GitHub Sponsors](https://img.shields.io/github/sponsors/unclecode?style=flat&logo=GitHub-Sponsors&label=Sponsors&color=pink)](https://github.com/sponsors/unclecode)
---
#### 🚀 Crawl4AI Cloud API — Closed Beta (Launching Soon)
Reliable, large-scale web extraction, now built to be _**drastically more cost-effective**_ than any of the existing solutions.
👉 **Apply [here](https://forms.gle/E9MyPaNXACnAMaqG7) for early access**
_Well be onboarding in phases and working closely with early users.
Limited slots._
---
<p align="center">
<a href="https://x.com/crawl4ai">
<img src="https://img.shields.io/badge/Follow%20on%20X-000000?style=for-the-badge&logo=x&logoColor=white" alt="Follow on X" />
@@ -27,11 +37,13 @@
Crawl4AI turns the web into clean, LLM ready Markdown for RAG, agents, and data pipelines. Fast, controllable, battle tested by a 50k+ star community.
[✨ Check out latest update v0.7.4](#-recent-updates)
[✨ Check out latest update v0.8.0](#-recent-updates)
✨ New in v0.7.4: Revolutionary LLM Table Extraction with intelligent chunking, enhanced concurrency fixes, memory management refactor, and critical stability improvements. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.4.md)
**New in v0.8.0**: Crash Recovery & Prefetch Mode! Deep crawl crash recovery with `resume_state` and `on_state_change` callbacks for long-running crawls. New `prefetch=True` mode for 5-10x faster URL discovery. Critical security fixes for Docker API (hooks disabled by default, file:// URLs blocked). [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.8.0.md)
✨ Recent v0.7.3: Undetected Browser Support, Multi-URL Configurations, Memory Monitoring, Enhanced Table Extraction, GitHub Sponsors. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.3.md)
✨ Recent v0.7.8: Stability & Bug Fix Release! 11 bug fixes addressing Docker API issues, LLM extraction improvements, URL handling fixes, and dependency updates. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.8.md)
✨ Previous v0.7.7: Complete Self-Hosting Platform with Real-time Monitoring! Enterprise-grade monitoring dashboard, comprehensive REST API, WebSocket streaming, and smart browser pool management. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.7.md)
<details>
<summary>🤓 <strong>My Personal Story</strong></summary>
@@ -177,7 +189,7 @@ No rate-limited APIs. No lock-in. Build and own your data pipeline with direct g
- 📸 **Screenshots**: Capture page screenshots during crawling for debugging or analysis.
- 📂 **Raw Data Crawling**: Directly process raw HTML (`raw:`) or local files (`file://`).
- 🔗 **Comprehensive Link Extraction**: Extracts internal, external links, and embedded iframe content.
- 🛠️ **Customizable Hooks**: Define hooks at every step to customize crawling behavior.
- 🛠️ **Customizable Hooks**: Define hooks at every step to customize crawling behavior (supports both string and function-based APIs).
- 💾 **Caching**: Cache data for improved speed and to avoid redundant fetches.
- 📄 **Metadata Extraction**: Retrieve structured metadata from web pages.
- 📡 **IFrame Content Extraction**: Seamless extraction from embedded iframe content.
@@ -294,6 +306,7 @@ pip install -e ".[all]" # Install all optional features
### New Docker Features
The new Docker implementation includes:
- **Real-time Monitoring Dashboard** with live system metrics and browser pool visibility
- **Browser pooling** with page pre-warming for faster response times
- **Interactive playground** to test and generate request code
- **MCP integration** for direct connection to AI tools like Claude Code
@@ -308,7 +321,8 @@ The new Docker implementation includes:
docker pull unclecode/crawl4ai:latest
docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:latest
# Visit the playground at http://localhost:11235/playground
# Visit the monitoring dashboard at http://localhost:11235/dashboard
# Or the playground at http://localhost:11235/playground
```
### Quick Test
@@ -337,7 +351,7 @@ else:
result = requests.get(f"http://localhost:11235/task/{task_id}")
```
For more examples, see our [Docker Examples](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/docker_example.py). For advanced configuration, environment variables, and usage examples, see our [Docker Deployment Guide](https://docs.crawl4ai.com/basic/docker-deployment/).
For more examples, see our [Docker Examples](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/docker_example.py). For advanced configuration, monitoring features, and production deployment, see our [Self-Hosting Guide](https://docs.crawl4ai.com/core/self-hosting/).
</details>
@@ -542,8 +556,199 @@ async def test_news_crawl():
</details>
---
> **💡 Tip:** Some websites may use **CAPTCHA** based verification mechanisms to prevent automated access. If your workflow encounters such challenges, you may optionally integrate a third-party CAPTCHA-handling service such as <strong>[CapSolver](https://www.capsolver.com/blog/Partners/crawl4ai-capsolver/?utm_source=crawl4ai&utm_medium=github_pr&utm_campaign=crawl4ai_integration)</strong>. They support reCAPTCHA v2/v3, Cloudflare Turnstile, Challenge, AWS WAF, and more. Please ensure that your usage complies with the target websites terms of service and applicable laws.
## ✨ Recent Updates
<details open>
<summary><strong>Version 0.8.0 Release Highlights - Crash Recovery & Prefetch Mode</strong></summary>
This release introduces crash recovery for deep crawls, a new prefetch mode for fast URL discovery, and critical security fixes for Docker deployments.
- **🔄 Deep Crawl Crash Recovery**:
- `on_state_change` callback fires after each URL for real-time state persistence
- `resume_state` parameter to continue from a saved checkpoint
- JSON-serializable state for Redis/database storage
- Works with BFS, DFS, and Best-First strategies
```python
from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
strategy = BFSDeepCrawlStrategy(
max_depth=3,
resume_state=saved_state, # Continue from checkpoint
on_state_change=save_to_redis, # Called after each URL
)
```
- **⚡ Prefetch Mode for Fast URL Discovery**:
- `prefetch=True` skips markdown, extraction, and media processing
- 5-10x faster than full processing
- Perfect for two-phase crawling: discover first, process selectively
```python
config = CrawlerRunConfig(prefetch=True)
result = await crawler.arun("https://example.com", config=config)
# Returns HTML and links only - no markdown generation
```
- **🔒 Security Fixes (Docker API)**:
- Hooks disabled by default (`CRAWL4AI_HOOKS_ENABLED=false`)
- `file://` URLs blocked on API endpoints to prevent LFI
- `__import__` removed from hook execution sandbox
[Full v0.8.0 Release Notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.8.0.md)
</details>
<details>
<summary><strong>Version 0.7.8 Release Highlights - Stability & Bug Fix Release</strong></summary>
This release focuses on stability with 11 bug fixes addressing issues reported by the community. No new features, but significant improvements to reliability.
- **🐳 Docker API Fixes**:
- Fixed `ContentRelevanceFilter` deserialization in deep crawl requests (#1642)
- Fixed `ProxyConfig` JSON serialization in `BrowserConfig.to_dict()` (#1629)
- Fixed `.cache` folder permissions in Docker image (#1638)
- **🤖 LLM Extraction Improvements**:
- Configurable rate limiter backoff with new `LLMConfig` parameters (#1269):
```python
from crawl4ai import LLMConfig
config = LLMConfig(
provider="openai/gpt-4o-mini",
backoff_base_delay=5, # Wait 5s on first retry
backoff_max_attempts=5, # Try up to 5 times
backoff_exponential_factor=3 # Multiply delay by 3 each attempt
)
```
- HTML input format support for `LLMExtractionStrategy` (#1178):
```python
from crawl4ai import LLMExtractionStrategy
strategy = LLMExtractionStrategy(
llm_config=config,
instruction="Extract table data",
input_format="html" # Now supports: "html", "markdown", "fit_markdown"
)
```
- Fixed raw HTML URL variable - extraction strategies now receive `"Raw HTML"` instead of HTML blob (#1116)
- **🔗 URL Handling**:
- Fixed relative URL resolution after JavaScript redirects (#1268)
- Fixed import statement formatting in extracted code (#1181)
- **📦 Dependency Updates**:
- Replaced deprecated PyPDF2 with pypdf (#1412)
- Pydantic v2 ConfigDict compatibility - no more deprecation warnings (#678)
- **🧠 AdaptiveCrawler**:
- Fixed query expansion to actually use LLM instead of hardcoded mock data (#1621)
[Full v0.7.8 Release Notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.8.md)
</details>
<details>
<summary><strong>Version 0.7.7 Release Highlights - The Self-Hosting & Monitoring Update</strong></summary>
- **📊 Real-time Monitoring Dashboard**: Interactive web UI with live system metrics and browser pool visibility
```python
# Access the monitoring dashboard
# Visit: http://localhost:11235/dashboard
# Real-time metrics include:
# - System health (CPU, memory, network, uptime)
# - Active and completed request tracking
# - Browser pool management (permanent/hot/cold)
# - Janitor cleanup events
# - Error monitoring with full context
```
- **🔌 Comprehensive Monitor API**: Complete REST API for programmatic access to all monitoring data
```python
import httpx
async with httpx.AsyncClient() as client:
# System health
health = await client.get("http://localhost:11235/monitor/health")
# Request tracking
requests = await client.get("http://localhost:11235/monitor/requests")
# Browser pool status
browsers = await client.get("http://localhost:11235/monitor/browsers")
# Endpoint statistics
stats = await client.get("http://localhost:11235/monitor/endpoints/stats")
```
- **⚡ WebSocket Streaming**: Real-time updates every 2 seconds for custom dashboards
- **🔥 Smart Browser Pool**: 3-tier architecture (permanent/hot/cold) with automatic promotion and cleanup
- **🧹 Janitor System**: Automatic resource management with event logging
- **🎮 Control Actions**: Manual browser management (kill, restart, cleanup) via API
- **📈 Production Metrics**: 6 critical metrics for operational excellence with Prometheus integration
- **🐛 Critical Bug Fixes**:
- Fixed async LLM extraction blocking issue (#1055)
- Enhanced DFS deep crawl strategy (#1607)
- Fixed sitemap parsing in AsyncUrlSeeder (#1598)
- Resolved browser viewport configuration (#1495)
- Fixed CDP timing with exponential backoff (#1528)
- Security update for pyOpenSSL (>=25.3.0)
[Full v0.7.7 Release Notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.7.md)
</details>
<details>
<summary><strong>Version 0.7.5 Release Highlights - The Docker Hooks & Security Update</strong></summary>
- **🔧 Docker Hooks System**: Complete pipeline customization with user-provided Python functions at 8 key points
- **✨ Function-Based Hooks API (NEW)**: Write hooks as regular Python functions with full IDE support:
```python
from crawl4ai import hooks_to_string
from crawl4ai.docker_client import Crawl4aiDockerClient
# Define hooks as regular Python functions
async def on_page_context_created(page, context, **kwargs):
"""Block images to speed up crawling"""
await context.route("**/*.{png,jpg,jpeg,gif,webp}", lambda route: route.abort())
await page.set_viewport_size({"width": 1920, "height": 1080})
return page
async def before_goto(page, context, url, **kwargs):
"""Add custom headers"""
await page.set_extra_http_headers({'X-Crawl4AI': 'v0.7.5'})
return page
# Option 1: Use hooks_to_string() utility for REST API
hooks_code = hooks_to_string({
"on_page_context_created": on_page_context_created,
"before_goto": before_goto
})
# Option 2: Docker client with automatic conversion (Recommended)
client = Crawl4aiDockerClient(base_url="http://localhost:11235")
results = await client.crawl(
urls=["https://httpbin.org/html"],
hooks={
"on_page_context_created": on_page_context_created,
"before_goto": before_goto
}
)
# ✓ Full IDE support, type checking, and reusability!
```
- **🤖 Enhanced LLM Integration**: Custom providers with temperature control and base_url configuration
- **🔒 HTTPS Preservation**: Secure internal link handling with `preserve_https_for_internal_links=True`
- **🐍 Python 3.10+ Support**: Modern language features and enhanced performance
- **🛠️ Bug Fixes**: Resolved multiple community-reported issues including URL processing, JWT authentication, and proxy configuration
[Full v0.7.5 Release Notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.5.md)
</details>
<details>
<summary><strong>Version 0.7.4 Release Highlights - The Intelligent Table Extraction & Performance Update</strong></summary>
@@ -919,6 +1124,40 @@ We envision a future where AI is powered by real human knowledge, ensuring data
For more details, see our [full mission statement](./MISSION.md).
</details>
## 🌟 Current Sponsors
### 🏢 Enterprise Sponsors & Partners
Our enterprise sponsors and technology partners help scale Crawl4AI to power production-grade data pipelines.
| Company | About | Sponsorship Tier |
|------|------|----------------------------|
| <a href="https://app.nstproxy.com/register?i=ecOqW9" target="_blank"><picture><source width="250" media="(prefers-color-scheme: dark)" srcset="https://gist.github.com/aravindkarnam/62f82bd4818d3079d9dd3c31df432cf8/raw/nst-light.svg"><source width="250" media="(prefers-color-scheme: light)" srcset="https://www.nstproxy.com/logo.svg"><img alt="nstproxy" src="ttps://www.nstproxy.com/logo.svg"></picture></a> | NstProxy is a trusted proxy provider with over 110M+ real residential IPs, city-level targeting, 99.99% uptime, and low pricing at $0.1/GB, it delivers unmatched stability, scale, and cost-efficiency. | 🥈 Silver |
| <a href="https://app.scrapeless.com/passport/register?utm_source=official&utm_term=crawl4ai" target="_blank"><picture><source width="250" media="(prefers-color-scheme: dark)" srcset="https://gist.githubusercontent.com/aravindkarnam/0d275b942705604263e5c32d2db27bc1/raw/Scrapeless-light-logo.svg"><source width="250" media="(prefers-color-scheme: light)" srcset="https://gist.githubusercontent.com/aravindkarnam/22d0525cc0f3021bf19ebf6e11a69ccd/raw/Scrapeless-dark-logo.svg"><img alt="Scrapeless" src="https://gist.githubusercontent.com/aravindkarnam/22d0525cc0f3021bf19ebf6e11a69ccd/raw/Scrapeless-dark-logo.svg"></picture></a> | Scrapeless provides production-grade infrastructure for Crawling, Automation, and AI Agents, offering Scraping Browser, 4 Proxy Types and Universal Scraping API. | 🥈 Silver |
| <a href="https://dashboard.capsolver.com/passport/register?inviteCode=ESVSECTX5Q23" target="_blank"><picture><source width="120" media="(prefers-color-scheme: dark)" srcset="https://docs.crawl4ai.com/uploads/sponsors/20251013045338_72a71fa4ee4d2f40.png"><source width="120" media="(prefers-color-scheme: light)" srcset="https://www.capsolver.com/assets/images/logo-text.png"><img alt="Capsolver" src="https://www.capsolver.com/assets/images/logo-text.png"></picture></a> | AI-powered Captcha solving service. Supports all major Captcha types, including reCAPTCHA, Cloudflare, and more | 🥉 Bronze |
| <a href="https://kipo.ai" target="_blank"><img src="https://docs.crawl4ai.com/uploads/sponsors/20251013045751_2d54f57f117c651e.png" alt="DataSync" width="120"/></a> | Helps engineers and buyers find, compare, and source electronic & industrial parts in seconds, with specs, pricing, lead times & alternatives.| 🥇 Gold |
| <a href="https://www.kidocode.com/" target="_blank"><img src="https://docs.crawl4ai.com/uploads/sponsors/20251013045045_bb8dace3f0440d65.svg" alt="Kidocode" width="120"/><p align="center">KidoCode</p></a> | Kidocode is a hybrid technology and entrepreneurship school for kids aged 518, offering both online and on-campus education. | 🥇 Gold |
| <a href="https://www.alephnull.sg/" target="_blank"><img src="https://docs.crawl4ai.com/uploads/sponsors/20251013050323_a9e8e8c4c3650421.svg" alt="Aleph null" width="120"/></a> | Singapore-based Aleph Null is Asias leading edtech hub, dedicated to student-centric, AI-driven education—empowering learners with the tools to thrive in a fast-changing world. | 🥇 Gold |
### 🧑‍🤝 Individual Sponsors
A heartfelt thanks to our individual supporters! Every contribution helps us keep our opensource mission alive and thriving!
<p align="left">
<a href="https://github.com/hafezparast"><img src="https://avatars.githubusercontent.com/u/14273305?s=60&v=4" style="border-radius:50%;" width="64px;"/></a>
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</p>
> Want to join them? [Sponsor Crawl4AI →](https://github.com/sponsors/unclecode)
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=unclecode/crawl4ai&type=Date)](https://star-history.com/#unclecode/crawl4ai&Date)

122
SECURITY.md Normal file
View File

@@ -0,0 +1,122 @@
# Security Policy
## Supported Versions
| Version | Supported |
| ------- | ------------------ |
| 0.8.x | :white_check_mark: |
| 0.7.x | :x: (upgrade recommended) |
| < 0.7 | :x: |
## Reporting a Vulnerability
We take security vulnerabilities seriously. If you discover a security issue, please report it responsibly.
### How to Report
**DO NOT** open a public GitHub issue for security vulnerabilities.
Instead, please report via one of these methods:
1. **GitHub Security Advisories (Preferred)**
- Go to [Security Advisories](https://github.com/unclecode/crawl4ai/security/advisories)
- Click "New draft security advisory"
- Fill in the details
2. **Email**
- Send details to: security@crawl4ai.com
- Use subject: `[SECURITY] Brief description`
- Include:
- Description of the vulnerability
- Steps to reproduce
- Potential impact
- Any suggested fixes
### What to Expect
- **Acknowledgment**: Within 48 hours
- **Initial Assessment**: Within 7 days
- **Resolution Timeline**: Depends on severity
- Critical: 24-72 hours
- High: 7 days
- Medium: 30 days
- Low: 90 days
### Disclosure Policy
- We follow responsible disclosure practices
- We will coordinate with you on disclosure timing
- Credit will be given to reporters (unless anonymity is requested)
- We may request CVE assignment for significant vulnerabilities
## Security Best Practices for Users
### Docker API Deployment
If you're running the Crawl4AI Docker API in production:
1. **Enable Authentication**
```yaml
# config.yml
security:
enabled: true
jwt_enabled: true
```
```bash
# Set a strong secret key
export SECRET_KEY="your-secure-random-key-here"
```
2. **Hooks are Disabled by Default** (v0.8.0+)
- Only enable if you trust all API users
- Set `CRAWL4AI_HOOKS_ENABLED=true` only when necessary
3. **Network Security**
- Run behind a reverse proxy (nginx, traefik)
- Use HTTPS in production
- Restrict access to trusted IPs if possible
4. **Container Security**
- Run as non-root user (default in our container)
- Use read-only filesystem where possible
- Limit container resources
### Library Usage
When using Crawl4AI as a Python library:
1. **Validate URLs** before crawling untrusted input
2. **Sanitize extracted content** before using in other systems
3. **Be cautious with hooks** - they execute arbitrary code
## Known Security Issues
### Fixed in v0.8.0
| ID | Severity | Description | Fix |
|----|----------|-------------|-----|
| CVE-pending-1 | CRITICAL | RCE via hooks `__import__` | Removed from allowed builtins |
| CVE-pending-2 | HIGH | LFI via `file://` URLs | URL scheme validation added |
See [Security Advisory](https://github.com/unclecode/crawl4ai/security/advisories) for details.
## Security Features
### v0.8.0+
- **URL Scheme Validation**: Blocks `file://`, `javascript:`, `data:` URLs on API
- **Hooks Disabled by Default**: Opt-in via `CRAWL4AI_HOOKS_ENABLED=true`
- **Restricted Hook Builtins**: No `__import__`, `eval`, `exec`, `open`
- **JWT Authentication**: Optional but recommended for production
- **Rate Limiting**: Configurable request limits
- **Security Headers**: X-Frame-Options, CSP, HSTS when enabled
## Acknowledgments
We thank the following security researchers for responsibly disclosing vulnerabilities:
- **[Neo by ProjectDiscovery](https://projectdiscovery.io/blog/introducing-neo)** - RCE and LFI vulnerabilities (December 2025)
---
*Last updated: January 2026*

View File

@@ -72,6 +72,8 @@ from .deep_crawling import (
BestFirstCrawlingStrategy,
DFSDeepCrawlStrategy,
DeepCrawlDecorator,
ContentRelevanceFilter,
ContentTypeScorer,
)
# NEW: Import AsyncUrlSeeder
from .async_url_seeder import AsyncUrlSeeder
@@ -103,7 +105,8 @@ from .browser_adapter import (
from .utils import (
start_colab_display_server,
setup_colab_environment
setup_colab_environment,
hooks_to_string
)
__all__ = [
@@ -183,6 +186,7 @@ __all__ = [
"ProxyConfig",
"start_colab_display_server",
"setup_colab_environment",
"hooks_to_string",
# C4A Script additions
"c4a_compile",
"c4a_validate",

View File

@@ -1,7 +1,7 @@
# crawl4ai/__version__.py
# This is the version that will be used for stable releases
__version__ = "0.7.4"
__version__ = "0.8.0"
# For nightly builds, this gets set during build process
__nightly_version__ = None

View File

@@ -728,18 +728,18 @@ class EmbeddingStrategy(CrawlStrategy):
provider = llm_config_dict.get('provider', 'openai/gpt-4o-mini') if llm_config_dict else 'openai/gpt-4o-mini'
api_token = llm_config_dict.get('api_token') if llm_config_dict else None
# response = perform_completion_with_backoff(
# provider=provider,
# prompt_with_variables=prompt,
# api_token=api_token,
# json_response=True
# )
response = perform_completion_with_backoff(
provider=provider,
prompt_with_variables=prompt,
api_token=api_token,
json_response=True
)
# variations = json.loads(response.choices[0].message.content)
variations = json.loads(response.choices[0].message.content)
# # Mock data with more variations for split
variations ={'queries': ['what are the best vegetables to use in fried rice?', 'how do I make vegetable fried rice from scratch?', 'can you provide a quick recipe for vegetable fried rice?', 'what cooking techniques are essential for perfect fried rice with vegetables?', 'how to add flavor to vegetable fried rice?', 'are there any tips for making healthy fried rice with vegetables?']}
# variations ={'queries': ['what are the best vegetables to use in fried rice?', 'how do I make vegetable fried rice from scratch?', 'can you provide a quick recipe for vegetable fried rice?', 'what cooking techniques are essential for perfect fried rice with vegetables?', 'how to add flavor to vegetable fried rice?', 'are there any tips for making healthy fried rice with vegetables?']}
# variations = {'queries': [

View File

@@ -1,6 +1,7 @@
import importlib
import os
from typing import Union
import warnings
import requests
from .config import (
DEFAULT_PROVIDER,
DEFAULT_PROVIDER_API_KEY,
@@ -26,14 +27,14 @@ from .table_extraction import TableExtractionStrategy, DefaultTableExtraction
from .cache_context import CacheMode
from .proxy_strategy import ProxyRotationStrategy
from typing import Union, List, Callable
import inspect
from typing import Any, Dict, Optional
from typing import Any, Callable, Dict, List, Optional, Union
from enum import Enum
# Type alias for URL matching
UrlMatcher = Union[str, Callable[[str], bool], List[Union[str, Callable[[str], bool]]]]
class MatchMode(Enum):
OR = "or"
AND = "and"
@@ -41,8 +42,7 @@ class MatchMode(Enum):
# from .proxy_strategy import ProxyConfig
def to_serializable_dict(obj: Any, ignore_default_value : bool = False) -> Dict:
def to_serializable_dict(obj: Any, ignore_default_value : bool = False):
"""
Recursively convert an object to a serializable dictionary using {type, params} structure
for complex objects.
@@ -109,8 +109,6 @@ def to_serializable_dict(obj: Any, ignore_default_value : bool = False) -> Dict:
# if value is not None:
# current_values[attr_name] = to_serializable_dict(value)
return {
"type": obj.__class__.__name__,
"params": current_values
@@ -136,12 +134,20 @@ def from_serializable_dict(data: Any) -> Any:
if data["type"] == "dict" and "value" in data:
return {k: from_serializable_dict(v) for k, v in data["value"].items()}
# Import from crawl4ai for class instances
import crawl4ai
if hasattr(crawl4ai, data["type"]):
cls = getattr(crawl4ai, data["type"])
cls = None
# If you are receiving an error while trying to convert a dict to an object:
# Either add a module to `modules_paths` list, or add the `data["type"]` to the crawl4ai __init__.py file
module_paths = ["crawl4ai"]
for module_path in module_paths:
try:
mod = importlib.import_module(module_path)
if hasattr(mod, data["type"]):
cls = getattr(mod, data["type"])
break
except (ImportError, AttributeError):
continue
if cls is not None:
# Handle Enum
if issubclass(cls, Enum):
return cls(data["params"])
@@ -367,6 +373,20 @@ class BrowserConfig:
use_managed_browser (bool): Launch the browser using a managed approach (e.g., via CDP), allowing
advanced manipulation. Default: False.
cdp_url (str): URL for the Chrome DevTools Protocol (CDP) endpoint. Default: "ws://localhost:9222/devtools/browser/".
browser_context_id (str or None): Pre-existing CDP browser context ID to use. When provided along with
cdp_url, the crawler will reuse this context instead of creating a new one.
Useful for cloud browser services that pre-create isolated contexts.
Default: None.
target_id (str or None): Pre-existing CDP target ID (page) to use. When provided along with
browser_context_id, the crawler will reuse this target instead of creating
a new page. Default: None.
cdp_cleanup_on_close (bool): When True and using cdp_url, the close() method will still clean up
the local Playwright client resources. Useful for cloud/server scenarios
where you don't own the remote browser but need to prevent memory leaks
from accumulated Playwright instances. Default: False.
create_isolated_context (bool): When True and using cdp_url, 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. Default: False.
debugging_port (int): Port for the browser debugging protocol. Default: 9222.
use_persistent_context (bool): Use a persistent browser context (like a persistent profile).
Automatically sets use_managed_browser=True. Default: False.
@@ -421,6 +441,10 @@ class BrowserConfig:
browser_mode: str = "dedicated",
use_managed_browser: bool = False,
cdp_url: str = None,
browser_context_id: str = None,
target_id: str = None,
cdp_cleanup_on_close: bool = False,
create_isolated_context: bool = False,
use_persistent_context: bool = False,
user_data_dir: str = None,
chrome_channel: str = "chromium",
@@ -453,13 +477,18 @@ class BrowserConfig:
debugging_port: int = 9222,
host: str = "localhost",
enable_stealth: bool = False,
init_scripts: List[str] = None,
):
self.browser_type = browser_type
self.headless = headless
self.headless = headless
self.browser_mode = browser_mode
self.use_managed_browser = use_managed_browser
self.cdp_url = cdp_url
self.browser_context_id = browser_context_id
self.target_id = target_id
self.cdp_cleanup_on_close = cdp_cleanup_on_close
self.create_isolated_context = create_isolated_context
self.use_persistent_context = use_persistent_context
self.user_data_dir = user_data_dir
self.chrome_channel = chrome_channel or self.browser_type or "chromium"
@@ -508,6 +537,7 @@ class BrowserConfig:
self.debugging_port = debugging_port
self.host = host
self.enable_stealth = enable_stealth
self.init_scripts = init_scripts if init_scripts is not None else []
fa_user_agenr_generator = ValidUAGenerator()
if self.user_agent_mode == "random":
@@ -555,6 +585,10 @@ class BrowserConfig:
browser_mode=kwargs.get("browser_mode", "dedicated"),
use_managed_browser=kwargs.get("use_managed_browser", False),
cdp_url=kwargs.get("cdp_url"),
browser_context_id=kwargs.get("browser_context_id"),
target_id=kwargs.get("target_id"),
cdp_cleanup_on_close=kwargs.get("cdp_cleanup_on_close", False),
create_isolated_context=kwargs.get("create_isolated_context", False),
use_persistent_context=kwargs.get("use_persistent_context", False),
user_data_dir=kwargs.get("user_data_dir"),
chrome_channel=kwargs.get("chrome_channel", "chromium"),
@@ -583,6 +617,7 @@ class BrowserConfig:
debugging_port=kwargs.get("debugging_port", 9222),
host=kwargs.get("host", "localhost"),
enable_stealth=kwargs.get("enable_stealth", False),
init_scripts=kwargs.get("init_scripts", []),
)
def to_dict(self):
@@ -592,12 +627,16 @@ class BrowserConfig:
"browser_mode": self.browser_mode,
"use_managed_browser": self.use_managed_browser,
"cdp_url": self.cdp_url,
"browser_context_id": self.browser_context_id,
"target_id": self.target_id,
"cdp_cleanup_on_close": self.cdp_cleanup_on_close,
"create_isolated_context": self.create_isolated_context,
"use_persistent_context": self.use_persistent_context,
"user_data_dir": self.user_data_dir,
"chrome_channel": self.chrome_channel,
"channel": self.channel,
"proxy": self.proxy,
"proxy_config": self.proxy_config,
"proxy_config": self.proxy_config.to_dict() if self.proxy_config else None,
"viewport_width": self.viewport_width,
"viewport_height": self.viewport_height,
"accept_downloads": self.accept_downloads,
@@ -618,9 +657,10 @@ class BrowserConfig:
"debugging_port": self.debugging_port,
"host": self.host,
"enable_stealth": self.enable_stealth,
"init_scripts": self.init_scripts,
}
return result
def clone(self, **kwargs):
@@ -649,6 +689,85 @@ class BrowserConfig:
return config
return BrowserConfig.from_kwargs(config)
def set_nstproxy(
self,
token: str,
channel_id: str,
country: str = "ANY",
state: str = "",
city: str = "",
protocol: str = "http",
session_duration: int = 10,
):
"""
Fetch a proxy from NSTProxy API and automatically assign it to proxy_config.
Get your NSTProxy token from: https://app.nstproxy.com/profile
Args:
token (str): NSTProxy API token.
channel_id (str): NSTProxy channel ID.
country (str, optional): Country code (default: "ANY").
state (str, optional): State code (default: "").
city (str, optional): City name (default: "").
protocol (str, optional): Proxy protocol ("http" or "socks5"). Defaults to "http".
session_duration (int, optional): Session duration in minutes (0 = rotate each request). Defaults to 10.
Raises:
ValueError: If the API response format is invalid.
PermissionError: If the API returns an error message.
"""
# --- Validate input early ---
if not token or not channel_id:
raise ValueError("[NSTProxy] token and channel_id are required")
if protocol not in ("http", "socks5"):
raise ValueError(f"[NSTProxy] Invalid protocol: {protocol}")
# --- Build NSTProxy API URL ---
params = {
"fType": 2,
"count": 1,
"channelId": channel_id,
"country": country,
"protocol": protocol,
"sessionDuration": session_duration,
"token": token,
}
if state:
params["state"] = state
if city:
params["city"] = city
url = "https://api.nstproxy.com/api/v1/generate/apiproxies"
try:
response = requests.get(url, params=params, timeout=10)
response.raise_for_status()
data = response.json()
# --- Handle API error response ---
if isinstance(data, dict) and data.get("err"):
raise PermissionError(f"[NSTProxy] API Error: {data.get('msg', 'Unknown error')}")
if not isinstance(data, list) or not data:
raise ValueError("[NSTProxy] Invalid API response — expected a non-empty list")
proxy_info = data[0]
# --- Apply proxy config ---
self.proxy_config = ProxyConfig(
server=f"{protocol}://{proxy_info['ip']}:{proxy_info['port']}",
username=proxy_info["username"],
password=proxy_info["password"],
)
except Exception as e:
print(f"[NSTProxy] ❌ Failed to set proxy: {e}")
raise
class VirtualScrollConfig:
"""Configuration for virtual scroll handling.
@@ -914,6 +1033,18 @@ class CrawlerRunConfig():
proxy_config (ProxyConfig or dict or None): Detailed proxy configuration, e.g. {"server": "...", "username": "..."}.
If None, no additional proxy config. Default: None.
# Sticky Proxy Session Parameters
proxy_session_id (str or None): When set, maintains the same proxy for all requests sharing this session ID.
The proxy is acquired on first request and reused for subsequent requests.
Session expires when explicitly released or crawler context is closed.
Default: None.
proxy_session_ttl (int or None): Time-to-live for sticky session in seconds.
After TTL expires, a new proxy is acquired on next request.
Default: None (session lasts until explicitly released or crawler closes).
proxy_session_auto_release (bool): If True, automatically release the proxy session after a batch operation.
Useful for arun_many() to clean up sessions automatically.
Default: False.
# Browser Location and Identity Parameters
locale (str or None): Locale to use for the browser context (e.g., "en-US").
Default: None.
@@ -942,6 +1073,15 @@ class CrawlerRunConfig():
shared_data (dict or None): Shared data to be passed between hooks.
Default: None.
# Cache Validation Parameters (Smart Cache)
check_cache_freshness (bool): If True, validates cached content freshness using HTTP
conditional requests (ETag/Last-Modified) and head fingerprinting
before returning cached results. Avoids full browser crawls when
content hasn't changed. Only applies when cache_mode allows reads.
Default: False.
cache_validation_timeout (float): Timeout in seconds for cache validation HTTP requests.
Default: 10.0.
# Page Navigation and Timing Parameters
wait_until (str): The condition to wait for when navigating, e.g. "domcontentloaded".
Default: "domcontentloaded".
@@ -1048,6 +1188,12 @@ class CrawlerRunConfig():
# Connection Parameters
stream (bool): If True, enables streaming of crawled URLs as they are processed when used with arun_many.
Default: False.
process_in_browser (bool): If True, forces raw:/file:// URLs to be processed through the browser
pipeline (enabling js_code, wait_for, scrolling, etc.). When False (default),
raw:/file:// URLs use a fast path that returns HTML directly without browser
interaction. This is automatically enabled when browser-requiring parameters
are detected (js_code, wait_for, screenshot, pdf, etc.).
Default: False.
check_robots_txt (bool): Whether to check robots.txt rules before crawling. Default: False
Default: False.
@@ -1093,6 +1239,10 @@ class CrawlerRunConfig():
scraping_strategy: ContentScrapingStrategy = None,
proxy_config: Union[ProxyConfig, dict, None] = None,
proxy_rotation_strategy: Optional[ProxyRotationStrategy] = None,
# Sticky Proxy Session Parameters
proxy_session_id: Optional[str] = None,
proxy_session_ttl: Optional[int] = None,
proxy_session_auto_release: bool = False,
# Browser Location and Identity Parameters
locale: Optional[str] = None,
timezone_id: Optional[str] = None,
@@ -1107,6 +1257,9 @@ class CrawlerRunConfig():
no_cache_read: bool = False,
no_cache_write: bool = False,
shared_data: dict = None,
# Cache Validation Parameters (Smart Cache)
check_cache_freshness: bool = False,
cache_validation_timeout: float = 10.0,
# Page Navigation and Timing Parameters
wait_until: str = "domcontentloaded",
page_timeout: int = PAGE_TIMEOUT,
@@ -1160,7 +1313,10 @@ class CrawlerRunConfig():
# Connection Parameters
method: str = "GET",
stream: bool = False,
prefetch: bool = False, # When True, return only HTML + links (skip heavy processing)
process_in_browser: bool = False, # Force browser processing for raw:/file:// URLs
url: str = None,
base_url: str = None, # Base URL for markdown link resolution (used with raw: HTML)
check_robots_txt: bool = False,
user_agent: str = None,
user_agent_mode: str = None,
@@ -1179,6 +1335,7 @@ class CrawlerRunConfig():
):
# TODO: Planning to set properties dynamically based on the __init__ signature
self.url = url
self.base_url = base_url # Base URL for markdown link resolution
# Content Processing Parameters
self.word_count_threshold = word_count_threshold
@@ -1203,7 +1360,12 @@ class CrawlerRunConfig():
self.proxy_config = ProxyConfig.from_string(proxy_config)
self.proxy_rotation_strategy = proxy_rotation_strategy
# Sticky Proxy Session Parameters
self.proxy_session_id = proxy_session_id
self.proxy_session_ttl = proxy_session_ttl
self.proxy_session_auto_release = proxy_session_auto_release
# Browser Location and Identity Parameters
self.locale = locale
self.timezone_id = timezone_id
@@ -1220,6 +1382,9 @@ class CrawlerRunConfig():
self.no_cache_read = no_cache_read
self.no_cache_write = no_cache_write
self.shared_data = shared_data
# Cache Validation (Smart Cache)
self.check_cache_freshness = check_cache_freshness
self.cache_validation_timeout = cache_validation_timeout
# Page Navigation and Timing Parameters
self.wait_until = wait_until
@@ -1286,6 +1451,8 @@ class CrawlerRunConfig():
# Connection Parameters
self.stream = stream
self.prefetch = prefetch # Prefetch mode: return only HTML + links
self.process_in_browser = process_in_browser # Force browser processing for raw:/file:// URLs
self.method = method
# Robots.txt Handling Parameters
@@ -1483,6 +1650,10 @@ class CrawlerRunConfig():
scraping_strategy=kwargs.get("scraping_strategy"),
proxy_config=kwargs.get("proxy_config"),
proxy_rotation_strategy=kwargs.get("proxy_rotation_strategy"),
# Sticky Proxy Session Parameters
proxy_session_id=kwargs.get("proxy_session_id"),
proxy_session_ttl=kwargs.get("proxy_session_ttl"),
proxy_session_auto_release=kwargs.get("proxy_session_auto_release", False),
# Browser Location and Identity Parameters
locale=kwargs.get("locale", None),
timezone_id=kwargs.get("timezone_id", None),
@@ -1558,6 +1729,8 @@ class CrawlerRunConfig():
# Connection Parameters
method=kwargs.get("method", "GET"),
stream=kwargs.get("stream", False),
prefetch=kwargs.get("prefetch", False),
process_in_browser=kwargs.get("process_in_browser", False),
check_robots_txt=kwargs.get("check_robots_txt", False),
user_agent=kwargs.get("user_agent"),
user_agent_mode=kwargs.get("user_agent_mode"),
@@ -1567,6 +1740,7 @@ class CrawlerRunConfig():
# Link Extraction Parameters
link_preview_config=kwargs.get("link_preview_config"),
url=kwargs.get("url"),
base_url=kwargs.get("base_url"),
# URL Matching Parameters
url_matcher=kwargs.get("url_matcher"),
match_mode=kwargs.get("match_mode", MatchMode.OR),
@@ -1606,6 +1780,9 @@ class CrawlerRunConfig():
"scraping_strategy": self.scraping_strategy,
"proxy_config": self.proxy_config,
"proxy_rotation_strategy": self.proxy_rotation_strategy,
"proxy_session_id": self.proxy_session_id,
"proxy_session_ttl": self.proxy_session_ttl,
"proxy_session_auto_release": self.proxy_session_auto_release,
"locale": self.locale,
"timezone_id": self.timezone_id,
"geolocation": self.geolocation,
@@ -1662,6 +1839,8 @@ class CrawlerRunConfig():
"capture_console_messages": self.capture_console_messages,
"method": self.method,
"stream": self.stream,
"prefetch": self.prefetch,
"process_in_browser": self.process_in_browser,
"check_robots_txt": self.check_robots_txt,
"user_agent": self.user_agent,
"user_agent_mode": self.user_agent_mode,
@@ -1712,7 +1891,10 @@ class LLMConfig:
frequency_penalty: Optional[float] = None,
presence_penalty: Optional[float] = None,
stop: Optional[List[str]] = None,
n: Optional[int] = None,
n: Optional[int] = None,
backoff_base_delay: Optional[int] = None,
backoff_max_attempts: Optional[int] = None,
backoff_exponential_factor: Optional[int] = None,
):
"""Configuaration class for LLM provider and API token."""
self.provider = provider
@@ -1741,6 +1923,9 @@ class LLMConfig:
self.presence_penalty = presence_penalty
self.stop = stop
self.n = n
self.backoff_base_delay = backoff_base_delay if backoff_base_delay is not None else 2
self.backoff_max_attempts = backoff_max_attempts if backoff_max_attempts is not None else 3
self.backoff_exponential_factor = backoff_exponential_factor if backoff_exponential_factor is not None else 2
@staticmethod
def from_kwargs(kwargs: dict) -> "LLMConfig":
@@ -1754,7 +1939,10 @@ class LLMConfig:
frequency_penalty=kwargs.get("frequency_penalty"),
presence_penalty=kwargs.get("presence_penalty"),
stop=kwargs.get("stop"),
n=kwargs.get("n")
n=kwargs.get("n"),
backoff_base_delay=kwargs.get("backoff_base_delay"),
backoff_max_attempts=kwargs.get("backoff_max_attempts"),
backoff_exponential_factor=kwargs.get("backoff_exponential_factor")
)
def to_dict(self):
@@ -1768,7 +1956,10 @@ class LLMConfig:
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"stop": self.stop,
"n": self.n
"n": self.n,
"backoff_base_delay": self.backoff_base_delay,
"backoff_max_attempts": self.backoff_max_attempts,
"backoff_exponential_factor": self.backoff_exponential_factor
}
def clone(self, **kwargs):
@@ -1805,6 +1996,8 @@ class SeedingConfig:
score_threshold: Optional[float] = None,
scoring_method: str = "bm25",
filter_nonsense_urls: bool = True,
cache_ttl_hours: int = 24,
validate_sitemap_lastmod: bool = True,
):
"""
Initialize URL seeding configuration.
@@ -1836,10 +2029,14 @@ class SeedingConfig:
Requires extract_head=True. Default: None
score_threshold: Minimum relevance score (0.0-1.0) to include URL.
Only applies when query is provided. Default: None
scoring_method: Scoring algorithm to use. Currently only "bm25" is supported.
scoring_method: Scoring algorithm to use. Currently only "bm25" is supported.
Future: "semantic". Default: "bm25"
filter_nonsense_urls: Filter out utility URLs like robots.txt, sitemap.xml,
filter_nonsense_urls: Filter out utility URLs like robots.txt, sitemap.xml,
ads.txt, favicon.ico, etc. Default: True
cache_ttl_hours: Hours before sitemap cache expires. Set to 0 to disable TTL
(only lastmod validation). Default: 24
validate_sitemap_lastmod: If True, compares sitemap's <lastmod> with cache
timestamp and refetches if sitemap is newer. Default: True
"""
self.source = source
self.pattern = pattern
@@ -1856,6 +2053,8 @@ class SeedingConfig:
self.score_threshold = score_threshold
self.scoring_method = scoring_method
self.filter_nonsense_urls = filter_nonsense_urls
self.cache_ttl_hours = cache_ttl_hours
self.validate_sitemap_lastmod = validate_sitemap_lastmod
# Add to_dict, from_kwargs, and clone methods for consistency
def to_dict(self) -> Dict[str, Any]:

View File

@@ -452,48 +452,48 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
if url.startswith(("http://", "https://", "view-source:")):
return await self._crawl_web(url, config)
elif url.startswith("file://"):
# initialize empty lists for console messages
captured_console = []
# Process local file
local_file_path = url[7:] # Remove 'file://' prefix
if not os.path.exists(local_file_path):
raise FileNotFoundError(f"Local file not found: {local_file_path}")
with open(local_file_path, "r", encoding="utf-8") as f:
html = f.read()
if config.screenshot:
screenshot_data = await self._generate_screenshot_from_html(html)
if config.capture_console_messages:
page, context = await self.browser_manager.get_page(crawlerRunConfig=config)
captured_console = await self._capture_console_messages(page, url)
return AsyncCrawlResponse(
html=html,
response_headers=response_headers,
status_code=status_code,
screenshot=screenshot_data,
get_delayed_content=None,
console_messages=captured_console,
elif url.startswith("file://") or url.startswith("raw://") or url.startswith("raw:"):
# Check if browser processing is required for file:// or raw: URLs
needs_browser = (
config.process_in_browser or
config.screenshot or
config.pdf or
config.capture_mhtml or
config.js_code or
config.wait_for or
config.scan_full_page or
config.remove_overlay_elements or
config.simulate_user or
config.magic or
config.process_iframes or
config.capture_console_messages or
config.capture_network_requests
)
#####
# Since both "raw:" and "raw://" start with "raw:", the first condition is always true for both, so "raw://" will be sliced as "//...", which is incorrect.
# Fix: Check for "raw://" first, then "raw:"
# Also, the prefix "raw://" is actually 6 characters long, not 7, so it should be sliced accordingly: url[6:]
#####
elif url.startswith("raw://") or url.startswith("raw:"):
# Process raw HTML content
# raw_html = url[4:] if url[:4] == "raw:" else url[7:]
raw_html = url[6:] if url.startswith("raw://") else url[4:]
html = raw_html
if config.screenshot:
screenshot_data = await self._generate_screenshot_from_html(html)
if needs_browser:
# Route through _crawl_web() for full browser pipeline
# _crawl_web() will detect file:// and raw: URLs and use set_content()
return await self._crawl_web(url, config)
# Fast path: return HTML directly without browser interaction
if url.startswith("file://"):
# Process local file
local_file_path = url[7:] # Remove 'file://' prefix
if not os.path.exists(local_file_path):
raise FileNotFoundError(f"Local file not found: {local_file_path}")
with open(local_file_path, "r", encoding="utf-8") as f:
html = f.read()
else:
# Process raw HTML content (raw:// or raw:)
html = url[6:] if url.startswith("raw://") else url[4:]
return AsyncCrawlResponse(
html=html,
response_headers=response_headers,
status_code=status_code,
screenshot=screenshot_data,
screenshot=None,
pdf_data=None,
mhtml_data=None,
get_delayed_content=None,
)
else:
@@ -666,67 +666,83 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
if not config.js_only:
await self.execute_hook("before_goto", page, context=context, url=url, config=config)
try:
# Generate a unique nonce for this request
if config.experimental.get("use_csp_nonce", False):
nonce = hashlib.sha256(os.urandom(32)).hexdigest()
# Check if this is a file:// or raw: URL that needs set_content() instead of goto()
is_local_content = url.startswith("file://") or url.startswith("raw://") or url.startswith("raw:")
# Add CSP headers to the request
await page.set_extra_http_headers(
{
"Content-Security-Policy": f"default-src 'self'; script-src 'self' 'nonce-{nonce}' 'strict-dynamic'"
}
)
response = await page.goto(
url, wait_until=config.wait_until, timeout=config.page_timeout
)
redirected_url = page.url
except Error as e:
# Allow navigation to be aborted when downloading files
# This is expected behavior for downloads in some browser engines
if 'net::ERR_ABORTED' in str(e) and self.browser_config.accept_downloads:
self.logger.info(
message=f"Navigation aborted, likely due to file download: {url}",
tag="GOTO",
params={"url": url},
)
response = None
if is_local_content:
# Load local content using set_content() instead of network navigation
if url.startswith("file://"):
local_file_path = url[7:] # Remove 'file://' prefix
if not os.path.exists(local_file_path):
raise FileNotFoundError(f"Local file not found: {local_file_path}")
with open(local_file_path, "r", encoding="utf-8") as f:
html_content = f.read()
else:
raise RuntimeError(f"Failed on navigating ACS-GOTO:\n{str(e)}")
# raw:// or raw:
html_content = url[6:] if url.startswith("raw://") else url[4:]
await page.set_content(html_content, wait_until=config.wait_until)
response = None
redirected_url = config.base_url or url
status_code = 200
response_headers = {}
else:
# Standard web navigation with goto()
try:
# Generate a unique nonce for this request
if config.experimental.get("use_csp_nonce", False):
nonce = hashlib.sha256(os.urandom(32)).hexdigest()
# Add CSP headers to the request
await page.set_extra_http_headers(
{
"Content-Security-Policy": f"default-src 'self'; script-src 'self' 'nonce-{nonce}' 'strict-dynamic'"
}
)
response = await page.goto(
url, wait_until=config.wait_until, timeout=config.page_timeout
)
redirected_url = page.url
except Error as e:
# Allow navigation to be aborted when downloading files
# This is expected behavior for downloads in some browser engines
if 'net::ERR_ABORTED' in str(e) and self.browser_config.accept_downloads:
self.logger.info(
message=f"Navigation aborted, likely due to file download: {url}",
tag="GOTO",
params={"url": url},
)
response = None
else:
raise RuntimeError(f"Failed on navigating ACS-GOTO:\n{str(e)}")
# ──────────────────────────────────────────────────────────────
# Walk the redirect chain. Playwright returns only the last
# hop, so we trace the `request.redirected_from` links until the
# first response that differs from the final one and surface its
# status-code.
# ──────────────────────────────────────────────────────────────
if response is None:
status_code = 200
response_headers = {}
else:
first_resp = response
req = response.request
while req and req.redirected_from:
prev_req = req.redirected_from
prev_resp = await prev_req.response()
if prev_resp: # keep earliest
first_resp = prev_resp
req = prev_req
status_code = first_resp.status
response_headers = first_resp.headers
await self.execute_hook(
"after_goto", page, context=context, url=url, response=response, config=config
)
# ──────────────────────────────────────────────────────────────
# Walk the redirect chain. Playwright returns only the last
# hop, so we trace the `request.redirected_from` links until the
# first response that differs from the final one and surface its
# status-code.
# ──────────────────────────────────────────────────────────────
if response is None:
status_code = 200
response_headers = {}
else:
first_resp = response
req = response.request
while req and req.redirected_from:
prev_req = req.redirected_from
prev_resp = await prev_req.response()
if prev_resp: # keep earliest
first_resp = prev_resp
req = prev_req
status_code = first_resp.status
response_headers = first_resp.headers
# if response is None:
# status_code = 200
# response_headers = {}
# else:
# status_code = response.status
# response_headers = response.headers
else:
status_code = 200
response_headers = {}
@@ -1023,6 +1039,12 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
final_messages = await self.adapter.retrieve_console_messages(page)
captured_console.extend(final_messages)
###
# This ensures we capture the current page URL at the time we return the response,
# which correctly reflects any JavaScript navigation that occurred.
###
redirected_url = page.url # Use current page URL to capture JS redirects
# Return complete response
return AsyncCrawlResponse(
html=html,
@@ -1383,9 +1405,10 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
try:
await self.adapter.evaluate(page,
f"""
(() => {{
(async () => {{
try {{
{remove_overlays_js}
const removeOverlays = {remove_overlays_js};
await removeOverlays();
return {{ success: true }};
}} catch (error) {{
return {{
@@ -1517,7 +1540,78 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
await page.goto(file_path)
return captured_console
async def _generate_media_from_html(
self, html: str, config: CrawlerRunConfig = None
) -> tuple:
"""
Generate media (screenshot, PDF, MHTML) from raw HTML content.
This method is used for raw: and file:// URLs where we have HTML content
but need to render it in a browser to generate media outputs.
Args:
html (str): The raw HTML content to render
config (CrawlerRunConfig, optional): Configuration for media options
Returns:
tuple: (screenshot_data, pdf_data, mhtml_data) - any can be None
"""
page = None
screenshot_data = None
pdf_data = None
mhtml_data = None
try:
# Get a browser page
config = config or CrawlerRunConfig()
page, context = await self.browser_manager.get_page(crawlerRunConfig=config)
# Load the HTML content into the page
await page.set_content(html, wait_until="domcontentloaded")
# Generate requested media
if config.pdf:
pdf_data = await self.export_pdf(page)
if config.capture_mhtml:
mhtml_data = await self.capture_mhtml(page)
if config.screenshot:
if config.screenshot_wait_for:
await asyncio.sleep(config.screenshot_wait_for)
screenshot_height_threshold = getattr(config, 'screenshot_height_threshold', None)
screenshot_data = await self.take_screenshot(
page, screenshot_height_threshold=screenshot_height_threshold
)
return screenshot_data, pdf_data, mhtml_data
except Exception as e:
error_message = f"Failed to generate media from HTML: {str(e)}"
self.logger.error(
message="HTML media generation failed: {error}",
tag="ERROR",
params={"error": error_message},
)
# Return error image for screenshot if it was requested
if config and config.screenshot:
img = Image.new("RGB", (800, 600), color="black")
draw = ImageDraw.Draw(img)
font = ImageFont.load_default()
draw.text((10, 10), error_message, fill=(255, 255, 255), font=font)
buffered = BytesIO()
img.save(buffered, format="JPEG")
screenshot_data = base64.b64encode(buffered.getvalue()).decode("utf-8")
return screenshot_data, pdf_data, mhtml_data
finally:
# Clean up the page
if page:
try:
await page.close()
except Exception:
pass
async def take_screenshot(self, page, **kwargs) -> str:
"""
Take a screenshot of the current page.
@@ -2286,9 +2380,28 @@ class AsyncHTTPCrawlerStrategy(AsyncCrawlerStrategy):
)
def _format_proxy_url(self, proxy_config) -> str:
"""Format ProxyConfig into aiohttp-compatible proxy URL."""
if not proxy_config:
return None
server = proxy_config.server
username = getattr(proxy_config, 'username', None)
password = getattr(proxy_config, 'password', None)
if username and password:
# Insert credentials into URL: http://user:pass@host:port
if '://' in server:
protocol, rest = server.split('://', 1)
return f"{protocol}://{username}:{password}@{rest}"
else:
return f"http://{username}:{password}@{server}"
return server
async def _handle_http(
self,
url: str,
self,
url: str,
config: CrawlerRunConfig
) -> AsyncCrawlResponse:
async with self._session_context() as session:
@@ -2297,7 +2410,7 @@ class AsyncHTTPCrawlerStrategy(AsyncCrawlerStrategy):
connect=10,
sock_read=30
)
headers = dict(self._BASE_HEADERS)
if self.browser_config.headers:
headers.update(self.browser_config.headers)
@@ -2309,6 +2422,12 @@ class AsyncHTTPCrawlerStrategy(AsyncCrawlerStrategy):
'headers': headers
}
# Add proxy support - use config.proxy_config (set by arun() from rotation strategy or direct config)
proxy_url = None
if config.proxy_config:
proxy_url = self._format_proxy_url(config.proxy_config)
request_kwargs['proxy'] = proxy_url
if self.browser_config.method == "POST":
if self.browser_config.data:
request_kwargs['data'] = self.browser_config.data
@@ -2379,7 +2498,10 @@ class AsyncHTTPCrawlerStrategy(AsyncCrawlerStrategy):
if scheme == 'file':
return await self._handle_file(parsed.path)
elif scheme == 'raw':
return await self._handle_raw(parsed.path)
# Don't use parsed.path - urlparse truncates at '#' which is common in CSS
# Strip prefix directly: "raw://" (6 chars) or "raw:" (4 chars)
raw_content = url[6:] if url.startswith("raw://") else url[4:]
return await self._handle_raw(raw_content)
else: # http or https
return await self._handle_http(url, config)

View File

@@ -1,10 +1,11 @@
import os
import time
from pathlib import Path
import aiosqlite
import asyncio
from typing import Optional, Dict
from contextlib import asynccontextmanager
import json
import json
from .models import CrawlResult, MarkdownGenerationResult, StringCompatibleMarkdown
import aiofiles
from .async_logger import AsyncLogger
@@ -262,6 +263,11 @@ class AsyncDatabaseManager:
"screenshot",
"response_headers",
"downloaded_files",
# Smart cache validation columns (added in 0.8.x)
"etag",
"last_modified",
"head_fingerprint",
"cached_at",
]
for column in new_columns:
@@ -275,6 +281,11 @@ class AsyncDatabaseManager:
await db.execute(
f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT "{{}}"'
)
elif new_column == "cached_at":
# Timestamp column for cache validation
await db.execute(
f"ALTER TABLE crawled_data ADD COLUMN {new_column} REAL DEFAULT 0"
)
else:
await db.execute(
f'ALTER TABLE crawled_data ADD COLUMN {new_column} TEXT DEFAULT ""'
@@ -378,6 +389,92 @@ class AsyncDatabaseManager:
)
return None
async def aget_cache_metadata(self, url: str) -> Optional[Dict]:
"""
Retrieve only cache validation metadata for a URL (lightweight query).
Returns dict with: url, etag, last_modified, head_fingerprint, cached_at, response_headers
This is used for cache validation without loading full content.
"""
async def _get_metadata(db):
async with db.execute(
"""SELECT url, etag, last_modified, head_fingerprint, cached_at, response_headers
FROM crawled_data WHERE url = ?""",
(url,)
) as cursor:
row = await cursor.fetchone()
if not row:
return None
columns = [description[0] for description in cursor.description]
row_dict = dict(zip(columns, row))
# Parse response_headers JSON
try:
row_dict["response_headers"] = (
json.loads(row_dict["response_headers"])
if row_dict["response_headers"] else {}
)
except json.JSONDecodeError:
row_dict["response_headers"] = {}
return row_dict
try:
return await self.execute_with_retry(_get_metadata)
except Exception as e:
self.logger.error(
message="Error retrieving cache metadata: {error}",
tag="ERROR",
force_verbose=True,
params={"error": str(e)},
)
return None
async def aupdate_cache_metadata(
self,
url: str,
etag: Optional[str] = None,
last_modified: Optional[str] = None,
head_fingerprint: Optional[str] = None,
):
"""
Update only the cache validation metadata for a URL.
Used to update etag/last_modified after a successful validation.
"""
async def _update(db):
updates = []
values = []
if etag is not None:
updates.append("etag = ?")
values.append(etag)
if last_modified is not None:
updates.append("last_modified = ?")
values.append(last_modified)
if head_fingerprint is not None:
updates.append("head_fingerprint = ?")
values.append(head_fingerprint)
if not updates:
return
values.append(url)
await db.execute(
f"UPDATE crawled_data SET {', '.join(updates)} WHERE url = ?",
tuple(values)
)
try:
await self.execute_with_retry(_update)
except Exception as e:
self.logger.error(
message="Error updating cache metadata: {error}",
tag="ERROR",
force_verbose=True,
params={"error": str(e)},
)
async def acache_url(self, result: CrawlResult):
"""Cache CrawlResult data"""
# Store content files and get hashes
@@ -425,15 +522,24 @@ class AsyncDatabaseManager:
for field, (content, content_type) in content_map.items():
content_hashes[field] = await self._store_content(content, content_type)
# Extract cache validation headers from response
response_headers = result.response_headers or {}
etag = response_headers.get("etag") or response_headers.get("ETag") or ""
last_modified = response_headers.get("last-modified") or response_headers.get("Last-Modified") or ""
# head_fingerprint is set by caller via result attribute (if available)
head_fingerprint = getattr(result, "head_fingerprint", None) or ""
cached_at = time.time()
async def _cache(db):
await db.execute(
"""
INSERT INTO crawled_data (
url, html, cleaned_html, markdown,
extracted_content, success, media, links, metadata,
screenshot, response_headers, downloaded_files
screenshot, response_headers, downloaded_files,
etag, last_modified, head_fingerprint, cached_at
)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
ON CONFLICT(url) DO UPDATE SET
html = excluded.html,
cleaned_html = excluded.cleaned_html,
@@ -445,7 +551,11 @@ class AsyncDatabaseManager:
metadata = excluded.metadata,
screenshot = excluded.screenshot,
response_headers = excluded.response_headers,
downloaded_files = excluded.downloaded_files
downloaded_files = excluded.downloaded_files,
etag = excluded.etag,
last_modified = excluded.last_modified,
head_fingerprint = excluded.head_fingerprint,
cached_at = excluded.cached_at
""",
(
result.url,
@@ -460,6 +570,10 @@ class AsyncDatabaseManager:
content_hashes["screenshot"],
json.dumps(result.response_headers or {}),
json.dumps(result.downloaded_files or []),
etag,
last_modified,
head_fingerprint,
cached_at,
),
)

View File

@@ -24,7 +24,7 @@ import os
import pathlib
import re
import time
from datetime import timedelta
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Sequence, Union
from urllib.parse import quote, urljoin
@@ -78,6 +78,103 @@ _link_rx = re.compile(
# ────────────────────────────────────────────────────────────────────────── helpers
def _parse_sitemap_lastmod(xml_content: bytes) -> Optional[str]:
"""Extract the most recent lastmod from sitemap XML."""
try:
if LXML:
root = etree.fromstring(xml_content)
# Get all lastmod elements (namespace-agnostic)
lastmods = root.xpath("//*[local-name()='lastmod']/text()")
if lastmods:
# Return the most recent one
return max(lastmods)
except Exception:
pass
return None
def _is_cache_valid(
cache_path: pathlib.Path,
ttl_hours: int,
validate_lastmod: bool,
current_lastmod: Optional[str] = None
) -> bool:
"""
Check if sitemap cache is still valid.
Returns False (invalid) if:
- File doesn't exist
- File is corrupted/unreadable
- TTL expired (if ttl_hours > 0)
- Sitemap lastmod is newer than cache (if validate_lastmod=True)
"""
if not cache_path.exists():
return False
try:
with open(cache_path, "r") as f:
data = json.load(f)
# Check version
if data.get("version") != 1:
return False
# Check TTL
if ttl_hours > 0:
created_at = datetime.fromisoformat(data["created_at"].replace("Z", "+00:00"))
age_hours = (datetime.now(timezone.utc) - created_at).total_seconds() / 3600
if age_hours > ttl_hours:
return False
# Check lastmod
if validate_lastmod and current_lastmod:
cached_lastmod = data.get("sitemap_lastmod")
if cached_lastmod and current_lastmod > cached_lastmod:
return False
# Check URL count (sanity check - if 0, likely corrupted)
if data.get("url_count", 0) == 0:
return False
return True
except (json.JSONDecodeError, KeyError, ValueError, IOError):
# Corrupted cache - return False to trigger refetch
return False
def _read_cache(cache_path: pathlib.Path) -> List[str]:
"""Read URLs from cache file. Returns empty list on error."""
try:
with open(cache_path, "r") as f:
data = json.load(f)
return data.get("urls", [])
except Exception:
return []
def _write_cache(
cache_path: pathlib.Path,
urls: List[str],
sitemap_url: str,
sitemap_lastmod: Optional[str]
) -> None:
"""Write URLs to cache with metadata."""
data = {
"version": 1,
"created_at": datetime.now(timezone.utc).isoformat(),
"sitemap_lastmod": sitemap_lastmod,
"sitemap_url": sitemap_url,
"url_count": len(urls),
"urls": urls
}
try:
with open(cache_path, "w") as f:
json.dump(data, f)
except Exception:
pass # Fail silently - cache is optional
def _match(url: str, pattern: str) -> bool:
if fnmatch.fnmatch(url, pattern):
return True
@@ -295,6 +392,10 @@ class AsyncUrlSeeder:
score_threshold = config.score_threshold
scoring_method = config.scoring_method
# Store cache config for use in _from_sitemaps
self._cache_ttl_hours = getattr(config, 'cache_ttl_hours', 24)
self._validate_sitemap_lastmod = getattr(config, 'validate_sitemap_lastmod', True)
# Ensure seeder's logger verbose matches the config's verbose if it's set
if self.logger and hasattr(self.logger, 'verbose') and config.verbose is not None:
self.logger.verbose = config.verbose
@@ -764,68 +865,222 @@ class AsyncUrlSeeder:
# ─────────────────────────────── Sitemaps
async def _from_sitemaps(self, domain: str, pattern: str, force: bool = False):
"""
1. Probe default sitemap locations.
2. If none exist, parse robots.txt for alternative sitemap URLs.
3. Yield only URLs that match `pattern`.
Discover URLs from sitemaps with smart TTL-based caching.
1. Check cache validity (TTL + lastmod)
2. If valid, yield from cache
3. If invalid or force=True, fetch fresh and update cache
4. FALLBACK: If anything fails, bypass cache and fetch directly
"""
# Get config values (passed via self during urls() call)
cache_ttl_hours = getattr(self, '_cache_ttl_hours', 24)
validate_lastmod = getattr(self, '_validate_sitemap_lastmod', True)
# ── cache file (same logic as _from_cc)
# Cache file path (new format: .json instead of .jsonl)
host = re.sub(r'^https?://', '', domain).rstrip('/')
host = re.sub('[/?#]+', '_', domain)
host_safe = re.sub('[/?#]+', '_', host)
digest = hashlib.md5(pattern.encode()).hexdigest()[:8]
path = self.cache_dir / f"sitemap_{host}_{digest}.jsonl"
cache_path = self.cache_dir / f"sitemap_{host_safe}_{digest}.json"
if path.exists() and not force:
self._log("info", "Loading sitemap URLs for {d} from cache: {p}",
params={"d": host, "p": str(path)}, tag="URL_SEED")
async with aiofiles.open(path, "r") as fp:
async for line in fp:
url = line.strip()
if _match(url, pattern):
yield url
return
# Check for old .jsonl format and delete it
old_cache_path = self.cache_dir / f"sitemap_{host_safe}_{digest}.jsonl"
if old_cache_path.exists():
try:
old_cache_path.unlink()
self._log("info", "Deleted old cache format: {p}",
params={"p": str(old_cache_path)}, tag="URL_SEED")
except Exception:
pass
# 1⃣ direct sitemap probe
# strip any scheme so we can handle https → http fallback
host = re.sub(r'^https?://', '', domain).rstrip('/')
# Step 1: Find sitemap URL and get lastmod (needed for validation)
sitemap_url = None
sitemap_lastmod = None
sitemap_content = None
schemes = ('https', 'http') # prefer TLS, downgrade if needed
schemes = ('https', 'http')
for scheme in schemes:
for suffix in ("/sitemap.xml", "/sitemap_index.xml"):
sm = f"{scheme}://{host}{suffix}"
sm = await self._resolve_head(sm)
if sm:
self._log("info", "Found sitemap at {url}", params={
"url": sm}, tag="URL_SEED")
async with aiofiles.open(path, "w") as fp:
resolved = await self._resolve_head(sm)
if resolved:
sitemap_url = resolved
# Fetch sitemap content to get lastmod
try:
r = await self.client.get(sitemap_url, timeout=15, follow_redirects=True)
if 200 <= r.status_code < 300:
sitemap_content = r.content
sitemap_lastmod = _parse_sitemap_lastmod(sitemap_content)
except Exception:
pass
break
if sitemap_url:
break
# Step 2: Check cache validity (skip if force=True)
if not force and cache_path.exists():
if _is_cache_valid(cache_path, cache_ttl_hours, validate_lastmod, sitemap_lastmod):
self._log("info", "Loading sitemap URLs from valid cache: {p}",
params={"p": str(cache_path)}, tag="URL_SEED")
cached_urls = _read_cache(cache_path)
for url in cached_urls:
if _match(url, pattern):
yield url
return
else:
self._log("info", "Cache invalid/expired, refetching sitemap for {d}",
params={"d": domain}, tag="URL_SEED")
# Step 3: Fetch fresh URLs
discovered_urls = []
if sitemap_url and sitemap_content:
self._log("info", "Found sitemap at {url}", params={"url": sitemap_url}, tag="URL_SEED")
# Parse sitemap (reuse content we already fetched)
async for u in self._iter_sitemap_content(sitemap_url, sitemap_content):
discovered_urls.append(u)
if _match(u, pattern):
yield u
elif sitemap_url:
# We have a sitemap URL but no content (fetch failed earlier), try again
self._log("info", "Found sitemap at {url}", params={"url": sitemap_url}, tag="URL_SEED")
async for u in self._iter_sitemap(sitemap_url):
discovered_urls.append(u)
if _match(u, pattern):
yield u
else:
# Fallback: robots.txt
robots = f"https://{host}/robots.txt"
try:
r = await self.client.get(robots, timeout=10, follow_redirects=True)
if 200 <= r.status_code < 300:
sitemap_lines = [l.split(":", 1)[1].strip()
for l in r.text.splitlines()
if l.lower().startswith("sitemap:")]
for sm in sitemap_lines:
async for u in self._iter_sitemap(sm):
await fp.write(u + "\n")
discovered_urls.append(u)
if _match(u, pattern):
yield u
else:
self._log("warning", "robots.txt unavailable for {d} HTTP{c}",
params={"d": domain, "c": r.status_code}, tag="URL_SEED")
return
# 2⃣ robots.txt fallback
robots = f"https://{domain.rstrip('/')}/robots.txt"
try:
r = await self.client.get(robots, timeout=10, follow_redirects=True)
if not 200 <= r.status_code < 300:
self._log("warning", "robots.txt unavailable for {d} HTTP{c}", params={
"d": domain, "c": r.status_code}, tag="URL_SEED")
except Exception as e:
self._log("warning", "Failed to fetch robots.txt for {d}: {e}",
params={"d": domain, "e": str(e)}, tag="URL_SEED")
return
sitemap_lines = [l.split(":", 1)[1].strip(
) for l in r.text.splitlines() if l.lower().startswith("sitemap:")]
except Exception as e:
self._log("warning", "Failed to fetch robots.txt for {d}: {e}", params={
"d": domain, "e": str(e)}, tag="URL_SEED")
return
if sitemap_lines:
async with aiofiles.open(path, "w") as fp:
for sm in sitemap_lines:
async for u in self._iter_sitemap(sm):
await fp.write(u + "\n")
if _match(u, pattern):
yield u
# Step 4: Write to cache (FALLBACK: if write fails, URLs still yielded above)
if discovered_urls:
_write_cache(cache_path, discovered_urls, sitemap_url or "", sitemap_lastmod)
self._log("info", "Cached {count} URLs for {d}",
params={"count": len(discovered_urls), "d": domain}, tag="URL_SEED")
async def _iter_sitemap_content(self, url: str, content: bytes):
"""Parse sitemap from already-fetched content."""
data = gzip.decompress(content) if url.endswith(".gz") else content
base_url = url
def _normalize_loc(raw: Optional[str]) -> Optional[str]:
if not raw:
return None
normalized = urljoin(base_url, raw.strip())
if not normalized:
return None
return normalized
# Detect if this is a sitemap index
is_sitemap_index = False
sub_sitemaps = []
regular_urls = []
if LXML:
try:
parser = etree.XMLParser(recover=True)
root = etree.fromstring(data, parser=parser)
sitemap_loc_nodes = root.xpath("//*[local-name()='sitemap']/*[local-name()='loc']")
url_loc_nodes = root.xpath("//*[local-name()='url']/*[local-name()='loc']")
if sitemap_loc_nodes:
is_sitemap_index = True
for sitemap_elem in sitemap_loc_nodes:
loc = _normalize_loc(sitemap_elem.text)
if loc:
sub_sitemaps.append(loc)
if not is_sitemap_index:
for loc_elem in url_loc_nodes:
loc = _normalize_loc(loc_elem.text)
if loc:
regular_urls.append(loc)
except Exception as e:
self._log("error", "LXML parsing error for sitemap {url}: {error}",
params={"url": url, "error": str(e)}, tag="URL_SEED")
return
else:
import xml.etree.ElementTree as ET
try:
root = ET.fromstring(data)
for elem in root.iter():
if '}' in elem.tag:
elem.tag = elem.tag.split('}')[1]
sitemaps = root.findall('.//sitemap')
url_entries = root.findall('.//url')
if sitemaps:
is_sitemap_index = True
for sitemap in sitemaps:
loc_elem = sitemap.find('loc')
loc = _normalize_loc(loc_elem.text if loc_elem is not None else None)
if loc:
sub_sitemaps.append(loc)
if not is_sitemap_index:
for url_elem in url_entries:
loc_elem = url_elem.find('loc')
loc = _normalize_loc(loc_elem.text if loc_elem is not None else None)
if loc:
regular_urls.append(loc)
except Exception as e:
self._log("error", "ElementTree parsing error for sitemap {url}: {error}",
params={"url": url, "error": str(e)}, tag="URL_SEED")
return
# Process based on type
if is_sitemap_index and sub_sitemaps:
self._log("info", "Processing sitemap index with {count} sub-sitemaps",
params={"count": len(sub_sitemaps)}, tag="URL_SEED")
queue_size = min(50000, len(sub_sitemaps) * 1000)
result_queue = asyncio.Queue(maxsize=queue_size)
completed_count = 0
total_sitemaps = len(sub_sitemaps)
async def process_subsitemap(sitemap_url: str):
try:
async for u in self._iter_sitemap(sitemap_url):
await result_queue.put(u)
except Exception as e:
self._log("error", "Error processing sub-sitemap {url}: {error}",
params={"url": sitemap_url, "error": str(e)}, tag="URL_SEED")
finally:
await result_queue.put(None)
tasks = [asyncio.create_task(process_subsitemap(sm)) for sm in sub_sitemaps]
while completed_count < total_sitemaps:
item = await result_queue.get()
if item is None:
completed_count += 1
else:
yield item
await asyncio.gather(*tasks, return_exceptions=True)
else:
for u in regular_urls:
yield u
async def _iter_sitemap(self, url: str):
try:
@@ -845,6 +1100,15 @@ class AsyncUrlSeeder:
return
data = gzip.decompress(r.content) if url.endswith(".gz") else r.content
base_url = str(r.url)
def _normalize_loc(raw: Optional[str]) -> Optional[str]:
if not raw:
return None
normalized = urljoin(base_url, raw.strip())
if not normalized:
return None
return normalized
# Detect if this is a sitemap index by checking for <sitemapindex> or presence of <sitemap> elements
is_sitemap_index = False
@@ -857,25 +1121,42 @@ class AsyncUrlSeeder:
# Use XML parser for sitemaps, not HTML parser
parser = etree.XMLParser(recover=True)
root = etree.fromstring(data, parser=parser)
# Namespace-agnostic lookups using local-name() so we honor custom or missing namespaces
sitemap_loc_nodes = root.xpath("//*[local-name()='sitemap']/*[local-name()='loc']")
url_loc_nodes = root.xpath("//*[local-name()='url']/*[local-name()='loc']")
# Define namespace for sitemap
ns = {'s': 'http://www.sitemaps.org/schemas/sitemap/0.9'}
self._log(
"debug",
"Parsed sitemap {url}: {sitemap_count} sitemap entries, {url_count} url entries discovered",
params={
"url": url,
"sitemap_count": len(sitemap_loc_nodes),
"url_count": len(url_loc_nodes),
},
tag="URL_SEED",
)
# Check for sitemap index entries
sitemap_locs = root.xpath('//s:sitemap/s:loc', namespaces=ns)
if sitemap_locs:
if sitemap_loc_nodes:
is_sitemap_index = True
for sitemap_elem in sitemap_locs:
loc = sitemap_elem.text.strip() if sitemap_elem.text else ""
for sitemap_elem in sitemap_loc_nodes:
loc = _normalize_loc(sitemap_elem.text)
if loc:
sub_sitemaps.append(loc)
# If not a sitemap index, get regular URLs
if not is_sitemap_index:
for loc_elem in root.xpath('//s:url/s:loc', namespaces=ns):
loc = loc_elem.text.strip() if loc_elem.text else ""
for loc_elem in url_loc_nodes:
loc = _normalize_loc(loc_elem.text)
if loc:
regular_urls.append(loc)
if not regular_urls:
self._log(
"warning",
"No <loc> entries found inside <url> tags for sitemap {url}. The sitemap might be empty or use an unexpected structure.",
params={"url": url},
tag="URL_SEED",
)
except Exception as e:
self._log("error", "LXML parsing error for sitemap {url}: {error}",
params={"url": url, "error": str(e)}, tag="URL_SEED")
@@ -892,19 +1173,39 @@ class AsyncUrlSeeder:
# Check for sitemap index entries
sitemaps = root.findall('.//sitemap')
url_entries = root.findall('.//url')
self._log(
"debug",
"ElementTree parsed sitemap {url}: {sitemap_count} sitemap entries, {url_count} url entries discovered",
params={
"url": url,
"sitemap_count": len(sitemaps),
"url_count": len(url_entries),
},
tag="URL_SEED",
)
if sitemaps:
is_sitemap_index = True
for sitemap in sitemaps:
loc_elem = sitemap.find('loc')
if loc_elem is not None and loc_elem.text:
sub_sitemaps.append(loc_elem.text.strip())
loc = _normalize_loc(loc_elem.text if loc_elem is not None else None)
if loc:
sub_sitemaps.append(loc)
# If not a sitemap index, get regular URLs
if not is_sitemap_index:
for url_elem in root.findall('.//url'):
for url_elem in url_entries:
loc_elem = url_elem.find('loc')
if loc_elem is not None and loc_elem.text:
regular_urls.append(loc_elem.text.strip())
loc = _normalize_loc(loc_elem.text if loc_elem is not None else None)
if loc:
regular_urls.append(loc)
if not regular_urls:
self._log(
"warning",
"No <loc> entries found inside <url> tags for sitemap {url}. The sitemap might be empty or use an unexpected structure.",
params={"url": url},
tag="URL_SEED",
)
except Exception as e:
self._log("error", "ElementTree parsing error for sitemap {url}: {error}",
params={"url": url, "error": str(e)}, tag="URL_SEED")

View File

@@ -47,7 +47,9 @@ from .utils import (
get_error_context,
RobotsParser,
preprocess_html_for_schema,
compute_head_fingerprint,
)
from .cache_validator import CacheValidator, CacheValidationResult
class AsyncWebCrawler:
@@ -267,6 +269,51 @@ class AsyncWebCrawler:
if cache_context.should_read():
cached_result = await async_db_manager.aget_cached_url(url)
# Smart Cache: Validate cache freshness if enabled
if cached_result and config.check_cache_freshness:
cache_metadata = await async_db_manager.aget_cache_metadata(url)
if cache_metadata:
async with CacheValidator(timeout=config.cache_validation_timeout) as validator:
validation = await validator.validate(
url=url,
stored_etag=cache_metadata.get("etag"),
stored_last_modified=cache_metadata.get("last_modified"),
stored_head_fingerprint=cache_metadata.get("head_fingerprint"),
)
if validation.status == CacheValidationResult.FRESH:
cached_result.cache_status = "hit_validated"
self.logger.info(
message="Cache validated: {reason}",
tag="CACHE",
params={"reason": validation.reason}
)
# Update metadata if we got new values
if validation.new_etag or validation.new_last_modified:
await async_db_manager.aupdate_cache_metadata(
url=url,
etag=validation.new_etag,
last_modified=validation.new_last_modified,
head_fingerprint=validation.new_head_fingerprint,
)
elif validation.status == CacheValidationResult.ERROR:
cached_result.cache_status = "hit_fallback"
self.logger.warning(
message="Cache validation failed, using cached: {reason}",
tag="CACHE",
params={"reason": validation.reason}
)
else:
# STALE or UNKNOWN - force recrawl
self.logger.info(
message="Cache stale: {reason}",
tag="CACHE",
params={"reason": validation.reason}
)
cached_result = None
elif cached_result:
cached_result.cache_status = "hit"
if cached_result:
html = sanitize_input_encode(cached_result.html)
extracted_content = sanitize_input_encode(
@@ -296,15 +343,32 @@ class AsyncWebCrawler:
# Update proxy configuration from rotation strategy if available
if config and config.proxy_rotation_strategy:
next_proxy: ProxyConfig = await config.proxy_rotation_strategy.get_next_proxy()
if next_proxy:
self.logger.info(
message="Switch proxy: {proxy}",
tag="PROXY",
params={"proxy": next_proxy.server}
# Handle sticky sessions - use same proxy for all requests with same session_id
if config.proxy_session_id:
next_proxy: ProxyConfig = await config.proxy_rotation_strategy.get_proxy_for_session(
config.proxy_session_id,
ttl=config.proxy_session_ttl
)
config.proxy_config = next_proxy
# config = config.clone(proxy_config=next_proxy)
if next_proxy:
self.logger.info(
message="Using sticky proxy session: {session_id} -> {proxy}",
tag="PROXY",
params={
"session_id": config.proxy_session_id,
"proxy": next_proxy.server
}
)
config.proxy_config = next_proxy
else:
# Existing behavior: rotate on each request
next_proxy: ProxyConfig = await config.proxy_rotation_strategy.get_next_proxy()
if next_proxy:
self.logger.info(
message="Switch proxy: {proxy}",
tag="PROXY",
params={"proxy": next_proxy.server}
)
config.proxy_config = next_proxy
# Fetch fresh content if needed
if not cached_result or not html:
@@ -383,6 +447,14 @@ class AsyncWebCrawler:
crawl_result.success = bool(html)
crawl_result.session_id = getattr(
config, "session_id", None)
crawl_result.cache_status = "miss"
# Compute head fingerprint for cache validation
if html:
head_end = html.lower().find('</head>')
if head_end != -1:
head_html = html[:head_end + 7]
crawl_result.head_fingerprint = compute_head_fingerprint(head_html)
self.logger.url_status(
url=cache_context.display_url,
@@ -459,6 +531,27 @@ class AsyncWebCrawler:
Returns:
CrawlResult: Processed result containing extracted and formatted content
"""
# === PREFETCH MODE SHORT-CIRCUIT ===
if getattr(config, 'prefetch', False):
from .utils import quick_extract_links
# Use base_url from config (for raw: URLs), redirected_url, or original url
effective_url = getattr(config, 'base_url', None) or kwargs.get('redirected_url') or url
links = quick_extract_links(html, effective_url)
return CrawlResult(
url=url,
html=html,
success=True,
links=links,
status_code=kwargs.get('status_code'),
response_headers=kwargs.get('response_headers'),
redirected_url=kwargs.get('redirected_url'),
ssl_certificate=kwargs.get('ssl_certificate'),
# All other fields default to None
)
# === END PREFETCH SHORT-CIRCUIT ===
cleaned_html = ""
try:
_url = url if not kwargs.get("is_raw_html", False) else "Raw HTML"
@@ -563,7 +656,8 @@ class AsyncWebCrawler:
markdown_result: MarkdownGenerationResult = (
markdown_generator.generate_markdown(
input_html=markdown_input_html,
base_url=params.get("redirected_url", url)
# Use explicit base_url if provided (for raw: HTML), otherwise redirected_url, then url
base_url=params.get("base_url") or params.get("redirected_url") or url
# html2text_options=kwargs.get('html2text', {})
)
)
@@ -617,7 +711,17 @@ class AsyncWebCrawler:
else config.chunking_strategy
)
sections = chunking.chunk(content)
extracted_content = config.extraction_strategy.run(url, sections)
# extracted_content = config.extraction_strategy.run(_url, sections)
# Use async version if available for better parallelism
if hasattr(config.extraction_strategy, 'arun'):
extracted_content = await config.extraction_strategy.arun(_url, sections)
else:
# Fallback to sync version run in thread pool to avoid blocking
extracted_content = await asyncio.to_thread(
config.extraction_strategy.run, url, sections
)
extracted_content = json.dumps(
extracted_content, indent=4, default=str, ensure_ascii=False
)
@@ -746,21 +850,45 @@ class AsyncWebCrawler:
# Handle stream setting - use first config's stream setting if config is a list
if isinstance(config, list):
stream = config[0].stream if config else False
primary_config = config[0] if config else None
else:
stream = config.stream
primary_config = config
# Helper to release sticky session if auto_release is enabled
async def maybe_release_session():
if (primary_config and
primary_config.proxy_session_id and
primary_config.proxy_session_auto_release and
primary_config.proxy_rotation_strategy):
await primary_config.proxy_rotation_strategy.release_session(
primary_config.proxy_session_id
)
self.logger.info(
message="Auto-released proxy session: {session_id}",
tag="PROXY",
params={"session_id": primary_config.proxy_session_id}
)
if stream:
async def result_transformer():
async for task_result in dispatcher.run_urls_stream(
crawler=self, urls=urls, config=config
):
yield transform_result(task_result)
try:
async for task_result in dispatcher.run_urls_stream(
crawler=self, urls=urls, config=config
):
yield transform_result(task_result)
finally:
# Auto-release session after streaming completes
await maybe_release_session()
return result_transformer()
else:
_results = await dispatcher.run_urls(crawler=self, urls=urls, config=config)
return [transform_result(res) for res in _results]
try:
_results = await dispatcher.run_urls(crawler=self, urls=urls, config=config)
return [transform_result(res) for res in _results]
finally:
# Auto-release session after batch completes
await maybe_release_session()
async def aseed_urls(
self,

View File

@@ -369,6 +369,9 @@ class ManagedBrowser:
]
if self.headless:
flags.append("--headless=new")
# Add viewport flag if specified in config
if self.browser_config.viewport_height and self.browser_config.viewport_width:
flags.append(f"--window-size={self.browser_config.viewport_width},{self.browser_config.viewport_height}")
# merge common launch flags
flags.extend(self.build_browser_flags(self.browser_config))
elif self.browser_type == "firefox":
@@ -658,9 +661,44 @@ class BrowserManager:
if self.config.cdp_url or self.config.use_managed_browser:
self.config.use_managed_browser = True
cdp_url = await self.managed_browser.start() if not self.config.cdp_url else self.config.cdp_url
# Add CDP endpoint verification before connecting
if not await self._verify_cdp_ready(cdp_url):
raise Exception(f"CDP endpoint at {cdp_url} is not ready after startup")
self.browser = await self.playwright.chromium.connect_over_cdp(cdp_url)
contexts = self.browser.contexts
if contexts:
# If browser_context_id is provided, we're using a pre-created context
if self.config.browser_context_id:
if self.logger:
self.logger.debug(
f"Using pre-existing browser context: {self.config.browser_context_id}",
tag="BROWSER"
)
# When connecting to a pre-created context, it should be in contexts
if contexts:
self.default_context = contexts[0]
if self.logger:
self.logger.debug(
f"Found {len(contexts)} existing context(s), using first one",
tag="BROWSER"
)
else:
# Context was created but not yet visible - wait a bit
await asyncio.sleep(0.2)
contexts = self.browser.contexts
if contexts:
self.default_context = contexts[0]
else:
# Still no contexts - this shouldn't happen with pre-created context
if self.logger:
self.logger.warning(
"Pre-created context not found, creating new one",
tag="BROWSER"
)
self.default_context = await self.create_browser_context()
elif contexts:
self.default_context = contexts[0]
else:
self.default_context = await self.create_browser_context()
@@ -678,6 +716,49 @@ class BrowserManager:
self.default_context = self.browser
async def _verify_cdp_ready(self, cdp_url: str) -> bool:
"""Verify CDP endpoint is ready with exponential backoff.
Supports multiple URL formats:
- HTTP URLs: http://localhost:9222
- HTTP URLs with query params: http://localhost:9222?browser_id=XXX
- WebSocket URLs: ws://localhost:9222/devtools/browser/XXX
"""
import aiohttp
from urllib.parse import urlparse, urlunparse
# If WebSocket URL, Playwright handles connection directly - skip HTTP verification
if cdp_url.startswith(('ws://', 'wss://')):
self.logger.debug(f"WebSocket CDP URL provided, skipping HTTP verification", tag="BROWSER")
return True
# Parse HTTP URL and properly construct /json/version endpoint
parsed = urlparse(cdp_url)
# Build URL with /json/version path, preserving query params
verify_url = urlunparse((
parsed.scheme,
parsed.netloc,
'/json/version', # Always use this path for verification
'', # params
parsed.query, # preserve query string
'' # fragment
))
self.logger.debug(f"Starting CDP verification for {verify_url}", tag="BROWSER")
for attempt in range(5):
try:
async with aiohttp.ClientSession() as session:
async with session.get(verify_url, timeout=aiohttp.ClientTimeout(total=2)) as response:
if response.status == 200:
self.logger.debug(f"CDP endpoint ready after {attempt + 1} attempts", tag="BROWSER")
return True
except Exception as e:
self.logger.debug(f"CDP check attempt {attempt + 1} failed: {e}", tag="BROWSER")
delay = 0.5 * (1.4 ** attempt)
self.logger.debug(f"Waiting {delay:.2f}s before next CDP check...", tag="BROWSER")
await asyncio.sleep(delay)
self.logger.debug(f"CDP verification failed after 5 attempts", tag="BROWSER")
return False
def _build_browser_args(self) -> dict:
"""Build browser launch arguments from config."""
@@ -814,18 +895,27 @@ class BrowserManager:
combined_headers.update(self.config.headers)
await context.set_extra_http_headers(combined_headers)
# Add default cookie
await context.add_cookies(
[
{
"name": "cookiesEnabled",
"value": "true",
"url": crawlerRunConfig.url
if crawlerRunConfig and crawlerRunConfig.url
else "https://crawl4ai.com/",
}
]
)
# Add default cookie (skip for raw:/file:// URLs which are not valid cookie URLs)
cookie_url = None
if crawlerRunConfig and crawlerRunConfig.url:
url = crawlerRunConfig.url
# Only set cookie for http/https URLs
if url.startswith(("http://", "https://")):
cookie_url = url
elif crawlerRunConfig.base_url and crawlerRunConfig.base_url.startswith(("http://", "https://")):
# Use base_url as fallback for raw:/file:// URLs
cookie_url = crawlerRunConfig.base_url
if cookie_url:
await context.add_cookies(
[
{
"name": "cookiesEnabled",
"value": "true",
"url": cookie_url,
}
]
)
# Handle navigator overrides
if crawlerRunConfig:
@@ -834,7 +924,12 @@ class BrowserManager:
or crawlerRunConfig.simulate_user
or crawlerRunConfig.magic
):
await context.add_init_script(load_js_script("navigator_overrider"))
await context.add_init_script(load_js_script("navigator_overrider"))
# Apply custom init_scripts from BrowserConfig (for stealth evasions, etc.)
if self.config.init_scripts:
for script in self.config.init_scripts:
await context.add_init_script(script)
async def create_browser_context(self, crawlerRunConfig: CrawlerRunConfig = None):
"""
@@ -1016,6 +1111,62 @@ class BrowserManager:
params={"error": str(e)}
)
async def _get_page_by_target_id(self, context: BrowserContext, target_id: str):
"""
Get an existing page by its CDP target ID.
This is used when connecting to a pre-created browser context with an existing page.
Playwright may not immediately see targets created via raw CDP commands, so we
use CDP to get all targets and find the matching one.
Args:
context: The browser context to search in
target_id: The CDP target ID to find
Returns:
Page object if found, None otherwise
"""
try:
# First check if Playwright already sees the page
for page in context.pages:
# Playwright's internal target ID might match
if hasattr(page, '_impl_obj') and hasattr(page._impl_obj, '_target_id'):
if page._impl_obj._target_id == target_id:
return page
# If not found, try using CDP to get targets
if hasattr(self.browser, '_impl_obj') and hasattr(self.browser._impl_obj, '_connection'):
cdp_session = await context.new_cdp_session(context.pages[0] if context.pages else None)
if cdp_session:
try:
result = await cdp_session.send("Target.getTargets")
targets = result.get("targetInfos", [])
for target in targets:
if target.get("targetId") == target_id:
# Found the target - if it's a page type, we can use it
if target.get("type") == "page":
# The page exists, let Playwright discover it
await asyncio.sleep(0.1)
# Refresh pages list
if context.pages:
return context.pages[0]
finally:
await cdp_session.detach()
# Fallback: if there are any pages now, return the first one
if context.pages:
return context.pages[0]
return None
except Exception as e:
if self.logger:
self.logger.warning(
message="Failed to get page by target ID: {error}",
tag="BROWSER",
params={"error": str(e)}
)
return None
async def get_page(self, crawlerRunConfig: CrawlerRunConfig):
"""
Get a page for the given session ID, creating a new one if needed.
@@ -1037,7 +1188,25 @@ class BrowserManager:
# If using a managed browser, just grab the shared default_context
if self.config.use_managed_browser:
if self.config.storage_state:
# If create_isolated_context is True, create isolated contexts for concurrent crawls
# Uses the same caching mechanism as non-CDP mode: cache context by config signature,
# but always create a new page. This prevents navigation conflicts while allowing
# context reuse for multiple URLs with the same config (e.g., batch/deep crawls).
if self.config.create_isolated_context:
config_signature = self._make_config_signature(crawlerRunConfig)
async with self._contexts_lock:
if config_signature in self.contexts_by_config:
context = self.contexts_by_config[config_signature]
else:
context = await self.create_browser_context(crawlerRunConfig)
await self.setup_context(context, crawlerRunConfig)
self.contexts_by_config[config_signature] = context
# Always create a new page for each crawl (isolation for navigation)
page = await context.new_page()
await self._apply_stealth_to_page(page)
elif self.config.storage_state:
context = await self.create_browser_context(crawlerRunConfig)
ctx = self.default_context # default context, one window only
ctx = await clone_runtime_state(context, ctx, crawlerRunConfig, self.config)
@@ -1060,6 +1229,14 @@ class BrowserManager:
pages = context.pages
if pages:
page = pages[0]
elif self.config.browser_context_id and self.config.target_id:
# Pre-existing context/target provided - use CDP to get the page
# This handles the case where Playwright doesn't see the target yet
page = await self._get_page_by_target_id(context, self.config.target_id)
if not page:
# Fallback: create new page in existing context
page = await context.new_page()
await self._apply_stealth_to_page(page)
else:
page = await context.new_page()
await self._apply_stealth_to_page(page)
@@ -1114,8 +1291,44 @@ class BrowserManager:
async def close(self):
"""Close all browser resources and clean up."""
if self.config.cdp_url:
# When using external CDP, we don't own the browser process.
# If cdp_cleanup_on_close is True, properly disconnect from the browser
# and clean up Playwright resources. This frees the browser for other clients.
if self.config.cdp_cleanup_on_close:
# First close all sessions (pages)
session_ids = list(self.sessions.keys())
for session_id in session_ids:
await self.kill_session(session_id)
# Close all contexts we created
for ctx in self.contexts_by_config.values():
try:
await ctx.close()
except Exception:
pass
self.contexts_by_config.clear()
# Disconnect from browser (doesn't terminate it, just releases connection)
if self.browser:
try:
await self.browser.close()
except Exception as e:
if self.logger:
self.logger.debug(
message="Error disconnecting from CDP browser: {error}",
tag="BROWSER",
params={"error": str(e)}
)
self.browser = None
# Allow time for CDP connection to fully release before another client connects
await asyncio.sleep(1.0)
# Stop Playwright instance to prevent memory leaks
if self.playwright:
await self.playwright.stop()
self.playwright = None
return
if self.config.sleep_on_close:
await asyncio.sleep(0.5)

270
crawl4ai/cache_validator.py Normal file
View File

@@ -0,0 +1,270 @@
"""
Cache validation using HTTP conditional requests and head fingerprinting.
Uses httpx for fast, lightweight HTTP requests (no browser needed).
This module enables smart cache validation to avoid unnecessary full browser crawls
when content hasn't changed.
Validation Strategy:
1. Send HEAD request with If-None-Match / If-Modified-Since headers
2. If server returns 304 Not Modified → cache is FRESH
3. If server returns 200 → fetch <head> and compare fingerprint
4. If fingerprint matches → cache is FRESH (minor changes only)
5. Otherwise → cache is STALE, need full recrawl
"""
import httpx
from dataclasses import dataclass
from typing import Optional, Tuple
from enum import Enum
from .utils import compute_head_fingerprint
class CacheValidationResult(Enum):
"""Result of cache validation check."""
FRESH = "fresh" # Content unchanged, use cache
STALE = "stale" # Content changed, need recrawl
UNKNOWN = "unknown" # Couldn't determine, need recrawl
ERROR = "error" # Request failed, use cache as fallback
@dataclass
class ValidationResult:
"""Detailed result of a cache validation attempt."""
status: CacheValidationResult
new_etag: Optional[str] = None
new_last_modified: Optional[str] = None
new_head_fingerprint: Optional[str] = None
reason: str = ""
class CacheValidator:
"""
Validates cache freshness using lightweight HTTP requests.
This validator uses httpx to make fast HTTP requests without needing
a full browser. It supports two validation methods:
1. HTTP Conditional Requests (Layer 3):
- Uses If-None-Match with stored ETag
- Uses If-Modified-Since with stored Last-Modified
- Server returns 304 if content unchanged
2. Head Fingerprinting (Layer 4):
- Fetches only the <head> section (~5KB)
- Compares fingerprint of key meta tags
- Catches changes even without server support for conditional requests
"""
def __init__(self, timeout: float = 10.0, user_agent: Optional[str] = None):
"""
Initialize the cache validator.
Args:
timeout: Request timeout in seconds
user_agent: Custom User-Agent string (optional)
"""
self.timeout = timeout
self.user_agent = user_agent or "Mozilla/5.0 (compatible; Crawl4AI/1.0)"
self._client: Optional[httpx.AsyncClient] = None
async def _get_client(self) -> httpx.AsyncClient:
"""Get or create the httpx client."""
if self._client is None:
self._client = httpx.AsyncClient(
http2=True,
timeout=self.timeout,
follow_redirects=True,
headers={"User-Agent": self.user_agent}
)
return self._client
async def validate(
self,
url: str,
stored_etag: Optional[str] = None,
stored_last_modified: Optional[str] = None,
stored_head_fingerprint: Optional[str] = None,
) -> ValidationResult:
"""
Validate if cached content is still fresh.
Args:
url: The URL to validate
stored_etag: Previously stored ETag header value
stored_last_modified: Previously stored Last-Modified header value
stored_head_fingerprint: Previously computed head fingerprint
Returns:
ValidationResult with status and any updated metadata
"""
client = await self._get_client()
# Build conditional request headers
headers = {}
if stored_etag:
headers["If-None-Match"] = stored_etag
if stored_last_modified:
headers["If-Modified-Since"] = stored_last_modified
try:
# Step 1: Try HEAD request with conditional headers
if headers:
response = await client.head(url, headers=headers)
if response.status_code == 304:
return ValidationResult(
status=CacheValidationResult.FRESH,
reason="Server returned 304 Not Modified"
)
# Got 200, extract new headers for potential update
new_etag = response.headers.get("etag")
new_last_modified = response.headers.get("last-modified")
# If we have fingerprint, compare it
if stored_head_fingerprint:
head_html, _, _ = await self._fetch_head(url)
if head_html:
new_fingerprint = compute_head_fingerprint(head_html)
if new_fingerprint and new_fingerprint == stored_head_fingerprint:
return ValidationResult(
status=CacheValidationResult.FRESH,
new_etag=new_etag,
new_last_modified=new_last_modified,
new_head_fingerprint=new_fingerprint,
reason="Head fingerprint matches"
)
elif new_fingerprint:
return ValidationResult(
status=CacheValidationResult.STALE,
new_etag=new_etag,
new_last_modified=new_last_modified,
new_head_fingerprint=new_fingerprint,
reason="Head fingerprint changed"
)
# Headers changed and no fingerprint match
return ValidationResult(
status=CacheValidationResult.STALE,
new_etag=new_etag,
new_last_modified=new_last_modified,
reason="Server returned 200, content may have changed"
)
# Step 2: No conditional headers available, try fingerprint only
if stored_head_fingerprint:
head_html, new_etag, new_last_modified = await self._fetch_head(url)
if head_html:
new_fingerprint = compute_head_fingerprint(head_html)
if new_fingerprint and new_fingerprint == stored_head_fingerprint:
return ValidationResult(
status=CacheValidationResult.FRESH,
new_etag=new_etag,
new_last_modified=new_last_modified,
new_head_fingerprint=new_fingerprint,
reason="Head fingerprint matches"
)
elif new_fingerprint:
return ValidationResult(
status=CacheValidationResult.STALE,
new_etag=new_etag,
new_last_modified=new_last_modified,
new_head_fingerprint=new_fingerprint,
reason="Head fingerprint changed"
)
# Step 3: No validation data available
return ValidationResult(
status=CacheValidationResult.UNKNOWN,
reason="No validation data available (no etag, last-modified, or fingerprint)"
)
except httpx.TimeoutException:
return ValidationResult(
status=CacheValidationResult.ERROR,
reason="Validation request timed out"
)
except httpx.RequestError as e:
return ValidationResult(
status=CacheValidationResult.ERROR,
reason=f"Validation request failed: {type(e).__name__}"
)
except Exception as e:
# On unexpected error, prefer using cache over failing
return ValidationResult(
status=CacheValidationResult.ERROR,
reason=f"Validation error: {str(e)}"
)
async def _fetch_head(self, url: str) -> Tuple[Optional[str], Optional[str], Optional[str]]:
"""
Fetch only the <head> section of a page.
Uses streaming to stop reading after </head> is found,
minimizing bandwidth usage.
Args:
url: The URL to fetch
Returns:
Tuple of (head_html, etag, last_modified)
"""
client = await self._get_client()
try:
async with client.stream(
"GET",
url,
headers={"Accept-Encoding": "identity"} # Disable compression for easier parsing
) as response:
etag = response.headers.get("etag")
last_modified = response.headers.get("last-modified")
if response.status_code != 200:
return None, etag, last_modified
# Read until </head> or max 64KB
chunks = []
total_bytes = 0
max_bytes = 65536
async for chunk in response.aiter_bytes(4096):
chunks.append(chunk)
total_bytes += len(chunk)
content = b''.join(chunks)
# Check for </head> (case insensitive)
if b'</head>' in content.lower() or b'</HEAD>' in content:
break
if total_bytes >= max_bytes:
break
html = content.decode('utf-8', errors='replace')
# Extract just the head section
head_end = html.lower().find('</head>')
if head_end != -1:
html = html[:head_end + 7]
return html, etag, last_modified
except Exception:
return None, None, None
async def close(self):
"""Close the HTTP client and release resources."""
if self._client:
await self._client.aclose()
self._client = None
async def __aenter__(self):
"""Async context manager entry."""
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
"""Async context manager exit."""
await self.close()

View File

@@ -980,6 +980,9 @@ class LLMContentFilter(RelevantContentFilter):
prompt,
api_token,
base_url=base_url,
base_delay=self.llm_config.backoff_base_delay,
max_attempts=self.llm_config.backoff_max_attempts,
exponential_factor=self.llm_config.backoff_exponential_factor,
extra_args=extra_args,
)

View File

@@ -542,6 +542,19 @@ class LXMLWebScrapingStrategy(ContentScrapingStrategy):
if el.tag in bypass_tags:
continue
# Skip elements inside <pre> or <code> tags where whitespace is significant
# This preserves whitespace-only spans (e.g., <span class="w"> </span>) in code blocks
is_in_code_block = False
ancestor = el.getparent()
while ancestor is not None:
if ancestor.tag in ("pre", "code"):
is_in_code_block = True
break
ancestor = ancestor.getparent()
if is_in_code_block:
continue
text_content = (el.text_content() or "").strip()
if (
len(text_content.split()) < word_count_threshold

View File

@@ -2,7 +2,7 @@
import asyncio
import logging
from datetime import datetime
from typing import AsyncGenerator, Optional, Set, Dict, List, Tuple
from typing import AsyncGenerator, Optional, Set, Dict, List, Tuple, Any, Callable, Awaitable
from urllib.parse import urlparse
from ..models import TraversalStats
@@ -41,6 +41,9 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
include_external: bool = False,
max_pages: int = infinity,
logger: Optional[logging.Logger] = None,
# Optional resume/callback parameters for crash recovery
resume_state: Optional[Dict[str, Any]] = None,
on_state_change: Optional[Callable[[Dict[str, Any]], Awaitable[None]]] = None,
):
self.max_depth = max_depth
self.filter_chain = filter_chain
@@ -57,6 +60,12 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
self.stats = TraversalStats(start_time=datetime.now())
self._cancel_event = asyncio.Event()
self._pages_crawled = 0
# Store for use in arun methods
self._resume_state = resume_state
self._on_state_change = on_state_change
self._last_state: Optional[Dict[str, Any]] = None
# Shadow list for queue items (only used when on_state_change is set)
self._queue_shadow: Optional[List[Tuple[float, int, str, Optional[str]]]] = None
async def can_process_url(self, url: str, depth: int) -> bool:
"""
@@ -135,16 +144,36 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
) -> AsyncGenerator[CrawlResult, None]:
"""
Core best-first crawl method using a priority queue.
The queue items are tuples of (score, depth, url, parent_url). Lower scores
are treated as higher priority. URLs are processed in batches for efficiency.
"""
queue: asyncio.PriorityQueue = asyncio.PriorityQueue()
# Push the initial URL with score 0 and depth 0.
initial_score = self.url_scorer.score(start_url) if self.url_scorer else 0
await queue.put((-initial_score, 0, start_url, None))
visited: Set[str] = set()
depths: Dict[str, int] = {start_url: 0}
# Conditional state initialization for resume support
if self._resume_state:
visited = set(self._resume_state.get("visited", []))
depths = dict(self._resume_state.get("depths", {}))
self._pages_crawled = self._resume_state.get("pages_crawled", 0)
# Restore queue from saved items
queue_items = self._resume_state.get("queue_items", [])
for item in queue_items:
await queue.put((item["score"], item["depth"], item["url"], item["parent_url"]))
# Initialize shadow list if callback is set
if self._on_state_change:
self._queue_shadow = [
(item["score"], item["depth"], item["url"], item["parent_url"])
for item in queue_items
]
else:
# Original initialization
initial_score = self.url_scorer.score(start_url) if self.url_scorer else 0
await queue.put((-initial_score, 0, start_url, None))
visited: Set[str] = set()
depths: Dict[str, int] = {start_url: 0}
# Initialize shadow list if callback is set
if self._on_state_change:
self._queue_shadow = [(-initial_score, 0, start_url, None)]
while not queue.empty() and not self._cancel_event.is_set():
# Stop if we've reached the max pages limit
@@ -166,6 +195,12 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
if queue.empty():
break
item = await queue.get()
# Remove from shadow list if tracking
if self._on_state_change and self._queue_shadow is not None:
try:
self._queue_shadow.remove(item)
except ValueError:
pass # Item may have been removed already
score, depth, url, parent_url = item
if url in visited:
continue
@@ -210,7 +245,26 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
for new_url, new_parent in new_links:
new_depth = depths.get(new_url, depth + 1)
new_score = self.url_scorer.score(new_url) if self.url_scorer else 0
await queue.put((-new_score, new_depth, new_url, new_parent))
queue_item = (-new_score, new_depth, new_url, new_parent)
await queue.put(queue_item)
# Add to shadow list if tracking
if self._on_state_change and self._queue_shadow is not None:
self._queue_shadow.append(queue_item)
# Capture state after EACH URL processed (if callback set)
if self._on_state_change and self._queue_shadow is not None:
state = {
"strategy_type": "best_first",
"visited": list(visited),
"queue_items": [
{"score": s, "depth": d, "url": u, "parent_url": p}
for s, d, u, p in self._queue_shadow
],
"depths": depths,
"pages_crawled": self._pages_crawled,
}
self._last_state = state
await self._on_state_change(state)
# End of crawl.
@@ -269,3 +323,15 @@ class BestFirstCrawlingStrategy(DeepCrawlStrategy):
"""
self._cancel_event.set()
self.stats.end_time = datetime.now()
def export_state(self) -> Optional[Dict[str, Any]]:
"""
Export current crawl state for external persistence.
Note: This returns the last captured state. For real-time state,
use the on_state_change callback.
Returns:
Dict with strategy state, or None if no state captured yet.
"""
return self._last_state

View File

@@ -2,7 +2,7 @@
import asyncio
import logging
from datetime import datetime
from typing import AsyncGenerator, Optional, Set, Dict, List, Tuple
from typing import AsyncGenerator, Optional, Set, Dict, List, Tuple, Any, Callable, Awaitable
from urllib.parse import urlparse
from ..models import TraversalStats
@@ -26,11 +26,14 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
self,
max_depth: int,
filter_chain: FilterChain = FilterChain(),
url_scorer: Optional[URLScorer] = None,
url_scorer: Optional[URLScorer] = None,
include_external: bool = False,
score_threshold: float = -infinity,
max_pages: int = infinity,
logger: Optional[logging.Logger] = None,
# Optional resume/callback parameters for crash recovery
resume_state: Optional[Dict[str, Any]] = None,
on_state_change: Optional[Callable[[Dict[str, Any]], Awaitable[None]]] = None,
):
self.max_depth = max_depth
self.filter_chain = filter_chain
@@ -48,6 +51,10 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
self.stats = TraversalStats(start_time=datetime.now())
self._cancel_event = asyncio.Event()
self._pages_crawled = 0
# Store for use in arun methods
self._resume_state = resume_state
self._on_state_change = on_state_change
self._last_state: Optional[Dict[str, Any]] = None
async def can_process_url(self, url: str, depth: int) -> bool:
"""
@@ -155,10 +162,21 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
Batch (non-streaming) mode:
Processes one BFS level at a time, then yields all the results.
"""
visited: Set[str] = set()
# current_level holds tuples: (url, parent_url)
current_level: List[Tuple[str, Optional[str]]] = [(start_url, None)]
depths: Dict[str, int] = {start_url: 0}
# Conditional state initialization for resume support
if self._resume_state:
visited = set(self._resume_state.get("visited", []))
current_level = [
(item["url"], item["parent_url"])
for item in self._resume_state.get("pending", [])
]
depths = dict(self._resume_state.get("depths", {}))
self._pages_crawled = self._resume_state.get("pages_crawled", 0)
else:
# Original initialization
visited: Set[str] = set()
# current_level holds tuples: (url, parent_url)
current_level: List[Tuple[str, Optional[str]]] = [(start_url, None)]
depths: Dict[str, int] = {start_url: 0}
results: List[CrawlResult] = []
@@ -174,11 +192,7 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
# Clone the config to disable deep crawling recursion and enforce batch mode.
batch_config = config.clone(deep_crawl_strategy=None, stream=False)
batch_results = await crawler.arun_many(urls=urls, config=batch_config)
# Update pages crawled counter - count only successful crawls
successful_results = [r for r in batch_results if r.success]
self._pages_crawled += len(successful_results)
for result in batch_results:
url = result.url
depth = depths.get(url, 0)
@@ -187,12 +201,27 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
parent_url = next((parent for (u, parent) in current_level if u == url), None)
result.metadata["parent_url"] = parent_url
results.append(result)
# Only discover links from successful crawls
if result.success:
# Increment pages crawled per URL for accurate state tracking
self._pages_crawled += 1
# Link discovery will handle the max pages limit internally
await self.link_discovery(result, url, depth, visited, next_level, depths)
# Capture state after EACH URL processed (if callback set)
if self._on_state_change:
state = {
"strategy_type": "bfs",
"visited": list(visited),
"pending": [{"url": u, "parent_url": p} for u, p in next_level],
"depths": depths,
"pages_crawled": self._pages_crawled,
}
self._last_state = state
await self._on_state_change(state)
current_level = next_level
return results
@@ -207,9 +236,20 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
Streaming mode:
Processes one BFS level at a time and yields results immediately as they arrive.
"""
visited: Set[str] = set()
current_level: List[Tuple[str, Optional[str]]] = [(start_url, None)]
depths: Dict[str, int] = {start_url: 0}
# Conditional state initialization for resume support
if self._resume_state:
visited = set(self._resume_state.get("visited", []))
current_level = [
(item["url"], item["parent_url"])
for item in self._resume_state.get("pending", [])
]
depths = dict(self._resume_state.get("depths", {}))
self._pages_crawled = self._resume_state.get("pages_crawled", 0)
else:
# Original initialization
visited: Set[str] = set()
current_level: List[Tuple[str, Optional[str]]] = [(start_url, None)]
depths: Dict[str, int] = {start_url: 0}
while current_level and not self._cancel_event.is_set():
next_level: List[Tuple[str, Optional[str]]] = []
@@ -244,7 +284,19 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
if result.success:
# Link discovery will handle the max pages limit internally
await self.link_discovery(result, url, depth, visited, next_level, depths)
# Capture state after EACH URL processed (if callback set)
if self._on_state_change:
state = {
"strategy_type": "bfs",
"visited": list(visited),
"pending": [{"url": u, "parent_url": p} for u, p in next_level],
"depths": depths,
"pages_crawled": self._pages_crawled,
}
self._last_state = state
await self._on_state_change(state)
# If we didn't get results back (e.g. due to errors), avoid getting stuck in an infinite loop
# by considering these URLs as visited but not counting them toward the max_pages limit
if results_count == 0 and urls:
@@ -258,3 +310,15 @@ class BFSDeepCrawlStrategy(DeepCrawlStrategy):
"""
self._cancel_event.set()
self.stats.end_time = datetime.now()
def export_state(self) -> Optional[Dict[str, Any]]:
"""
Export current crawl state for external persistence.
Note: This returns the last captured state. For real-time state,
use the on_state_change callback.
Returns:
Dict with strategy state, or None if no state captured yet.
"""
return self._last_state

View File

@@ -4,14 +4,26 @@ from typing import AsyncGenerator, Optional, Set, Dict, List, Tuple
from ..models import CrawlResult
from .bfs_strategy import BFSDeepCrawlStrategy # noqa
from ..types import AsyncWebCrawler, CrawlerRunConfig
from ..utils import normalize_url_for_deep_crawl
class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
"""
Depth-First Search (DFS) deep crawling strategy.
Depth-first deep crawling with familiar BFS rules.
Inherits URL validation and link discovery from BFSDeepCrawlStrategy.
Overrides _arun_batch and _arun_stream to use a stack (LIFO) for DFS traversal.
We reuse the same filters, scoring, and page limits from :class:`BFSDeepCrawlStrategy`,
but walk the graph with a stack so we fully explore one branch before hopping to the
next. DFS also keeps its own ``_dfs_seen`` set so we can drop duplicate links at
discovery time without accidentally marking them as “already crawled”.
"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._dfs_seen: Set[str] = set()
def _reset_seen(self, start_url: str) -> None:
"""Start each crawl with a clean dedupe set seeded with the root URL."""
self._dfs_seen = {start_url}
async def _arun_batch(
self,
start_url: str,
@@ -19,14 +31,32 @@ class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
config: CrawlerRunConfig,
) -> List[CrawlResult]:
"""
Batch (non-streaming) DFS mode.
Uses a stack to traverse URLs in DFS order, aggregating CrawlResults into a list.
Crawl level-by-level but emit results at the end.
We keep a stack of ``(url, parent, depth)`` tuples, pop one at a time, and
hand it to ``crawler.arun_many`` with deep crawling disabled so we remain
in control of traversal. Every successful page bumps ``_pages_crawled`` and
seeds new stack items discovered via :meth:`link_discovery`.
"""
visited: Set[str] = set()
# Stack items: (url, parent_url, depth)
stack: List[Tuple[str, Optional[str], int]] = [(start_url, None, 0)]
depths: Dict[str, int] = {start_url: 0}
results: List[CrawlResult] = []
# Conditional state initialization for resume support
if self._resume_state:
visited = set(self._resume_state.get("visited", []))
stack = [
(item["url"], item["parent_url"], item["depth"])
for item in self._resume_state.get("stack", [])
]
depths = dict(self._resume_state.get("depths", {}))
self._pages_crawled = self._resume_state.get("pages_crawled", 0)
self._dfs_seen = set(self._resume_state.get("dfs_seen", []))
results: List[CrawlResult] = []
else:
# Original initialization
visited: Set[str] = set()
# Stack items: (url, parent_url, depth)
stack: List[Tuple[str, Optional[str], int]] = [(start_url, None, 0)]
depths: Dict[str, int] = {start_url: 0}
results: List[CrawlResult] = []
self._reset_seen(start_url)
while stack and not self._cancel_event.is_set():
url, parent, depth = stack.pop()
@@ -62,6 +92,22 @@ class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
for new_url, new_parent in reversed(new_links):
new_depth = depths.get(new_url, depth + 1)
stack.append((new_url, new_parent, new_depth))
# Capture state after each URL processed (if callback set)
if self._on_state_change:
state = {
"strategy_type": "dfs",
"visited": list(visited),
"stack": [
{"url": u, "parent_url": p, "depth": d}
for u, p, d in stack
],
"depths": depths,
"pages_crawled": self._pages_crawled,
"dfs_seen": list(self._dfs_seen),
}
self._last_state = state
await self._on_state_change(state)
return results
async def _arun_stream(
@@ -71,12 +117,28 @@ class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
config: CrawlerRunConfig,
) -> AsyncGenerator[CrawlResult, None]:
"""
Streaming DFS mode.
Uses a stack to traverse URLs in DFS order and yields CrawlResults as they become available.
Same traversal as :meth:`_arun_batch`, but yield pages immediately.
Each popped URL is crawled, its metadata annotated, then the result gets
yielded before we even look at the next stack entry. Successful crawls
still feed :meth:`link_discovery`, keeping DFS order intact.
"""
visited: Set[str] = set()
stack: List[Tuple[str, Optional[str], int]] = [(start_url, None, 0)]
depths: Dict[str, int] = {start_url: 0}
# Conditional state initialization for resume support
if self._resume_state:
visited = set(self._resume_state.get("visited", []))
stack = [
(item["url"], item["parent_url"], item["depth"])
for item in self._resume_state.get("stack", [])
]
depths = dict(self._resume_state.get("depths", {}))
self._pages_crawled = self._resume_state.get("pages_crawled", 0)
self._dfs_seen = set(self._resume_state.get("dfs_seen", []))
else:
# Original initialization
visited: Set[str] = set()
stack: List[Tuple[str, Optional[str], int]] = [(start_url, None, 0)]
depths: Dict[str, int] = {start_url: 0}
self._reset_seen(start_url)
while stack and not self._cancel_event.is_set():
url, parent, depth = stack.pop()
@@ -108,3 +170,108 @@ class DFSDeepCrawlStrategy(BFSDeepCrawlStrategy):
for new_url, new_parent in reversed(new_links):
new_depth = depths.get(new_url, depth + 1)
stack.append((new_url, new_parent, new_depth))
# Capture state after each URL processed (if callback set)
if self._on_state_change:
state = {
"strategy_type": "dfs",
"visited": list(visited),
"stack": [
{"url": u, "parent_url": p, "depth": d}
for u, p, d in stack
],
"depths": depths,
"pages_crawled": self._pages_crawled,
"dfs_seen": list(self._dfs_seen),
}
self._last_state = state
await self._on_state_change(state)
async def link_discovery(
self,
result: CrawlResult,
source_url: str,
current_depth: int,
_visited: Set[str],
next_level: List[Tuple[str, Optional[str]]],
depths: Dict[str, int],
) -> None:
"""
Find the next URLs we should push onto the DFS stack.
Parameters
----------
result : CrawlResult
Output of the page we just crawled; its ``links`` block is our raw material.
source_url : str
URL of the parent page; stored so callers can track ancestry.
current_depth : int
Depth of the parent; children naturally sit at ``current_depth + 1``.
_visited : Set[str]
Present to match the BFS signature, but we rely on ``_dfs_seen`` instead.
next_level : list of tuples
The stack buffer supplied by the caller; we append new ``(url, parent)`` items here.
depths : dict
Shared depth map so future metadata tagging knows how deep each URL lives.
Notes
-----
- ``_dfs_seen`` keeps us from pushing duplicates without touching the traversal guard.
- Validation, scoring, and capacity trimming mirror the BFS version so behaviour stays consistent.
"""
next_depth = current_depth + 1
if next_depth > self.max_depth:
return
remaining_capacity = self.max_pages - self._pages_crawled
if remaining_capacity <= 0:
self.logger.info(
f"Max pages limit ({self.max_pages}) reached, stopping link discovery"
)
return
links = result.links.get("internal", [])
if self.include_external:
links += result.links.get("external", [])
seen = self._dfs_seen
valid_links: List[Tuple[str, float]] = []
for link in links:
raw_url = link.get("href")
if not raw_url:
continue
normalized_url = normalize_url_for_deep_crawl(raw_url, source_url)
if not normalized_url or normalized_url in seen:
continue
if not await self.can_process_url(raw_url, next_depth):
self.stats.urls_skipped += 1
continue
score = self.url_scorer.score(normalized_url) if self.url_scorer else 0
if score < self.score_threshold:
self.logger.debug(
f"URL {normalized_url} skipped: score {score} below threshold {self.score_threshold}"
)
self.stats.urls_skipped += 1
continue
seen.add(normalized_url)
valid_links.append((normalized_url, score))
if len(valid_links) > remaining_capacity:
if self.url_scorer:
valid_links.sort(key=lambda x: x[1], reverse=True)
valid_links = valid_links[:remaining_capacity]
self.logger.info(
f"Limiting to {remaining_capacity} URLs due to max_pages limit"
)
for url, score in valid_links:
if score:
result.metadata = result.metadata or {}
result.metadata["score"] = score
next_level.append((url, source_url))
depths[url] = next_depth

View File

@@ -509,18 +509,22 @@ class DomainFilter(URLFilter):
class ContentRelevanceFilter(URLFilter):
"""BM25-based relevance filter using head section content"""
__slots__ = ("query_terms", "threshold", "k1", "b", "avgdl")
__slots__ = ("query_terms", "threshold", "k1", "b", "avgdl", "query")
def __init__(
self,
query: str,
query: Union[str, List[str]],
threshold: float,
k1: float = 1.2,
b: float = 0.75,
avgdl: int = 1000,
):
super().__init__(name="BM25RelevanceFilter")
self.query_terms = self._tokenize(query)
if isinstance(query, list):
self.query = " ".join(query)
else:
self.query = query
self.query_terms = self._tokenize(self.query)
self.threshold = threshold
self.k1 = k1 # TF saturation parameter
self.b = b # Length normalization parameter

View File

@@ -1,4 +1,4 @@
from typing import List, Optional, Union, AsyncGenerator, Dict, Any
from typing import List, Optional, Union, AsyncGenerator, Dict, Any, Callable
import httpx
import json
from urllib.parse import urljoin
@@ -7,6 +7,7 @@ import asyncio
from .async_configs import BrowserConfig, CrawlerRunConfig
from .models import CrawlResult
from .async_logger import AsyncLogger, LogLevel
from .utils import hooks_to_string
class Crawl4aiClientError(Exception):
@@ -70,17 +71,41 @@ class Crawl4aiDockerClient:
self.logger.error(f"Server unreachable: {str(e)}", tag="ERROR")
raise ConnectionError(f"Cannot connect to server: {str(e)}")
def _prepare_request(self, urls: List[str], browser_config: Optional[BrowserConfig] = None,
crawler_config: Optional[CrawlerRunConfig] = None) -> Dict[str, Any]:
def _prepare_request(
self,
urls: List[str],
browser_config: Optional[BrowserConfig] = None,
crawler_config: Optional[CrawlerRunConfig] = None,
hooks: Optional[Union[Dict[str, Callable], Dict[str, str]]] = None,
hooks_timeout: int = 30
) -> Dict[str, Any]:
"""Prepare request data from configs."""
if self._token:
self._http_client.headers["Authorization"] = f"Bearer {self._token}"
return {
request_data = {
"urls": urls,
"browser_config": browser_config.dump() if browser_config else {},
"crawler_config": crawler_config.dump() if crawler_config else {}
}
# Handle hooks if provided
if hooks:
# Check if hooks are already strings or need conversion
if any(callable(v) for v in hooks.values()):
# Convert function objects to strings
hooks_code = hooks_to_string(hooks)
else:
# Already in string format
hooks_code = hooks
request_data["hooks"] = {
"code": hooks_code,
"timeout": hooks_timeout
}
return request_data
async def _request(self, method: str, endpoint: str, **kwargs) -> httpx.Response:
"""Make an HTTP request with error handling."""
url = urljoin(self.base_url, endpoint)
@@ -102,16 +127,42 @@ class Crawl4aiDockerClient:
self,
urls: List[str],
browser_config: Optional[BrowserConfig] = None,
crawler_config: Optional[CrawlerRunConfig] = None
crawler_config: Optional[CrawlerRunConfig] = None,
hooks: Optional[Union[Dict[str, Callable], Dict[str, str]]] = None,
hooks_timeout: int = 30
) -> Union[CrawlResult, List[CrawlResult], AsyncGenerator[CrawlResult, None]]:
"""Execute a crawl operation."""
"""
Execute a crawl operation.
Args:
urls: List of URLs to crawl
browser_config: Browser configuration
crawler_config: Crawler configuration
hooks: Optional hooks - can be either:
- Dict[str, Callable]: Function objects that will be converted to strings
- Dict[str, str]: Already stringified hook code
hooks_timeout: Timeout in seconds for each hook execution (1-120)
Returns:
Single CrawlResult, list of results, or async generator for streaming
Example with function hooks:
>>> async def my_hook(page, context, **kwargs):
... await page.set_viewport_size({"width": 1920, "height": 1080})
... return page
>>>
>>> result = await client.crawl(
... ["https://example.com"],
... hooks={"on_page_context_created": my_hook}
... )
"""
await self._check_server()
data = self._prepare_request(urls, browser_config, crawler_config)
data = self._prepare_request(urls, browser_config, crawler_config, hooks, hooks_timeout)
is_streaming = crawler_config and crawler_config.stream
self.logger.info(f"Crawling {len(urls)} URLs {'(streaming)' if is_streaming else ''}", tag="CRAWL")
if is_streaming:
async def stream_results() -> AsyncGenerator[CrawlResult, None]:
async with self._http_client.stream("POST", f"{self.base_url}/crawl/stream", json=data) as response:
@@ -128,12 +179,12 @@ class Crawl4aiDockerClient:
else:
yield CrawlResult(**result)
return stream_results()
response = await self._request("POST", "/crawl", json=data)
response = await self._request("POST", "/crawl", json=data, timeout=hooks_timeout)
result_data = response.json()
if not result_data.get("success", False):
raise RequestError(f"Crawl failed: {result_data.get('msg', 'Unknown error')}")
results = [CrawlResult(**r) for r in result_data.get("results", [])]
self.logger.success(f"Crawl completed with {len(results)} results", tag="CRAWL")
return results[0] if len(results) == 1 else results

View File

@@ -94,6 +94,20 @@ class ExtractionStrategy(ABC):
extracted_content.extend(future.result())
return extracted_content
async def arun(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]:
"""
Async version: Process sections of text in parallel using asyncio.
Default implementation runs the sync version in a thread pool.
Subclasses can override this for true async processing.
:param url: The URL of the webpage.
:param sections: List of sections (strings) to process.
:return: A list of processed JSON blocks.
"""
import asyncio
return await asyncio.to_thread(self.run, url, sections, *q, **kwargs)
class NoExtractionStrategy(ExtractionStrategy):
"""
@@ -635,6 +649,9 @@ class LLMExtractionStrategy(ExtractionStrategy):
base_url=self.llm_config.base_url,
json_response=self.force_json_response,
extra_args=self.extra_args,
base_delay=self.llm_config.backoff_base_delay,
max_attempts=self.llm_config.backoff_max_attempts,
exponential_factor=self.llm_config.backoff_exponential_factor
) # , json_response=self.extract_type == "schema")
# Track usage
usage = TokenUsage(
@@ -780,6 +797,180 @@ class LLMExtractionStrategy(ExtractionStrategy):
return extracted_content
async def aextract(self, url: str, ix: int, html: str) -> List[Dict[str, Any]]:
"""
Async version: Extract meaningful blocks or chunks from the given HTML using an LLM.
How it works:
1. Construct a prompt with variables.
2. Make an async request to the LLM using the prompt.
3. Parse the response and extract blocks or chunks.
Args:
url: The URL of the webpage.
ix: Index of the block.
html: The HTML content of the webpage.
Returns:
A list of extracted blocks or chunks.
"""
from .utils import aperform_completion_with_backoff
if self.verbose:
print(f"[LOG] Call LLM for {url} - block index: {ix}")
variable_values = {
"URL": url,
"HTML": escape_json_string(sanitize_html(html)),
}
prompt_with_variables = PROMPT_EXTRACT_BLOCKS
if self.instruction:
variable_values["REQUEST"] = self.instruction
prompt_with_variables = PROMPT_EXTRACT_BLOCKS_WITH_INSTRUCTION
if self.extract_type == "schema" and self.schema:
variable_values["SCHEMA"] = json.dumps(self.schema, indent=2)
prompt_with_variables = PROMPT_EXTRACT_SCHEMA_WITH_INSTRUCTION
if self.extract_type == "schema" and not self.schema:
prompt_with_variables = PROMPT_EXTRACT_INFERRED_SCHEMA
for variable in variable_values:
prompt_with_variables = prompt_with_variables.replace(
"{" + variable + "}", variable_values[variable]
)
try:
response = await aperform_completion_with_backoff(
self.llm_config.provider,
prompt_with_variables,
self.llm_config.api_token,
base_url=self.llm_config.base_url,
json_response=self.force_json_response,
extra_args=self.extra_args,
base_delay=self.llm_config.backoff_base_delay,
max_attempts=self.llm_config.backoff_max_attempts,
exponential_factor=self.llm_config.backoff_exponential_factor
)
# Track usage
usage = TokenUsage(
completion_tokens=response.usage.completion_tokens,
prompt_tokens=response.usage.prompt_tokens,
total_tokens=response.usage.total_tokens,
completion_tokens_details=response.usage.completion_tokens_details.__dict__
if response.usage.completion_tokens_details
else {},
prompt_tokens_details=response.usage.prompt_tokens_details.__dict__
if response.usage.prompt_tokens_details
else {},
)
self.usages.append(usage)
# Update totals
self.total_usage.completion_tokens += usage.completion_tokens
self.total_usage.prompt_tokens += usage.prompt_tokens
self.total_usage.total_tokens += usage.total_tokens
try:
content = response.choices[0].message.content
blocks = None
if self.force_json_response:
blocks = json.loads(content)
if isinstance(blocks, dict):
if len(blocks) == 1 and isinstance(list(blocks.values())[0], list):
blocks = list(blocks.values())[0]
else:
blocks = [blocks]
elif isinstance(blocks, list):
blocks = blocks
else:
blocks = extract_xml_data(["blocks"], content)["blocks"]
blocks = json.loads(blocks)
for block in blocks:
block["error"] = False
except Exception:
parsed, unparsed = split_and_parse_json_objects(
response.choices[0].message.content
)
blocks = parsed
if unparsed:
blocks.append(
{"index": 0, "error": True, "tags": ["error"], "content": unparsed}
)
if self.verbose:
print(
"[LOG] Extracted",
len(blocks),
"blocks from URL:",
url,
"block index:",
ix,
)
return blocks
except Exception as e:
if self.verbose:
print(f"[LOG] Error in LLM extraction: {e}")
return [
{
"index": ix,
"error": True,
"tags": ["error"],
"content": str(e),
}
]
async def arun(self, url: str, sections: List[str]) -> List[Dict[str, Any]]:
"""
Async version: Process sections with true parallelism using asyncio.gather.
Args:
url: The URL of the webpage.
sections: List of sections (strings) to process.
Returns:
A list of extracted blocks or chunks.
"""
import asyncio
merged_sections = self._merge(
sections,
self.chunk_token_threshold,
overlap=int(self.chunk_token_threshold * self.overlap_rate),
)
extracted_content = []
# Create tasks for all sections to run in parallel
tasks = [
self.aextract(url, ix, sanitize_input_encode(section))
for ix, section in enumerate(merged_sections)
]
# Execute all tasks concurrently
results = await asyncio.gather(*tasks, return_exceptions=True)
# Process results
for result in results:
if isinstance(result, Exception):
if self.verbose:
print(f"Error in async extraction: {result}")
extracted_content.append(
{
"index": 0,
"error": True,
"tags": ["error"],
"content": str(result),
}
)
else:
extracted_content.extend(result)
return extracted_content
def show_usage(self) -> None:
"""Print a detailed token usage report showing total and per-request usage."""
print("\n=== Token Usage Summary ===")
@@ -1086,44 +1277,18 @@ class JsonElementExtractionStrategy(ExtractionStrategy):
}
@staticmethod
def generate_schema(
html: str,
schema_type: str = "CSS", # or XPATH
query: str = None,
target_json_example: str = None,
llm_config: 'LLMConfig' = create_llm_config(),
provider: str = None,
api_token: str = None,
**kwargs
) -> dict:
def _build_schema_prompt(html: str, schema_type: str, query: str = None, target_json_example: str = None) -> str:
"""
Generate extraction schema from HTML content and optional query.
Args:
html (str): The HTML content to analyze
query (str, optional): Natural language description of what data to extract
provider (str): Legacy Parameter. LLM provider to use
api_token (str): Legacy Parameter. API token for LLM provider
llm_config (LLMConfig): LLM configuration object
prompt (str, optional): Custom prompt template to use
**kwargs: Additional args passed to LLM processor
Build the prompt for schema generation. Shared by sync and async methods.
Returns:
dict: Generated schema following the JsonElementExtractionStrategy format
str: Combined system and user prompt
"""
from .prompts import JSON_SCHEMA_BUILDER
from .utils import perform_completion_with_backoff
for name, message in JsonElementExtractionStrategy._GENERATE_SCHEMA_UNWANTED_PROPS.items():
if locals()[name] is not None:
raise AttributeError(f"Setting '{name}' is deprecated. {message}")
# Use default or custom prompt
prompt_template = JSON_SCHEMA_BUILDER if schema_type == "CSS" else JSON_SCHEMA_BUILDER_XPATH
# Build the prompt
system_message = {
"role": "system",
"content": f"""You specialize in generating special JSON schemas for web scraping. This schema uses CSS or XPATH selectors to present a repetitive pattern in crawled HTML, such as a product in a product list or a search result item in a list of search results. We use this JSON schema to pass to a language model along with the HTML content to extract structured data from the HTML. The language model uses the JSON schema to extract data from the HTML and retrieve values for fields in the JSON schema, following the schema.
system_content = f"""You specialize in generating special JSON schemas for web scraping. This schema uses CSS or XPATH selectors to present a repetitive pattern in crawled HTML, such as a product in a product list or a search result item in a list of search results. We use this JSON schema to pass to a language model along with the HTML content to extract structured data from the HTML. The language model uses the JSON schema to extract data from the HTML and retrieve values for fields in the JSON schema, following the schema.
Generating this HTML manually is not feasible, so you need to generate the JSON schema using the HTML content. The HTML copied from the crawled website is provided below, which we believe contains the repetitive pattern.
@@ -1144,31 +1309,27 @@ In this scenario, use your best judgment to generate the schema. You need to exa
# What are the instructions and details for this schema generation?
{prompt_template}"""
}
user_message = {
"role": "user",
"content": f"""
user_content = f"""
HTML to analyze:
```html
{html}
```
"""
}
if query:
user_message["content"] += f"\n\n## Query or explanation of target/goal data item:\n{query}"
user_content += f"\n\n## Query or explanation of target/goal data item:\n{query}"
if target_json_example:
user_message["content"] += f"\n\n## Example of target JSON object:\n```json\n{target_json_example}\n```"
user_content += f"\n\n## Example of target JSON object:\n```json\n{target_json_example}\n```"
if query and not target_json_example:
user_message["content"] += """IMPORTANT: To remind you, in this process, we are not providing a rigid example of the adjacent objects we seek. We rely on your understanding of the explanation provided in the above section. Make sure to grasp what we are looking for and, based on that, create the best schema.."""
user_content += """IMPORTANT: To remind you, in this process, we are not providing a rigid example of the adjacent objects we seek. We rely on your understanding of the explanation provided in the above section. Make sure to grasp what we are looking for and, based on that, create the best schema.."""
elif not query and target_json_example:
user_message["content"] += """IMPORTANT: Please remember that in this process, we provided a proper example of a target JSON object. Make sure to adhere to the structure and create a schema that exactly fits this example. If you find that some elements on the page do not match completely, vote for the majority."""
user_content += """IMPORTANT: Please remember that in this process, we provided a proper example of a target JSON object. Make sure to adhere to the structure and create a schema that exactly fits this example. If you find that some elements on the page do not match completely, vote for the majority."""
elif not query and not target_json_example:
user_message["content"] += """IMPORTANT: Since we neither have a query nor an example, it is crucial to rely solely on the HTML content provided. Leverage your expertise to determine the schema based on the repetitive patterns observed in the content."""
user_message["content"] += """IMPORTANT:
user_content += """IMPORTANT: Since we neither have a query nor an example, it is crucial to rely solely on the HTML content provided. Leverage your expertise to determine the schema based on the repetitive patterns observed in the content."""
user_content += """IMPORTANT:
0/ Ensure your schema remains reliable by avoiding selectors that appear to generate dynamically and are not dependable. You want a reliable schema, as it consistently returns the same data even after many page reloads.
1/ DO NOT USE use base64 kind of classes, they are temporary and not reliable.
2/ Every selector must refer to only one unique element. You should ensure your selector points to a single element and is unique to the place that contains the information. You have to use available techniques based on CSS or XPATH requested schema to make sure your selector is unique and also not fragile, meaning if we reload the page now or in the future, the selector should remain reliable.
@@ -1177,20 +1338,98 @@ In this scenario, use your best judgment to generate the schema. You need to exa
Analyze the HTML and generate a JSON schema that follows the specified format. Only output valid JSON schema, nothing else.
"""
return "\n\n".join([system_content, user_content])
@staticmethod
def generate_schema(
html: str,
schema_type: str = "CSS",
query: str = None,
target_json_example: str = None,
llm_config: 'LLMConfig' = create_llm_config(),
provider: str = None,
api_token: str = None,
**kwargs
) -> dict:
"""
Generate extraction schema from HTML content and optional query (sync version).
Args:
html (str): The HTML content to analyze
query (str, optional): Natural language description of what data to extract
provider (str): Legacy Parameter. LLM provider to use
api_token (str): Legacy Parameter. API token for LLM provider
llm_config (LLMConfig): LLM configuration object
**kwargs: Additional args passed to LLM processor
Returns:
dict: Generated schema following the JsonElementExtractionStrategy format
"""
from .utils import perform_completion_with_backoff
for name, message in JsonElementExtractionStrategy._GENERATE_SCHEMA_UNWANTED_PROPS.items():
if locals()[name] is not None:
raise AttributeError(f"Setting '{name}' is deprecated. {message}")
prompt = JsonElementExtractionStrategy._build_schema_prompt(html, schema_type, query, target_json_example)
try:
# Call LLM with backoff handling
response = perform_completion_with_backoff(
provider=llm_config.provider,
prompt_with_variables="\n\n".join([system_message["content"], user_message["content"]]),
json_response = True,
prompt_with_variables=prompt,
json_response=True,
api_token=llm_config.api_token,
base_url=llm_config.base_url,
extra_args=kwargs
)
return json.loads(response.choices[0].message.content)
except Exception as e:
raise Exception(f"Failed to generate schema: {str(e)}")
@staticmethod
async def agenerate_schema(
html: str,
schema_type: str = "CSS",
query: str = None,
target_json_example: str = None,
llm_config: 'LLMConfig' = None,
**kwargs
) -> dict:
"""
Generate extraction schema from HTML content (async version).
Use this method when calling from async contexts (e.g., FastAPI) to avoid
issues with certain LLM providers (e.g., Gemini/Vertex AI) that require
async execution.
Args:
html (str): The HTML content to analyze
schema_type (str): "CSS" or "XPATH"
query (str, optional): Natural language description of what data to extract
target_json_example (str, optional): Example of desired JSON output
llm_config (LLMConfig): LLM configuration object
**kwargs: Additional args passed to LLM processor
Returns:
dict: Generated schema following the JsonElementExtractionStrategy format
"""
from .utils import aperform_completion_with_backoff
if llm_config is None:
llm_config = create_llm_config()
prompt = JsonElementExtractionStrategy._build_schema_prompt(html, schema_type, query, target_json_example)
try:
response = await aperform_completion_with_backoff(
provider=llm_config.provider,
prompt_with_variables=prompt,
json_response=True,
api_token=llm_config.api_token,
base_url=llm_config.base_url,
extra_args=kwargs
)
# Extract and return schema
return json.loads(response.choices[0].message.content)
except Exception as e:
raise Exception(f"Failed to generate schema: {str(e)}")

View File

@@ -1,4 +1,4 @@
from pydantic import BaseModel, HttpUrl, PrivateAttr, Field
from pydantic import BaseModel, HttpUrl, PrivateAttr, Field, ConfigDict
from typing import List, Dict, Optional, Callable, Awaitable, Union, Any
from typing import AsyncGenerator
from typing import Generic, TypeVar
@@ -152,9 +152,12 @@ class CrawlResult(BaseModel):
network_requests: Optional[List[Dict[str, Any]]] = None
console_messages: Optional[List[Dict[str, Any]]] = None
tables: List[Dict] = Field(default_factory=list) # NEW [{headers,rows,caption,summary}]
# Cache validation metadata (Smart Cache)
head_fingerprint: Optional[str] = None
cached_at: Optional[float] = None
cache_status: Optional[str] = None # "hit", "hit_validated", "hit_fallback", "miss"
class Config:
arbitrary_types_allowed = True
model_config = ConfigDict(arbitrary_types_allowed=True)
# NOTE: The StringCompatibleMarkdown class, custom __init__ method, property getters/setters,
# and model_dump override all exist to support a smooth transition from markdown as a string
@@ -332,8 +335,7 @@ class AsyncCrawlResponse(BaseModel):
network_requests: Optional[List[Dict[str, Any]]] = None
console_messages: Optional[List[Dict[str, Any]]] = None
class Config:
arbitrary_types_allowed = True
model_config = ConfigDict(arbitrary_types_allowed=True)
###############################
# Scraping Models

View File

@@ -15,9 +15,9 @@ from .utils import (
clean_pdf_text_to_html,
)
# Remove direct PyPDF2 imports from the top
# import PyPDF2
# from PyPDF2 import PdfReader
# Remove direct pypdf imports from the top
# import pypdf
# from pypdf import PdfReader
logger = logging.getLogger(__name__)
@@ -59,9 +59,9 @@ class NaivePDFProcessorStrategy(PDFProcessorStrategy):
save_images_locally: bool = False, image_save_dir: Optional[Path] = None, batch_size: int = 4):
# Import check at initialization time
try:
import PyPDF2
import pypdf
except ImportError:
raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
raise ImportError("pypdf is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
self.image_dpi = image_dpi
self.image_quality = image_quality
@@ -75,9 +75,9 @@ class NaivePDFProcessorStrategy(PDFProcessorStrategy):
def process(self, pdf_path: Path) -> PDFProcessResult:
# Import inside method to allow dependency to be optional
try:
from PyPDF2 import PdfReader
from pypdf import PdfReader
except ImportError:
raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
raise ImportError("pypdf is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
start_time = time()
result = PDFProcessResult(
@@ -125,15 +125,15 @@ class NaivePDFProcessorStrategy(PDFProcessorStrategy):
"""Like process() but processes PDF pages in parallel batches"""
# Import inside method to allow dependency to be optional
try:
from PyPDF2 import PdfReader
import PyPDF2 # For type checking
from pypdf import PdfReader
import pypdf # For type checking
except ImportError:
raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
raise ImportError("pypdf is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
import concurrent.futures
import threading
# Initialize PyPDF2 thread support
# Initialize pypdf thread support
if not hasattr(threading.current_thread(), "_children"):
threading.current_thread()._children = set()
@@ -232,11 +232,11 @@ class NaivePDFProcessorStrategy(PDFProcessorStrategy):
return pdf_page
def _extract_images(self, page, image_dir: Optional[Path]) -> List[Dict]:
# Import PyPDF2 for type checking only when needed
# Import pypdf for type checking only when needed
try:
import PyPDF2
from pypdf.generic import IndirectObject
except ImportError:
raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
raise ImportError("pypdf is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
if not self.extract_images:
return []
@@ -266,7 +266,7 @@ class NaivePDFProcessorStrategy(PDFProcessorStrategy):
width = xobj.get('/Width', 0)
height = xobj.get('/Height', 0)
color_space = xobj.get('/ColorSpace', '/DeviceRGB')
if isinstance(color_space, PyPDF2.generic.IndirectObject):
if isinstance(color_space, IndirectObject):
color_space = color_space.get_object()
# Handle different image encodings
@@ -277,7 +277,7 @@ class NaivePDFProcessorStrategy(PDFProcessorStrategy):
if '/FlateDecode' in filters:
try:
decode_parms = xobj.get('/DecodeParms', {})
if isinstance(decode_parms, PyPDF2.generic.IndirectObject):
if isinstance(decode_parms, IndirectObject):
decode_parms = decode_parms.get_object()
predictor = decode_parms.get('/Predictor', 1)
@@ -416,10 +416,10 @@ class NaivePDFProcessorStrategy(PDFProcessorStrategy):
# Import inside method to allow dependency to be optional
if reader is None:
try:
from PyPDF2 import PdfReader
from pypdf import PdfReader
reader = PdfReader(pdf_path)
except ImportError:
raise ImportError("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
raise ImportError("pypdf is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
meta = reader.metadata or {}
created = self._parse_pdf_date(meta.get('/CreationDate', ''))
@@ -459,11 +459,11 @@ if __name__ == "__main__":
from pathlib import Path
try:
# Import PyPDF2 only when running the file directly
import PyPDF2
from PyPDF2 import PdfReader
# Import pypdf only when running the file directly
import pypdf
from pypdf import PdfReader
except ImportError:
print("PyPDF2 is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
print("pypdf is required for PDF processing. Install with 'pip install crawl4ai[pdf]'")
exit(1)
current_dir = Path(__file__).resolve().parent

View File

@@ -1,7 +1,9 @@
from typing import List, Dict, Optional
from typing import List, Dict, Optional, Tuple
from abc import ABC, abstractmethod
from itertools import cycle
import os
import asyncio
import time
########### ATTENTION PEOPLE OF EARTH ###########
@@ -120,7 +122,7 @@ class ProxyConfig:
class ProxyRotationStrategy(ABC):
"""Base abstract class for proxy rotation strategies"""
@abstractmethod
async def get_next_proxy(self) -> Optional[ProxyConfig]:
"""Get next proxy configuration from the strategy"""
@@ -131,18 +133,81 @@ class ProxyRotationStrategy(ABC):
"""Add proxy configurations to the strategy"""
pass
class RoundRobinProxyStrategy:
"""Simple round-robin proxy rotation strategy using ProxyConfig objects"""
@abstractmethod
async def get_proxy_for_session(
self,
session_id: str,
ttl: Optional[int] = None
) -> Optional[ProxyConfig]:
"""
Get or create a sticky proxy for a session.
If session_id already has an assigned proxy (and hasn't expired), return it.
If session_id is new, acquire a new proxy and associate it.
Args:
session_id: Unique session identifier
ttl: Optional time-to-live in seconds for this session
Returns:
ProxyConfig for this session
"""
pass
@abstractmethod
async def release_session(self, session_id: str) -> None:
"""
Release a sticky session, making the proxy available for reuse.
Args:
session_id: Session to release
"""
pass
@abstractmethod
def get_session_proxy(self, session_id: str) -> Optional[ProxyConfig]:
"""
Get the proxy for an existing session without creating new one.
Args:
session_id: Session to look up
Returns:
ProxyConfig if session exists and hasn't expired, None otherwise
"""
pass
@abstractmethod
def get_active_sessions(self) -> Dict[str, ProxyConfig]:
"""
Get all active sticky sessions.
Returns:
Dictionary mapping session_id to ProxyConfig
"""
pass
class RoundRobinProxyStrategy(ProxyRotationStrategy):
"""Simple round-robin proxy rotation strategy using ProxyConfig objects.
Supports sticky sessions where a session_id can be bound to a specific proxy
for the duration of the session. This is useful for deep crawling where
you want to maintain the same IP address across multiple requests.
"""
def __init__(self, proxies: List[ProxyConfig] = None):
"""
Initialize with optional list of proxy configurations
Args:
proxies: List of ProxyConfig objects
"""
self._proxies = []
self._proxies: List[ProxyConfig] = []
self._proxy_cycle = None
# Session tracking: maps session_id -> (ProxyConfig, created_at, ttl)
self._sessions: Dict[str, Tuple[ProxyConfig, float, Optional[int]]] = {}
self._session_lock = asyncio.Lock()
if proxies:
self.add_proxies(proxies)
@@ -156,3 +221,121 @@ class RoundRobinProxyStrategy:
if not self._proxy_cycle:
return None
return next(self._proxy_cycle)
async def get_proxy_for_session(
self,
session_id: str,
ttl: Optional[int] = None
) -> Optional[ProxyConfig]:
"""
Get or create a sticky proxy for a session.
If session_id already has an assigned proxy (and hasn't expired), return it.
If session_id is new, acquire a new proxy and associate it.
Args:
session_id: Unique session identifier
ttl: Optional time-to-live in seconds for this session
Returns:
ProxyConfig for this session
"""
async with self._session_lock:
# Check if session exists and hasn't expired
if session_id in self._sessions:
proxy, created_at, session_ttl = self._sessions[session_id]
# Check TTL expiration
effective_ttl = ttl if ttl is not None else session_ttl
if effective_ttl is not None:
elapsed = time.time() - created_at
if elapsed >= effective_ttl:
# Session expired, remove it and get new proxy
del self._sessions[session_id]
else:
return proxy
else:
return proxy
# Acquire new proxy for this session
proxy = await self.get_next_proxy()
if proxy:
self._sessions[session_id] = (proxy, time.time(), ttl)
return proxy
async def release_session(self, session_id: str) -> None:
"""
Release a sticky session, making the proxy available for reuse.
Args:
session_id: Session to release
"""
async with self._session_lock:
if session_id in self._sessions:
del self._sessions[session_id]
def get_session_proxy(self, session_id: str) -> Optional[ProxyConfig]:
"""
Get the proxy for an existing session without creating new one.
Args:
session_id: Session to look up
Returns:
ProxyConfig if session exists and hasn't expired, None otherwise
"""
if session_id not in self._sessions:
return None
proxy, created_at, ttl = self._sessions[session_id]
# Check TTL expiration
if ttl is not None:
elapsed = time.time() - created_at
if elapsed >= ttl:
return None
return proxy
def get_active_sessions(self) -> Dict[str, ProxyConfig]:
"""
Get all active sticky sessions (excluding expired ones).
Returns:
Dictionary mapping session_id to ProxyConfig
"""
current_time = time.time()
active_sessions = {}
for session_id, (proxy, created_at, ttl) in self._sessions.items():
# Skip expired sessions
if ttl is not None:
elapsed = current_time - created_at
if elapsed >= ttl:
continue
active_sessions[session_id] = proxy
return active_sessions
async def cleanup_expired_sessions(self) -> int:
"""
Remove all expired sessions from tracking.
Returns:
Number of sessions removed
"""
async with self._session_lock:
current_time = time.time()
expired = []
for session_id, (proxy, created_at, ttl) in self._sessions.items():
if ttl is not None:
elapsed = current_time - created_at
if elapsed >= ttl:
expired.append(session_id)
for session_id in expired:
del self._sessions[session_id]
return len(expired)

View File

@@ -795,6 +795,9 @@ Return only a JSON array of extracted tables following the specified format."""
api_token=self.llm_config.api_token,
base_url=self.llm_config.base_url,
json_response=True,
base_delay=self.llm_config.backoff_base_delay,
max_attempts=self.llm_config.backoff_max_attempts,
exponential_factor=self.llm_config.backoff_exponential_factor,
extra_args=self.extra_args
)
@@ -1116,6 +1119,9 @@ Return only a JSON array of extracted tables following the specified format."""
api_token=self.llm_config.api_token,
base_url=self.llm_config.base_url,
json_response=True,
base_delay=self.llm_config.backoff_base_delay,
max_attempts=self.llm_config.backoff_max_attempts,
exponential_factor=self.llm_config.backoff_exponential_factor,
extra_args=self.extra_args
)

View File

@@ -47,6 +47,7 @@ from urllib.parse import (
urljoin, urlparse, urlunparse,
parse_qsl, urlencode, quote, unquote
)
import inspect
# Monkey patch to fix wildcard handling in urllib.robotparser
@@ -1744,6 +1745,9 @@ def perform_completion_with_backoff(
api_token,
json_response=False,
base_url=None,
base_delay=2,
max_attempts=3,
exponential_factor=2,
**kwargs,
):
"""
@@ -1760,6 +1764,9 @@ def perform_completion_with_backoff(
api_token (str): The API token for authentication.
json_response (bool): Whether to request a JSON response. Defaults to False.
base_url (Optional[str]): The base URL for the API. Defaults to None.
base_delay (int): The base delay in seconds. Defaults to 2.
max_attempts (int): The maximum number of attempts. Defaults to 3.
exponential_factor (int): The exponential factor. Defaults to 2.
**kwargs: Additional arguments for the API request.
Returns:
@@ -1768,9 +1775,8 @@ def perform_completion_with_backoff(
from litellm import completion
from litellm.exceptions import RateLimitError
max_attempts = 3
base_delay = 2 # Base delay in seconds, you can adjust this based on your needs
import litellm
litellm.drop_params = True # Auto-drop unsupported params (e.g., temperature for O-series/GPT-5)
extra_args = {"temperature": 0.01, "api_key": api_token, "base_url": base_url}
if json_response:
@@ -1797,7 +1803,7 @@ def perform_completion_with_backoff(
# Check if we have exhausted our max attempts
if attempt < max_attempts - 1:
# Calculate the delay and wait
delay = base_delay * (2**attempt) # Exponential backoff formula
delay = base_delay * (exponential_factor**attempt) # Exponential backoff formula
print(f"Waiting for {delay} seconds before retrying...")
time.sleep(delay)
else:
@@ -1824,6 +1830,87 @@ def perform_completion_with_backoff(
# ]
async def aperform_completion_with_backoff(
provider,
prompt_with_variables,
api_token,
json_response=False,
base_url=None,
base_delay=2,
max_attempts=3,
exponential_factor=2,
**kwargs,
):
"""
Async version: Perform an API completion request with exponential backoff.
How it works:
1. Sends an async completion request to the API.
2. Retries on rate-limit errors with exponential delays (async).
3. Returns the API response or an error after all retries.
Args:
provider (str): The name of the API provider.
prompt_with_variables (str): The input prompt for the completion request.
api_token (str): The API token for authentication.
json_response (bool): Whether to request a JSON response. Defaults to False.
base_url (Optional[str]): The base URL for the API. Defaults to None.
base_delay (int): The base delay in seconds. Defaults to 2.
max_attempts (int): The maximum number of attempts. Defaults to 3.
exponential_factor (int): The exponential factor. Defaults to 2.
**kwargs: Additional arguments for the API request.
Returns:
dict: The API response or an error message after all retries.
"""
from litellm import acompletion
from litellm.exceptions import RateLimitError
import litellm
import asyncio
litellm.drop_params = True # Auto-drop unsupported params (e.g., temperature for O-series/GPT-5)
extra_args = {"temperature": 0.01, "api_key": api_token, "base_url": base_url}
if json_response:
extra_args["response_format"] = {"type": "json_object"}
if kwargs.get("extra_args"):
extra_args.update(kwargs["extra_args"])
for attempt in range(max_attempts):
try:
response = await acompletion(
model=provider,
messages=[{"role": "user", "content": prompt_with_variables}],
**extra_args,
)
return response # Return the successful response
except RateLimitError as e:
print("Rate limit error:", str(e))
if attempt == max_attempts - 1:
# Last attempt failed, raise the error.
raise
# Check if we have exhausted our max attempts
if attempt < max_attempts - 1:
# Calculate the delay and wait
delay = base_delay * (exponential_factor**attempt) # Exponential backoff formula
print(f"Waiting for {delay} seconds before retrying...")
await asyncio.sleep(delay)
else:
# Return an error response after exhausting all retries
return [
{
"index": 0,
"tags": ["error"],
"content": ["Rate limit error. Please try again later."],
}
]
except Exception as e:
raise e # Raise any other exceptions immediately
def extract_blocks(url, html, provider=DEFAULT_PROVIDER, api_token=None, base_url=None):
"""
Extract content blocks from website HTML using an AI provider.
@@ -2378,6 +2465,54 @@ def normalize_url_tmp(href, base_url):
return href.strip()
def quick_extract_links(html: str, base_url: str) -> Dict[str, List[Dict[str, str]]]:
"""
Fast link extraction for prefetch mode.
Only extracts <a href> tags - no media, no cleaning, no heavy processing.
Args:
html: Raw HTML string
base_url: Base URL for resolving relative links
Returns:
{"internal": [{"href": "...", "text": "..."}], "external": [...]}
"""
from lxml.html import document_fromstring
try:
doc = document_fromstring(html)
except Exception:
return {"internal": [], "external": []}
base_domain = get_base_domain(base_url)
internal: List[Dict[str, str]] = []
external: List[Dict[str, str]] = []
seen: Set[str] = set()
for a in doc.xpath("//a[@href]"):
href = a.get("href", "").strip()
if not href or href.startswith(("#", "javascript:", "mailto:", "tel:")):
continue
# Normalize URL
normalized = normalize_url_for_deep_crawl(href, base_url)
if not normalized or normalized in seen:
continue
seen.add(normalized)
# Extract text (truncated for memory efficiency)
text = (a.text_content() or "").strip()[:200]
link_data = {"href": normalized, "text": text}
if is_external_url(normalized, base_domain):
external.append(link_data)
else:
internal.append(link_data)
return {"internal": internal, "external": external}
def get_base_domain(url: str) -> str:
"""
Extract the base domain from a given URL, handling common edge cases.
@@ -2745,6 +2880,67 @@ def generate_content_hash(content: str) -> str:
# return hashlib.sha256(content.encode()).hexdigest()
def compute_head_fingerprint(head_html: str) -> str:
"""
Compute a fingerprint of <head> content for cache validation.
Focuses on content that typically changes when page updates:
- <title>
- <meta name="description">
- <meta property="og:title|og:description|og:image|og:updated_time">
- <meta property="article:modified_time">
- <meta name="last-modified">
Uses xxhash for speed, combines multiple signals into a single hash.
Args:
head_html: The HTML content of the <head> section
Returns:
A hex string fingerprint, or empty string if no signals found
"""
if not head_html:
return ""
head_lower = head_html.lower()
signals = []
# Extract title
title_match = re.search(r'<title[^>]*>(.*?)</title>', head_lower, re.DOTALL)
if title_match:
signals.append(title_match.group(1).strip())
# Meta tags to extract (name or property attribute, and the value to match)
meta_tags = [
("name", "description"),
("name", "last-modified"),
("property", "og:title"),
("property", "og:description"),
("property", "og:image"),
("property", "og:updated_time"),
("property", "article:modified_time"),
]
for attr_type, attr_value in meta_tags:
# Handle both attribute orders: attr="value" content="..." and content="..." attr="value"
patterns = [
rf'<meta[^>]*{attr_type}=["\']{ re.escape(attr_value)}["\'][^>]*content=["\']([^"\']*)["\']',
rf'<meta[^>]*content=["\']([^"\']*)["\'][^>]*{attr_type}=["\']{re.escape(attr_value)}["\']',
]
for pattern in patterns:
match = re.search(pattern, head_lower)
if match:
signals.append(match.group(1).strip())
break # Found this tag, move to next
if not signals:
return ""
# Combine signals and hash
combined = '|'.join(signals)
return xxhash.xxh64(combined.encode()).hexdigest()
def ensure_content_dirs(base_path: str) -> Dict[str, str]:
"""Create content directories if they don't exist"""
dirs = {
@@ -3529,4 +3725,52 @@ def get_memory_stats() -> Tuple[float, float, float]:
available_gb = get_true_available_memory_gb()
used_percent = get_true_memory_usage_percent()
return used_percent, available_gb, total_gb
return used_percent, available_gb, total_gb
# Hook utilities for Docker API
def hooks_to_string(hooks: Dict[str, Callable]) -> Dict[str, str]:
"""
Convert hook function objects to string representations for Docker API.
This utility simplifies the process of using hooks with the Docker API by converting
Python function objects into the string format required by the API.
Args:
hooks: Dictionary mapping hook point names to Python function objects.
Functions should be async and follow hook signature requirements.
Returns:
Dictionary mapping hook point names to string representations of the functions.
Example:
>>> async def my_hook(page, context, **kwargs):
... await page.set_viewport_size({"width": 1920, "height": 1080})
... return page
>>>
>>> hooks_dict = {"on_page_context_created": my_hook}
>>> api_hooks = hooks_to_string(hooks_dict)
>>> # api_hooks is now ready to use with Docker API
Raises:
ValueError: If a hook is not callable or source cannot be extracted
"""
result = {}
for hook_name, hook_func in hooks.items():
if not callable(hook_func):
raise ValueError(f"Hook '{hook_name}' must be a callable function, got {type(hook_func)}")
try:
# Get the source code of the function
source = inspect.getsource(hook_func)
# Remove any leading indentation to get clean source
source = textwrap.dedent(source)
result[hook_name] = source
except (OSError, TypeError) as e:
raise ValueError(
f"Cannot extract source code for hook '{hook_name}'. "
f"Make sure the function is defined in a file (not interactively). Error: {e}"
)
return result

View File

@@ -12,6 +12,7 @@
- [Python SDK](#python-sdk)
- [Understanding Request Schema](#understanding-request-schema)
- [REST API Examples](#rest-api-examples)
- [Asynchronous Jobs with Webhooks](#asynchronous-jobs-with-webhooks)
- [Additional API Endpoints](#additional-api-endpoints)
- [HTML Extraction Endpoint](#html-extraction-endpoint)
- [Screenshot Endpoint](#screenshot-endpoint)
@@ -58,15 +59,13 @@ Pull and run images directly from Docker Hub without building locally.
#### 1. Pull the Image
Our latest release candidate is `0.7.0-r1`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
> ⚠️ **Important Note**: The `latest` tag currently points to the stable `0.6.0` version. After testing and validation, `0.7.0` (without -r1) will be released and `latest` will be updated. For now, please use `0.7.0-r1` to test the new features.
Our latest stable release is `0.8.0`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
```bash
# Pull the release candidate (for testing new features)
docker pull unclecode/crawl4ai:0.7.0-r1
# Pull the latest stable version (0.8.0)
docker pull unclecode/crawl4ai:0.8.0
# Or pull the current stable version (0.6.0)
# Or use the latest tag (points to 0.8.0)
docker pull unclecode/crawl4ai:latest
```
@@ -101,7 +100,7 @@ EOL
-p 11235:11235 \
--name crawl4ai \
--shm-size=1g \
unclecode/crawl4ai:0.7.0-r1
unclecode/crawl4ai:0.8.0
```
* **With LLM support:**
@@ -112,7 +111,7 @@ EOL
--name crawl4ai \
--env-file .llm.env \
--shm-size=1g \
unclecode/crawl4ai:0.7.0-r1
unclecode/crawl4ai:0.8.0
```
> The server will be available at `http://localhost:11235`. Visit `/playground` to access the interactive testing interface.
@@ -185,7 +184,7 @@ The `docker-compose.yml` file in the project root provides a simplified approach
```bash
# Pulls and runs the release candidate from Docker Hub
# Automatically selects the correct architecture
IMAGE=unclecode/crawl4ai:0.7.0-r1 docker compose up -d
IMAGE=unclecode/crawl4ai:0.8.0 docker compose up -d
```
* **Build and Run Locally:**
@@ -648,6 +647,194 @@ async def test_stream_crawl(token: str = None): # Made token optional
# asyncio.run(test_stream_crawl())
```
### Asynchronous Jobs with Webhooks
For long-running crawls or when you want to avoid keeping connections open, use the job queue endpoints. Instead of polling for results, configure a webhook to receive notifications when jobs complete.
#### Why Use Jobs & Webhooks?
- **No Polling Required** - Get notified when crawls complete instead of constantly checking status
- **Better Resource Usage** - Free up client connections while jobs run in the background
- **Scalable Architecture** - Ideal for high-volume crawling with TypeScript/Node.js clients or microservices
- **Reliable Delivery** - Automatic retry with exponential backoff (5 attempts: 1s → 2s → 4s → 8s → 16s)
#### How It Works
1. **Submit Job** → POST to `/crawl/job` with optional `webhook_config`
2. **Get Task ID** → Receive a `task_id` immediately
3. **Job Runs** → Crawl executes in the background
4. **Webhook Fired** → Server POSTs completion notification to your webhook URL
5. **Fetch Results** → If data wasn't included in webhook, GET `/crawl/job/{task_id}`
#### Quick Example
```bash
# Submit a crawl job with webhook notification
curl -X POST http://localhost:11235/crawl/job \
-H "Content-Type: application/json" \
-d '{
"urls": ["https://example.com"],
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/crawl-complete",
"webhook_data_in_payload": false
}
}'
# Response: {"task_id": "crawl_a1b2c3d4"}
```
**Your webhook receives:**
```json
{
"task_id": "crawl_a1b2c3d4",
"task_type": "crawl",
"status": "completed",
"timestamp": "2025-10-21T10:30:00.000000+00:00",
"urls": ["https://example.com"]
}
```
Then fetch the results:
```bash
curl http://localhost:11235/crawl/job/crawl_a1b2c3d4
```
#### Include Data in Webhook
Set `webhook_data_in_payload: true` to receive the full crawl results directly in the webhook:
```bash
curl -X POST http://localhost:11235/crawl/job \
-H "Content-Type: application/json" \
-d '{
"urls": ["https://example.com"],
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/crawl-complete",
"webhook_data_in_payload": true
}
}'
```
**Your webhook receives the complete data:**
```json
{
"task_id": "crawl_a1b2c3d4",
"task_type": "crawl",
"status": "completed",
"timestamp": "2025-10-21T10:30:00.000000+00:00",
"urls": ["https://example.com"],
"data": {
"markdown": "...",
"html": "...",
"links": {...},
"metadata": {...}
}
}
```
#### Webhook Authentication
Add custom headers for authentication:
```json
{
"urls": ["https://example.com"],
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/crawl",
"webhook_data_in_payload": false,
"webhook_headers": {
"X-Webhook-Secret": "your-secret-token",
"X-Service-ID": "crawl4ai-prod"
}
}
}
```
#### Global Default Webhook
Configure a default webhook URL in `config.yml` for all jobs:
```yaml
webhooks:
enabled: true
default_url: "https://myapp.com/webhooks/default"
data_in_payload: false
retry:
max_attempts: 5
initial_delay_ms: 1000
max_delay_ms: 32000
timeout_ms: 30000
```
Now jobs without `webhook_config` automatically use the default webhook.
#### Job Status Polling (Without Webhooks)
If you prefer polling instead of webhooks, just omit `webhook_config`:
```bash
# Submit job
curl -X POST http://localhost:11235/crawl/job \
-H "Content-Type: application/json" \
-d '{"urls": ["https://example.com"]}'
# Response: {"task_id": "crawl_xyz"}
# Poll for status
curl http://localhost:11235/crawl/job/crawl_xyz
```
The response includes `status` field: `"processing"`, `"completed"`, or `"failed"`.
#### LLM Extraction Jobs with Webhooks
The same webhook system works for LLM extraction jobs via `/llm/job`:
```bash
# Submit LLM extraction job with webhook
curl -X POST http://localhost:11235/llm/job \
-H "Content-Type: application/json" \
-d '{
"url": "https://example.com/article",
"q": "Extract the article title, author, and main points",
"provider": "openai/gpt-4o-mini",
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/llm-complete",
"webhook_data_in_payload": true,
"webhook_headers": {
"X-Webhook-Secret": "your-secret-token"
}
}
}'
# Response: {"task_id": "llm_1234567890"}
```
**Your webhook receives:**
```json
{
"task_id": "llm_1234567890",
"task_type": "llm_extraction",
"status": "completed",
"timestamp": "2025-10-22T12:30:00.000000+00:00",
"urls": ["https://example.com/article"],
"data": {
"extracted_content": {
"title": "Understanding Web Scraping",
"author": "John Doe",
"main_points": ["Point 1", "Point 2", "Point 3"]
}
}
}
```
**Key Differences for LLM Jobs:**
- Task type is `"llm_extraction"` instead of `"crawl"`
- Extracted data is in `data.extracted_content`
- Single URL only (not an array)
- Supports schema-based extraction with `schema` parameter
> 💡 **Pro tip**: See [WEBHOOK_EXAMPLES.md](./WEBHOOK_EXAMPLES.md) for detailed examples including TypeScript client code, Flask webhook handlers, and failure handling.
---
## Metrics & Monitoring
@@ -826,10 +1013,11 @@ We're here to help you succeed with Crawl4AI! Here's how to get support:
In this guide, we've covered everything you need to get started with Crawl4AI's Docker deployment:
- Building and running the Docker container
- Configuring the environment
- Configuring the environment
- Using the interactive playground for testing
- Making API requests with proper typing
- Using the Python SDK
- Asynchronous job queues with webhook notifications
- Leveraging specialized endpoints for screenshots, PDFs, and JavaScript execution
- Connecting via the Model Context Protocol (MCP)
- Monitoring your deployment

View File

@@ -0,0 +1,378 @@
# Webhook Feature Examples
This document provides examples of how to use the webhook feature for crawl jobs in Crawl4AI.
## Overview
The webhook feature allows you to receive notifications when crawl jobs complete, eliminating the need for polling. Webhooks are sent with exponential backoff retry logic to ensure reliable delivery.
## Configuration
### Global Configuration (config.yml)
You can configure default webhook settings in `config.yml`:
```yaml
webhooks:
enabled: true
default_url: null # Optional: default webhook URL for all jobs
data_in_payload: false # Optional: default behavior for including data
retry:
max_attempts: 5
initial_delay_ms: 1000 # 1s, 2s, 4s, 8s, 16s exponential backoff
max_delay_ms: 32000
timeout_ms: 30000 # 30s timeout per webhook call
headers: # Optional: default headers to include
User-Agent: "Crawl4AI-Webhook/1.0"
```
## API Usage Examples
### Example 1: Basic Webhook (Notification Only)
Send a webhook notification without including the crawl data in the payload.
**Request:**
```bash
curl -X POST http://localhost:11235/crawl/job \
-H "Content-Type: application/json" \
-d '{
"urls": ["https://example.com"],
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/crawl-complete",
"webhook_data_in_payload": false
}
}'
```
**Response:**
```json
{
"task_id": "crawl_a1b2c3d4"
}
```
**Webhook Payload Received:**
```json
{
"task_id": "crawl_a1b2c3d4",
"task_type": "crawl",
"status": "completed",
"timestamp": "2025-10-21T10:30:00.000000+00:00",
"urls": ["https://example.com"]
}
```
Your webhook handler should then fetch the results:
```bash
curl http://localhost:11235/crawl/job/crawl_a1b2c3d4
```
### Example 2: Webhook with Data Included
Include the full crawl results in the webhook payload.
**Request:**
```bash
curl -X POST http://localhost:11235/crawl/job \
-H "Content-Type: application/json" \
-d '{
"urls": ["https://example.com"],
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/crawl-complete",
"webhook_data_in_payload": true
}
}'
```
**Webhook Payload Received:**
```json
{
"task_id": "crawl_a1b2c3d4",
"task_type": "crawl",
"status": "completed",
"timestamp": "2025-10-21T10:30:00.000000+00:00",
"urls": ["https://example.com"],
"data": {
"markdown": "...",
"html": "...",
"links": {...},
"metadata": {...}
}
}
```
### Example 3: Webhook with Custom Headers
Include custom headers for authentication or identification.
**Request:**
```bash
curl -X POST http://localhost:11235/crawl/job \
-H "Content-Type: application/json" \
-d '{
"urls": ["https://example.com"],
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/crawl-complete",
"webhook_data_in_payload": false,
"webhook_headers": {
"X-Webhook-Secret": "my-secret-token",
"X-Service-ID": "crawl4ai-production"
}
}
}'
```
The webhook will be sent with these additional headers plus the default headers from config.
### Example 4: Failure Notification
When a crawl job fails, a webhook is sent with error details.
**Webhook Payload on Failure:**
```json
{
"task_id": "crawl_a1b2c3d4",
"task_type": "crawl",
"status": "failed",
"timestamp": "2025-10-21T10:30:00.000000+00:00",
"urls": ["https://example.com"],
"error": "Connection timeout after 30s"
}
```
### Example 5: Using Global Default Webhook
If you set a `default_url` in config.yml, jobs without webhook_config will use it:
**config.yml:**
```yaml
webhooks:
enabled: true
default_url: "https://myapp.com/webhooks/default"
data_in_payload: false
```
**Request (no webhook_config needed):**
```bash
curl -X POST http://localhost:11235/crawl/job \
-H "Content-Type: application/json" \
-d '{
"urls": ["https://example.com"]
}'
```
The webhook will be sent to the default URL configured in config.yml.
### Example 6: LLM Extraction Job with Webhook
Use webhooks with the LLM extraction endpoint for asynchronous processing.
**Request:**
```bash
curl -X POST http://localhost:11235/llm/job \
-H "Content-Type: application/json" \
-d '{
"url": "https://example.com/article",
"q": "Extract the article title, author, and publication date",
"schema": "{\"type\": \"object\", \"properties\": {\"title\": {\"type\": \"string\"}, \"author\": {\"type\": \"string\"}, \"date\": {\"type\": \"string\"}}}",
"cache": false,
"provider": "openai/gpt-4o-mini",
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/llm-complete",
"webhook_data_in_payload": true
}
}'
```
**Response:**
```json
{
"task_id": "llm_1698765432_12345"
}
```
**Webhook Payload Received:**
```json
{
"task_id": "llm_1698765432_12345",
"task_type": "llm_extraction",
"status": "completed",
"timestamp": "2025-10-21T10:30:00.000000+00:00",
"urls": ["https://example.com/article"],
"data": {
"extracted_content": {
"title": "Understanding Web Scraping",
"author": "John Doe",
"date": "2025-10-21"
}
}
}
```
## Webhook Handler Example
Here's a simple Python Flask webhook handler that supports both crawl and LLM extraction jobs:
```python
from flask import Flask, request, jsonify
import requests
app = Flask(__name__)
@app.route('/webhooks/crawl-complete', methods=['POST'])
def handle_crawl_webhook():
payload = request.json
task_id = payload['task_id']
task_type = payload['task_type']
status = payload['status']
if status == 'completed':
# If data not in payload, fetch it
if 'data' not in payload:
# Determine endpoint based on task type
endpoint = 'crawl' if task_type == 'crawl' else 'llm'
response = requests.get(f'http://localhost:11235/{endpoint}/job/{task_id}')
data = response.json()
else:
data = payload['data']
# Process based on task type
if task_type == 'crawl':
print(f"Processing crawl results for {task_id}")
# Handle crawl results
results = data.get('results', [])
for result in results:
print(f" - {result.get('url')}: {len(result.get('markdown', ''))} chars")
elif task_type == 'llm_extraction':
print(f"Processing LLM extraction for {task_id}")
# Handle LLM extraction
# Note: Webhook sends 'extracted_content', API returns 'result'
extracted = data.get('extracted_content', data.get('result', {}))
print(f" - Extracted: {extracted}")
# Your business logic here...
elif status == 'failed':
error = payload.get('error', 'Unknown error')
print(f"{task_type} job {task_id} failed: {error}")
# Handle failure...
return jsonify({"status": "received"}), 200
if __name__ == '__main__':
app.run(port=8080)
```
## Retry Logic
The webhook delivery service uses exponential backoff retry logic:
- **Attempts:** Up to 5 attempts by default
- **Delays:** 1s → 2s → 4s → 8s → 16s
- **Timeout:** 30 seconds per attempt
- **Retry Conditions:**
- Server errors (5xx status codes)
- Network errors
- Timeouts
- **No Retry:**
- Client errors (4xx status codes)
- Successful delivery (2xx status codes)
## Benefits
1. **No Polling Required** - Eliminates constant API calls to check job status
2. **Real-time Notifications** - Immediate notification when jobs complete
3. **Reliable Delivery** - Exponential backoff ensures webhooks are delivered
4. **Flexible** - Choose between notification-only or full data delivery
5. **Secure** - Support for custom headers for authentication
6. **Configurable** - Global defaults or per-job configuration
7. **Universal Support** - Works with both `/crawl/job` and `/llm/job` endpoints
## TypeScript Client Example
```typescript
interface WebhookConfig {
webhook_url: string;
webhook_data_in_payload?: boolean;
webhook_headers?: Record<string, string>;
}
interface CrawlJobRequest {
urls: string[];
browser_config?: Record<string, any>;
crawler_config?: Record<string, any>;
webhook_config?: WebhookConfig;
}
interface LLMJobRequest {
url: string;
q: string;
schema?: string;
cache?: boolean;
provider?: string;
webhook_config?: WebhookConfig;
}
async function createCrawlJob(request: CrawlJobRequest) {
const response = await fetch('http://localhost:11235/crawl/job', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(request)
});
const { task_id } = await response.json();
return task_id;
}
async function createLLMJob(request: LLMJobRequest) {
const response = await fetch('http://localhost:11235/llm/job', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(request)
});
const { task_id } = await response.json();
return task_id;
}
// Usage - Crawl Job
const crawlTaskId = await createCrawlJob({
urls: ['https://example.com'],
webhook_config: {
webhook_url: 'https://myapp.com/webhooks/crawl-complete',
webhook_data_in_payload: false,
webhook_headers: {
'X-Webhook-Secret': 'my-secret'
}
}
});
// Usage - LLM Extraction Job
const llmTaskId = await createLLMJob({
url: 'https://example.com/article',
q: 'Extract the main points from this article',
provider: 'openai/gpt-4o-mini',
webhook_config: {
webhook_url: 'https://myapp.com/webhooks/llm-complete',
webhook_data_in_payload: true,
webhook_headers: {
'X-Webhook-Secret': 'my-secret'
}
}
});
```
## Monitoring and Debugging
Webhook delivery attempts are logged at INFO level:
- Successful deliveries
- Retry attempts with delays
- Final failures after max attempts
Check the application logs for webhook delivery status:
```bash
docker logs crawl4ai-container | grep -i webhook
```

View File

@@ -46,6 +46,7 @@ from utils import (
get_llm_temperature,
get_llm_base_url
)
from webhook import WebhookDeliveryService
import psutil, time
@@ -107,7 +108,10 @@ async def handle_llm_qa(
prompt_with_variables=prompt,
api_token=get_llm_api_key(config), # Returns None to let litellm handle it
temperature=get_llm_temperature(config),
base_url=get_llm_base_url(config)
base_url=get_llm_base_url(config),
base_delay=config["llm"].get("backoff_base_delay", 2),
max_attempts=config["llm"].get("backoff_max_attempts", 3),
exponential_factor=config["llm"].get("backoff_exponential_factor", 2)
)
return response.choices[0].message.content
@@ -127,10 +131,14 @@ async def process_llm_extraction(
schema: Optional[str] = None,
cache: str = "0",
provider: Optional[str] = None,
webhook_config: Optional[Dict] = None,
temperature: Optional[float] = None,
base_url: Optional[str] = None
) -> None:
"""Process LLM extraction in background."""
# Initialize webhook service
webhook_service = WebhookDeliveryService(config)
try:
# Validate provider
is_valid, error_msg = validate_llm_provider(config, provider)
@@ -139,6 +147,16 @@ async def process_llm_extraction(
"status": TaskStatus.FAILED,
"error": error_msg
})
# Send webhook notification on failure
await webhook_service.notify_job_completion(
task_id=task_id,
task_type="llm_extraction",
status="failed",
urls=[url],
webhook_config=webhook_config,
error=error_msg
)
return
api_key = get_llm_api_key(config, provider) # Returns None to let litellm handle it
llm_strategy = LLMExtractionStrategy(
@@ -169,17 +187,40 @@ async def process_llm_extraction(
"status": TaskStatus.FAILED,
"error": result.error_message
})
# Send webhook notification on failure
await webhook_service.notify_job_completion(
task_id=task_id,
task_type="llm_extraction",
status="failed",
urls=[url],
webhook_config=webhook_config,
error=result.error_message
)
return
try:
content = json.loads(result.extracted_content)
except json.JSONDecodeError:
content = result.extracted_content
result_data = {"extracted_content": content}
await redis.hset(f"task:{task_id}", mapping={
"status": TaskStatus.COMPLETED,
"result": json.dumps(content)
})
# Send webhook notification on successful completion
await webhook_service.notify_job_completion(
task_id=task_id,
task_type="llm_extraction",
status="completed",
urls=[url],
webhook_config=webhook_config,
result=result_data
)
except Exception as e:
logger.error(f"LLM extraction error: {str(e)}", exc_info=True)
await redis.hset(f"task:{task_id}", mapping={
@@ -187,6 +228,16 @@ async def process_llm_extraction(
"error": str(e)
})
# Send webhook notification on failure
await webhook_service.notify_job_completion(
task_id=task_id,
task_type="llm_extraction",
status="failed",
urls=[url],
webhook_config=webhook_config,
error=str(e)
)
async def handle_markdown_request(
url: str,
filter_type: FilterType,
@@ -275,6 +326,7 @@ async def handle_llm_request(
cache: str = "0",
config: Optional[dict] = None,
provider: Optional[str] = None,
webhook_config: Optional[Dict] = None,
temperature: Optional[float] = None,
api_base_url: Optional[str] = None
) -> JSONResponse:
@@ -308,6 +360,7 @@ async def handle_llm_request(
base_url,
config,
provider,
webhook_config,
temperature,
api_base_url
)
@@ -355,6 +408,7 @@ async def create_new_task(
base_url: str,
config: dict,
provider: Optional[str] = None,
webhook_config: Optional[Dict] = None,
temperature: Optional[float] = None,
api_base_url: Optional[str] = None
) -> JSONResponse:
@@ -365,12 +419,18 @@ async def create_new_task(
from datetime import datetime
task_id = f"llm_{int(datetime.now().timestamp())}_{id(background_tasks)}"
await redis.hset(f"task:{task_id}", mapping={
task_data = {
"status": TaskStatus.PROCESSING,
"created_at": datetime.now().isoformat(),
"url": decoded_url
})
}
# Store webhook config if provided
if webhook_config:
task_data["webhook_config"] = json.dumps(webhook_config)
await redis.hset(f"task:{task_id}", mapping=task_data)
background_tasks.add_task(
process_llm_extraction,
@@ -382,6 +442,7 @@ async def create_new_task(
schema,
cache,
provider,
webhook_config,
temperature,
api_base_url
)
@@ -723,6 +784,7 @@ async def handle_crawl_job(
browser_config: Dict,
crawler_config: Dict,
config: Dict,
webhook_config: Optional[Dict] = None,
) -> Dict:
"""
Fire-and-forget version of handle_crawl_request.
@@ -730,13 +792,24 @@ async def handle_crawl_job(
lets /crawl/job/{task_id} polling fetch the result.
"""
task_id = f"crawl_{uuid4().hex[:8]}"
await redis.hset(f"task:{task_id}", mapping={
# Store task data in Redis
task_data = {
"status": TaskStatus.PROCESSING, # <-- keep enum values consistent
"created_at": datetime.now(timezone.utc).replace(tzinfo=None).isoformat(),
"url": json.dumps(urls), # store list as JSON string
"result": "",
"error": "",
})
}
# Store webhook config if provided
if webhook_config:
task_data["webhook_config"] = json.dumps(webhook_config)
await redis.hset(f"task:{task_id}", mapping=task_data)
# Initialize webhook service
webhook_service = WebhookDeliveryService(config)
async def _runner():
try:
@@ -750,6 +823,17 @@ async def handle_crawl_job(
"status": TaskStatus.COMPLETED,
"result": json.dumps(result),
})
# Send webhook notification on successful completion
await webhook_service.notify_job_completion(
task_id=task_id,
task_type="crawl",
status="completed",
urls=urls,
webhook_config=webhook_config,
result=result
)
await asyncio.sleep(5) # Give Redis time to process the update
except Exception as exc:
await redis.hset(f"task:{task_id}", mapping={
@@ -757,5 +841,15 @@ async def handle_crawl_job(
"error": str(exc),
})
# Send webhook notification on failure
await webhook_service.notify_job_completion(
task_id=task_id,
task_type="crawl",
status="failed",
urls=urls,
webhook_config=webhook_config,
error=str(exc)
)
background_tasks.add_task(_runner)
return {"task_id": task_id}

View File

@@ -37,6 +37,10 @@ rate_limiting:
storage_uri: "memory://" # Use "redis://localhost:6379" for production
# Security Configuration
# WARNING: For production deployments, enable security and use proper SECRET_KEY:
# - Set jwt_enabled: true for authentication
# - Set SECRET_KEY environment variable to a secure random value
# - Set CRAWL4AI_HOOKS_ENABLED=true only if you need hooks (RCE risk)
security:
enabled: false
jwt_enabled: false
@@ -87,4 +91,17 @@ observability:
enabled: True
endpoint: "/metrics"
health_check:
endpoint: "/health"
endpoint: "/health"
# Webhook Configuration
webhooks:
enabled: true
default_url: null # Optional: default webhook URL for all jobs
data_in_payload: false # Optional: default behavior for including data
retry:
max_attempts: 5
initial_delay_ms: 1000 # 1s, 2s, 4s, 8s, 16s exponential backoff
max_delay_ms: 32000
timeout_ms: 30000 # 30s timeout per webhook call
headers: # Optional: default headers to include
User-Agent: "Crawl4AI-Webhook/1.0"

View File

@@ -117,18 +117,18 @@ class UserHookManager:
"""
try:
# Create a safe namespace for the hook
# Use a more complete builtins that includes __import__
# SECURITY: No __import__ to prevent arbitrary module imports (RCE risk)
import builtins
safe_builtins = {}
# Add safe built-in functions
# Add safe built-in functions (no __import__ for security)
allowed_builtins = [
'print', 'len', 'str', 'int', 'float', 'bool',
'list', 'dict', 'set', 'tuple', 'range', 'enumerate',
'zip', 'map', 'filter', 'any', 'all', 'sum', 'min', 'max',
'sorted', 'reversed', 'abs', 'round', 'isinstance', 'type',
'getattr', 'hasattr', 'setattr', 'callable', 'iter', 'next',
'__import__', '__build_class__' # Required for exec
'__build_class__' # Required for class definitions in exec
]
for name in allowed_builtins:

View File

@@ -12,6 +12,7 @@ from api import (
handle_crawl_job,
handle_task_status,
)
from schemas import WebhookConfig
# ------------- dependency placeholders -------------
_redis = None # will be injected from server.py
@@ -37,6 +38,7 @@ class LlmJobPayload(BaseModel):
schema: Optional[str] = None
cache: bool = False
provider: Optional[str] = None
webhook_config: Optional[WebhookConfig] = None
temperature: Optional[float] = None
base_url: Optional[str] = None
@@ -45,6 +47,7 @@ class CrawlJobPayload(BaseModel):
urls: list[HttpUrl]
browser_config: Dict = {}
crawler_config: Dict = {}
webhook_config: Optional[WebhookConfig] = None
# ---------- LLM job ---------------------------------------------------------
@@ -55,6 +58,10 @@ async def llm_job_enqueue(
request: Request,
_td: Dict = Depends(lambda: _token_dep()), # late-bound dep
):
webhook_config = None
if payload.webhook_config:
webhook_config = payload.webhook_config.model_dump(mode='json')
return await handle_llm_request(
_redis,
background_tasks,
@@ -65,6 +72,7 @@ async def llm_job_enqueue(
cache=payload.cache,
config=_config,
provider=payload.provider,
webhook_config=webhook_config,
temperature=payload.temperature,
api_base_url=payload.base_url,
)
@@ -86,6 +94,10 @@ async def crawl_job_enqueue(
background_tasks: BackgroundTasks,
_td: Dict = Depends(lambda: _token_dep()),
):
webhook_config = None
if payload.webhook_config:
webhook_config = payload.webhook_config.model_dump(mode='json')
return await handle_crawl_job(
_redis,
background_tasks,
@@ -93,6 +105,7 @@ async def crawl_job_enqueue(
payload.browser_config,
payload.crawler_config,
config=_config,
webhook_config=webhook_config,
)

View File

@@ -12,6 +12,6 @@ pydantic>=2.11
rank-bm25==0.2.2
anyio==4.9.0
PyJWT==2.10.1
mcp>=1.6.0
mcp>=1.18.0
websockets>=15.0.1
httpx[http2]>=0.27.2

View File

@@ -1,6 +1,6 @@
from typing import List, Optional, Dict
from enum import Enum
from pydantic import BaseModel, Field
from pydantic import BaseModel, Field, HttpUrl
from utils import FilterType
@@ -85,4 +85,22 @@ class JSEndpointRequest(BaseModel):
scripts: List[str] = Field(
...,
description="List of separated JavaScript snippets to execute"
)
)
class WebhookConfig(BaseModel):
"""Configuration for webhook notifications."""
webhook_url: HttpUrl
webhook_data_in_payload: bool = False
webhook_headers: Optional[Dict[str, str]] = None
class WebhookPayload(BaseModel):
"""Payload sent to webhook endpoints."""
task_id: str
task_type: str # "crawl", "llm_extraction", etc.
status: str # "completed" or "failed"
timestamp: str # ISO 8601 format
urls: List[str]
error: Optional[str] = None
data: Optional[Dict] = None # Included only if webhook_data_in_payload=True

View File

@@ -79,6 +79,10 @@ __version__ = "0.5.1-d1"
MAX_PAGES = config["crawler"]["pool"].get("max_pages", 30)
GLOBAL_SEM = asyncio.Semaphore(MAX_PAGES)
# ── security feature flags ───────────────────────────────────
# Hooks are disabled by default for security (RCE risk). Set to "true" to enable.
HOOKS_ENABLED = os.environ.get("CRAWL4AI_HOOKS_ENABLED", "false").lower() == "true"
# ── default browser config helper ─────────────────────────────
def get_default_browser_config() -> BrowserConfig:
"""Get default BrowserConfig from config.yml."""
@@ -236,6 +240,19 @@ async def add_security_headers(request: Request, call_next):
resp.headers.update(config["security"]["headers"])
return resp
# ───────────────── URL validation helper ─────────────────
ALLOWED_URL_SCHEMES = ("http://", "https://")
ALLOWED_URL_SCHEMES_WITH_RAW = ("http://", "https://", "raw:", "raw://")
def validate_url_scheme(url: str, allow_raw: bool = False) -> None:
"""Validate URL scheme to prevent file:// LFI attacks."""
allowed = ALLOWED_URL_SCHEMES_WITH_RAW if allow_raw else ALLOWED_URL_SCHEMES
if not url.startswith(allowed):
schemes = ", ".join(allowed)
raise HTTPException(400, f"URL must start with {schemes}")
# ───────────────── safe configdump helper ─────────────────
ALLOWED_TYPES = {
"CrawlerRunConfig": CrawlerRunConfig,
@@ -337,6 +354,7 @@ async def generate_html(
Crawls the URL, preprocesses the raw HTML for schema extraction, and returns the processed HTML.
Use when you need sanitized HTML structures for building schemas or further processing.
"""
validate_url_scheme(body.url, allow_raw=True)
from crawler_pool import get_crawler
cfg = CrawlerRunConfig()
try:
@@ -368,6 +386,7 @@ async def generate_screenshot(
Use when you need an image snapshot of the rendered page. Its recommened to provide an output path to save the screenshot.
Then in result instead of the screenshot you will get a path to the saved file.
"""
validate_url_scheme(body.url)
from crawler_pool import get_crawler
try:
cfg = CrawlerRunConfig(screenshot=True, screenshot_wait_for=body.screenshot_wait_for)
@@ -402,6 +421,7 @@ async def generate_pdf(
Use when you need a printable or archivable snapshot of the page. It is recommended to provide an output path to save the PDF.
Then in result instead of the PDF you will get a path to the saved file.
"""
validate_url_scheme(body.url)
from crawler_pool import get_crawler
try:
cfg = CrawlerRunConfig(pdf=True)
@@ -474,6 +494,7 @@ async def execute_js(
```
"""
validate_url_scheme(body.url)
from crawler_pool import get_crawler
try:
cfg = CrawlerRunConfig(js_code=body.scripts)
@@ -600,6 +621,8 @@ async def crawl(
"""
if not crawl_request.urls:
raise HTTPException(400, "At least one URL required")
if crawl_request.hooks and not HOOKS_ENABLED:
raise HTTPException(403, "Hooks are disabled. Set CRAWL4AI_HOOKS_ENABLED=true to enable.")
# Check whether it is a redirection for a streaming request
crawler_config = CrawlerRunConfig.load(crawl_request.crawler_config)
if crawler_config.stream:
@@ -635,6 +658,8 @@ async def crawl_stream(
):
if not crawl_request.urls:
raise HTTPException(400, "At least one URL required")
if crawl_request.hooks and not HOOKS_ENABLED:
raise HTTPException(403, "Hooks are disabled. Set CRAWL4AI_HOOKS_ENABLED=true to enable.")
return await stream_process(crawl_request=crawl_request)

View File

@@ -0,0 +1,196 @@
#!/usr/bin/env python3
"""
Security Integration Tests for Crawl4AI Docker API.
Tests that security fixes are working correctly against a running server.
Usage:
python run_security_tests.py [base_url]
Example:
python run_security_tests.py http://localhost:11235
"""
import subprocess
import sys
import re
# Colors for terminal output
GREEN = '\033[0;32m'
RED = '\033[0;31m'
YELLOW = '\033[1;33m'
NC = '\033[0m' # No Color
PASSED = 0
FAILED = 0
def run_curl(args: list) -> str:
"""Run curl command and return output."""
try:
result = subprocess.run(
['curl', '-s'] + args,
capture_output=True,
text=True,
timeout=30
)
return result.stdout + result.stderr
except subprocess.TimeoutExpired:
return "TIMEOUT"
except Exception as e:
return str(e)
def test_expect(name: str, expect_pattern: str, curl_args: list) -> bool:
"""Run a test and check if output matches expected pattern."""
global PASSED, FAILED
result = run_curl(curl_args)
if re.search(expect_pattern, result, re.IGNORECASE):
print(f"{GREEN}{NC} {name}")
PASSED += 1
return True
else:
print(f"{RED}{NC} {name}")
print(f" Expected pattern: {expect_pattern}")
print(f" Got: {result[:200]}")
FAILED += 1
return False
def main():
global PASSED, FAILED
base_url = sys.argv[1] if len(sys.argv) > 1 else "http://localhost:11235"
print("=" * 60)
print("Crawl4AI Security Integration Tests")
print(f"Target: {base_url}")
print("=" * 60)
print()
# Check server availability
print("Checking server availability...")
result = run_curl(['-o', '/dev/null', '-w', '%{http_code}', f'{base_url}/health'])
if '200' not in result:
print(f"{RED}ERROR: Server not reachable at {base_url}{NC}")
print("Please start the server first.")
sys.exit(1)
print(f"{GREEN}Server is running{NC}")
print()
# === Part A: Security Tests ===
print("=== Part A: Security Tests ===")
print("(Vulnerabilities must be BLOCKED)")
print()
test_expect(
"A1: Hooks disabled by default (403)",
r"403|disabled|Hooks are disabled",
['-X', 'POST', f'{base_url}/crawl',
'-H', 'Content-Type: application/json',
'-d', '{"urls":["https://example.com"],"hooks":{"code":{"on_page_context_created":"async def hook(page, context, **kwargs): return page"}}}']
)
test_expect(
"A2: file:// blocked on /execute_js (400)",
r"400|must start with",
['-X', 'POST', f'{base_url}/execute_js',
'-H', 'Content-Type: application/json',
'-d', '{"url":"file:///etc/passwd","scripts":["1"]}']
)
test_expect(
"A3: file:// blocked on /screenshot (400)",
r"400|must start with",
['-X', 'POST', f'{base_url}/screenshot',
'-H', 'Content-Type: application/json',
'-d', '{"url":"file:///etc/passwd"}']
)
test_expect(
"A4: file:// blocked on /pdf (400)",
r"400|must start with",
['-X', 'POST', f'{base_url}/pdf',
'-H', 'Content-Type: application/json',
'-d', '{"url":"file:///etc/passwd"}']
)
test_expect(
"A5: file:// blocked on /html (400)",
r"400|must start with",
['-X', 'POST', f'{base_url}/html',
'-H', 'Content-Type: application/json',
'-d', '{"url":"file:///etc/passwd"}']
)
print()
# === Part B: Functionality Tests ===
print("=== Part B: Functionality Tests ===")
print("(Normal operations must WORK)")
print()
test_expect(
"B1: Basic crawl works",
r"success.*true|results",
['-X', 'POST', f'{base_url}/crawl',
'-H', 'Content-Type: application/json',
'-d', '{"urls":["https://example.com"]}']
)
test_expect(
"B2: /md works with https://",
r"success.*true|markdown",
['-X', 'POST', f'{base_url}/md',
'-H', 'Content-Type: application/json',
'-d', '{"url":"https://example.com"}']
)
test_expect(
"B3: Health endpoint works",
r"ok",
[f'{base_url}/health']
)
print()
# === Part C: Edge Cases ===
print("=== Part C: Edge Cases ===")
print("(Malformed input must be REJECTED)")
print()
test_expect(
"C1: javascript: URL rejected (400)",
r"400|must start with",
['-X', 'POST', f'{base_url}/execute_js',
'-H', 'Content-Type: application/json',
'-d', '{"url":"javascript:alert(1)","scripts":["1"]}']
)
test_expect(
"C2: data: URL rejected (400)",
r"400|must start with",
['-X', 'POST', f'{base_url}/execute_js',
'-H', 'Content-Type: application/json',
'-d', '{"url":"data:text/html,<h1>test</h1>","scripts":["1"]}']
)
print()
print("=" * 60)
print("Results")
print("=" * 60)
print(f"Passed: {GREEN}{PASSED}{NC}")
print(f"Failed: {RED}{FAILED}{NC}")
print()
if FAILED > 0:
print(f"{RED}SOME TESTS FAILED{NC}")
sys.exit(1)
else:
print(f"{GREEN}ALL TESTS PASSED{NC}")
sys.exit(0)
if __name__ == '__main__':
main()

View File

@@ -0,0 +1,170 @@
#!/usr/bin/env python3
"""
Unit tests for security fixes.
These tests verify the security fixes at the code level without needing a running server.
"""
import sys
import os
# Add parent directory to path to import modules
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import unittest
class TestURLValidation(unittest.TestCase):
"""Test URL scheme validation helper."""
def setUp(self):
"""Set up test fixtures."""
# Import the validation constants and function
self.ALLOWED_URL_SCHEMES = ("http://", "https://")
self.ALLOWED_URL_SCHEMES_WITH_RAW = ("http://", "https://", "raw:", "raw://")
def validate_url_scheme(self, url: str, allow_raw: bool = False) -> bool:
"""Local version of validate_url_scheme for testing."""
allowed = self.ALLOWED_URL_SCHEMES_WITH_RAW if allow_raw else self.ALLOWED_URL_SCHEMES
return url.startswith(allowed)
# === SECURITY TESTS: These URLs must be BLOCKED ===
def test_file_url_blocked(self):
"""file:// URLs must be blocked (LFI vulnerability)."""
self.assertFalse(self.validate_url_scheme("file:///etc/passwd"))
self.assertFalse(self.validate_url_scheme("file:///etc/passwd", allow_raw=True))
def test_file_url_blocked_windows(self):
"""file:// URLs with Windows paths must be blocked."""
self.assertFalse(self.validate_url_scheme("file:///C:/Windows/System32/config/sam"))
def test_javascript_url_blocked(self):
"""javascript: URLs must be blocked (XSS)."""
self.assertFalse(self.validate_url_scheme("javascript:alert(1)"))
def test_data_url_blocked(self):
"""data: URLs must be blocked."""
self.assertFalse(self.validate_url_scheme("data:text/html,<script>alert(1)</script>"))
def test_ftp_url_blocked(self):
"""ftp: URLs must be blocked."""
self.assertFalse(self.validate_url_scheme("ftp://example.com/file"))
def test_empty_url_blocked(self):
"""Empty URLs must be blocked."""
self.assertFalse(self.validate_url_scheme(""))
def test_relative_url_blocked(self):
"""Relative URLs must be blocked."""
self.assertFalse(self.validate_url_scheme("/etc/passwd"))
self.assertFalse(self.validate_url_scheme("../../../etc/passwd"))
# === FUNCTIONALITY TESTS: These URLs must be ALLOWED ===
def test_http_url_allowed(self):
"""http:// URLs must be allowed."""
self.assertTrue(self.validate_url_scheme("http://example.com"))
self.assertTrue(self.validate_url_scheme("http://localhost:8080"))
def test_https_url_allowed(self):
"""https:// URLs must be allowed."""
self.assertTrue(self.validate_url_scheme("https://example.com"))
self.assertTrue(self.validate_url_scheme("https://example.com/path?query=1"))
def test_raw_url_allowed_when_enabled(self):
"""raw: URLs must be allowed when allow_raw=True."""
self.assertTrue(self.validate_url_scheme("raw:<html></html>", allow_raw=True))
self.assertTrue(self.validate_url_scheme("raw://<html></html>", allow_raw=True))
def test_raw_url_blocked_when_disabled(self):
"""raw: URLs must be blocked when allow_raw=False."""
self.assertFalse(self.validate_url_scheme("raw:<html></html>", allow_raw=False))
self.assertFalse(self.validate_url_scheme("raw://<html></html>", allow_raw=False))
class TestHookBuiltins(unittest.TestCase):
"""Test that dangerous builtins are removed from hooks."""
def test_import_not_in_allowed_builtins(self):
"""__import__ must NOT be in allowed_builtins."""
allowed_builtins = [
'print', 'len', 'str', 'int', 'float', 'bool',
'list', 'dict', 'set', 'tuple', 'range', 'enumerate',
'zip', 'map', 'filter', 'any', 'all', 'sum', 'min', 'max',
'sorted', 'reversed', 'abs', 'round', 'isinstance', 'type',
'getattr', 'hasattr', 'setattr', 'callable', 'iter', 'next',
'__build_class__' # Required for class definitions in exec
]
self.assertNotIn('__import__', allowed_builtins)
self.assertNotIn('eval', allowed_builtins)
self.assertNotIn('exec', allowed_builtins)
self.assertNotIn('compile', allowed_builtins)
self.assertNotIn('open', allowed_builtins)
def test_build_class_in_allowed_builtins(self):
"""__build_class__ must be in allowed_builtins (needed for class definitions)."""
allowed_builtins = [
'print', 'len', 'str', 'int', 'float', 'bool',
'list', 'dict', 'set', 'tuple', 'range', 'enumerate',
'zip', 'map', 'filter', 'any', 'all', 'sum', 'min', 'max',
'sorted', 'reversed', 'abs', 'round', 'isinstance', 'type',
'getattr', 'hasattr', 'setattr', 'callable', 'iter', 'next',
'__build_class__'
]
self.assertIn('__build_class__', allowed_builtins)
class TestHooksEnabled(unittest.TestCase):
"""Test HOOKS_ENABLED environment variable logic."""
def test_hooks_disabled_by_default(self):
"""Hooks must be disabled by default."""
# Simulate the default behavior
hooks_enabled = os.environ.get("CRAWL4AI_HOOKS_ENABLED", "false").lower() == "true"
# Clear any existing env var to test default
original = os.environ.pop("CRAWL4AI_HOOKS_ENABLED", None)
try:
hooks_enabled = os.environ.get("CRAWL4AI_HOOKS_ENABLED", "false").lower() == "true"
self.assertFalse(hooks_enabled)
finally:
if original is not None:
os.environ["CRAWL4AI_HOOKS_ENABLED"] = original
def test_hooks_enabled_when_true(self):
"""Hooks must be enabled when CRAWL4AI_HOOKS_ENABLED=true."""
original = os.environ.get("CRAWL4AI_HOOKS_ENABLED")
try:
os.environ["CRAWL4AI_HOOKS_ENABLED"] = "true"
hooks_enabled = os.environ.get("CRAWL4AI_HOOKS_ENABLED", "false").lower() == "true"
self.assertTrue(hooks_enabled)
finally:
if original is not None:
os.environ["CRAWL4AI_HOOKS_ENABLED"] = original
else:
os.environ.pop("CRAWL4AI_HOOKS_ENABLED", None)
def test_hooks_disabled_when_false(self):
"""Hooks must be disabled when CRAWL4AI_HOOKS_ENABLED=false."""
original = os.environ.get("CRAWL4AI_HOOKS_ENABLED")
try:
os.environ["CRAWL4AI_HOOKS_ENABLED"] = "false"
hooks_enabled = os.environ.get("CRAWL4AI_HOOKS_ENABLED", "false").lower() == "true"
self.assertFalse(hooks_enabled)
finally:
if original is not None:
os.environ["CRAWL4AI_HOOKS_ENABLED"] = original
else:
os.environ.pop("CRAWL4AI_HOOKS_ENABLED", None)
if __name__ == '__main__':
print("=" * 60)
print("Crawl4AI Security Fixes - Unit Tests")
print("=" * 60)
print()
# Run tests with verbosity
unittest.main(verbosity=2)

159
deploy/docker/webhook.py Normal file
View File

@@ -0,0 +1,159 @@
"""
Webhook delivery service for Crawl4AI.
This module provides webhook notification functionality with exponential backoff retry logic.
"""
import asyncio
import httpx
import logging
from typing import Dict, Optional
from datetime import datetime, timezone
logger = logging.getLogger(__name__)
class WebhookDeliveryService:
"""Handles webhook delivery with exponential backoff retry logic."""
def __init__(self, config: Dict):
"""
Initialize the webhook delivery service.
Args:
config: Application configuration dictionary containing webhook settings
"""
self.config = config.get("webhooks", {})
self.max_attempts = self.config.get("retry", {}).get("max_attempts", 5)
self.initial_delay = self.config.get("retry", {}).get("initial_delay_ms", 1000) / 1000
self.max_delay = self.config.get("retry", {}).get("max_delay_ms", 32000) / 1000
self.timeout = self.config.get("retry", {}).get("timeout_ms", 30000) / 1000
async def send_webhook(
self,
webhook_url: str,
payload: Dict,
headers: Optional[Dict[str, str]] = None
) -> bool:
"""
Send webhook with exponential backoff retry logic.
Args:
webhook_url: The URL to send the webhook to
payload: The JSON payload to send
headers: Optional custom headers
Returns:
bool: True if delivered successfully, False otherwise
"""
default_headers = self.config.get("headers", {})
merged_headers = {**default_headers, **(headers or {})}
merged_headers["Content-Type"] = "application/json"
async with httpx.AsyncClient(timeout=self.timeout) as client:
for attempt in range(self.max_attempts):
try:
logger.info(
f"Sending webhook (attempt {attempt + 1}/{self.max_attempts}) to {webhook_url}"
)
response = await client.post(
webhook_url,
json=payload,
headers=merged_headers
)
# Success or client error (don't retry client errors)
if response.status_code < 500:
if 200 <= response.status_code < 300:
logger.info(f"Webhook delivered successfully to {webhook_url}")
return True
else:
logger.warning(
f"Webhook rejected with status {response.status_code}: {response.text[:200]}"
)
return False # Client error - don't retry
# Server error - retry with backoff
logger.warning(
f"Webhook failed with status {response.status_code}, will retry"
)
except httpx.TimeoutException as exc:
logger.error(f"Webhook timeout (attempt {attempt + 1}): {exc}")
except httpx.RequestError as exc:
logger.error(f"Webhook request error (attempt {attempt + 1}): {exc}")
except Exception as exc:
logger.error(f"Webhook delivery error (attempt {attempt + 1}): {exc}")
# Calculate exponential backoff delay
if attempt < self.max_attempts - 1:
delay = min(self.initial_delay * (2 ** attempt), self.max_delay)
logger.info(f"Retrying in {delay}s...")
await asyncio.sleep(delay)
logger.error(
f"Webhook delivery failed after {self.max_attempts} attempts to {webhook_url}"
)
return False
async def notify_job_completion(
self,
task_id: str,
task_type: str,
status: str,
urls: list,
webhook_config: Optional[Dict],
result: Optional[Dict] = None,
error: Optional[str] = None
):
"""
Notify webhook of job completion.
Args:
task_id: The task identifier
task_type: Type of task (e.g., "crawl", "llm_extraction")
status: Task status ("completed" or "failed")
urls: List of URLs that were crawled
webhook_config: Webhook configuration from the job request
result: Optional crawl result data
error: Optional error message if failed
"""
# Determine webhook URL
webhook_url = None
data_in_payload = self.config.get("data_in_payload", False)
custom_headers = None
if webhook_config:
webhook_url = webhook_config.get("webhook_url")
data_in_payload = webhook_config.get("webhook_data_in_payload", data_in_payload)
custom_headers = webhook_config.get("webhook_headers")
if not webhook_url:
webhook_url = self.config.get("default_url")
if not webhook_url:
logger.debug("No webhook URL configured, skipping notification")
return
# Check if webhooks are enabled
if not self.config.get("enabled", True):
logger.debug("Webhooks are disabled, skipping notification")
return
# Build payload
payload = {
"task_id": task_id,
"task_type": task_type,
"status": status,
"timestamp": datetime.now(timezone.utc).isoformat(),
"urls": urls
}
if error:
payload["error"] = error
if data_in_payload and result:
payload["data"] = result
# Send webhook (fire and forget - don't block on completion)
await self.send_webhook(webhook_url, payload, custom_headers)

View File

@@ -6,15 +6,16 @@ x-base-config: &base-config
- "11235:11235" # Gunicorn port
env_file:
- .llm.env # API keys (create from .llm.env.example)
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY:-}
- DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY:-}
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
- GROQ_API_KEY=${GROQ_API_KEY:-}
- TOGETHER_API_KEY=${TOGETHER_API_KEY:-}
- MISTRAL_API_KEY=${MISTRAL_API_KEY:-}
- GEMINI_API_TOKEN=${GEMINI_API_TOKEN:-}
- LLM_PROVIDER=${LLM_PROVIDER:-} # Optional: Override default provider (e.g., "anthropic/claude-3-opus")
# Uncomment to set default environment variables (will overwrite .llm.env)
# environment:
# - OPENAI_API_KEY=${OPENAI_API_KEY:-}
# - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY:-}
# - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
# - GROQ_API_KEY=${GROQ_API_KEY:-}
# - TOGETHER_API_KEY=${TOGETHER_API_KEY:-}
# - MISTRAL_API_KEY=${MISTRAL_API_KEY:-}
# - GEMINI_API_KEY=${GEMINI_API_KEY:-}
# - LLM_PROVIDER=${LLM_PROVIDER:-} # Optional: Override default provider (e.g., "anthropic/claude-3-opus")
volumes:
- /dev/shm:/dev/shm # Chromium performance
deploy:

View File

@@ -0,0 +1,243 @@
# Crawl4AI v0.8.0 Release Notes
**Release Date**: January 2026
**Previous Version**: v0.7.6
**Status**: Release Candidate
---
## Highlights
- **Critical Security Fixes** for Docker API deployment
- **11 New Features** including crash recovery, prefetch mode, and proxy improvements
- **Breaking Changes** - see migration guide below
---
## Breaking Changes
### 1. Docker API: Hooks Disabled by Default
**What changed**: Hooks are now disabled by default on the Docker API.
**Why**: Security fix for Remote Code Execution (RCE) vulnerability.
**Who is affected**: Users of the Docker API who use the `hooks` parameter in `/crawl` requests.
**Migration**:
```bash
# To re-enable hooks (only if you trust all API users):
export CRAWL4AI_HOOKS_ENABLED=true
```
### 2. Docker API: file:// URLs Blocked
**What changed**: The endpoints `/execute_js`, `/screenshot`, `/pdf`, and `/html` now reject `file://` URLs.
**Why**: Security fix for Local File Inclusion (LFI) vulnerability.
**Who is affected**: Users who were reading local files via the Docker API.
**Migration**: Use the Python library directly for local file processing:
```python
# Instead of API call with file:// URL, use library:
from crawl4ai import AsyncWebCrawler
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(url="file:///path/to/file.html")
```
---
## Security Fixes
### Critical: Remote Code Execution via Hooks (CVE Pending)
**Severity**: CRITICAL (CVSS 10.0)
**Affected**: Docker API deployment (all versions before v0.8.0)
**Vector**: `POST /crawl` with malicious `hooks` parameter
**Details**: The `__import__` builtin was available in hook code, allowing attackers to import `os`, `subprocess`, etc. and execute arbitrary commands.
**Fix**:
1. Removed `__import__` from allowed builtins
2. Hooks disabled by default (`CRAWL4AI_HOOKS_ENABLED=false`)
### High: Local File Inclusion via file:// URLs (CVE Pending)
**Severity**: HIGH (CVSS 8.6)
**Affected**: Docker API deployment (all versions before v0.8.0)
**Vector**: `POST /execute_js` (and other endpoints) with `file:///etc/passwd`
**Details**: API endpoints accepted `file://` URLs, allowing attackers to read arbitrary files from the server.
**Fix**: URL scheme validation now only allows `http://`, `https://`, and `raw:` URLs.
### Credits
Discovered by **Neo by ProjectDiscovery** ([projectdiscovery.io](https://projectdiscovery.io)) - December 2025
---
## New Features
### 1. init_scripts Support for BrowserConfig
Pre-page-load JavaScript injection for stealth evasions.
```python
config = BrowserConfig(
init_scripts=[
"Object.defineProperty(navigator, 'webdriver', {get: () => false})"
]
)
```
### 2. CDP Connection Improvements
- WebSocket URL support (`ws://`, `wss://`)
- Proper cleanup with `cdp_cleanup_on_close=True`
- Browser reuse across multiple connections
### 3. Crash Recovery for Deep Crawl Strategies
All deep crawl strategies (BFS, DFS, Best-First) now support crash recovery:
```python
from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
strategy = BFSDeepCrawlStrategy(
max_depth=3,
resume_state=saved_state, # Resume from checkpoint
on_state_change=save_callback # Persist state in real-time
)
```
### 4. PDF and MHTML for raw:/file:// URLs
Generate PDFs and MHTML from cached HTML content.
### 5. Screenshots for raw:/file:// URLs
Render cached HTML and capture screenshots.
### 6. base_url Parameter for CrawlerRunConfig
Proper URL resolution for raw: HTML processing:
```python
config = CrawlerRunConfig(base_url='https://example.com')
result = await crawler.arun(url='raw:{html}', config=config)
```
### 7. Prefetch Mode for Two-Phase Deep Crawling
Fast link extraction without full page processing:
```python
config = CrawlerRunConfig(prefetch=True)
```
### 8. Proxy Rotation and Configuration
Enhanced proxy rotation with sticky sessions support.
### 9. Proxy Support for HTTP Strategy
Non-browser crawler now supports proxies.
### 10. Browser Pipeline for raw:/file:// URLs
New `process_in_browser` parameter for browser operations on local content:
```python
config = CrawlerRunConfig(
process_in_browser=True, # Force browser processing
screenshot=True
)
result = await crawler.arun(url='raw:<html>...</html>', config=config)
```
### 11. Smart TTL Cache for Sitemap URL Seeder
Intelligent cache invalidation for sitemaps:
```python
config = SeedingConfig(
cache_ttl_hours=24,
validate_sitemap_lastmod=True
)
```
---
## Bug Fixes
### raw: URL Parsing Truncates at # Character
**Problem**: CSS color codes like `#eee` were being truncated.
**Before**: `raw:body{background:#eee}``body{background:`
**After**: `raw:body{background:#eee}``body{background:#eee}`
### Caching System Improvements
Various fixes to cache validation and persistence.
---
## Documentation Updates
- Multi-sample schema generation documentation
- URL seeder smart TTL cache parameters
- Security documentation (SECURITY.md)
---
## Upgrade Guide
### From v0.7.x to v0.8.0
1. **Update the package**:
```bash
pip install --upgrade crawl4ai
```
2. **Docker API users**:
- Hooks are now disabled by default
- If you need hooks: `export CRAWL4AI_HOOKS_ENABLED=true`
- `file://` URLs no longer work on API (use library directly)
3. **Review security settings**:
```yaml
# config.yml - recommended for production
security:
enabled: true
jwt_enabled: true
```
4. **Test your integration** before deploying to production
### Breaking Change Checklist
- [ ] Check if you use `hooks` parameter in API calls
- [ ] Check if you use `file://` URLs via the API
- [ ] Update environment variables if needed
- [ ] Review security configuration
---
## Full Changelog
See [CHANGELOG.md](../CHANGELOG.md) for complete version history.
---
## Contributors
Thanks to all contributors who made this release possible.
Special thanks to **Neo by ProjectDiscovery** for responsible security disclosure.
---
*For questions or issues, please open a [GitHub Issue](https://github.com/unclecode/crawl4ai/issues).*

View File

@@ -10,7 +10,6 @@ Today I'm releasing Crawl4AI v0.7.4—the Intelligent Table Extraction & Perform
- **🚀 LLMTableExtraction**: Revolutionary table extraction with intelligent chunking for massive tables
- **⚡ Enhanced Concurrency**: True concurrency improvements for fast-completing tasks in batch operations
- **🧹 Memory Management Refactor**: Streamlined memory utilities and better resource management
- **🔧 Browser Manager Fixes**: Resolved race conditions in concurrent page creation
- **⌨️ Cross-Platform Browser Profiler**: Improved keyboard handling and quit mechanisms
- **🔗 Advanced URL Processing**: Better handling of raw URLs and base tag link resolution
@@ -158,40 +157,6 @@ async with AsyncWebCrawler() as crawler:
- **Monitoring Systems**: Faster health checks and status page monitoring
- **Data Aggregation**: Improved performance for real-time data collection
## 🧹 Memory Management Refactor: Cleaner Architecture
**The Problem:** Memory utilities were scattered and difficult to maintain, with potential import conflicts and unclear organization.
**My Solution:** I consolidated all memory-related utilities into the main `utils.py` module, creating a cleaner, more maintainable architecture.
### Improved Memory Handling
```python
# All memory utilities now consolidated
from crawl4ai.utils import get_true_memory_usage_percent, MemoryMonitor
# Enhanced memory monitoring
monitor = MemoryMonitor()
monitor.start_monitoring()
async with AsyncWebCrawler() as crawler:
# Memory-efficient batch processing
results = await crawler.arun_many(large_url_list)
# Get accurate memory metrics
memory_usage = get_true_memory_usage_percent()
memory_report = monitor.get_report()
print(f"Memory efficiency: {memory_report['efficiency']:.1f}%")
print(f"Peak usage: {memory_report['peak_mb']:.1f} MB")
```
**Expected Real-World Impact:**
- **Production Stability**: More reliable memory tracking and management
- **Code Maintainability**: Cleaner architecture for easier debugging
- **Import Clarity**: Resolved potential conflicts and import issues
- **Developer Experience**: Simpler API for memory monitoring
## 🔧 Critical Stability Fixes
### Browser Manager Race Condition Resolution

318
docs/blog/release-v0.7.5.md Normal file
View File

@@ -0,0 +1,318 @@
# 🚀 Crawl4AI v0.7.5: The Docker Hooks & Security Update
*September 29, 2025 • 8 min read*
---
Today I'm releasing Crawl4AI v0.7.5—focused on extensibility and security. This update introduces the Docker Hooks System for pipeline customization, enhanced LLM integration, and important security improvements.
## 🎯 What's New at a Glance
- **Docker Hooks System**: Custom Python functions at key pipeline points with function-based API
- **Function-Based Hooks**: New `hooks_to_string()` utility with Docker client auto-conversion
- **Enhanced LLM Integration**: Custom providers with temperature control
- **HTTPS Preservation**: Secure internal link handling
- **Bug Fixes**: Resolved multiple community-reported issues
- **Improved Docker Error Handling**: Better debugging and reliability
## 🔧 Docker Hooks System: Pipeline Customization
Every scraping project needs custom logic—authentication, performance optimization, content processing. Traditional solutions require forking or complex workarounds. Docker Hooks let you inject custom Python functions at 8 key points in the crawling pipeline.
### Real Example: Authentication & Performance
```python
import requests
# Real working hooks for httpbin.org
hooks_config = {
"on_page_context_created": """
async def hook(page, context, **kwargs):
print("Hook: Setting up page context")
# Block images to speed up crawling
await context.route("**/*.{png,jpg,jpeg,gif,webp}", lambda route: route.abort())
print("Hook: Images blocked")
return page
""",
"before_retrieve_html": """
async def hook(page, context, **kwargs):
print("Hook: Before retrieving HTML")
# Scroll to bottom to load lazy content
await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
await page.wait_for_timeout(1000)
print("Hook: Scrolled to bottom")
return page
""",
"before_goto": """
async def hook(page, context, url, **kwargs):
print(f"Hook: About to navigate to {url}")
# Add custom headers
await page.set_extra_http_headers({
'X-Test-Header': 'crawl4ai-hooks-test'
})
return page
"""
}
# Test with Docker API
payload = {
"urls": ["https://httpbin.org/html"],
"hooks": {
"code": hooks_config,
"timeout": 30
}
}
response = requests.post("http://localhost:11235/crawl", json=payload)
result = response.json()
if result.get('success'):
print("✅ Hooks executed successfully!")
print(f"Content length: {len(result.get('markdown', ''))} characters")
```
**Available Hook Points:**
- `on_browser_created`: Browser setup
- `on_page_context_created`: Page context configuration
- `before_goto`: Pre-navigation setup
- `after_goto`: Post-navigation processing
- `on_user_agent_updated`: User agent changes
- `on_execution_started`: Crawl initialization
- `before_retrieve_html`: Pre-extraction processing
- `before_return_html`: Final HTML processing
### Function-Based Hooks API
Writing hooks as strings works, but lacks IDE support and type checking. v0.7.5 introduces a function-based approach with automatic conversion!
**Option 1: Using the `hooks_to_string()` Utility**
```python
from crawl4ai import hooks_to_string
import requests
# Define hooks as regular Python functions (with full IDE support!)
async def on_page_context_created(page, context, **kwargs):
"""Block images to speed up crawling"""
await context.route("**/*.{png,jpg,jpeg,gif,webp}", lambda route: route.abort())
await page.set_viewport_size({"width": 1920, "height": 1080})
return page
async def before_goto(page, context, url, **kwargs):
"""Add custom headers"""
await page.set_extra_http_headers({
'X-Crawl4AI': 'v0.7.5',
'X-Custom-Header': 'my-value'
})
return page
# Convert functions to strings
hooks_code = hooks_to_string({
"on_page_context_created": on_page_context_created,
"before_goto": before_goto
})
# Use with REST API
payload = {
"urls": ["https://httpbin.org/html"],
"hooks": {"code": hooks_code, "timeout": 30}
}
response = requests.post("http://localhost:11235/crawl", json=payload)
```
**Option 2: Docker Client with Automatic Conversion (Recommended!)**
```python
from crawl4ai.docker_client import Crawl4aiDockerClient
# Define hooks as functions (same as above)
async def on_page_context_created(page, context, **kwargs):
await context.route("**/*.{png,jpg,jpeg,gif,webp}", lambda route: route.abort())
return page
async def before_retrieve_html(page, context, **kwargs):
# Scroll to load lazy content
await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
await page.wait_for_timeout(1000)
return page
# Use Docker client - conversion happens automatically!
client = Crawl4aiDockerClient(base_url="http://localhost:11235")
results = await client.crawl(
urls=["https://httpbin.org/html"],
hooks={
"on_page_context_created": on_page_context_created,
"before_retrieve_html": before_retrieve_html
},
hooks_timeout=30
)
if results and results.success:
print(f"✅ Hooks executed! HTML length: {len(results.html)}")
```
**Benefits of Function-Based Hooks:**
- ✅ Full IDE support (autocomplete, syntax highlighting)
- ✅ Type checking and linting
- ✅ Easier to test and debug
- ✅ Reusable across projects
- ✅ Automatic conversion in Docker client
- ✅ No breaking changes - string hooks still work!
## 🤖 Enhanced LLM Integration
Enhanced LLM integration with custom providers, temperature control, and base URL configuration.
### Multi-Provider Support
```python
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
from crawl4ai.extraction_strategy import LLMExtractionStrategy
# Test with different providers
async def test_llm_providers():
# OpenAI with custom temperature
openai_strategy = LLMExtractionStrategy(
provider="gemini/gemini-2.5-flash-lite",
api_token="your-api-token",
temperature=0.7, # New in v0.7.5
instruction="Summarize this page in one sentence"
)
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
"https://example.com",
config=CrawlerRunConfig(extraction_strategy=openai_strategy)
)
if result.success:
print("✅ LLM extraction completed")
print(result.extracted_content)
# Docker API with enhanced LLM config
llm_payload = {
"url": "https://example.com",
"f": "llm",
"q": "Summarize this page in one sentence.",
"provider": "gemini/gemini-2.5-flash-lite",
"temperature": 0.7
}
response = requests.post("http://localhost:11235/md", json=llm_payload)
```
**New Features:**
- Custom `temperature` parameter for creativity control
- `base_url` for custom API endpoints
- Multi-provider environment variable support
- Docker API integration
## 🔒 HTTPS Preservation
**The Problem:** Modern web apps require HTTPS everywhere. When crawlers downgrade internal links from HTTPS to HTTP, authentication breaks and security warnings appear.
**Solution:** HTTPS preservation maintains secure protocols throughout crawling.
```python
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, FilterChain, URLPatternFilter, BFSDeepCrawlStrategy
async def test_https_preservation():
# Enable HTTPS preservation
url_filter = URLPatternFilter(
patterns=["^(https:\/\/)?quotes\.toscrape\.com(\/.*)?$"]
)
config = CrawlerRunConfig(
exclude_external_links=True,
preserve_https_for_internal_links=True, # New in v0.7.5
deep_crawl_strategy=BFSDeepCrawlStrategy(
max_depth=2,
max_pages=5,
filter_chain=FilterChain([url_filter])
)
)
async with AsyncWebCrawler() as crawler:
async for result in await crawler.arun(
url="https://quotes.toscrape.com",
config=config
):
# All internal links maintain HTTPS
internal_links = [link['href'] for link in result.links['internal']]
https_links = [link for link in internal_links if link.startswith('https://')]
print(f"HTTPS links preserved: {len(https_links)}/{len(internal_links)}")
for link in https_links[:3]:
print(f"{link}")
```
## 🛠️ Bug Fixes and Improvements
### Major Fixes
- **URL Processing**: Fixed '+' sign preservation in query parameters (#1332)
- **Proxy Configuration**: Enhanced proxy string parsing (old `proxy` parameter deprecated)
- **Docker Error Handling**: Comprehensive error messages with status codes
- **Memory Management**: Fixed leaks in long-running sessions
- **JWT Authentication**: Fixed Docker JWT validation issues (#1442)
- **Playwright Stealth**: Fixed stealth features for Playwright integration (#1481)
- **API Configuration**: Fixed config handling to prevent overriding user-provided settings (#1505)
- **Docker Filter Serialization**: Resolved JSON encoding errors in deep crawl strategy (#1419)
- **LLM Provider Support**: Fixed custom LLM provider integration for adaptive crawler (#1291)
- **Performance Issues**: Resolved backoff strategy failures and timeout handling (#989)
### Community-Reported Issues Fixed
This release addresses multiple issues reported by the community through GitHub issues and Discord discussions:
- Fixed browser configuration reference errors
- Resolved dependency conflicts with cssselect
- Improved error messaging for failed authentications
- Enhanced compatibility with various proxy configurations
- Fixed edge cases in URL normalization
### Configuration Updates
```python
# Old proxy config (deprecated)
# browser_config = BrowserConfig(proxy="http://proxy:8080")
# New enhanced proxy config
browser_config = BrowserConfig(
proxy_config={
"server": "http://proxy:8080",
"username": "optional-user",
"password": "optional-pass"
}
)
```
## 🔄 Breaking Changes
1. **Python 3.10+ Required**: Upgrade from Python 3.9
2. **Proxy Parameter Deprecated**: Use new `proxy_config` structure
3. **New Dependency**: Added `cssselect` for better CSS handling
## 🚀 Get Started
```bash
# Install latest version
pip install crawl4ai==0.7.5
# Docker deployment
docker pull unclecode/crawl4ai:latest
docker run -p 11235:11235 unclecode/crawl4ai:latest
```
**Try the Demo:**
```bash
# Run working examples
python docs/releases_review/demo_v0.7.5.py
```
**Resources:**
- 📖 Documentation: [docs.crawl4ai.com](https://docs.crawl4ai.com)
- 🐙 GitHub: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
- 💬 Discord: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
- 🐦 Twitter: [@unclecode](https://x.com/unclecode)
Happy crawling! 🕷️

314
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View File

@@ -0,0 +1,314 @@
# Crawl4AI v0.7.6 Release Notes
*Release Date: October 22, 2025*
I'm excited to announce Crawl4AI v0.7.6, featuring a complete webhook infrastructure for the Docker job queue API! This release eliminates polling and brings real-time notifications to both crawling and LLM extraction workflows.
## 🎯 What's New
### Webhook Support for Docker Job Queue API
The headline feature of v0.7.6 is comprehensive webhook support for asynchronous job processing. No more constant polling to check if your jobs are done - get instant notifications when they complete!
**Key Capabilities:**
-**Universal Webhook Support**: Both `/crawl/job` and `/llm/job` endpoints now support webhooks
-**Flexible Delivery Modes**: Choose notification-only or include full data in the webhook payload
-**Reliable Delivery**: Exponential backoff retry mechanism (5 attempts: 1s → 2s → 4s → 8s → 16s)
-**Custom Authentication**: Add custom headers for webhook authentication
-**Global Configuration**: Set default webhook URL in `config.yml` for all jobs
-**Task Type Identification**: Distinguish between `crawl` and `llm_extraction` tasks
### How It Works
Instead of constantly checking job status:
**OLD WAY (Polling):**
```python
# Submit job
response = requests.post("http://localhost:11235/crawl/job", json=payload)
task_id = response.json()['task_id']
# Poll until complete
while True:
status = requests.get(f"http://localhost:11235/crawl/job/{task_id}")
if status.json()['status'] == 'completed':
break
time.sleep(5) # Wait and try again
```
**NEW WAY (Webhooks):**
```python
# Submit job with webhook
payload = {
"urls": ["https://example.com"],
"webhook_config": {
"webhook_url": "https://myapp.com/webhook",
"webhook_data_in_payload": True
}
}
response = requests.post("http://localhost:11235/crawl/job", json=payload)
# Done! Webhook will notify you when complete
# Your webhook handler receives the results automatically
```
### Crawl Job Webhooks
```bash
curl -X POST http://localhost:11235/crawl/job \
-H "Content-Type: application/json" \
-d '{
"urls": ["https://example.com"],
"browser_config": {"headless": true},
"crawler_config": {"cache_mode": "bypass"},
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/crawl-complete",
"webhook_data_in_payload": false,
"webhook_headers": {
"X-Webhook-Secret": "your-secret-token"
}
}
}'
```
### LLM Extraction Job Webhooks (NEW!)
```bash
curl -X POST http://localhost:11235/llm/job \
-H "Content-Type: application/json" \
-d '{
"url": "https://example.com/article",
"q": "Extract the article title, author, and publication date",
"schema": "{\"type\":\"object\",\"properties\":{\"title\":{\"type\":\"string\"}}}",
"provider": "openai/gpt-4o-mini",
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/llm-complete",
"webhook_data_in_payload": true
}
}'
```
### Webhook Payload Structure
**Success (with data):**
```json
{
"task_id": "llm_1698765432",
"task_type": "llm_extraction",
"status": "completed",
"timestamp": "2025-10-22T10:30:00.000000+00:00",
"urls": ["https://example.com/article"],
"data": {
"extracted_content": {
"title": "Understanding Web Scraping",
"author": "John Doe",
"date": "2025-10-22"
}
}
}
```
**Failure:**
```json
{
"task_id": "crawl_abc123",
"task_type": "crawl",
"status": "failed",
"timestamp": "2025-10-22T10:30:00.000000+00:00",
"urls": ["https://example.com"],
"error": "Connection timeout after 30s"
}
```
### Simple Webhook Handler Example
```python
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/webhook', methods=['POST'])
def handle_webhook():
payload = request.json
task_id = payload['task_id']
task_type = payload['task_type']
status = payload['status']
if status == 'completed':
if 'data' in payload:
# Process data directly
data = payload['data']
else:
# Fetch from API
endpoint = 'crawl' if task_type == 'crawl' else 'llm'
response = requests.get(f'http://localhost:11235/{endpoint}/job/{task_id}')
data = response.json()
# Your business logic here
print(f"Job {task_id} completed!")
elif status == 'failed':
error = payload.get('error', 'Unknown error')
print(f"Job {task_id} failed: {error}")
return jsonify({"status": "received"}), 200
app.run(port=8080)
```
## 📊 Performance Improvements
- **Reduced Server Load**: Eliminates constant polling requests
- **Lower Latency**: Instant notification vs. polling interval delay
- **Better Resource Usage**: Frees up client connections while jobs run in background
- **Scalable Architecture**: Handles high-volume crawling workflows efficiently
## 🐛 Bug Fixes
- Fixed webhook configuration serialization for Pydantic HttpUrl fields
- Improved error handling in webhook delivery service
- Enhanced Redis task storage for webhook config persistence
## 🌍 Expected Real-World Impact
### For Web Scraping Workflows
- **Reduced Costs**: Less API calls = lower bandwidth and server costs
- **Better UX**: Instant notifications improve user experience
- **Scalability**: Handle 100s of concurrent jobs without polling overhead
### For LLM Extraction Pipelines
- **Async Processing**: Submit LLM extraction jobs and move on
- **Batch Processing**: Queue multiple extractions, get notified as they complete
- **Integration**: Easy integration with workflow automation tools (Zapier, n8n, etc.)
### For Microservices
- **Event-Driven**: Perfect for event-driven microservice architectures
- **Decoupling**: Decouple job submission from result processing
- **Reliability**: Automatic retries ensure webhooks are delivered
## 🔄 Breaking Changes
**None!** This release is fully backward compatible.
- Webhook configuration is optional
- Existing code continues to work without modification
- Polling is still supported for jobs without webhook config
## 📚 Documentation
### New Documentation
- **[WEBHOOK_EXAMPLES.md](../deploy/docker/WEBHOOK_EXAMPLES.md)** - Comprehensive webhook usage guide
- **[docker_webhook_example.py](../docs/examples/docker_webhook_example.py)** - Working code examples
### Updated Documentation
- **[Docker README](../deploy/docker/README.md)** - Added webhook sections
- API documentation with webhook examples
## 🛠️ Migration Guide
No migration needed! Webhooks are opt-in:
1. **To use webhooks**: Add `webhook_config` to your job payload
2. **To keep polling**: Continue using your existing code
### Quick Start
```python
# Just add webhook_config to your existing payload
payload = {
# Your existing configuration
"urls": ["https://example.com"],
"browser_config": {...},
"crawler_config": {...},
# NEW: Add webhook configuration
"webhook_config": {
"webhook_url": "https://myapp.com/webhook",
"webhook_data_in_payload": True
}
}
```
## 🔧 Configuration
### Global Webhook Configuration (config.yml)
```yaml
webhooks:
enabled: true
default_url: "https://myapp.com/webhooks/default" # Optional
data_in_payload: false
retry:
max_attempts: 5
initial_delay_ms: 1000
max_delay_ms: 32000
timeout_ms: 30000
headers:
User-Agent: "Crawl4AI-Webhook/1.0"
```
## 🚀 Upgrade Instructions
### Docker
```bash
# Pull the latest image
docker pull unclecode/crawl4ai:0.7.6
# Or use latest tag
docker pull unclecode/crawl4ai:latest
# Run with webhook support
docker run -d \
-p 11235:11235 \
--env-file .llm.env \
--name crawl4ai \
unclecode/crawl4ai:0.7.6
```
### Python Package
```bash
pip install --upgrade crawl4ai
```
## 💡 Pro Tips
1. **Use notification-only mode** for large results - fetch data separately to avoid large webhook payloads
2. **Set custom headers** for webhook authentication and request tracking
3. **Configure global default webhook** for consistent handling across all jobs
4. **Implement idempotent webhook handlers** - same webhook may be delivered multiple times on retry
5. **Use structured schemas** with LLM extraction for predictable webhook data
## 🎬 Demo
Try the release demo:
```bash
python docs/releases_review/demo_v0.7.6.py
```
This comprehensive demo showcases:
- Crawl job webhooks (notification-only and with data)
- LLM extraction webhooks (with JSON schema support)
- Custom headers for authentication
- Webhook retry mechanism
- Real-time webhook receiver
## 🙏 Acknowledgments
Thank you to the community for the feedback that shaped this feature! Special thanks to everyone who requested webhook support for asynchronous job processing.
## 📞 Support
- **Documentation**: https://docs.crawl4ai.com
- **GitHub Issues**: https://github.com/unclecode/crawl4ai/issues
- **Discord**: https://discord.gg/crawl4ai
---
**Happy crawling with webhooks!** 🕷️🪝
*- unclecode*

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# 🚀 Crawl4AI v0.7.7: The Self-Hosting & Monitoring Update
*November 14, 2025 • 10 min read*
---
Today I'm releasing Crawl4AI v0.7.7—the Self-Hosting & Monitoring Update. This release transforms Crawl4AI Docker from a simple containerized crawler into a complete self-hosting platform with enterprise-grade real-time monitoring, full operational transparency, and production-ready observability.
## 🎯 What's New at a Glance
- **📊 Real-time Monitoring Dashboard**: Interactive web UI with live system metrics and browser pool status
- **🔌 Comprehensive Monitor API**: Complete REST API for programmatic access to all monitoring data
- **⚡ WebSocket Streaming**: Real-time updates every 2 seconds for custom dashboards
- **🎮 Control Actions**: Manual browser management (kill, restart, cleanup)
- **🔥 Smart Browser Pool**: 3-tier architecture (permanent/hot/cold) with automatic promotion
- **🧹 Janitor Cleanup System**: Automatic resource management with event logging
- **📈 Production Metrics**: 6 critical metrics for operational excellence
- **🏭 Integration Ready**: Prometheus, alerting, and log aggregation examples
- **🐛 Critical Bug Fixes**: Async LLM extraction, DFS crawling, viewport config, and more
## 📊 Real-time Monitoring Dashboard: Complete Visibility
**The Problem:** Running Crawl4AI in Docker was like flying blind. Users had no visibility into what was happening inside the container—memory usage, active requests, browser pools, or errors. Troubleshooting required checking logs, and there was no way to monitor performance or manually intervene when issues occurred.
**My Solution:** I built a complete real-time monitoring system with an interactive dashboard, comprehensive REST API, WebSocket streaming, and manual control actions. Now you have full transparency and control over your crawling infrastructure.
### The Self-Hosting Value Proposition
Before v0.7.7, Docker was just a containerized crawler. After v0.7.7, it's a complete self-hosting platform that gives you:
- **🔒 Data Privacy**: Your data never leaves your infrastructure
- **💰 Cost Control**: No per-request pricing or rate limits
- **🎯 Full Customization**: Complete control over configurations and strategies
- **📊 Complete Transparency**: Real-time visibility into every aspect
- **⚡ Performance**: Direct access without network overhead
- **🛡️ Enterprise Security**: Keep workflows behind your firewall
### Interactive Monitoring Dashboard
Access the dashboard at `http://localhost:11235/dashboard` to see:
- **System Health Overview**: CPU, memory, network, and uptime in real-time
- **Live Request Tracking**: Active and completed requests with full details
- **Browser Pool Management**: Interactive table with permanent/hot/cold browsers
- **Janitor Events Log**: Automatic cleanup activities
- **Error Monitoring**: Full context error logs
The dashboard updates every 2 seconds via WebSocket, giving you live visibility into your crawling operations.
## 🔌 Monitor API: Programmatic Access
**The Problem:** Monitoring dashboards are great for humans, but automation and integration require programmatic access.
**My Solution:** A comprehensive REST API that exposes all monitoring data for integration with your existing infrastructure.
### System Health Endpoint
```python
import httpx
import asyncio
async def monitor_system_health():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/health")
health = response.json()
print(f"Container Metrics:")
print(f" CPU: {health['container']['cpu_percent']:.1f}%")
print(f" Memory: {health['container']['memory_percent']:.1f}%")
print(f" Uptime: {health['container']['uptime_seconds']}s")
print(f"\nBrowser Pool:")
print(f" Permanent: {health['pool']['permanent']['active']} active")
print(f" Hot Pool: {health['pool']['hot']['count']} browsers")
print(f" Cold Pool: {health['pool']['cold']['count']} browsers")
print(f"\nStatistics:")
print(f" Total Requests: {health['stats']['total_requests']}")
print(f" Success Rate: {health['stats']['success_rate_percent']:.1f}%")
print(f" Avg Latency: {health['stats']['avg_latency_ms']:.0f}ms")
asyncio.run(monitor_system_health())
```
### Request Tracking
```python
async def track_requests():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/requests")
requests_data = response.json()
print(f"Active Requests: {len(requests_data['active'])}")
print(f"Completed Requests: {len(requests_data['completed'])}")
# See details of recent requests
for req in requests_data['completed'][:5]:
status_icon = "" if req['success'] else ""
print(f"{status_icon} {req['endpoint']} - {req['latency_ms']:.0f}ms")
```
### Browser Pool Management
```python
async def monitor_browser_pool():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/browsers")
browsers = response.json()
print(f"Pool Summary:")
print(f" Total Browsers: {browsers['summary']['total_count']}")
print(f" Total Memory: {browsers['summary']['total_memory_mb']} MB")
print(f" Reuse Rate: {browsers['summary']['reuse_rate_percent']:.1f}%")
# List all browsers
for browser in browsers['permanent']:
print(f"🔥 Permanent: {browser['browser_id'][:8]}... | "
f"Requests: {browser['request_count']} | "
f"Memory: {browser['memory_mb']:.0f} MB")
```
### Endpoint Performance Statistics
```python
async def get_endpoint_stats():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/endpoints/stats")
stats = response.json()
print("Endpoint Analytics:")
for endpoint, data in stats.items():
print(f" {endpoint}:")
print(f" Requests: {data['count']}")
print(f" Avg Latency: {data['avg_latency_ms']:.0f}ms")
print(f" Success Rate: {data['success_rate_percent']:.1f}%")
```
### Complete API Reference
The Monitor API includes these endpoints:
- `GET /monitor/health` - System health with pool statistics
- `GET /monitor/requests` - Active and completed request tracking
- `GET /monitor/browsers` - Browser pool details and efficiency
- `GET /monitor/endpoints/stats` - Per-endpoint performance analytics
- `GET /monitor/timeline?minutes=5` - Time-series data for charts
- `GET /monitor/logs/janitor?limit=10` - Cleanup activity logs
- `GET /monitor/logs/errors?limit=10` - Error logs with context
- `POST /monitor/actions/cleanup` - Force immediate cleanup
- `POST /monitor/actions/kill_browser` - Kill specific browser
- `POST /monitor/actions/restart_browser` - Restart browser
- `POST /monitor/stats/reset` - Reset accumulated statistics
## ⚡ WebSocket Streaming: Real-time Updates
**The Problem:** Polling the API every few seconds wastes resources and adds latency. Real-time dashboards need instant updates.
**My Solution:** WebSocket streaming with 2-second update intervals for building custom real-time dashboards.
### WebSocket Integration Example
```python
import websockets
import json
import asyncio
async def monitor_realtime():
uri = "ws://localhost:11235/monitor/ws"
async with websockets.connect(uri) as websocket:
print("Connected to real-time monitoring stream")
while True:
# Receive update every 2 seconds
data = await websocket.recv()
update = json.loads(data)
# Access all monitoring data
print(f"\n--- Update at {update['timestamp']} ---")
print(f"Memory: {update['health']['container']['memory_percent']:.1f}%")
print(f"Active Requests: {len(update['requests']['active'])}")
print(f"Total Browsers: {update['browsers']['summary']['total_count']}")
if update['errors']:
print(f"⚠️ Recent Errors: {len(update['errors'])}")
asyncio.run(monitor_realtime())
```
**Expected Real-World Impact:**
- **Custom Dashboards**: Build tailored monitoring UIs for your team
- **Real-time Alerting**: Trigger alerts instantly when metrics exceed thresholds
- **Integration**: Feed live data into monitoring tools like Grafana
- **Automation**: React to events in real-time without polling
## 🔥 Smart Browser Pool: 3-Tier Architecture
**The Problem:** Creating a new browser for every request is slow and memory-intensive. Traditional browser pools are static and inefficient.
**My Solution:** A smart 3-tier browser pool that automatically adapts to usage patterns.
### How It Works
```python
import httpx
async def demonstrate_browser_pool():
async with httpx.AsyncClient() as client:
# Request 1-3: Default config → Uses permanent browser
print("Phase 1: Using permanent browser")
for i in range(3):
await client.post(
"http://localhost:11235/crawl",
json={"urls": [f"https://httpbin.org/html?req={i}"]}
)
print(f" Request {i+1}: Reused permanent browser")
# Request 4-6: Custom viewport → Cold pool (first use)
print("\nPhase 2: Custom config creates cold pool browser")
viewport_config = {"viewport": {"width": 1280, "height": 720}}
for i in range(4):
await client.post(
"http://localhost:11235/crawl",
json={
"urls": [f"https://httpbin.org/json?v={i}"],
"browser_config": viewport_config
}
)
if i < 2:
print(f" Request {i+1}: Cold pool browser")
else:
print(f" Request {i+1}: Promoted to hot pool! (after 3 uses)")
# Check pool status
response = await client.get("http://localhost:11235/monitor/browsers")
browsers = response.json()
print(f"\nPool Status:")
print(f" Permanent: {len(browsers['permanent'])} (always active)")
print(f" Hot: {len(browsers['hot'])} (frequently used configs)")
print(f" Cold: {len(browsers['cold'])} (on-demand)")
print(f" Reuse Rate: {browsers['summary']['reuse_rate_percent']:.1f}%")
asyncio.run(demonstrate_browser_pool())
```
**Pool Tiers:**
- **🔥 Permanent Browser**: Always-on, default configuration, instant response
- **♨️ Hot Pool**: Browsers promoted after 3+ uses, kept warm for quick access
- **❄️ Cold Pool**: On-demand browsers for variant configs, cleaned up when idle
**Expected Real-World Impact:**
- **Memory Efficiency**: 10x reduction in memory usage vs creating browsers per request
- **Performance**: Instant access to frequently-used configurations
- **Automatic Optimization**: Pool adapts to your usage patterns
- **Resource Management**: Janitor automatically cleans up idle browsers
## 🧹 Janitor System: Automatic Cleanup
**The Problem:** Long-running crawlers accumulate idle browsers and consume memory over time.
**My Solution:** An automatic janitor system that monitors and cleans up idle resources.
```python
async def monitor_janitor_activity():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/logs/janitor?limit=5")
logs = response.json()
print("Recent Cleanup Activities:")
for log in logs:
print(f" {log['timestamp']}: {log['message']}")
# Example output:
# 2025-11-14 10:30:00: Cleaned up 2 cold pool browsers (idle > 5min)
# 2025-11-14 10:25:00: Browser reuse rate: 85.3%
# 2025-11-14 10:20:00: Hot pool browser promoted (10 requests)
```
## 🎮 Control Actions: Manual Management
**The Problem:** Sometimes you need to manually intervene—kill a stuck browser, force cleanup, or restart resources.
**My Solution:** Manual control actions via the API for operational troubleshooting.
### Force Cleanup
```python
async def force_cleanup():
async with httpx.AsyncClient() as client:
response = await client.post("http://localhost:11235/monitor/actions/cleanup")
result = response.json()
print(f"Cleanup completed:")
print(f" Browsers cleaned: {result.get('cleaned_count', 0)}")
print(f" Memory freed: {result.get('memory_freed_mb', 0):.1f} MB")
```
### Kill Specific Browser
```python
async def kill_stuck_browser(browser_id: str):
async with httpx.AsyncClient() as client:
response = await client.post(
"http://localhost:11235/monitor/actions/kill_browser",
json={"browser_id": browser_id}
)
if response.status_code == 200:
print(f"✅ Browser {browser_id} killed successfully")
```
### Reset Statistics
```python
async def reset_stats():
async with httpx.AsyncClient() as client:
response = await client.post("http://localhost:11235/monitor/stats/reset")
print("📊 Statistics reset for fresh monitoring")
```
## 📈 Production Integration Patterns
### Prometheus Integration
```python
# Export metrics for Prometheus scraping
async def export_prometheus_metrics():
async with httpx.AsyncClient() as client:
health = await client.get("http://localhost:11235/monitor/health")
data = health.json()
# Export in Prometheus format
metrics = f"""
# HELP crawl4ai_memory_usage_percent Memory usage percentage
# TYPE crawl4ai_memory_usage_percent gauge
crawl4ai_memory_usage_percent {data['container']['memory_percent']}
# HELP crawl4ai_request_success_rate Request success rate
# TYPE crawl4ai_request_success_rate gauge
crawl4ai_request_success_rate {data['stats']['success_rate_percent']}
# HELP crawl4ai_browser_pool_count Total browsers in pool
# TYPE crawl4ai_browser_pool_count gauge
crawl4ai_browser_pool_count {data['pool']['permanent']['active'] + data['pool']['hot']['count'] + data['pool']['cold']['count']}
"""
return metrics
```
### Alerting Example
```python
async def check_alerts():
async with httpx.AsyncClient() as client:
health = await client.get("http://localhost:11235/monitor/health")
data = health.json()
# Memory alert
if data['container']['memory_percent'] > 80:
print("🚨 ALERT: Memory usage above 80%")
# Trigger cleanup
await client.post("http://localhost:11235/monitor/actions/cleanup")
# Success rate alert
if data['stats']['success_rate_percent'] < 90:
print("🚨 ALERT: Success rate below 90%")
# Check error logs
errors = await client.get("http://localhost:11235/monitor/logs/errors")
print(f"Recent errors: {len(errors.json())}")
# Latency alert
if data['stats']['avg_latency_ms'] > 5000:
print("🚨 ALERT: Average latency above 5s")
```
### Key Metrics to Track
```python
CRITICAL_METRICS = {
"memory_usage": {
"current": "container.memory_percent",
"target": "<80%",
"alert_threshold": ">80%",
"action": "Force cleanup or scale"
},
"success_rate": {
"current": "stats.success_rate_percent",
"target": ">95%",
"alert_threshold": "<90%",
"action": "Check error logs"
},
"avg_latency": {
"current": "stats.avg_latency_ms",
"target": "<2000ms",
"alert_threshold": ">5000ms",
"action": "Investigate slow requests"
},
"browser_reuse_rate": {
"current": "browsers.summary.reuse_rate_percent",
"target": ">80%",
"alert_threshold": "<60%",
"action": "Check pool configuration"
},
"total_browsers": {
"current": "browsers.summary.total_count",
"target": "<15",
"alert_threshold": ">20",
"action": "Check for browser leaks"
},
"error_frequency": {
"current": "len(errors)",
"target": "<5/hour",
"alert_threshold": ">10/hour",
"action": "Review error patterns"
}
}
```
## 🐛 Critical Bug Fixes
This release includes significant bug fixes that improve stability and performance:
### Async LLM Extraction (#1590)
**The Problem:** LLM extraction was blocking async execution, causing URLs to be processed sequentially instead of in parallel (issue #1055).
**The Fix:** Resolved the blocking issue to enable true parallel processing for LLM extraction.
```python
# Before v0.7.7: Sequential processing
# After v0.7.7: True parallel processing
async with AsyncWebCrawler() as crawler:
urls = ["url1", "url2", "url3", "url4"]
# Now processes truly in parallel with LLM extraction
results = await crawler.arun_many(
urls,
config=CrawlerRunConfig(
extraction_strategy=LLMExtractionStrategy(...)
)
)
# 4x faster for parallel LLM extraction!
```
**Expected Impact:** Major performance improvement for batch LLM extraction workflows.
### DFS Deep Crawling (#1607)
**The Problem:** DFS (Depth-First Search) deep crawl strategy had implementation issues.
**The Fix:** Enhanced DFSDeepCrawlStrategy with proper seen URL tracking and improved documentation.
### Browser & Crawler Config Documentation (#1609)
**The Problem:** Documentation didn't match the actual `async_configs.py` implementation.
**The Fix:** Updated all configuration documentation to accurately reflect the current implementation.
### Sitemap Seeder (#1598)
**The Problem:** Sitemap parsing and URL normalization issues in AsyncUrlSeeder (issue #1559).
**The Fix:** Added comprehensive tests and fixes for sitemap namespace parsing and URL normalization.
### Remove Overlay Elements (#1529)
**The Problem:** The `remove_overlay_elements` functionality wasn't working (issue #1396).
**The Fix:** Fixed by properly calling the injected JavaScript function.
### Viewport Configuration (#1495)
**The Problem:** Viewport configuration wasn't working in managed browsers (issue #1490).
**The Fix:** Added proper viewport size configuration support for browser launch.
### Managed Browser CDP Timing (#1528)
**The Problem:** CDP (Chrome DevTools Protocol) endpoint verification had timing issues causing connection failures (issue #1445).
**The Fix:** Added exponential backoff for CDP endpoint verification to handle timing variations.
### Security Updates
- **pyOpenSSL**: Updated from >=24.3.0 to >=25.3.0 to address security vulnerability
- Added verification tests for the security update
### Docker Fixes
- **Port Standardization**: Fixed inconsistent port usage (11234 vs 11235) - now standardized to 11235
- **LLM Environment**: Fixed LLM API key handling for multi-provider support (PR #1537)
- **Error Handling**: Improved Docker API error messages with comprehensive status codes
- **Serialization**: Fixed `fit_html` property serialization in `/crawl` and `/crawl/stream` endpoints
### Other Important Fixes
- **arun_many Returns**: Fixed function to always return a list, even on exception (PR #1530)
- **Webhook Serialization**: Properly serialize Pydantic HttpUrl in webhook config
- **LLMConfig Documentation**: Fixed casing and variable name consistency (issue #1551)
- **Python Version**: Dropped Python 3.9 support, now requires Python >=3.10
## 📊 Expected Real-World Impact
### For DevOps & Infrastructure Teams
- **Full Visibility**: Know exactly what's happening inside your crawling infrastructure
- **Proactive Monitoring**: Catch issues before they become problems
- **Resource Optimization**: Identify memory leaks and performance bottlenecks
- **Operational Control**: Manual intervention when automated systems need help
### For Production Deployments
- **Enterprise Observability**: Prometheus, Grafana, and alerting integration
- **Debugging**: Real-time logs and error tracking
- **Capacity Planning**: Historical metrics for scaling decisions
- **SLA Monitoring**: Track success rates and latency against targets
### For Development Teams
- **Local Monitoring**: Understand crawler behavior during development
- **Performance Testing**: Measure impact of configuration changes
- **Troubleshooting**: Quickly identify and fix issues
- **Learning**: See exactly how the browser pool works
## 🔄 Breaking Changes
**None!** This release is fully backward compatible.
- All existing Docker configurations continue to work
- No API changes to existing endpoints
- Monitoring is additive functionality
- No migration required
## 🚀 Upgrade Instructions
### Docker
```bash
# Pull the latest version
docker pull unclecode/crawl4ai:0.7.7
# Or use the latest tag
docker pull unclecode/crawl4ai:latest
# Run with monitoring enabled (default)
docker run -d \
-p 11235:11235 \
--shm-size=1g \
--name crawl4ai \
unclecode/crawl4ai:0.7.7
# Access the monitoring dashboard
open http://localhost:11235/dashboard
```
### Python Package
```bash
# Upgrade to latest version
pip install --upgrade crawl4ai
# Or install specific version
pip install crawl4ai==0.7.7
```
## 🎬 Try the Demo
Run the comprehensive demo that showcases all monitoring features:
```bash
python docs/releases_review/demo_v0.7.7.py
```
**The demo includes:**
1. System health overview with live metrics
2. Request tracking with active/completed monitoring
3. Browser pool management (permanent/hot/cold)
4. Complete Monitor API endpoint examples
5. WebSocket streaming demonstration
6. Control actions (cleanup, kill, restart)
7. Production metrics and alerting patterns
8. Self-hosting value proposition
## 📚 Documentation
### New Documentation
- **[Self-Hosting Guide](https://docs.crawl4ai.com/core/self-hosting/)** - Complete self-hosting documentation with monitoring
- **Demo Script**: `docs/releases_review/demo_v0.7.7.py` - Working examples
### Updated Documentation
- **Docker Deployment** → **Self-Hosting** (renamed for better positioning)
- Added comprehensive monitoring sections
- Production integration patterns
- WebSocket streaming examples
## 💡 Pro Tips
1. **Start with the dashboard** - Visit `/dashboard` to get familiar with the monitoring system
2. **Track the 6 key metrics** - Memory, success rate, latency, reuse rate, browser count, errors
3. **Set up alerting early** - Use the Monitor API to build alerts before issues occur
4. **Monitor browser pool efficiency** - Aim for >80% reuse rate for optimal performance
5. **Use WebSocket for custom dashboards** - Build tailored monitoring UIs for your team
6. **Leverage Prometheus integration** - Export metrics for long-term storage and analysis
7. **Check janitor logs** - Understand automatic cleanup patterns
8. **Use control actions judiciously** - Manual interventions are for exceptional cases
## 🙏 Acknowledgments
Thank you to our community for the feedback, bug reports, and feature requests that shaped this release. Special thanks to everyone who contributed to the issues that were fixed in this version.
The monitoring system was built based on real user needs for production deployments, and your input made it comprehensive and practical.
## 📞 Support & Resources
- **📖 Documentation**: [docs.crawl4ai.com](https://docs.crawl4ai.com)
- **🐙 GitHub**: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
- **💬 Discord**: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
- **🐦 Twitter**: [@unclecode](https://x.com/unclecode)
- **📊 Dashboard**: `http://localhost:11235/dashboard` (when running)
---
**Crawl4AI v0.7.7 delivers complete self-hosting with enterprise-grade monitoring. You now have full visibility and control over your web crawling infrastructure. The monitoring dashboard, comprehensive API, and WebSocket streaming give you everything needed for production deployments. Try the self-hosting platform—it's a game changer for operational excellence!**
**Happy crawling with full visibility!** 🕷️📊
*- unclecode*

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@@ -0,0 +1,327 @@
# Crawl4AI v0.7.8: Stability & Bug Fix Release
*December 2025*
---
I'm releasing Crawl4AI v0.7.8—a focused stability release that addresses 11 bugs reported by the community. While there are no new features in this release, these fixes resolve important issues affecting Docker deployments, LLM extraction, URL handling, and dependency compatibility.
## What's Fixed at a Glance
- **Docker API**: Fixed ContentRelevanceFilter deserialization, ProxyConfig serialization, and cache folder permissions
- **LLM Extraction**: Configurable rate limiter backoff, HTML input format support, and proper URL handling for raw HTML
- **URL Handling**: Correct relative URL resolution after JavaScript redirects
- **Dependencies**: Replaced deprecated PyPDF2 with pypdf, Pydantic v2 ConfigDict compatibility
- **AdaptiveCrawler**: Fixed query expansion to actually use LLM instead of hardcoded mock data
## Bug Fixes
### Docker & API Fixes
#### ContentRelevanceFilter Deserialization (#1642)
**The Problem:** When sending deep crawl requests to the Docker API with `ContentRelevanceFilter`, the server failed to deserialize the filter, causing requests to fail.
**The Fix:** I added `ContentRelevanceFilter` to the public exports and enhanced the deserialization logic with dynamic imports.
```python
# This now works correctly in Docker API
import httpx
request = {
"urls": ["https://docs.example.com"],
"crawler_config": {
"deep_crawl_strategy": {
"type": "BFSDeepCrawlStrategy",
"max_depth": 2,
"filter_chain": [
{
"type": "ContentRelevanceFilter",
"query": "API documentation",
"threshold": 0.3
}
]
}
}
}
async with httpx.AsyncClient() as client:
response = await client.post("http://localhost:11235/crawl", json=request)
# Previously failed, now works!
```
#### ProxyConfig JSON Serialization (#1629)
**The Problem:** `BrowserConfig.to_dict()` failed when `proxy_config` was set because `ProxyConfig` wasn't being serialized to a dictionary.
**The Fix:** `ProxyConfig.to_dict()` is now called during serialization.
```python
from crawl4ai import BrowserConfig
from crawl4ai.async_configs import ProxyConfig
proxy = ProxyConfig(
server="http://proxy.example.com:8080",
username="user",
password="pass"
)
config = BrowserConfig(headless=True, proxy_config=proxy)
# Previously raised TypeError, now works
config_dict = config.to_dict()
json.dumps(config_dict) # Valid JSON
```
#### Docker Cache Folder Permissions (#1638)
**The Problem:** The `.cache` folder in the Docker image had incorrect permissions, causing crawling to fail when caching was enabled.
**The Fix:** Corrected ownership and permissions during image build.
```bash
# Cache now works correctly in Docker
docker run -d -p 11235:11235 \
--shm-size=1g \
-v ./my-cache:/app/.cache \
unclecode/crawl4ai:0.7.8
```
---
### LLM & Extraction Fixes
#### Configurable Rate Limiter Backoff (#1269)
**The Problem:** The LLM rate limiting backoff parameters were hardcoded, making it impossible to adjust retry behavior for different API rate limits.
**The Fix:** `LLMConfig` now accepts three new parameters for complete control over retry behavior.
```python
from crawl4ai import LLMConfig
# Default behavior (unchanged)
default_config = LLMConfig(provider="openai/gpt-4o-mini")
# backoff_base_delay=2, backoff_max_attempts=3, backoff_exponential_factor=2
# Custom configuration for APIs with strict rate limits
custom_config = LLMConfig(
provider="openai/gpt-4o-mini",
backoff_base_delay=5, # Wait 5 seconds on first retry
backoff_max_attempts=5, # Try up to 5 times
backoff_exponential_factor=3 # Multiply delay by 3 each attempt
)
# Retry sequence: 5s -> 15s -> 45s -> 135s -> 405s
```
#### LLM Strategy HTML Input Support (#1178)
**The Problem:** `LLMExtractionStrategy` always sent markdown to the LLM, but some extraction tasks work better with HTML structure preserved.
**The Fix:** Added `input_format` parameter supporting `"markdown"`, `"html"`, `"fit_markdown"`, `"cleaned_html"`, and `"fit_html"`.
```python
from crawl4ai import LLMExtractionStrategy, LLMConfig
# Default: markdown input (unchanged)
markdown_strategy = LLMExtractionStrategy(
llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
instruction="Extract product information"
)
# NEW: HTML input - preserves table/list structure
html_strategy = LLMExtractionStrategy(
llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
instruction="Extract the data table preserving structure",
input_format="html"
)
# NEW: Filtered markdown - only relevant content
fit_strategy = LLMExtractionStrategy(
llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
instruction="Summarize the main content",
input_format="fit_markdown"
)
```
#### Raw HTML URL Variable (#1116)
**The Problem:** When using `url="raw:<html>..."`, the entire HTML content was being passed to extraction strategies as the URL parameter, polluting LLM prompts.
**The Fix:** The URL is now correctly set to `"Raw HTML"` for raw HTML inputs.
```python
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
html = "<html><body><h1>Test</h1></body></html>"
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=f"raw:{html}",
config=CrawlerRunConfig(extraction_strategy=my_strategy)
)
# extraction_strategy receives url="Raw HTML" instead of the HTML blob
```
---
### URL Handling Fix
#### Relative URLs After Redirects (#1268)
**The Problem:** When JavaScript caused a page redirect, relative links were resolved against the original URL instead of the final URL.
**The Fix:** `redirected_url` now captures the actual page URL after all JavaScript execution completes.
```python
from crawl4ai import AsyncWebCrawler
async with AsyncWebCrawler() as crawler:
# Page at /old-page redirects via JS to /new-page
result = await crawler.arun(url="https://example.com/old-page")
# BEFORE: redirected_url = "https://example.com/old-page"
# AFTER: redirected_url = "https://example.com/new-page"
# Links are now correctly resolved against the final URL
for link in result.links['internal']:
print(link['href']) # Relative links resolved correctly
```
---
### Dependency & Compatibility Fixes
#### PyPDF2 Replaced with pypdf (#1412)
**The Problem:** PyPDF2 was deprecated in 2022 and is no longer maintained.
**The Fix:** Replaced with the actively maintained `pypdf` library.
```python
# Installation (unchanged)
pip install crawl4ai[pdf]
# The PDF processor now uses pypdf internally
# No code changes required - API remains the same
```
#### Pydantic v2 ConfigDict Compatibility (#678)
**The Problem:** Using the deprecated `class Config` syntax caused deprecation warnings with Pydantic v2.
**The Fix:** Migrated to `model_config = ConfigDict(...)` syntax.
```python
# No more deprecation warnings when importing crawl4ai models
from crawl4ai.models import CrawlResult
from crawl4ai import CrawlerRunConfig, BrowserConfig
# All models are now Pydantic v2 compatible
```
---
### AdaptiveCrawler Fix
#### Query Expansion Using LLM (#1621)
**The Problem:** The `EmbeddingStrategy` in AdaptiveCrawler had commented-out LLM code and was using hardcoded mock query variations instead.
**The Fix:** Uncommented and activated the LLM call for actual query expansion.
```python
# AdaptiveCrawler query expansion now actually uses the LLM
# Instead of hardcoded variations like:
# variations = {'queries': ['what are the best vegetables...']}
# The LLM generates relevant query variations based on your actual query
```
---
### Code Formatting Fix
#### Import Statement Formatting (#1181)
**The Problem:** When extracting code from web pages, import statements were sometimes concatenated without proper line separation.
**The Fix:** Import statements now maintain proper newline separation.
```python
# BEFORE: "import osimport sysfrom pathlib import Path"
# AFTER:
# import os
# import sys
# from pathlib import Path
```
---
## Breaking Changes
**None!** This release is fully backward compatible.
- All existing code continues to work without modification
- New parameters have sensible defaults matching previous behavior
- No API changes to existing functionality
---
## Upgrade Instructions
### Python Package
```bash
pip install --upgrade crawl4ai
# or
pip install crawl4ai==0.7.8
```
### Docker
```bash
# Pull the latest version
docker pull unclecode/crawl4ai:0.7.8
# Run
docker run -d -p 11235:11235 --shm-size=1g unclecode/crawl4ai:0.7.8
```
---
## Verification
Run the verification tests to confirm all fixes are working:
```bash
python docs/releases_review/demo_v0.7.8.py
```
This runs actual tests that verify each bug fix is properly implemented.
---
## Acknowledgments
Thank you to everyone who reported these issues and provided detailed reproduction steps. Your bug reports make Crawl4AI better for everyone.
Issues fixed: #1642, #1638, #1629, #1621, #1412, #1269, #1268, #1181, #1178, #1116, #678
---
## Support & Resources
- **Documentation**: [docs.crawl4ai.com](https://docs.crawl4ai.com)
- **GitHub**: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
- **Discord**: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
- **Twitter**: [@unclecode](https://x.com/unclecode)
---
**This stability release ensures Crawl4AI works reliably across Docker deployments, LLM extraction workflows, and various edge cases. Thank you for your continued support and feedback!**
**Happy crawling!**
*- unclecode*

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@@ -0,0 +1,243 @@
# Crawl4AI v0.8.0 Release Notes
**Release Date**: January 2026
**Previous Version**: v0.7.6
**Status**: Release Candidate
---
## Highlights
- **Critical Security Fixes** for Docker API deployment
- **11 New Features** including crash recovery, prefetch mode, and proxy improvements
- **Breaking Changes** - see migration guide below
---
## Breaking Changes
### 1. Docker API: Hooks Disabled by Default
**What changed**: Hooks are now disabled by default on the Docker API.
**Why**: Security fix for Remote Code Execution (RCE) vulnerability.
**Who is affected**: Users of the Docker API who use the `hooks` parameter in `/crawl` requests.
**Migration**:
```bash
# To re-enable hooks (only if you trust all API users):
export CRAWL4AI_HOOKS_ENABLED=true
```
### 2. Docker API: file:// URLs Blocked
**What changed**: The endpoints `/execute_js`, `/screenshot`, `/pdf`, and `/html` now reject `file://` URLs.
**Why**: Security fix for Local File Inclusion (LFI) vulnerability.
**Who is affected**: Users who were reading local files via the Docker API.
**Migration**: Use the Python library directly for local file processing:
```python
# Instead of API call with file:// URL, use library:
from crawl4ai import AsyncWebCrawler
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(url="file:///path/to/file.html")
```
---
## Security Fixes
### Critical: Remote Code Execution via Hooks (CVE Pending)
**Severity**: CRITICAL (CVSS 10.0)
**Affected**: Docker API deployment (all versions before v0.8.0)
**Vector**: `POST /crawl` with malicious `hooks` parameter
**Details**: The `__import__` builtin was available in hook code, allowing attackers to import `os`, `subprocess`, etc. and execute arbitrary commands.
**Fix**:
1. Removed `__import__` from allowed builtins
2. Hooks disabled by default (`CRAWL4AI_HOOKS_ENABLED=false`)
### High: Local File Inclusion via file:// URLs (CVE Pending)
**Severity**: HIGH (CVSS 8.6)
**Affected**: Docker API deployment (all versions before v0.8.0)
**Vector**: `POST /execute_js` (and other endpoints) with `file:///etc/passwd`
**Details**: API endpoints accepted `file://` URLs, allowing attackers to read arbitrary files from the server.
**Fix**: URL scheme validation now only allows `http://`, `https://`, and `raw:` URLs.
### Credits
Discovered by **Neo by ProjectDiscovery** ([projectdiscovery.io](https://projectdiscovery.io)) - December 2025
---
## New Features
### 1. init_scripts Support for BrowserConfig
Pre-page-load JavaScript injection for stealth evasions.
```python
config = BrowserConfig(
init_scripts=[
"Object.defineProperty(navigator, 'webdriver', {get: () => false})"
]
)
```
### 2. CDP Connection Improvements
- WebSocket URL support (`ws://`, `wss://`)
- Proper cleanup with `cdp_cleanup_on_close=True`
- Browser reuse across multiple connections
### 3. Crash Recovery for Deep Crawl Strategies
All deep crawl strategies (BFS, DFS, Best-First) now support crash recovery:
```python
from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
strategy = BFSDeepCrawlStrategy(
max_depth=3,
resume_state=saved_state, # Resume from checkpoint
on_state_change=save_callback # Persist state in real-time
)
```
### 4. PDF and MHTML for raw:/file:// URLs
Generate PDFs and MHTML from cached HTML content.
### 5. Screenshots for raw:/file:// URLs
Render cached HTML and capture screenshots.
### 6. base_url Parameter for CrawlerRunConfig
Proper URL resolution for raw: HTML processing:
```python
config = CrawlerRunConfig(base_url='https://example.com')
result = await crawler.arun(url='raw:{html}', config=config)
```
### 7. Prefetch Mode for Two-Phase Deep Crawling
Fast link extraction without full page processing:
```python
config = CrawlerRunConfig(prefetch=True)
```
### 8. Proxy Rotation and Configuration
Enhanced proxy rotation with sticky sessions support.
### 9. Proxy Support for HTTP Strategy
Non-browser crawler now supports proxies.
### 10. Browser Pipeline for raw:/file:// URLs
New `process_in_browser` parameter for browser operations on local content:
```python
config = CrawlerRunConfig(
process_in_browser=True, # Force browser processing
screenshot=True
)
result = await crawler.arun(url='raw:<html>...</html>', config=config)
```
### 11. Smart TTL Cache for Sitemap URL Seeder
Intelligent cache invalidation for sitemaps:
```python
config = SeedingConfig(
cache_ttl_hours=24,
validate_sitemap_lastmod=True
)
```
---
## Bug Fixes
### raw: URL Parsing Truncates at # Character
**Problem**: CSS color codes like `#eee` were being truncated.
**Before**: `raw:body{background:#eee}``body{background:`
**After**: `raw:body{background:#eee}``body{background:#eee}`
### Caching System Improvements
Various fixes to cache validation and persistence.
---
## Documentation Updates
- Multi-sample schema generation documentation
- URL seeder smart TTL cache parameters
- Security documentation (SECURITY.md)
---
## Upgrade Guide
### From v0.7.x to v0.8.0
1. **Update the package**:
```bash
pip install --upgrade crawl4ai
```
2. **Docker API users**:
- Hooks are now disabled by default
- If you need hooks: `export CRAWL4AI_HOOKS_ENABLED=true`
- `file://` URLs no longer work on API (use library directly)
3. **Review security settings**:
```yaml
# config.yml - recommended for production
security:
enabled: true
jwt_enabled: true
```
4. **Test your integration** before deploying to production
### Breaking Change Checklist
- [ ] Check if you use `hooks` parameter in API calls
- [ ] Check if you use `file://` URLs via the API
- [ ] Update environment variables if needed
- [ ] Review security configuration
---
## Full Changelog
See [CHANGELOG.md](../CHANGELOG.md) for complete version history.
---
## Contributors
Thanks to all contributors who made this release possible.
Special thanks to **Neo by ProjectDiscovery** for responsible security disclosure.
---
*For questions or issues, please open a [GitHub Issue](https://github.com/unclecode/crawl4ai/issues).*

View File

@@ -18,7 +18,7 @@ A comprehensive web-based tutorial for learning and experimenting with C4A-Scrip
2. **Install Dependencies**
```bash
pip install flask
pip install -r requirements.txt
```
3. **Launch the Server**
@@ -28,7 +28,7 @@ A comprehensive web-based tutorial for learning and experimenting with C4A-Scrip
4. **Open in Browser**
```
http://localhost:8080
http://localhost:8000
```
**🌐 Try Online**: [Live Demo](https://docs.crawl4ai.com/c4a-script/demo)
@@ -325,7 +325,7 @@ Powers the recording functionality:
### Configuration
```python
# server.py configuration
PORT = 8080
PORT = 8000
DEBUG = True
THREADED = True
```
@@ -343,9 +343,9 @@ THREADED = True
**Port Already in Use**
```bash
# Kill existing process
lsof -ti:8080 | xargs kill -9
lsof -ti:8000 | xargs kill -9
# Or use different port
python server.py --port 8081
python server.py --port 8001
```
**Blockly Not Loading**

View File

@@ -216,7 +216,7 @@ def get_examples():
'name': 'Handle Cookie Banner',
'description': 'Accept cookies and close newsletter popup',
'script': '''# Handle cookie banner and newsletter
GO http://127.0.0.1:8080/playground/
GO http://127.0.0.1:8000/playground/
WAIT `body` 2
IF (EXISTS `.cookie-banner`) THEN CLICK `.accept`
IF (EXISTS `.newsletter-popup`) THEN CLICK `.close`'''

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@@ -0,0 +1,62 @@
import asyncio
import capsolver
from crawl4ai import *
# TODO: set your config
# Docs: https://docs.capsolver.com/guide/captcha/awsWaf/
api_key = "CAP-xxxxxxxxxxxxxxxxxxxxx" # your api key of capsolver
site_url = "https://nft.porsche.com/onboarding@6" # page url of your target site
cookie_domain = ".nft.porsche.com" # the domain name to which you want to apply the cookie
captcha_type = "AntiAwsWafTaskProxyLess" # type of your target captcha
capsolver.api_key = api_key
async def main():
browser_config = BrowserConfig(
verbose=True,
headless=False,
use_persistent_context=True,
)
async with AsyncWebCrawler(config=browser_config) as crawler:
await crawler.arun(
url=site_url,
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
# get aws waf cookie using capsolver sdk
solution = capsolver.solve({
"type": captcha_type,
"websiteURL": site_url,
})
cookie = solution["cookie"]
print("aws waf cookie:", cookie)
js_code = """
document.cookie = \'aws-waf-token=""" + cookie + """;domain=""" + cookie_domain + """;path=/\';
location.reload();
"""
wait_condition = """() => {
return document.title === \'Join Porsches journey into Web3\';
}"""
run_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test",
js_code=js_code,
js_only=True,
wait_for=f"js:{wait_condition}"
)
result_next = await crawler.arun(
url=site_url,
config=run_config,
)
print(result_next.markdown)
if __name__ == "__main__":
asyncio.run(main())

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@@ -0,0 +1,60 @@
import asyncio
import capsolver
from crawl4ai import *
# TODO: set your config
# Docs: https://docs.capsolver.com/guide/captcha/cloudflare_challenge/
api_key = "CAP-xxxxxxxxxxxxxxxxxxxxx" # your api key of capsolver
site_url = "https://gitlab.com/users/sign_in" # page url of your target site
captcha_type = "AntiCloudflareTask" # type of your target captcha
# your http proxy to solve cloudflare challenge
proxy_server = "proxy.example.com:8080"
proxy_username = "myuser"
proxy_password = "mypass"
capsolver.api_key = api_key
async def main():
# get challenge cookie using capsolver sdk
solution = capsolver.solve({
"type": captcha_type,
"websiteURL": site_url,
"proxy": f"{proxy_server}:{proxy_username}:{proxy_password}",
})
cookies = solution["cookies"]
user_agent = solution["userAgent"]
print("challenge cookies:", cookies)
cookies_list = []
for name, value in cookies.items():
cookies_list.append({
"name": name,
"value": value,
"url": site_url,
})
browser_config = BrowserConfig(
verbose=True,
headless=False,
use_persistent_context=True,
user_agent=user_agent,
cookies=cookies_list,
proxy_config={
"server": f"http://{proxy_server}",
"username": proxy_username,
"password": proxy_password,
},
)
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(
url=site_url,
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
print(result.markdown)
if __name__ == "__main__":
asyncio.run(main())

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@@ -0,0 +1,64 @@
import asyncio
import capsolver
from crawl4ai import *
# TODO: set your config
# Docs: https://docs.capsolver.com/guide/captcha/cloudflare_turnstile/
api_key = "CAP-xxxxxxxxxxxxxxxxxxxxx" # your api key of capsolver
site_key = "0x4AAAAAAAGlwMzq_9z6S9Mh" # site key of your target site
site_url = "https://clifford.io/demo/cloudflare-turnstile" # page url of your target site
captcha_type = "AntiTurnstileTaskProxyLess" # type of your target captcha
capsolver.api_key = api_key
async def main():
browser_config = BrowserConfig(
verbose=True,
headless=False,
use_persistent_context=True,
)
async with AsyncWebCrawler(config=browser_config) as crawler:
await crawler.arun(
url=site_url,
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
# get turnstile token using capsolver sdk
solution = capsolver.solve({
"type": captcha_type,
"websiteURL": site_url,
"websiteKey": site_key,
})
token = solution["token"]
print("turnstile token:", token)
js_code = """
document.querySelector(\'input[name="cf-turnstile-response"]\').value = \'"""+token+"""\';
document.querySelector(\'button[type="submit"]\').click();
"""
wait_condition = """() => {
const items = document.querySelectorAll(\'h1\');
return items.length === 0;
}"""
run_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test",
js_code=js_code,
js_only=True,
wait_for=f"js:{wait_condition}"
)
result_next = await crawler.arun(
url=site_url,
config=run_config,
)
print(result_next.markdown)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -0,0 +1,67 @@
import asyncio
import capsolver
from crawl4ai import *
# TODO: set your config
# Docs: https://docs.capsolver.com/guide/captcha/ReCaptchaV2/
api_key = "CAP-xxxxxxxxxxxxxxxxxxxxx" # your api key of capsolver
site_key = "6LfW6wATAAAAAHLqO2pb8bDBahxlMxNdo9g947u9" # site key of your target site
site_url = "https://recaptcha-demo.appspot.com/recaptcha-v2-checkbox.php" # page url of your target site
captcha_type = "ReCaptchaV2TaskProxyLess" # type of your target captcha
capsolver.api_key = api_key
async def main():
browser_config = BrowserConfig(
verbose=True,
headless=False,
use_persistent_context=True,
)
async with AsyncWebCrawler(config=browser_config) as crawler:
await crawler.arun(
url=site_url,
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
# get recaptcha token using capsolver sdk
solution = capsolver.solve({
"type": captcha_type,
"websiteURL": site_url,
"websiteKey": site_key,
})
token = solution["gRecaptchaResponse"]
print("recaptcha token:", token)
js_code = """
const textarea = document.getElementById(\'g-recaptcha-response\');
if (textarea) {
textarea.value = \"""" + token + """\";
document.querySelector(\'button.form-field[type="submit"]\').click();
}
"""
wait_condition = """() => {
const items = document.querySelectorAll(\'h2\');
return items.length > 1;
}"""
run_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test",
js_code=js_code,
js_only=True,
wait_for=f"js:{wait_condition}"
)
result_next = await crawler.arun(
url=site_url,
config=run_config,
)
print(result_next.markdown)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -0,0 +1,75 @@
import asyncio
import capsolver
from crawl4ai import *
# TODO: set your config
# Docs: https://docs.capsolver.com/guide/captcha/ReCaptchaV3/
api_key = "CAP-xxxxxxxxxxxxxxxxxxxxx" # your api key of capsolver
site_key = "6LdKlZEpAAAAAAOQjzC2v_d36tWxCl6dWsozdSy9" # site key of your target site
site_url = "https://recaptcha-demo.appspot.com/recaptcha-v3-request-scores.php" # page url of your target site
page_action = "examples/v3scores" # page action of your target site
captcha_type = "ReCaptchaV3TaskProxyLess" # type of your target captcha
capsolver.api_key = api_key
async def main():
browser_config = BrowserConfig(
verbose=True,
headless=False,
use_persistent_context=True,
)
# get recaptcha token using capsolver sdk
solution = capsolver.solve({
"type": captcha_type,
"websiteURL": site_url,
"websiteKey": site_key,
"pageAction": page_action,
})
token = solution["gRecaptchaResponse"]
print("recaptcha token:", token)
async with AsyncWebCrawler(config=browser_config) as crawler:
await crawler.arun(
url=site_url,
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
js_code = """
const originalFetch = window.fetch;
window.fetch = function(...args) {
if (typeof args[0] === 'string' && args[0].includes('/recaptcha-v3-verify.php')) {
const url = new URL(args[0], window.location.origin);
url.searchParams.set('action', '""" + token + """');
args[0] = url.toString();
document.querySelector('.token').innerHTML = "fetch('/recaptcha-v3-verify.php?action=examples/v3scores&token=""" + token + """')";
console.log('Fetch URL hooked:', args[0]);
}
return originalFetch.apply(this, args);
};
"""
wait_condition = """() => {
return document.querySelector('.step3:not(.hidden)');
}"""
run_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test",
js_code=js_code,
js_only=True,
wait_for=f"js:{wait_condition}"
)
result_next = await crawler.arun(
url=site_url,
config=run_config,
)
print(result_next.markdown)
if __name__ == "__main__":
asyncio.run(main())

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import time
import asyncio
from crawl4ai import *
# TODO: the user data directory that includes the capsolver extension
user_data_dir = "/browser-profile/Default1"
"""
The capsolver extension supports more features, such as:
- Telling the extension when to start solving captcha.
- Calling functions to check whether the captcha has been solved, etc.
Reference blog: https://docs.capsolver.com/guide/automation-tool-integration/
"""
browser_config = BrowserConfig(
verbose=True,
headless=False,
user_data_dir=user_data_dir,
use_persistent_context=True,
)
async def main():
async with AsyncWebCrawler(config=browser_config) as crawler:
result_initial = await crawler.arun(
url="https://nft.porsche.com/onboarding@6",
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
# do something later
time.sleep(300)
if __name__ == "__main__":
asyncio.run(main())

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import time
import asyncio
from crawl4ai import *
# TODO: the user data directory that includes the capsolver extension
user_data_dir = "/browser-profile/Default1"
"""
The capsolver extension supports more features, such as:
- Telling the extension when to start solving captcha.
- Calling functions to check whether the captcha has been solved, etc.
Reference blog: https://docs.capsolver.com/guide/automation-tool-integration/
"""
browser_config = BrowserConfig(
verbose=True,
headless=False,
user_data_dir=user_data_dir,
use_persistent_context=True,
)
async def main():
async with AsyncWebCrawler(config=browser_config) as crawler:
result_initial = await crawler.arun(
url="https://gitlab.com/users/sign_in",
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
# do something later
time.sleep(300)
if __name__ == "__main__":
asyncio.run(main())

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import time
import asyncio
from crawl4ai import *
# TODO: the user data directory that includes the capsolver extension
user_data_dir = "/browser-profile/Default1"
"""
The capsolver extension supports more features, such as:
- Telling the extension when to start solving captcha.
- Calling functions to check whether the captcha has been solved, etc.
Reference blog: https://docs.capsolver.com/guide/automation-tool-integration/
"""
browser_config = BrowserConfig(
verbose=True,
headless=False,
user_data_dir=user_data_dir,
use_persistent_context=True,
)
async def main():
async with AsyncWebCrawler(config=browser_config) as crawler:
result_initial = await crawler.arun(
url="https://clifford.io/demo/cloudflare-turnstile",
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
# do something later
time.sleep(300)
if __name__ == "__main__":
asyncio.run(main())

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import time
import asyncio
from crawl4ai import *
# TODO: the user data directory that includes the capsolver extension
user_data_dir = "/browser-profile/Default1"
"""
The capsolver extension supports more features, such as:
- Telling the extension when to start solving captcha.
- Calling functions to check whether the captcha has been solved, etc.
Reference blog: https://docs.capsolver.com/guide/automation-tool-integration/
"""
browser_config = BrowserConfig(
verbose=True,
headless=False,
user_data_dir=user_data_dir,
use_persistent_context=True,
)
async def main():
async with AsyncWebCrawler(config=browser_config) as crawler:
result_initial = await crawler.arun(
url="https://recaptcha-demo.appspot.com/recaptcha-v2-checkbox.php",
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
# do something later
time.sleep(300)
if __name__ == "__main__":
asyncio.run(main())

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import time
import asyncio
from crawl4ai import *
# TODO: the user data directory that includes the capsolver extension
user_data_dir = "/browser-profile/Default1"
"""
The capsolver extension supports more features, such as:
- Telling the extension when to start solving captcha.
- Calling functions to check whether the captcha has been solved, etc.
Reference blog: https://docs.capsolver.com/guide/automation-tool-integration/
"""
browser_config = BrowserConfig(
verbose=True,
headless=False,
user_data_dir=user_data_dir,
use_persistent_context=True,
)
async def main():
async with AsyncWebCrawler(config=browser_config) as crawler:
result_initial = await crawler.arun(
url="https://recaptcha-demo.appspot.com/recaptcha-v3-request-scores.php",
cache_mode=CacheMode.BYPASS,
session_id="session_captcha_test"
)
# do something later
time.sleep(300)
if __name__ == "__main__":
asyncio.run(main())

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import json
import asyncio
from urllib.parse import quote, urlencode
from crawl4ai import CrawlerRunConfig, BrowserConfig, AsyncWebCrawler
# Scrapeless provides a free anti-detection fingerprint browser client and cloud browsers:
# https://www.scrapeless.com/en/blog/scrapeless-nstbrowser-strategic-integration
async def main():
# customize browser fingerprint
fingerprint = {
"userAgent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/134.1.2.3 Safari/537.36",
"platform": "Windows",
"screen": {
"width": 1280, "height": 1024
},
"localization": {
"languages": ["zh-HK", "en-US", "en"], "timezone": "Asia/Hong_Kong",
}
}
fingerprint_json = json.dumps(fingerprint)
encoded_fingerprint = quote(fingerprint_json)
scrapeless_params = {
"token": "your token",
"sessionTTL": 1000,
"sessionName": "Demo",
"fingerprint": encoded_fingerprint,
# Sets the target country/region for the proxy, sending requests via an IP address from that region. You can specify a country code (e.g., US for the United States, GB for the United Kingdom, ANY for any country). See country codes for all supported options.
# "proxyCountry": "ANY",
# create profile on scrapeless
# "profileId": "your profileId",
# For more usage details, please refer to https://docs.scrapeless.com/en/scraping-browser/quickstart/getting-started
}
query_string = urlencode(scrapeless_params)
scrapeless_connection_url = f"wss://browser.scrapeless.com/api/v2/browser?{query_string}"
async with AsyncWebCrawler(
config=BrowserConfig(
headless=False,
browser_mode="cdp",
cdp_url=scrapeless_connection_url,
)
) as crawler:
result = await crawler.arun(
url="https://www.scrapeless.com/en",
config=CrawlerRunConfig(
wait_for="css:.content",
scan_full_page=True,
),
)
print("-" * 20)
print(f'Status Code: {result.status_code}')
print("-" * 20)
print(f'Title: {result.metadata["title"]}')
print(f'Description: {result.metadata["description"]}')
print("-" * 20)
if __name__ == "__main__":
asyncio.run(main())

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#!/usr/bin/env python3
"""
Deep Crawl Crash Recovery Example
This example demonstrates how to implement crash recovery for long-running
deep crawls. The feature is useful for:
- Cloud deployments with spot/preemptible instances
- Long-running crawls that may be interrupted
- Distributed crawling with state coordination
Key concepts:
- `on_state_change`: Callback fired after each URL is processed
- `resume_state`: Pass saved state to continue from a checkpoint
- `export_state()`: Get the last captured state manually
Works with all strategies: BFSDeepCrawlStrategy, DFSDeepCrawlStrategy,
BestFirstCrawlingStrategy
"""
import asyncio
import json
import os
from pathlib import Path
from typing import Dict, Any, List
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
# File to store crawl state (in production, use Redis/database)
STATE_FILE = Path("crawl_state.json")
async def save_state_to_file(state: Dict[str, Any]) -> None:
"""
Callback to save state after each URL is processed.
In production, you might save to:
- Redis: await redis.set("crawl_state", json.dumps(state))
- Database: await db.execute("UPDATE crawls SET state = ?", json.dumps(state))
- S3: await s3.put_object(Bucket="crawls", Key="state.json", Body=json.dumps(state))
"""
with open(STATE_FILE, "w") as f:
json.dump(state, f, indent=2)
print(f" [State saved] Pages: {state['pages_crawled']}, Pending: {len(state['pending'])}")
def load_state_from_file() -> Dict[str, Any] | None:
"""Load previously saved state, if it exists."""
if STATE_FILE.exists():
with open(STATE_FILE, "r") as f:
return json.load(f)
return None
async def example_basic_state_persistence():
"""
Example 1: Basic state persistence with file storage.
The on_state_change callback is called after each URL is processed,
allowing you to save progress in real-time.
"""
print("\n" + "=" * 60)
print("Example 1: Basic State Persistence")
print("=" * 60)
# Clean up any previous state
if STATE_FILE.exists():
STATE_FILE.unlink()
strategy = BFSDeepCrawlStrategy(
max_depth=2,
max_pages=5,
on_state_change=save_state_to_file, # Save after each URL
)
config = CrawlerRunConfig(
deep_crawl_strategy=strategy,
verbose=False,
)
print("\nStarting crawl with state persistence...")
async with AsyncWebCrawler(verbose=False) as crawler:
results = await crawler.arun("https://books.toscrape.com", config=config)
# Show final state
if STATE_FILE.exists():
with open(STATE_FILE, "r") as f:
final_state = json.load(f)
print(f"\nFinal state saved to {STATE_FILE}:")
print(f" - Strategy: {final_state['strategy_type']}")
print(f" - Pages crawled: {final_state['pages_crawled']}")
print(f" - URLs visited: {len(final_state['visited'])}")
print(f" - URLs pending: {len(final_state['pending'])}")
print(f"\nCrawled {len(results)} pages total")
async def example_crash_and_resume():
"""
Example 2: Simulate a crash and resume from checkpoint.
This demonstrates the full crash recovery workflow:
1. Start crawling with state persistence
2. "Crash" after N pages
3. Resume from saved state
4. Verify no duplicate work
"""
print("\n" + "=" * 60)
print("Example 2: Crash and Resume")
print("=" * 60)
# Clean up any previous state
if STATE_FILE.exists():
STATE_FILE.unlink()
crash_after = 3
crawled_urls_phase1: List[str] = []
async def save_and_maybe_crash(state: Dict[str, Any]) -> None:
"""Save state, then simulate crash after N pages."""
# Always save state first
await save_state_to_file(state)
crawled_urls_phase1.clear()
crawled_urls_phase1.extend(state["visited"])
# Simulate crash after reaching threshold
if state["pages_crawled"] >= crash_after:
raise Exception("Simulated crash! (This is intentional)")
# Phase 1: Start crawl that will "crash"
print(f"\n--- Phase 1: Crawl until 'crash' after {crash_after} pages ---")
strategy1 = BFSDeepCrawlStrategy(
max_depth=2,
max_pages=10,
on_state_change=save_and_maybe_crash,
)
config = CrawlerRunConfig(
deep_crawl_strategy=strategy1,
verbose=False,
)
try:
async with AsyncWebCrawler(verbose=False) as crawler:
await crawler.arun("https://books.toscrape.com", config=config)
except Exception as e:
print(f"\n Crash occurred: {e}")
print(f" URLs crawled before crash: {len(crawled_urls_phase1)}")
# Phase 2: Resume from checkpoint
print("\n--- Phase 2: Resume from checkpoint ---")
saved_state = load_state_from_file()
if not saved_state:
print(" ERROR: No saved state found!")
return
print(f" Loaded state: {saved_state['pages_crawled']} pages, {len(saved_state['pending'])} pending")
crawled_urls_phase2: List[str] = []
async def track_resumed_crawl(state: Dict[str, Any]) -> None:
"""Track new URLs crawled in phase 2."""
await save_state_to_file(state)
new_urls = set(state["visited"]) - set(saved_state["visited"])
for url in new_urls:
if url not in crawled_urls_phase2:
crawled_urls_phase2.append(url)
strategy2 = BFSDeepCrawlStrategy(
max_depth=2,
max_pages=10,
resume_state=saved_state, # Resume from checkpoint!
on_state_change=track_resumed_crawl,
)
config2 = CrawlerRunConfig(
deep_crawl_strategy=strategy2,
verbose=False,
)
async with AsyncWebCrawler(verbose=False) as crawler:
results = await crawler.arun("https://books.toscrape.com", config=config2)
# Verify no duplicates
already_crawled = set(saved_state["visited"])
duplicates = set(crawled_urls_phase2) & already_crawled
print(f"\n--- Results ---")
print(f" Phase 1 URLs: {len(crawled_urls_phase1)}")
print(f" Phase 2 new URLs: {len(crawled_urls_phase2)}")
print(f" Duplicate crawls: {len(duplicates)} (should be 0)")
print(f" Total results: {len(results)}")
if len(duplicates) == 0:
print("\n SUCCESS: No duplicate work after resume!")
else:
print(f"\n WARNING: Found duplicates: {duplicates}")
async def example_export_state():
"""
Example 3: Manual state export using export_state().
If you don't need real-time persistence, you can export
the state manually after the crawl completes.
"""
print("\n" + "=" * 60)
print("Example 3: Manual State Export")
print("=" * 60)
strategy = BFSDeepCrawlStrategy(
max_depth=1,
max_pages=3,
# No callback - state is still tracked internally
)
config = CrawlerRunConfig(
deep_crawl_strategy=strategy,
verbose=False,
)
print("\nCrawling without callback...")
async with AsyncWebCrawler(verbose=False) as crawler:
results = await crawler.arun("https://books.toscrape.com", config=config)
# Export state after crawl completes
# Note: This only works if on_state_change was set during crawl
# For this example, we'd need to set on_state_change to get state
print(f"\nCrawled {len(results)} pages")
print("(For manual export, set on_state_change to capture state)")
async def example_state_structure():
"""
Example 4: Understanding the state structure.
Shows the complete state dictionary that gets saved.
"""
print("\n" + "=" * 60)
print("Example 4: State Structure")
print("=" * 60)
captured_state = None
async def capture_state(state: Dict[str, Any]) -> None:
nonlocal captured_state
captured_state = state
strategy = BFSDeepCrawlStrategy(
max_depth=1,
max_pages=2,
on_state_change=capture_state,
)
config = CrawlerRunConfig(
deep_crawl_strategy=strategy,
verbose=False,
)
async with AsyncWebCrawler(verbose=False) as crawler:
await crawler.arun("https://books.toscrape.com", config=config)
if captured_state:
print("\nState structure:")
print(json.dumps(captured_state, indent=2, default=str)[:1000] + "...")
print("\n\nKey fields:")
print(f" strategy_type: '{captured_state['strategy_type']}'")
print(f" visited: List of {len(captured_state['visited'])} URLs")
print(f" pending: List of {len(captured_state['pending'])} queued items")
print(f" depths: Dict mapping URL -> depth level")
print(f" pages_crawled: {captured_state['pages_crawled']}")
async def main():
"""Run all examples."""
print("=" * 60)
print("Deep Crawl Crash Recovery Examples")
print("=" * 60)
await example_basic_state_persistence()
await example_crash_and_resume()
await example_state_structure()
# # Cleanup
# if STATE_FILE.exists():
# STATE_FILE.unlink()
# print(f"\n[Cleaned up {STATE_FILE}]")
if __name__ == "__main__":
asyncio.run(main())

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"""
Simple demonstration of the DFS deep crawler visiting multiple pages.
Run with: python docs/examples/dfs_crawl_demo.py
"""
import asyncio
from crawl4ai.async_configs import BrowserConfig, CrawlerRunConfig
from crawl4ai.async_webcrawler import AsyncWebCrawler
from crawl4ai.cache_context import CacheMode
from crawl4ai.deep_crawling.dfs_strategy import DFSDeepCrawlStrategy
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
async def main() -> None:
dfs_strategy = DFSDeepCrawlStrategy(
max_depth=3,
max_pages=50,
include_external=False,
)
config = CrawlerRunConfig(
deep_crawl_strategy=dfs_strategy,
cache_mode=CacheMode.BYPASS,
markdown_generator=DefaultMarkdownGenerator(),
stream=True,
)
seed_url = "https://docs.python.org/3/" # Plenty of internal links
async with AsyncWebCrawler(config=BrowserConfig(headless=True)) as crawler:
async for result in await crawler.arun(url=seed_url, config=config):
depth = result.metadata.get("depth")
status = "SUCCESS" if result.success else "FAILED"
print(f"[{status}] depth={depth} url={result.url}")
if __name__ == "__main__":
asyncio.run(main())

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#!/usr/bin/env python3
"""
Comprehensive hooks examples using Docker Client with function objects.
This approach is recommended because:
- Write hooks as regular Python functions
- Full IDE support (autocomplete, type checking)
- Automatic conversion to API format
- Reusable and testable code
- Clean, readable syntax
"""
import asyncio
from crawl4ai import Crawl4aiDockerClient
# API_BASE_URL = "http://localhost:11235"
API_BASE_URL = "http://localhost:11234"
# ============================================================================
# Hook Function Definitions
# ============================================================================
# --- All Hooks Demo ---
async def browser_created_hook(browser, **kwargs):
"""Called after browser is created"""
print("[HOOK] Browser created and ready")
return browser
async def page_context_hook(page, context, **kwargs):
"""Setup page environment"""
print("[HOOK] Setting up page environment")
# Set viewport
await page.set_viewport_size({"width": 1920, "height": 1080})
# Add cookies
await context.add_cookies([{
"name": "test_session",
"value": "abc123xyz",
"domain": ".httpbin.org",
"path": "/"
}])
# Block resources
await context.route("**/*.{png,jpg,jpeg,gif}", lambda route: route.abort())
await context.route("**/analytics/*", lambda route: route.abort())
print("[HOOK] Environment configured")
return page
async def user_agent_hook(page, context, user_agent, **kwargs):
"""Called when user agent is updated"""
print(f"[HOOK] User agent: {user_agent[:50]}...")
return page
async def before_goto_hook(page, context, url, **kwargs):
"""Called before navigating to URL"""
print(f"[HOOK] Navigating to: {url}")
await page.set_extra_http_headers({
"X-Custom-Header": "crawl4ai-test",
"Accept-Language": "en-US"
})
return page
async def after_goto_hook(page, context, url, response, **kwargs):
"""Called after page loads"""
print(f"[HOOK] Page loaded: {url}")
await page.wait_for_timeout(1000)
try:
await page.wait_for_selector("body", timeout=2000)
print("[HOOK] Body element ready")
except:
print("[HOOK] Timeout, continuing")
return page
async def execution_started_hook(page, context, **kwargs):
"""Called when custom JS execution starts"""
print("[HOOK] JS execution started")
await page.evaluate("console.log('[HOOK] Custom JS');")
return page
async def before_retrieve_hook(page, context, **kwargs):
"""Called before retrieving HTML"""
print("[HOOK] Preparing HTML retrieval")
# Scroll for lazy content
await page.evaluate("window.scrollTo(0, document.body.scrollHeight);")
await page.wait_for_timeout(500)
await page.evaluate("window.scrollTo(0, 0);")
print("[HOOK] Scrolling complete")
return page
async def before_return_hook(page, context, html, **kwargs):
"""Called before returning HTML"""
print(f"[HOOK] HTML ready: {len(html)} chars")
metrics = await page.evaluate('''() => ({
images: document.images.length,
links: document.links.length,
scripts: document.scripts.length
})''')
print(f"[HOOK] Metrics - Images: {metrics['images']}, Links: {metrics['links']}")
return page
# --- Authentication Hooks ---
async def auth_context_hook(page, context, **kwargs):
"""Setup authentication context"""
print("[HOOK] Setting up authentication")
# Add auth cookies
await context.add_cookies([{
"name": "auth_token",
"value": "fake_jwt_token",
"domain": ".httpbin.org",
"path": "/",
"httpOnly": True
}])
# Set localStorage
await page.evaluate('''
localStorage.setItem('user_id', '12345');
localStorage.setItem('auth_time', new Date().toISOString());
''')
print("[HOOK] Auth context ready")
return page
async def auth_headers_hook(page, context, url, **kwargs):
"""Add authentication headers"""
print(f"[HOOK] Adding auth headers for {url}")
import base64
credentials = base64.b64encode(b"user:passwd").decode('ascii')
await page.set_extra_http_headers({
'Authorization': f'Basic {credentials}',
'X-API-Key': 'test-key-123'
})
return page
# --- Performance Optimization Hooks ---
async def performance_hook(page, context, **kwargs):
"""Optimize page for performance"""
print("[HOOK] Optimizing for performance")
# Block resource-heavy content
await context.route("**/*.{png,jpg,jpeg,gif,webp,svg}", lambda r: r.abort())
await context.route("**/*.{woff,woff2,ttf}", lambda r: r.abort())
await context.route("**/*.{mp4,webm,ogg}", lambda r: r.abort())
await context.route("**/googletagmanager.com/*", lambda r: r.abort())
await context.route("**/google-analytics.com/*", lambda r: r.abort())
await context.route("**/facebook.com/*", lambda r: r.abort())
# Disable animations
await page.add_style_tag(content='''
*, *::before, *::after {
animation-duration: 0s !important;
transition-duration: 0s !important;
}
''')
print("[HOOK] Optimizations applied")
return page
async def cleanup_hook(page, context, **kwargs):
"""Clean page before extraction"""
print("[HOOK] Cleaning page")
await page.evaluate('''() => {
const selectors = [
'.ad', '.ads', '.advertisement',
'.popup', '.modal', '.overlay',
'.cookie-banner', '.newsletter'
];
selectors.forEach(sel => {
document.querySelectorAll(sel).forEach(el => el.remove());
});
document.querySelectorAll('script, style').forEach(el => el.remove());
}''')
print("[HOOK] Page cleaned")
return page
# --- Content Extraction Hooks ---
async def wait_dynamic_content_hook(page, context, url, response, **kwargs):
"""Wait for dynamic content to load"""
print(f"[HOOK] Waiting for dynamic content on {url}")
await page.wait_for_timeout(2000)
# Click "Load More" if exists
try:
load_more = await page.query_selector('[class*="load-more"], button:has-text("Load More")')
if load_more:
await load_more.click()
await page.wait_for_timeout(1000)
print("[HOOK] Clicked 'Load More'")
except:
pass
return page
async def extract_metadata_hook(page, context, **kwargs):
"""Extract page metadata"""
print("[HOOK] Extracting metadata")
metadata = await page.evaluate('''() => {
const getMeta = (name) => {
const el = document.querySelector(`meta[name="${name}"], meta[property="${name}"]`);
return el ? el.getAttribute('content') : null;
};
return {
title: document.title,
description: getMeta('description'),
author: getMeta('author'),
keywords: getMeta('keywords'),
};
}''')
print(f"[HOOK] Metadata: {metadata}")
# Infinite scroll
for i in range(3):
await page.evaluate("window.scrollTo(0, document.body.scrollHeight);")
await page.wait_for_timeout(1000)
print(f"[HOOK] Scroll {i+1}/3")
return page
# --- Multi-URL Hooks ---
async def url_specific_hook(page, context, url, **kwargs):
"""Apply URL-specific logic"""
print(f"[HOOK] Processing URL: {url}")
# URL-specific headers
if 'html' in url:
await page.set_extra_http_headers({"X-Type": "HTML"})
elif 'json' in url:
await page.set_extra_http_headers({"X-Type": "JSON"})
return page
async def track_progress_hook(page, context, url, response, **kwargs):
"""Track crawl progress"""
status = response.status if response else 'unknown'
print(f"[HOOK] Loaded {url} - Status: {status}")
return page
# ============================================================================
# Test Functions
# ============================================================================
async def test_all_hooks_comprehensive():
"""Test all 8 hook types"""
print("=" * 70)
print("Test 1: All Hooks Comprehensive Demo (Docker Client)")
print("=" * 70)
async with Crawl4aiDockerClient(base_url=API_BASE_URL, verbose=False) as client:
print("\nCrawling with all 8 hooks...")
# Define hooks with function objects
hooks = {
"on_browser_created": browser_created_hook,
"on_page_context_created": page_context_hook,
"on_user_agent_updated": user_agent_hook,
"before_goto": before_goto_hook,
"after_goto": after_goto_hook,
"on_execution_started": execution_started_hook,
"before_retrieve_html": before_retrieve_hook,
"before_return_html": before_return_hook
}
result = await client.crawl(
["https://httpbin.org/html"],
hooks=hooks,
hooks_timeout=30
)
print("\n✅ Success!")
print(f" URL: {result.url}")
print(f" Success: {result.success}")
print(f" HTML: {len(result.html)} chars")
async def test_authentication_workflow():
"""Test authentication with hooks"""
print("\n" + "=" * 70)
print("Test 2: Authentication Workflow (Docker Client)")
print("=" * 70)
async with Crawl4aiDockerClient(base_url=API_BASE_URL, verbose=False) as client:
print("\nTesting authentication...")
hooks = {
"on_page_context_created": auth_context_hook,
"before_goto": auth_headers_hook
}
result = await client.crawl(
["https://httpbin.org/basic-auth/user/passwd"],
hooks=hooks,
hooks_timeout=15
)
print("\n✅ Authentication completed")
if result.success:
if '"authenticated"' in result.html and 'true' in result.html:
print(" ✅ Basic auth successful!")
else:
print(" ⚠️ Auth status unclear")
else:
print(f" ❌ Failed: {result.error_message}")
async def test_performance_optimization():
"""Test performance optimization"""
print("\n" + "=" * 70)
print("Test 3: Performance Optimization (Docker Client)")
print("=" * 70)
async with Crawl4aiDockerClient(base_url=API_BASE_URL, verbose=False) as client:
print("\nTesting performance hooks...")
hooks = {
"on_page_context_created": performance_hook,
"before_retrieve_html": cleanup_hook
}
result = await client.crawl(
["https://httpbin.org/html"],
hooks=hooks,
hooks_timeout=10
)
print("\n✅ Optimization completed")
print(f" HTML size: {len(result.html):,} chars")
print(" Resources blocked, ads removed")
async def test_content_extraction():
"""Test content extraction"""
print("\n" + "=" * 70)
print("Test 4: Content Extraction (Docker Client)")
print("=" * 70)
async with Crawl4aiDockerClient(base_url=API_BASE_URL, verbose=False) as client:
print("\nTesting extraction hooks...")
hooks = {
"after_goto": wait_dynamic_content_hook,
"before_retrieve_html": extract_metadata_hook
}
result = await client.crawl(
["https://www.kidocode.com/"],
hooks=hooks,
hooks_timeout=20
)
print("\n✅ Extraction completed")
print(f" URL: {result.url}")
print(f" Success: {result.success}")
print(f" Metadata: {result.metadata}")
async def test_multi_url_crawl():
"""Test hooks with multiple URLs"""
print("\n" + "=" * 70)
print("Test 5: Multi-URL Crawl (Docker Client)")
print("=" * 70)
async with Crawl4aiDockerClient(base_url=API_BASE_URL, verbose=False) as client:
print("\nCrawling multiple URLs...")
hooks = {
"before_goto": url_specific_hook,
"after_goto": track_progress_hook
}
results = await client.crawl(
[
"https://httpbin.org/html",
"https://httpbin.org/json",
"https://httpbin.org/xml"
],
hooks=hooks,
hooks_timeout=15
)
print("\n✅ Multi-URL crawl completed")
print(f"\n Crawled {len(results)} URLs:")
for i, result in enumerate(results, 1):
status = "" if result.success else ""
print(f" {status} {i}. {result.url}")
async def test_reusable_hook_library():
"""Test using reusable hook library"""
print("\n" + "=" * 70)
print("Test 6: Reusable Hook Library (Docker Client)")
print("=" * 70)
# Create a library of reusable hooks
class HookLibrary:
@staticmethod
async def block_images(page, context, **kwargs):
"""Block all images"""
await context.route("**/*.{png,jpg,jpeg,gif}", lambda r: r.abort())
print("[LIBRARY] Images blocked")
return page
@staticmethod
async def block_analytics(page, context, **kwargs):
"""Block analytics"""
await context.route("**/analytics/*", lambda r: r.abort())
await context.route("**/google-analytics.com/*", lambda r: r.abort())
print("[LIBRARY] Analytics blocked")
return page
@staticmethod
async def scroll_infinite(page, context, **kwargs):
"""Handle infinite scroll"""
for i in range(5):
prev = await page.evaluate("document.body.scrollHeight")
await page.evaluate("window.scrollTo(0, document.body.scrollHeight);")
await page.wait_for_timeout(1000)
curr = await page.evaluate("document.body.scrollHeight")
if curr == prev:
break
print("[LIBRARY] Infinite scroll complete")
return page
async with Crawl4aiDockerClient(base_url=API_BASE_URL, verbose=False) as client:
print("\nUsing hook library...")
hooks = {
"on_page_context_created": HookLibrary.block_images,
"before_retrieve_html": HookLibrary.scroll_infinite
}
result = await client.crawl(
["https://www.kidocode.com/"],
hooks=hooks,
hooks_timeout=20
)
print("\n✅ Library hooks completed")
print(f" Success: {result.success}")
# ============================================================================
# Main
# ============================================================================
async def main():
"""Run all Docker client hook examples"""
print("🔧 Crawl4AI Docker Client - Hooks Examples (Function-Based)")
print("Using Python function objects with automatic conversion")
print("=" * 70)
tests = [
("All Hooks Demo", test_all_hooks_comprehensive),
("Authentication", test_authentication_workflow),
("Performance", test_performance_optimization),
("Extraction", test_content_extraction),
("Multi-URL", test_multi_url_crawl),
("Hook Library", test_reusable_hook_library)
]
for i, (name, test_func) in enumerate(tests, 1):
try:
await test_func()
print(f"\n✅ Test {i}/{len(tests)}: {name} completed\n")
except Exception as e:
print(f"\n❌ Test {i}/{len(tests)}: {name} failed: {e}\n")
import traceback
traceback.print_exc()
print("=" * 70)
print("🎉 All Docker client hook examples completed!")
print("\n💡 Key Benefits of Function-Based Hooks:")
print(" • Write as regular Python functions")
print(" • Full IDE support (autocomplete, types)")
print(" • Automatic conversion to API format")
print(" • Reusable across projects")
print(" • Clean, readable code")
print(" • Easy to test and debug")
print("=" * 70)
if __name__ == "__main__":
asyncio.run(main())

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"""
Docker Webhook Example for Crawl4AI
This example demonstrates how to use webhooks with the Crawl4AI job queue API.
Instead of polling for results, webhooks notify your application when jobs complete.
Supports both:
- /crawl/job - Raw crawling with markdown extraction
- /llm/job - LLM-powered content extraction
Prerequisites:
1. Crawl4AI Docker container running on localhost:11235
2. Flask installed: pip install flask requests
3. LLM API key configured in .llm.env (for LLM extraction examples)
Usage:
1. Run this script: python docker_webhook_example.py
2. The webhook server will start on http://localhost:8080
3. Jobs will be submitted and webhooks will be received automatically
"""
import requests
import json
import time
from flask import Flask, request, jsonify
from threading import Thread
# Configuration
CRAWL4AI_BASE_URL = "http://localhost:11235"
WEBHOOK_BASE_URL = "http://localhost:8080" # Your webhook receiver URL
# Initialize Flask app for webhook receiver
app = Flask(__name__)
# Store received webhook data for demonstration
received_webhooks = []
@app.route('/webhooks/crawl-complete', methods=['POST'])
def handle_crawl_webhook():
"""
Webhook handler that receives notifications when crawl jobs complete.
Payload structure:
{
"task_id": "crawl_abc123",
"task_type": "crawl",
"status": "completed" or "failed",
"timestamp": "2025-10-21T10:30:00.000000+00:00",
"urls": ["https://example.com"],
"error": "error message" (only if failed),
"data": {...} (only if webhook_data_in_payload=True)
}
"""
payload = request.json
print(f"\n{'='*60}")
print(f"📬 Webhook received for task: {payload['task_id']}")
print(f" Status: {payload['status']}")
print(f" Timestamp: {payload['timestamp']}")
print(f" URLs: {payload['urls']}")
if payload['status'] == 'completed':
# If data is in payload, process it directly
if 'data' in payload:
print(f" ✅ Data included in webhook")
data = payload['data']
# Process the crawl results here
for result in data.get('results', []):
print(f" - Crawled: {result.get('url')}")
print(f" - Markdown length: {len(result.get('markdown', ''))}")
else:
# Fetch results from API if not included
print(f" 📥 Fetching results from API...")
task_id = payload['task_id']
result_response = requests.get(f"{CRAWL4AI_BASE_URL}/crawl/job/{task_id}")
if result_response.ok:
data = result_response.json()
print(f" ✅ Results fetched successfully")
# Process the crawl results here
for result in data['result'].get('results', []):
print(f" - Crawled: {result.get('url')}")
print(f" - Markdown length: {len(result.get('markdown', ''))}")
elif payload['status'] == 'failed':
print(f" ❌ Job failed: {payload.get('error', 'Unknown error')}")
print(f"{'='*60}\n")
# Store webhook for demonstration
received_webhooks.append(payload)
# Return 200 OK to acknowledge receipt
return jsonify({"status": "received"}), 200
@app.route('/webhooks/llm-complete', methods=['POST'])
def handle_llm_webhook():
"""
Webhook handler that receives notifications when LLM extraction jobs complete.
Payload structure:
{
"task_id": "llm_1698765432_12345",
"task_type": "llm_extraction",
"status": "completed" or "failed",
"timestamp": "2025-10-21T10:30:00.000000+00:00",
"urls": ["https://example.com/article"],
"error": "error message" (only if failed),
"data": {"extracted_content": {...}} (only if webhook_data_in_payload=True)
}
"""
payload = request.json
print(f"\n{'='*60}")
print(f"🤖 LLM Webhook received for task: {payload['task_id']}")
print(f" Task Type: {payload['task_type']}")
print(f" Status: {payload['status']}")
print(f" Timestamp: {payload['timestamp']}")
print(f" URL: {payload['urls'][0]}")
if payload['status'] == 'completed':
# If data is in payload, process it directly
if 'data' in payload:
print(f" ✅ Data included in webhook")
data = payload['data']
# Webhook wraps extracted content in 'extracted_content' field
extracted = data.get('extracted_content', {})
print(f" - Extracted content:")
print(f" {json.dumps(extracted, indent=8)}")
else:
# Fetch results from API if not included
print(f" 📥 Fetching results from API...")
task_id = payload['task_id']
result_response = requests.get(f"{CRAWL4AI_BASE_URL}/llm/job/{task_id}")
if result_response.ok:
data = result_response.json()
print(f" ✅ Results fetched successfully")
# API returns unwrapped content in 'result' field
extracted = data['result']
print(f" - Extracted content:")
print(f" {json.dumps(extracted, indent=8)}")
elif payload['status'] == 'failed':
print(f" ❌ Job failed: {payload.get('error', 'Unknown error')}")
print(f"{'='*60}\n")
# Store webhook for demonstration
received_webhooks.append(payload)
# Return 200 OK to acknowledge receipt
return jsonify({"status": "received"}), 200
def start_webhook_server():
"""Start the Flask webhook server in a separate thread"""
app.run(host='0.0.0.0', port=8080, debug=False, use_reloader=False)
def submit_crawl_job_with_webhook(urls, webhook_url, include_data=False):
"""
Submit a crawl job with webhook notification.
Args:
urls: List of URLs to crawl
webhook_url: URL to receive webhook notifications
include_data: Whether to include full results in webhook payload
Returns:
task_id: The job's task identifier
"""
payload = {
"urls": urls,
"browser_config": {"headless": True},
"crawler_config": {"cache_mode": "bypass"},
"webhook_config": {
"webhook_url": webhook_url,
"webhook_data_in_payload": include_data,
# Optional: Add custom headers for authentication
# "webhook_headers": {
# "X-Webhook-Secret": "your-secret-token"
# }
}
}
print(f"\n🚀 Submitting crawl job...")
print(f" URLs: {urls}")
print(f" Webhook: {webhook_url}")
print(f" Include data: {include_data}")
response = requests.post(
f"{CRAWL4AI_BASE_URL}/crawl/job",
json=payload,
headers={"Content-Type": "application/json"}
)
if response.ok:
data = response.json()
task_id = data['task_id']
print(f" ✅ Job submitted successfully")
print(f" Task ID: {task_id}")
return task_id
else:
print(f" ❌ Failed to submit job: {response.text}")
return None
def submit_llm_job_with_webhook(url, query, webhook_url, include_data=False, schema=None, provider=None):
"""
Submit an LLM extraction job with webhook notification.
Args:
url: URL to extract content from
query: Instruction for the LLM (e.g., "Extract article title and author")
webhook_url: URL to receive webhook notifications
include_data: Whether to include full results in webhook payload
schema: Optional JSON schema for structured extraction
provider: Optional LLM provider (e.g., "openai/gpt-4o-mini")
Returns:
task_id: The job's task identifier
"""
payload = {
"url": url,
"q": query,
"cache": False,
"webhook_config": {
"webhook_url": webhook_url,
"webhook_data_in_payload": include_data,
# Optional: Add custom headers for authentication
# "webhook_headers": {
# "X-Webhook-Secret": "your-secret-token"
# }
}
}
if schema:
payload["schema"] = schema
if provider:
payload["provider"] = provider
print(f"\n🤖 Submitting LLM extraction job...")
print(f" URL: {url}")
print(f" Query: {query}")
print(f" Webhook: {webhook_url}")
print(f" Include data: {include_data}")
if provider:
print(f" Provider: {provider}")
response = requests.post(
f"{CRAWL4AI_BASE_URL}/llm/job",
json=payload,
headers={"Content-Type": "application/json"}
)
if response.ok:
data = response.json()
task_id = data['task_id']
print(f" ✅ Job submitted successfully")
print(f" Task ID: {task_id}")
return task_id
else:
print(f" ❌ Failed to submit job: {response.text}")
return None
def submit_job_without_webhook(urls):
"""
Submit a job without webhook (traditional polling approach).
Args:
urls: List of URLs to crawl
Returns:
task_id: The job's task identifier
"""
payload = {
"urls": urls,
"browser_config": {"headless": True},
"crawler_config": {"cache_mode": "bypass"}
}
print(f"\n🚀 Submitting crawl job (without webhook)...")
print(f" URLs: {urls}")
response = requests.post(
f"{CRAWL4AI_BASE_URL}/crawl/job",
json=payload
)
if response.ok:
data = response.json()
task_id = data['task_id']
print(f" ✅ Job submitted successfully")
print(f" Task ID: {task_id}")
return task_id
else:
print(f" ❌ Failed to submit job: {response.text}")
return None
def poll_job_status(task_id, timeout=60):
"""
Poll for job status (used when webhook is not configured).
Args:
task_id: The job's task identifier
timeout: Maximum time to wait in seconds
"""
print(f"\n⏳ Polling for job status...")
start_time = time.time()
while time.time() - start_time < timeout:
response = requests.get(f"{CRAWL4AI_BASE_URL}/crawl/job/{task_id}")
if response.ok:
data = response.json()
status = data.get('status', 'unknown')
if status == 'completed':
print(f" ✅ Job completed!")
return data
elif status == 'failed':
print(f" ❌ Job failed: {data.get('error', 'Unknown error')}")
return data
else:
print(f" ⏳ Status: {status}, waiting...")
time.sleep(2)
else:
print(f" ❌ Failed to get status: {response.text}")
return None
print(f" ⏰ Timeout reached")
return None
def main():
"""Run the webhook demonstration"""
# Check if Crawl4AI is running
try:
health = requests.get(f"{CRAWL4AI_BASE_URL}/health", timeout=5)
print(f"✅ Crawl4AI is running: {health.json()}")
except:
print(f"❌ Cannot connect to Crawl4AI at {CRAWL4AI_BASE_URL}")
print(" Please make sure Docker container is running:")
print(" docker run -d -p 11235:11235 --name crawl4ai unclecode/crawl4ai:latest")
return
# Start webhook server in background thread
print(f"\n🌐 Starting webhook server at {WEBHOOK_BASE_URL}...")
webhook_thread = Thread(target=start_webhook_server, daemon=True)
webhook_thread.start()
time.sleep(2) # Give server time to start
# Example 1: Job with webhook (notification only, fetch data separately)
print(f"\n{'='*60}")
print("Example 1: Webhook Notification Only")
print(f"{'='*60}")
task_id_1 = submit_crawl_job_with_webhook(
urls=["https://example.com"],
webhook_url=f"{WEBHOOK_BASE_URL}/webhooks/crawl-complete",
include_data=False
)
# Example 2: Job with webhook (data included in payload)
time.sleep(5) # Wait a bit between requests
print(f"\n{'='*60}")
print("Example 2: Webhook with Full Data")
print(f"{'='*60}")
task_id_2 = submit_crawl_job_with_webhook(
urls=["https://www.python.org"],
webhook_url=f"{WEBHOOK_BASE_URL}/webhooks/crawl-complete",
include_data=True
)
# Example 3: LLM extraction with webhook (notification only)
time.sleep(5) # Wait a bit between requests
print(f"\n{'='*60}")
print("Example 3: LLM Extraction with Webhook (Notification Only)")
print(f"{'='*60}")
task_id_3 = submit_llm_job_with_webhook(
url="https://www.example.com",
query="Extract the main heading and description from this page.",
webhook_url=f"{WEBHOOK_BASE_URL}/webhooks/llm-complete",
include_data=False,
provider="openai/gpt-4o-mini"
)
# Example 4: LLM extraction with webhook (data included + schema)
time.sleep(5) # Wait a bit between requests
print(f"\n{'='*60}")
print("Example 4: LLM Extraction with Schema and Full Data")
print(f"{'='*60}")
# Define a schema for structured extraction
schema = json.dumps({
"type": "object",
"properties": {
"title": {"type": "string", "description": "Page title"},
"description": {"type": "string", "description": "Page description"}
},
"required": ["title"]
})
task_id_4 = submit_llm_job_with_webhook(
url="https://www.python.org",
query="Extract the title and description of this website",
webhook_url=f"{WEBHOOK_BASE_URL}/webhooks/llm-complete",
include_data=True,
schema=schema,
provider="openai/gpt-4o-mini"
)
# Example 5: Traditional polling (no webhook)
time.sleep(5) # Wait a bit between requests
print(f"\n{'='*60}")
print("Example 5: Traditional Polling (No Webhook)")
print(f"{'='*60}")
task_id_5 = submit_job_without_webhook(
urls=["https://github.com"]
)
if task_id_5:
result = poll_job_status(task_id_5)
if result and result.get('status') == 'completed':
print(f" ✅ Results retrieved via polling")
# Wait for webhooks to arrive
print(f"\n⏳ Waiting for webhooks to be received...")
time.sleep(30) # Give jobs time to complete and webhooks to arrive (longer for LLM)
# Summary
print(f"\n{'='*60}")
print("Summary")
print(f"{'='*60}")
print(f"Total webhooks received: {len(received_webhooks)}")
crawl_webhooks = [w for w in received_webhooks if w['task_type'] == 'crawl']
llm_webhooks = [w for w in received_webhooks if w['task_type'] == 'llm_extraction']
print(f"\n📊 Breakdown:")
print(f" - Crawl webhooks: {len(crawl_webhooks)}")
print(f" - LLM extraction webhooks: {len(llm_webhooks)}")
print(f"\n📋 Details:")
for i, webhook in enumerate(received_webhooks, 1):
task_type = webhook['task_type']
icon = "🕷️" if task_type == "crawl" else "🤖"
print(f"{i}. {icon} Task {webhook['task_id']}: {webhook['status']} ({task_type})")
print(f"\n✅ Demo completed!")
print(f"\n💡 Pro tips:")
print(f" - In production, your webhook URL should be publicly accessible")
print(f" (e.g., https://myapp.com/webhooks) or use ngrok for testing")
print(f" - Both /crawl/job and /llm/job support the same webhook configuration")
print(f" - Use webhook_data_in_payload=true to get results directly in the webhook")
print(f" - LLM jobs may take longer, adjust timeouts accordingly")
if __name__ == "__main__":
main()

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"""
NSTProxy Integration Examples for crawl4ai
------------------------------------------
NSTProxy is a premium residential proxy provider.
👉 Purchase Proxies: https://nstproxy.com
💰 Use coupon code "crawl4ai" for 10% off your plan.
"""
import asyncio, requests
from crawl4ai import AsyncWebCrawler, BrowserConfig
async def main():
"""
Example: Dynamically fetch a proxy from NSTProxy API before crawling.
"""
NST_TOKEN = "YOUR_NST_PROXY_TOKEN" # Get from https://app.nstproxy.com/profile
CHANNEL_ID = "YOUR_NST_PROXY_CHANNEL_ID" # Your NSTProxy Channel ID
country = "ANY" # e.g. "ANY", "US", "DE"
# Fetch proxy from NSTProxy API
api_url = (
f"https://api.nstproxy.com/api/v1/generate/apiproxies"
f"?fType=2&channelId={CHANNEL_ID}&country={country}"
f"&protocol=http&sessionDuration=10&count=1&token={NST_TOKEN}"
)
response = requests.get(api_url, timeout=10).json()
proxy = response[0]
ip = proxy.get("ip")
port = proxy.get("port")
username = proxy.get("username", "")
password = proxy.get("password", "")
browser_config = BrowserConfig(proxy_config={
"server": f"http://{ip}:{port}",
"username": username,
"password": password,
})
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com")
print("[API Proxy] Status:", result.status_code)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -0,0 +1,31 @@
"""
NSTProxy Integration Examples for crawl4ai
------------------------------------------
NSTProxy is a premium residential proxy provider.
👉 Purchase Proxies: https://nstproxy.com
💰 Use coupon code "crawl4ai" for 10% off your plan.
"""
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig
async def main():
"""
Example: Use NSTProxy with manual username/password authentication.
"""
browser_config = BrowserConfig(proxy_config={
"server": "http://gate.nstproxy.io:24125",
"username": "your_username",
"password": "your_password",
})
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com")
print("[Auth Proxy] Status:", result.status_code)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -0,0 +1,29 @@
"""
NSTProxy Integration Examples for crawl4ai
------------------------------------------
NSTProxy is a premium residential proxy provider.
👉 Purchase Proxies: https://nstproxy.com
💰 Use coupon code "crawl4ai" for 10% off your plan.
"""
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig
async def main():
# Using HTTP proxy
browser_config = BrowserConfig(proxy_config={"server": "http://gate.nstproxy.io:24125"})
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com")
print("[HTTP Proxy] Status:", result.status_code)
# Using SOCKS proxy
browser_config = BrowserConfig(proxy_config={"server": "socks5://gate.nstproxy.io:24125"})
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com")
print("[SOCKS5 Proxy] Status:", result.status_code)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -0,0 +1,39 @@
"""
NSTProxy Integration Examples for crawl4ai
------------------------------------------
NSTProxy is a premium residential proxy provider.
👉 Purchase Proxies: https://nstproxy.com
💰 Use coupon code "crawl4ai" for 10% off your plan.
"""
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig
async def main():
"""
Example: Using NSTProxy with AsyncWebCrawler.
"""
NST_TOKEN = "YOUR_NST_PROXY_TOKEN" # Get from https://app.nstproxy.com/profile
CHANNEL_ID = "YOUR_NST_PROXY_CHANNEL_ID" # Your NSTProxy Channel ID
browser_config = BrowserConfig()
browser_config.set_nstproxy(
token=NST_TOKEN,
channel_id=CHANNEL_ID,
country="ANY", # e.g. "US", "JP", or "ANY"
state="", # optional, leave empty if not needed
city="", # optional, leave empty if not needed
session_duration=0 # Session duration in minutes,0 = rotate on every request
)
# === Run crawler ===
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com")
print("[Nstproxy] Status:", result.status_code)
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -0,0 +1,279 @@
#!/usr/bin/env python3
"""
Prefetch Mode and Two-Phase Crawling Example
Prefetch mode is a fast path that skips heavy processing and returns
only HTML + links. This is ideal for:
- Site mapping: Quickly discover all URLs
- Selective crawling: Find URLs first, then process only what you need
- Link validation: Check which pages exist without full processing
- Crawl planning: Estimate size before committing resources
Key concept:
- `prefetch=True` in CrawlerRunConfig enables fast link-only extraction
- Skips: markdown generation, content scraping, media extraction, LLM extraction
- Returns: HTML and links dictionary
Performance benefit: ~5-10x faster than full processing
"""
import asyncio
import time
from typing import List, Dict
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
async def example_basic_prefetch():
"""
Example 1: Basic prefetch mode.
Shows how prefetch returns HTML and links without heavy processing.
"""
print("\n" + "=" * 60)
print("Example 1: Basic Prefetch Mode")
print("=" * 60)
async with AsyncWebCrawler(verbose=False) as crawler:
# Enable prefetch mode
config = CrawlerRunConfig(prefetch=True)
print("\nFetching with prefetch=True...")
result = await crawler.arun("https://books.toscrape.com", config=config)
print(f"\nResult summary:")
print(f" Success: {result.success}")
print(f" HTML length: {len(result.html) if result.html else 0} chars")
print(f" Internal links: {len(result.links.get('internal', []))}")
print(f" External links: {len(result.links.get('external', []))}")
# These should be None/empty in prefetch mode
print(f"\n Skipped processing:")
print(f" Markdown: {result.markdown}")
print(f" Cleaned HTML: {result.cleaned_html}")
print(f" Extracted content: {result.extracted_content}")
# Show some discovered links
internal_links = result.links.get("internal", [])
if internal_links:
print(f"\n Sample internal links:")
for link in internal_links[:5]:
print(f" - {link['href'][:60]}...")
async def example_performance_comparison():
"""
Example 2: Compare prefetch vs full processing performance.
"""
print("\n" + "=" * 60)
print("Example 2: Performance Comparison")
print("=" * 60)
url = "https://books.toscrape.com"
async with AsyncWebCrawler(verbose=False) as crawler:
# Warm up - first request is slower due to browser startup
await crawler.arun(url, config=CrawlerRunConfig())
# Prefetch mode timing
start = time.time()
prefetch_result = await crawler.arun(url, config=CrawlerRunConfig(prefetch=True))
prefetch_time = time.time() - start
# Full processing timing
start = time.time()
full_result = await crawler.arun(url, config=CrawlerRunConfig())
full_time = time.time() - start
print(f"\nTiming comparison:")
print(f" Prefetch mode: {prefetch_time:.3f}s")
print(f" Full processing: {full_time:.3f}s")
print(f" Speedup: {full_time / prefetch_time:.1f}x faster")
print(f"\nOutput comparison:")
print(f" Prefetch - Links found: {len(prefetch_result.links.get('internal', []))}")
print(f" Full - Links found: {len(full_result.links.get('internal', []))}")
print(f" Full - Markdown length: {len(full_result.markdown.raw_markdown) if full_result.markdown else 0}")
async def example_two_phase_crawl():
"""
Example 3: Two-phase crawling pattern.
Phase 1: Fast discovery with prefetch
Phase 2: Full processing on selected URLs
"""
print("\n" + "=" * 60)
print("Example 3: Two-Phase Crawling")
print("=" * 60)
async with AsyncWebCrawler(verbose=False) as crawler:
# ═══════════════════════════════════════════════════════════
# Phase 1: Fast URL discovery
# ═══════════════════════════════════════════════════════════
print("\n--- Phase 1: Fast Discovery ---")
prefetch_config = CrawlerRunConfig(prefetch=True)
start = time.time()
discovery = await crawler.arun("https://books.toscrape.com", config=prefetch_config)
discovery_time = time.time() - start
all_urls = [link["href"] for link in discovery.links.get("internal", [])]
print(f" Discovered {len(all_urls)} URLs in {discovery_time:.2f}s")
# Filter to URLs we care about (e.g., book detail pages)
# On books.toscrape.com, book pages contain "catalogue/" but not "category/"
book_urls = [
url for url in all_urls
if "catalogue/" in url and "category/" not in url
][:5] # Limit to 5 for demo
print(f" Filtered to {len(book_urls)} book pages")
# ═══════════════════════════════════════════════════════════
# Phase 2: Full processing on selected URLs
# ═══════════════════════════════════════════════════════════
print("\n--- Phase 2: Full Processing ---")
full_config = CrawlerRunConfig(
word_count_threshold=10,
remove_overlay_elements=True,
)
results = []
start = time.time()
for url in book_urls:
result = await crawler.arun(url, config=full_config)
if result.success:
results.append(result)
title = result.url.split("/")[-2].replace("-", " ").title()[:40]
md_len = len(result.markdown.raw_markdown) if result.markdown else 0
print(f" Processed: {title}... ({md_len} chars)")
processing_time = time.time() - start
print(f"\n Processed {len(results)} pages in {processing_time:.2f}s")
# ═══════════════════════════════════════════════════════════
# Summary
# ═══════════════════════════════════════════════════════════
print(f"\n--- Summary ---")
print(f" Discovery phase: {discovery_time:.2f}s ({len(all_urls)} URLs)")
print(f" Processing phase: {processing_time:.2f}s ({len(results)} pages)")
print(f" Total time: {discovery_time + processing_time:.2f}s")
print(f" URLs skipped: {len(all_urls) - len(book_urls)} (not matching filter)")
async def example_prefetch_with_deep_crawl():
"""
Example 4: Combine prefetch with deep crawl strategy.
Use prefetch mode during deep crawl for maximum speed.
"""
print("\n" + "=" * 60)
print("Example 4: Prefetch with Deep Crawl")
print("=" * 60)
from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
async with AsyncWebCrawler(verbose=False) as crawler:
# Deep crawl with prefetch - maximum discovery speed
config = CrawlerRunConfig(
prefetch=True, # Fast mode
deep_crawl_strategy=BFSDeepCrawlStrategy(
max_depth=1,
max_pages=10,
)
)
print("\nDeep crawling with prefetch mode...")
start = time.time()
result_container = await crawler.arun("https://books.toscrape.com", config=config)
# Handle iterator result from deep crawl
if hasattr(result_container, '__iter__'):
results = list(result_container)
else:
results = [result_container]
elapsed = time.time() - start
# Collect all discovered links
all_internal_links = set()
all_external_links = set()
for result in results:
for link in result.links.get("internal", []):
all_internal_links.add(link["href"])
for link in result.links.get("external", []):
all_external_links.add(link["href"])
print(f"\nResults:")
print(f" Pages crawled: {len(results)}")
print(f" Total internal links discovered: {len(all_internal_links)}")
print(f" Total external links discovered: {len(all_external_links)}")
print(f" Time: {elapsed:.2f}s")
async def example_prefetch_with_raw_html():
"""
Example 5: Prefetch with raw HTML input.
You can also use prefetch mode with raw: URLs for cached content.
"""
print("\n" + "=" * 60)
print("Example 5: Prefetch with Raw HTML")
print("=" * 60)
sample_html = """
<html>
<head><title>Sample Page</title></head>
<body>
<h1>Hello World</h1>
<nav>
<a href="/page1">Internal Page 1</a>
<a href="/page2">Internal Page 2</a>
<a href="https://example.com/external">External Link</a>
</nav>
<main>
<p>This is the main content with <a href="/page3">another link</a>.</p>
</main>
</body>
</html>
"""
async with AsyncWebCrawler(verbose=False) as crawler:
config = CrawlerRunConfig(
prefetch=True,
base_url="https://mysite.com", # For resolving relative links
)
result = await crawler.arun(f"raw:{sample_html}", config=config)
print(f"\nExtracted from raw HTML:")
print(f" Internal links: {len(result.links.get('internal', []))}")
for link in result.links.get("internal", []):
print(f" - {link['href']} ({link['text']})")
print(f"\n External links: {len(result.links.get('external', []))}")
for link in result.links.get("external", []):
print(f" - {link['href']} ({link['text']})")
async def main():
"""Run all examples."""
print("=" * 60)
print("Prefetch Mode and Two-Phase Crawling Examples")
print("=" * 60)
await example_basic_prefetch()
await example_performance_comparison()
await example_two_phase_crawl()
await example_prefetch_with_deep_crawl()
await example_prefetch_with_raw_html()
if __name__ == "__main__":
asyncio.run(main())

View File

@@ -82,6 +82,42 @@ If you installed Crawl4AI (which installs Playwright under the hood), you alread
---
### Creating a Profile Using the Crawl4AI CLI (Easiest)
If you prefer a guided, interactive setup, use the built-in CLI to create and manage persistent browser profiles.
1.Launch the profile manager:
```bash
crwl profiles
```
2.Choose "Create new profile" and enter a profile name. A Chromium window opens so you can log in to sites and configure settings. When finished, return to the terminal and press `q` to save the profile.
3.Profiles are saved under `~/.crawl4ai/profiles/<profile_name>` (for example: `/home/<you>/.crawl4ai/profiles/test_profile_1`) along with a `storage_state.json` for cookies and session data.
4.Optionally, choose "List profiles" in the CLI to view available profiles and their paths.
5.Use the saved path with `BrowserConfig.user_data_dir`:
```python
from crawl4ai import AsyncWebCrawler, BrowserConfig
profile_path = "/home/<you>/.crawl4ai/profiles/test_profile_1"
browser_config = BrowserConfig(
headless=True,
use_managed_browser=True,
user_data_dir=profile_path,
browser_type="chromium",
)
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com/private")
```
The CLI also supports listing and deleting profiles, and even testing a crawl directly from the menu.
---
## 3. Using Managed Browsers in Crawl4AI
Once you have a data directory with your session data, pass it to **`BrowserConfig`**:

View File

@@ -1,98 +1,304 @@
# Proxy
# Proxy & Security
This guide covers proxy configuration and security features in Crawl4AI, including SSL certificate analysis and proxy rotation strategies.
## Understanding Proxy Configuration
Crawl4AI recommends configuring proxies per request through `CrawlerRunConfig.proxy_config`. This gives you precise control, enables rotation strategies, and keeps examples simple enough to copy, paste, and run.
## Basic Proxy Setup
Simple proxy configuration with `BrowserConfig`:
Configure proxies that apply to each crawl operation:
```python
from crawl4ai.async_configs import BrowserConfig
# Using HTTP proxy
browser_config = BrowserConfig(proxy_config={"server": "http://proxy.example.com:8080"})
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com")
# Using SOCKS proxy
browser_config = BrowserConfig(proxy_config={"server": "socks5://proxy.example.com:1080"})
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com")
```
## Authenticated Proxy
Use an authenticated proxy with `BrowserConfig`:
```python
from crawl4ai.async_configs import BrowserConfig
browser_config = BrowserConfig(proxy_config={
"server": "http://[host]:[port]",
"username": "[username]",
"password": "[password]",
})
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com")
```
## Rotating Proxies
Example using a proxy rotation service dynamically:
```python
import re
from crawl4ai import (
AsyncWebCrawler,
BrowserConfig,
CrawlerRunConfig,
CacheMode,
RoundRobinProxyStrategy,
)
import asyncio
from crawl4ai import ProxyConfig
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, ProxyConfig
run_config = CrawlerRunConfig(proxy_config=ProxyConfig(server="http://proxy.example.com:8080"))
# run_config = CrawlerRunConfig(proxy_config={"server": "http://proxy.example.com:8080"})
# run_config = CrawlerRunConfig(proxy_config="http://proxy.example.com:8080")
async def main():
# Load proxies and create rotation strategy
browser_config = BrowserConfig()
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com", config=run_config)
print(f"Success: {result.success} -> {result.url}")
if __name__ == "__main__":
asyncio.run(main())
```
!!! note "Why request-level?"
`CrawlerRunConfig.proxy_config` keeps each request self-contained, so swapping proxies or rotation strategies is just a matter of building a new run configuration.
## Supported Proxy Formats
The `ProxyConfig.from_string()` method supports multiple formats:
```python
from crawl4ai import ProxyConfig
# HTTP proxy with authentication
proxy1 = ProxyConfig.from_string("http://user:pass@192.168.1.1:8080")
# HTTPS proxy
proxy2 = ProxyConfig.from_string("https://proxy.example.com:8080")
# SOCKS5 proxy
proxy3 = ProxyConfig.from_string("socks5://proxy.example.com:1080")
# Simple IP:port format
proxy4 = ProxyConfig.from_string("192.168.1.1:8080")
# IP:port:user:pass format
proxy5 = ProxyConfig.from_string("192.168.1.1:8080:user:pass")
```
## Authenticated Proxies
For proxies requiring authentication:
```python
import asyncio
from crawl4ai import AsyncWebCrawler,BrowserConfig, CrawlerRunConfig, ProxyConfig
run_config = CrawlerRunConfig(
proxy_config=ProxyConfig(
server="http://proxy.example.com:8080",
username="your_username",
password="your_password",
)
)
# Or dictionary style:
# run_config = CrawlerRunConfig(proxy_config={
# "server": "http://proxy.example.com:8080",
# "username": "your_username",
# "password": "your_password",
# })
async def main():
browser_config = BrowserConfig()
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com", config=run_config)
print(f"Success: {result.success} -> {result.url}")
if __name__ == "__main__":
asyncio.run(main())
```
## Environment Variable Configuration
Load proxies from environment variables for easy configuration:
```python
import os
from crawl4ai import ProxyConfig, CrawlerRunConfig
# Set environment variable
os.environ["PROXIES"] = "ip1:port1:user1:pass1,ip2:port2:user2:pass2,ip3:port3"
# Load all proxies
proxies = ProxyConfig.from_env()
print(f"Loaded {len(proxies)} proxies")
# Use first proxy
if proxies:
run_config = CrawlerRunConfig(proxy_config=proxies[0])
```
## Rotating Proxies
Crawl4AI supports automatic proxy rotation to distribute requests across multiple proxy servers. Rotation is applied per request using a rotation strategy on `CrawlerRunConfig`.
### Proxy Rotation (recommended)
```python
import asyncio
import re
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, ProxyConfig
from crawl4ai.proxy_strategy import RoundRobinProxyStrategy
async def main():
# Load proxies from environment
proxies = ProxyConfig.from_env()
#eg: export PROXIES="ip1:port1:username1:password1,ip2:port2:username2:password2"
if not proxies:
print("No proxies found in environment. Set PROXIES env variable!")
print("No proxies found! Set PROXIES environment variable.")
return
# Create rotation strategy
proxy_strategy = RoundRobinProxyStrategy(proxies)
# Create configs
# Configure per-request with proxy rotation
browser_config = BrowserConfig(headless=True, verbose=False)
run_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
proxy_rotation_strategy=proxy_strategy
proxy_rotation_strategy=proxy_strategy,
)
async with AsyncWebCrawler(config=browser_config) as crawler:
urls = ["https://httpbin.org/ip"] * (len(proxies) * 2) # Test each proxy twice
print("\n📈 Initializing crawler with proxy rotation...")
async with AsyncWebCrawler(config=browser_config) as crawler:
print("\n🚀 Starting batch crawl with proxy rotation...")
results = await crawler.arun_many(
urls=urls,
config=run_config
)
for result in results:
if result.success:
ip_match = re.search(r'(?:[0-9]{1,3}\.){3}[0-9]{1,3}', result.html)
current_proxy = run_config.proxy_config if run_config.proxy_config else None
print(f"🚀 Testing {len(proxies)} proxies with rotation...")
results = await crawler.arun_many(urls=urls, config=run_config)
if current_proxy and ip_match:
print(f"URL {result.url}")
print(f"Proxy {current_proxy.server} -> Response IP: {ip_match.group(0)}")
verified = ip_match.group(0) == current_proxy.ip
if verified:
print(f"✅ Proxy working! IP matches: {current_proxy.ip}")
else:
print("❌ Proxy failed or IP mismatch!")
print("---")
for i, result in enumerate(results):
if result.success:
# Extract IP from response
ip_match = re.search(r'(?:[0-9]{1,3}\.){3}[0-9]{1,3}', result.html)
if ip_match:
detected_ip = ip_match.group(0)
proxy_index = i % len(proxies)
expected_ip = proxies[proxy_index].ip
asyncio.run(main())
print(f"✅ Request {i+1}: Proxy {proxy_index+1} -> IP {detected_ip}")
if detected_ip == expected_ip:
print(" 🎯 IP matches proxy configuration")
else:
print(f" ⚠️ IP mismatch (expected {expected_ip})")
else:
print(f"❌ Request {i+1}: Could not extract IP from response")
else:
print(f"❌ Request {i+1}: Failed - {result.error_message}")
if __name__ == "__main__":
asyncio.run(main())
```
## SSL Certificate Analysis
Combine proxy usage with SSL certificate inspection for enhanced security analysis. SSL certificate fetching is configured per request via `CrawlerRunConfig`.
### Per-Request SSL Certificate Analysis
```python
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
run_config = CrawlerRunConfig(
proxy_config={
"server": "http://proxy.example.com:8080",
"username": "user",
"password": "pass",
},
fetch_ssl_certificate=True, # Enable SSL certificate analysis for this request
)
async def main():
browser_config = BrowserConfig()
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(url="https://example.com", config=run_config)
if result.success:
print(f"✅ Crawled via proxy: {result.url}")
# Analyze SSL certificate
if result.ssl_certificate:
cert = result.ssl_certificate
print("🔒 SSL Certificate Info:")
print(f" Issuer: {cert.issuer}")
print(f" Subject: {cert.subject}")
print(f" Valid until: {cert.valid_until}")
print(f" Fingerprint: {cert.fingerprint}")
# Export certificate
cert.to_json("certificate.json")
print("💾 Certificate exported to certificate.json")
else:
print("⚠️ No SSL certificate information available")
if __name__ == "__main__":
asyncio.run(main())
```
## Security Best Practices
### 1. Proxy Rotation for Anonymity
```python
from crawl4ai import CrawlerRunConfig, ProxyConfig
from crawl4ai.proxy_strategy import RoundRobinProxyStrategy
# Use multiple proxies to avoid IP blocking
proxies = ProxyConfig.from_env("PROXIES")
strategy = RoundRobinProxyStrategy(proxies)
# Configure rotation per request (recommended)
run_config = CrawlerRunConfig(proxy_rotation_strategy=strategy)
# For a fixed proxy across all requests, just reuse the same run_config instance
static_run_config = run_config
```
### 2. SSL Certificate Verification
```python
from crawl4ai import CrawlerRunConfig
# Always verify SSL certificates when possible
# Per-request (affects specific requests)
run_config = CrawlerRunConfig(fetch_ssl_certificate=True)
```
### 3. Environment Variable Security
```bash
# Use environment variables for sensitive proxy credentials
# Avoid hardcoding usernames/passwords in code
export PROXIES="ip1:port1:user1:pass1,ip2:port2:user2:pass2"
```
### 4. SOCKS5 for Enhanced Security
```python
from crawl4ai import CrawlerRunConfig
# Prefer SOCKS5 proxies for better protocol support
run_config = CrawlerRunConfig(proxy_config="socks5://proxy.example.com:1080")
```
## Migration from Deprecated `proxy` Parameter
- "Deprecation Notice"
The legacy `proxy` argument on `BrowserConfig` is deprecated. Configure proxies through `CrawlerRunConfig.proxy_config` so each request fully describes its network settings.
```python
# Old (deprecated) approach
# from crawl4ai import BrowserConfig
# browser_config = BrowserConfig(proxy="http://proxy.example.com:8080")
# New (preferred) approach
from crawl4ai import CrawlerRunConfig
run_config = CrawlerRunConfig(proxy_config="http://proxy.example.com:8080")
```
### Safe Logging of Proxies
```python
from crawl4ai import ProxyConfig
def safe_proxy_repr(proxy: ProxyConfig):
if getattr(proxy, "username", None):
return f"{proxy.server} (auth: ****)"
return proxy.server
```
## Troubleshooting
### Common Issues
- "Proxy connection failed"
- Verify the proxy server is reachable from your network.
- Double-check authentication credentials.
- Ensure the protocol matches (`http`, `https`, or `socks5`).
- "SSL certificate errors"
- Some proxies break SSL inspection; switch proxies if you see repeated failures.
- Consider temporarily disabling certificate fetching to isolate the issue.
- "Environment variables not loading"
- Confirm `PROXIES` (or your custom env var) is set before running the script.
- Check formatting: `ip:port:user:pass,ip:port:user:pass`.
- "Proxy rotation not working"
- Ensure `ProxyConfig.from_env()` actually loaded entries (`len(proxies) > 0`).
- Attach `proxy_rotation_strategy` to `CrawlerRunConfig`.
- Validate the proxy definitions you pass into the strategy.

View File

@@ -21,21 +21,35 @@ browser_cfg = BrowserConfig(
|-----------------------|----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
| **`browser_type`** | `"chromium"`, `"firefox"`, `"webkit"`<br/>*(default: `"chromium"`)* | Which browser engine to use. `"chromium"` is typical for many sites, `"firefox"` or `"webkit"` for specialized tests. |
| **`headless`** | `bool` (default: `True`) | Headless means no visible UI. `False` is handy for debugging. |
| **`browser_mode`** | `str` (default: `"dedicated"`) | How browser is initialized: `"dedicated"` (new instance), `"builtin"` (CDP background), `"custom"` (explicit CDP), `"docker"` (container). |
| **`use_managed_browser`** | `bool` (default: `False`) | Launch browser via CDP for advanced control. Set automatically based on `browser_mode`. |
| **`cdp_url`** | `str` (default: `None`) | Chrome DevTools Protocol endpoint URL (e.g., `"ws://localhost:9222/devtools/browser/"`). Set automatically based on `browser_mode`. |
| **`debugging_port`** | `int` (default: `9222`) | Port for browser debugging protocol. |
| **`host`** | `str` (default: `"localhost"`) | Host for browser connection. |
| **`viewport_width`** | `int` (default: `1080`) | Initial page width (in px). Useful for testing responsive layouts. |
| **`viewport_height`** | `int` (default: `600`) | Initial page height (in px). |
| **`viewport`** | `dict` (default: `None`) | Viewport dimensions dict. If set, overrides `viewport_width` and `viewport_height`. |
| **`proxy`** | `str` (deprecated) | Deprecated. Use `proxy_config` instead. If set, it will be auto-converted internally. |
| **`proxy_config`** | `dict` (default: `None`) | For advanced or multi-proxy needs, specify details like `{"server": "...", "username": "...", ...}`. |
| **`proxy_config`** | `ProxyConfig or dict` (default: `None`)| For advanced or multi-proxy needs, specify `ProxyConfig` object or dict like `{"server": "...", "username": "...", "password": "..."}`. |
| **`use_persistent_context`** | `bool` (default: `False`) | If `True`, uses a **persistent** browser context (keep cookies, sessions across runs). Also sets `use_managed_browser=True`. |
| **`user_data_dir`** | `str or None` (default: `None`) | Directory to store user data (profiles, cookies). Must be set if you want permanent sessions. |
| **`chrome_channel`** | `str` (default: `"chromium"`) | Chrome channel to launch (e.g., "chrome", "msedge"). Only for `browser_type="chromium"`. Auto-set to empty for Firefox/WebKit. |
| **`channel`** | `str` (default: `"chromium"`) | Alias for `chrome_channel`. |
| **`accept_downloads`** | `bool` (default: `False`) | Whether to allow file downloads. Requires `downloads_path` if `True`. |
| **`downloads_path`** | `str or None` (default: `None`) | Directory to store downloaded files. |
| **`storage_state`** | `str or dict or None` (default: `None`)| In-memory storage state (cookies, localStorage) to restore browser state. |
| **`ignore_https_errors`** | `bool` (default: `True`) | If `True`, continues despite invalid certificates (common in dev/staging). |
| **`java_script_enabled`** | `bool` (default: `True`) | Disable if you want no JS overhead, or if only static content is needed. |
| **`sleep_on_close`** | `bool` (default: `False`) | Add a small delay when closing browser (can help with cleanup issues). |
| **`cookies`** | `list` (default: `[]`) | Pre-set cookies, each a dict like `{"name": "session", "value": "...", "url": "..."}`. |
| **`headers`** | `dict` (default: `{}`) | Extra HTTP headers for every request, e.g. `{"Accept-Language": "en-US"}`. |
| **`user_agent`** | `str` (default: Chrome-based UA) | Your custom or random user agent. `user_agent_mode="random"` can shuffle it. |
| **`light_mode`** | `bool` (default: `False`) | Disables some background features for performance gains. |
| **`user_agent`** | `str` (default: Chrome-based UA) | Your custom user agent string. |
| **`user_agent_mode`** | `str` (default: `""`) | Set to `"random"` to randomize user agent from a pool (helps with bot detection). |
| **`user_agent_generator_config`** | `dict` (default: `{}`) | Configuration dict for user agent generation when `user_agent_mode="random"`. |
| **`text_mode`** | `bool` (default: `False`) | If `True`, tries to disable images/other heavy content for speed. |
| **`use_managed_browser`** | `bool` (default: `False`) | For advanced “managed” interactions (debugging, CDP usage). Typically set automatically if persistent context is on. |
| **`light_mode`** | `bool` (default: `False`) | Disables some background features for performance gains. |
| **`extra_args`** | `list` (default: `[]`) | Additional flags for the underlying browser process, e.g. `["--disable-extensions"]`. |
| **`enable_stealth`** | `bool` (default: `False`) | Enable playwright-stealth mode to bypass bot detection. Cannot be used with `browser_mode="builtin"`. |
**Tips**:
- Set `headless=False` to visually **debug** how pages load or how interactions proceed.
@@ -70,6 +84,7 @@ We group them by category.
|------------------------------|--------------------------------------|-------------------------------------------------------------------------------------------------|
| **`word_count_threshold`** | `int` (default: ~200) | Skips text blocks below X words. Helps ignore trivial sections. |
| **`extraction_strategy`** | `ExtractionStrategy` (default: None) | If set, extracts structured data (CSS-based, LLM-based, etc.). |
| **`chunking_strategy`** | `ChunkingStrategy` (default: RegexChunking()) | Strategy to chunk content before extraction. Can be customized for different chunking approaches. |
| **`markdown_generator`** | `MarkdownGenerationStrategy` (None) | If you want specialized markdown output (citations, filtering, chunking, etc.). Can be customized with options such as `content_source` parameter to select the HTML input source ('cleaned_html', 'raw_html', or 'fit_html'). |
| **`css_selector`** | `str` (None) | Retains only the part of the page matching this selector. Affects the entire extraction process. |
| **`target_elements`** | `List[str]` (None) | List of CSS selectors for elements to focus on for markdown generation and data extraction, while still processing the entire page for links, media, etc. Provides more flexibility than `css_selector`. |
@@ -78,32 +93,50 @@ We group them by category.
| **`only_text`** | `bool` (False) | If `True`, tries to extract text-only content. |
| **`prettiify`** | `bool` (False) | If `True`, beautifies final HTML (slower, purely cosmetic). |
| **`keep_data_attributes`** | `bool` (False) | If `True`, preserve `data-*` attributes in cleaned HTML. |
| **`keep_attrs`** | `list` (default: []) | List of HTML attributes to keep during processing (e.g., `["id", "class", "data-value"]`). |
| **`remove_forms`** | `bool` (False) | If `True`, remove all `<form>` elements. |
| **`parser_type`** | `str` (default: "lxml") | HTML parser to use (e.g., "lxml", "html.parser"). |
| **`scraping_strategy`** | `ContentScrapingStrategy` (default: LXMLWebScrapingStrategy()) | Strategy to use for content scraping. Can be customized for different scraping needs (e.g., PDF extraction). |
---
### B) **Caching & Session**
### B) **Browser Location and Identity**
| **Parameter** | **Type / Default** | **What It Does** |
|------------------------|---------------------------|--------------------------------------------------------------------------------------------------------|
| **`locale`** | `str or None` (None) | Browser's locale (e.g., "en-US", "fr-FR") for language preferences. |
| **`timezone_id`** | `str or None` (None) | Browser's timezone (e.g., "America/New_York", "Europe/Paris"). |
| **`geolocation`** | `GeolocationConfig or None` (None) | GPS coordinates configuration. Use `GeolocationConfig(latitude=..., longitude=..., accuracy=...)`. |
| **`fetch_ssl_certificate`** | `bool` (False) | If `True`, fetches and includes SSL certificate information in the result. |
| **`proxy_config`** | `ProxyConfig or dict or None` (None) | Proxy configuration for this specific crawl. Can override browser-level proxy settings. |
| **`proxy_rotation_strategy`** | `ProxyRotationStrategy` (None) | Strategy for rotating proxies during crawl operations. |
---
### C) **Caching & Session**
| **Parameter** | **Type / Default** | **What It Does** |
|-------------------------|------------------------|------------------------------------------------------------------------------------------------------------------------------|
| **`cache_mode`** | `CacheMode or None` | Controls how caching is handled (`ENABLED`, `BYPASS`, `DISABLED`, etc.). If `None`, typically defaults to `ENABLED`. |
| **`session_id`** | `str or None` | Assign a unique ID to reuse a single browser session across multiple `arun()` calls. |
| **`bypass_cache`** | `bool` (False) | If `True`, acts like `CacheMode.BYPASS`. |
| **`disable_cache`** | `bool` (False) | If `True`, acts like `CacheMode.DISABLED`. |
| **`no_cache_read`** | `bool` (False) | If `True`, acts like `CacheMode.WRITE_ONLY` (writes cache but never reads). |
| **`no_cache_write`** | `bool` (False) | If `True`, acts like `CacheMode.READ_ONLY` (reads cache but never writes). |
| **`bypass_cache`** | `bool` (False) | **Deprecated.** If `True`, acts like `CacheMode.BYPASS`. Use `cache_mode` instead. |
| **`disable_cache`** | `bool` (False) | **Deprecated.** If `True`, acts like `CacheMode.DISABLED`. Use `cache_mode` instead. |
| **`no_cache_read`** | `bool` (False) | **Deprecated.** If `True`, acts like `CacheMode.WRITE_ONLY` (writes cache but never reads). Use `cache_mode` instead. |
| **`no_cache_write`** | `bool` (False) | **Deprecated.** If `True`, acts like `CacheMode.READ_ONLY` (reads cache but never writes). Use `cache_mode` instead. |
| **`shared_data`** | `dict or None` (None) | Shared data to be passed between hooks and accessible across crawl operations. |
Use these for controlling whether you read or write from a local content cache. Handy for large batch crawls or repeated site visits.
---
### C) **Page Navigation & Timing**
### D) **Page Navigation & Timing**
| **Parameter** | **Type / Default** | **What It Does** |
|----------------------------|-------------------------|----------------------------------------------------------------------------------------------------------------------|
| **`wait_until`** | `str` (domcontentloaded)| Condition for navigation to complete. Often `"networkidle"` or `"domcontentloaded"`. |
| **`wait_until`** | `str` (domcontentloaded)| Condition for navigation to "complete". Often `"networkidle"` or `"domcontentloaded"`. |
| **`page_timeout`** | `int` (60000 ms) | Timeout for page navigation or JS steps. Increase for slow sites. |
| **`wait_for`** | `str or None` | Wait for a CSS (`"css:selector"`) or JS (`"js:() => bool"`) condition before content extraction. |
| **`wait_for_timeout`** | `int or None` (None) | Specific timeout in ms for the `wait_for` condition. If None, uses `page_timeout`. |
| **`wait_for_images`** | `bool` (False) | Wait for images to load before finishing. Slows down if you only want text. |
| **`delay_before_return_html`** | `float` (0.1) | Additional pause (seconds) before final HTML is captured. Good for last-second updates. |
| **`check_robots_txt`** | `bool` (False) | Whether to check and respect robots.txt rules before crawling. If True, caches robots.txt for efficiency. |
@@ -112,15 +145,17 @@ Use these for controlling whether you read or write from a local content cache.
---
### D) **Page Interaction**
### E) **Page Interaction**
| **Parameter** | **Type / Default** | **What It Does** |
|----------------------------|--------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------|
| **`js_code`** | `str or list[str]` (None) | JavaScript to run after load. E.g. `"document.querySelector('button')?.click();"`. |
| **`js_only`** | `bool` (False) | If `True`, indicates were reusing an existing session and only applying JS. No full reload. |
| **`c4a_script`** | `str or list[str]` (None) | C4A script that compiles to JavaScript. Alternative to writing raw JS. |
| **`js_only`** | `bool` (False) | If `True`, indicates we're reusing an existing session and only applying JS. No full reload. |
| **`ignore_body_visibility`** | `bool` (True) | Skip checking if `<body>` is visible. Usually best to keep `True`. |
| **`scan_full_page`** | `bool` (False) | If `True`, auto-scroll the page to load dynamic content (infinite scroll). |
| **`scroll_delay`** | `float` (0.2) | Delay between scroll steps if `scan_full_page=True`. |
| **`max_scroll_steps`** | `int or None` (None) | Maximum number of scroll steps during full page scan. If None, scrolls until entire page is loaded. |
| **`process_iframes`** | `bool` (False) | Inlines iframe content for single-page extraction. |
| **`remove_overlay_elements`** | `bool` (False) | Removes potential modals/popups blocking the main content. |
| **`simulate_user`** | `bool` (False) | Simulate user interactions (mouse movements) to avoid bot detection. |
@@ -132,7 +167,7 @@ If your page is a single-page app with repeated JS updates, set `js_only=True` i
---
### E) **Media Handling**
### F) **Media Handling**
| **Parameter** | **Type / Default** | **What It Does** |
|--------------------------------------------|---------------------|-----------------------------------------------------------------------------------------------------------|
@@ -141,13 +176,16 @@ If your page is a single-page app with repeated JS updates, set `js_only=True` i
| **`screenshot_height_threshold`** | `int` (~20000) | If the page is taller than this, alternate screenshot strategies are used. |
| **`pdf`** | `bool` (False) | If `True`, returns a PDF in `result.pdf`. |
| **`capture_mhtml`** | `bool` (False) | If `True`, captures an MHTML snapshot of the page in `result.mhtml`. MHTML includes all page resources (CSS, images, etc.) in a single file. |
| **`image_description_min_word_threshold`** | `int` (~50) | Minimum words for an images alt text or description to be considered valid. |
| **`image_description_min_word_threshold`** | `int` (~50) | Minimum words for an image's alt text or description to be considered valid. |
| **`image_score_threshold`** | `int` (~3) | Filter out low-scoring images. The crawler scores images by relevance (size, context, etc.). |
| **`exclude_external_images`** | `bool` (False) | Exclude images from other domains. |
| **`exclude_all_images`** | `bool` (False) | If `True`, excludes all images from processing (both internal and external). |
| **`table_score_threshold`** | `int` (7) | Minimum score threshold for processing a table. Lower values include more tables. |
| **`table_extraction`** | `TableExtractionStrategy` (DefaultTableExtraction) | Strategy for table extraction. Defaults to DefaultTableExtraction with configured threshold. |
---
### F) **Link/Domain Handling**
### G) **Link/Domain Handling**
| **Parameter** | **Type / Default** | **What It Does** |
|------------------------------|-------------------------|-----------------------------------------------------------------------------------------------------------------------------|
@@ -155,23 +193,39 @@ If your page is a single-page app with repeated JS updates, set `js_only=True` i
| **`exclude_external_links`** | `bool` (False) | Removes all links pointing outside the current domain. |
| **`exclude_social_media_links`** | `bool` (False) | Strips links specifically to social sites (like Facebook or Twitter). |
| **`exclude_domains`** | `list` ([]) | Provide a custom list of domains to exclude (like `["ads.com", "trackers.io"]`). |
| **`exclude_internal_links`** | `bool` (False) | If `True`, excludes internal links from the results. |
| **`score_links`** | `bool` (False) | If `True`, calculates intrinsic quality scores for all links using URL structure, text quality, and contextual metrics. |
| **`preserve_https_for_internal_links`** | `bool` (False) | If `True`, preserves HTTPS scheme for internal links even when the server redirects to HTTP. Useful for security-conscious crawling. |
Use these for link-level content filtering (often to keep crawls “internal” or to remove spammy domains).
---
### G) **Debug & Logging**
### H) **Debug, Logging & Network Monitoring**
| **Parameter** | **Type / Default** | **What It Does** |
|----------------|--------------------|---------------------------------------------------------------------------|
| **`verbose`** | `bool` (True) | Prints logs detailing each step of crawling, interactions, or errors. |
| **`log_console`** | `bool` (False) | Logs the pages JavaScript console output if you want deeper JS debugging.|
| **`log_console`** | `bool` (False) | Logs the page's JavaScript console output if you want deeper JS debugging.|
| **`capture_network_requests`** | `bool` (False) | If `True`, captures network requests made by the page in `result.captured_requests`. |
| **`capture_console_messages`** | `bool` (False) | If `True`, captures console messages from the page in `result.console_messages`. |
---
### I) **Connection & HTTP Parameters**
### H) **Virtual Scroll Configuration**
| **Parameter** | **Type / Default** | **What It Does** |
|-----------------------------|-------------------------|----------------------------------------------------------------------------------------------------------------------|
| **`method`** | `str` ("GET") | HTTP method to use when using AsyncHTTPCrawlerStrategy (e.g., "GET", "POST"). |
| **`stream`** | `bool` (False) | If `True`, enables streaming mode for `arun_many()` to process URLs as they complete rather than waiting for all. |
| **`url`** | `str or None` (None) | URL for this specific config. Not typically set directly but used internally for URL-specific configurations. |
| **`user_agent`** | `str or None` (None) | Custom User-Agent string for this crawl. Can override browser-level user agent. |
| **`user_agent_mode`** | `str or None` (None) | Set to `"random"` to randomize user agent. Can override browser-level setting. |
| **`user_agent_generator_config`** | `dict` ({}) | Configuration for user agent generation when `user_agent_mode="random"`. |
---
### J) **Virtual Scroll Configuration**
| **Parameter** | **Type / Default** | **What It Does** |
|------------------------------|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|
@@ -211,7 +265,7 @@ See [Virtual Scroll documentation](../../advanced/virtual-scroll.md) for detaile
---
### I) **URL Matching Configuration**
### K) **URL Matching Configuration**
| **Parameter** | **Type / Default** | **What It Does** |
|------------------------|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|
@@ -274,7 +328,25 @@ default_config = CrawlerRunConfig() # No url_matcher = matches everything
- If no config matches a URL and there's no default config (one without `url_matcher`), the URL will fail with "No matching configuration found"
- Always include a default config as the last item if you want to handle all URLs
---## 2.2 Helper Methods
---
### L) **Advanced Crawling Features**
| **Parameter** | **Type / Default** | **What It Does** |
|-----------------------------|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|
| **`deep_crawl_strategy`** | `DeepCrawlStrategy or None` (None) | Strategy for deep/recursive crawling. Enables automatic link following and multi-level site crawling. |
| **`link_preview_config`** | `LinkPreviewConfig or dict or None` (None) | Configuration for link head extraction and scoring. Fetches and scores link metadata without full page loads. |
| **`experimental`** | `dict or None` (None) | Dictionary for experimental/beta features not yet integrated into main parameters. Use with caution. |
**Deep Crawl Strategy** enables automatic site exploration by following links according to defined rules. Useful for sitemap generation or comprehensive site archiving.
**Link Preview Config** allows efficient link discovery and scoring by fetching only the `<head>` section of linked pages, enabling smart crawl prioritization without the overhead of full page loads.
**Experimental** parameters are features in beta testing. They may change or be removed in future versions. Check documentation for currently available experimental features.
---
## 2.2 Helper Methods
Both `BrowserConfig` and `CrawlerRunConfig` provide a `clone()` method to create modified copies:
@@ -367,10 +439,19 @@ LLMConfig is useful to pass LLM provider config to strategies and functions that
| **`provider`** | `"ollama/llama3","groq/llama3-70b-8192","groq/llama3-8b-8192", "openai/gpt-4o-mini" ,"openai/gpt-4o","openai/o1-mini","openai/o1-preview","openai/o3-mini","openai/o3-mini-high","anthropic/claude-3-haiku-20240307","anthropic/claude-3-opus-20240229","anthropic/claude-3-sonnet-20240229","anthropic/claude-3-5-sonnet-20240620","gemini/gemini-pro","gemini/gemini-1.5-pro","gemini/gemini-2.0-flash","gemini/gemini-2.0-flash-exp","gemini/gemini-2.0-flash-lite-preview-02-05","deepseek/deepseek-chat"`<br/>*(default: `"openai/gpt-4o-mini"`)* | Which LLM provider to use.
| **`api_token`** |1.Optional. When not provided explicitly, api_token will be read from environment variables based on provider. For example: If a gemini model is passed as provider then,`"GEMINI_API_KEY"` will be read from environment variables <br/> 2. API token of LLM provider <br/> eg: `api_token = "gsk_1ClHGGJ7Lpn4WGybR7vNWGdyb3FY7zXEw3SCiy0BAVM9lL8CQv"` <br/> 3. Environment variable - use with prefix "env:" <br/> eg:`api_token = "env: GROQ_API_KEY"` | API token to use for the given provider
| **`base_url`** |Optional. Custom API endpoint | If your provider has a custom endpoint
| **`backoff_base_delay`** |Optional. `int` *(default: `2`)* | Seconds to wait before the first retry when the provider throttles a request.
| **`backoff_max_attempts`** |Optional. `int` *(default: `3`)* | Total tries (initial call + retries) before surfacing an error.
| **`backoff_exponential_factor`** |Optional. `int` *(default: `2`)* | Multiplier that increases the wait time for each retry (`delay = base_delay * factor^attempt`).
## 3.2 Example Usage
```python
llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
llm_config = LLMConfig(
provider="openai/gpt-4o-mini",
api_token=os.getenv("OPENAI_API_KEY"),
backoff_base_delay=1, # optional
backoff_max_attempts=5, # optional
backoff_exponential_factor=3, # optional
)
```
## 4. Putting It All Together

View File

@@ -18,7 +18,7 @@ A comprehensive web-based tutorial for learning and experimenting with C4A-Scrip
2. **Install Dependencies**
```bash
pip install flask
pip install -r requirements.txt
```
3. **Launch the Server**
@@ -28,7 +28,7 @@ A comprehensive web-based tutorial for learning and experimenting with C4A-Scrip
4. **Open in Browser**
```
http://localhost:8080
http://localhost:8000
```
**🌐 Try Online**: [Live Demo](https://docs.crawl4ai.com/c4a-script/demo)
@@ -325,7 +325,7 @@ Powers the recording functionality:
### Configuration
```python
# server.py configuration
PORT = 8080
PORT = 8000
DEBUG = True
THREADED = True
```
@@ -343,9 +343,9 @@ THREADED = True
**Port Already in Use**
```bash
# Kill existing process
lsof -ti:8080 | xargs kill -9
lsof -ti:8000 | xargs kill -9
# Or use different port
python server.py --port 8081
python server.py --port 8001
```
**Blockly Not Loading**

View File

@@ -216,7 +216,7 @@ def get_examples():
'name': 'Handle Cookie Banner',
'description': 'Accept cookies and close newsletter popup',
'script': '''# Handle cookie banner and newsletter
GO http://127.0.0.1:8080/playground/
GO http://127.0.0.1:8000/playground/
WAIT `body` 2
IF (EXISTS `.cookie-banner`) THEN CLICK `.accept`
IF (EXISTS `.newsletter-popup`) THEN CLICK `.close`'''
@@ -283,7 +283,7 @@ WAIT `.success-message` 5'''
return jsonify(examples)
if __name__ == '__main__':
port = int(os.environ.get('PORT', 8080))
port = int(os.environ.get('PORT', 8000))
print(f"""
╔══════════════════════════════════════════════════════════╗
║ C4A-Script Interactive Tutorial Server ║

Binary file not shown.

View File

@@ -20,22 +20,69 @@ Ever wondered why your AI coding assistant struggles with your library despite c
## Latest Release
### [Crawl4AI v0.7.4 The Intelligent Table Extraction & Performance Update](../blog/release-v0.7.4.md)
*August 17, 2025*
### [Crawl4AI v0.8.0 Crash Recovery & Prefetch Mode](../blog/release-v0.8.0.md)
*January 2026*
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.
Crawl4AI v0.8.0 introduces crash recovery for deep crawls, a new prefetch mode for fast URL discovery, and critical security fixes for Docker deployments.
Key highlights:
- **🚀 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
- **🔗 Advanced URL Processing**: Better handling of raw URLs and base tag link resolution
- **🔄 Deep Crawl Crash Recovery**: `on_state_change` callback for real-time state persistence, `resume_state` to continue from checkpoints
- **⚡ Prefetch Mode**: `prefetch=True` for 5-10x faster URL discovery, perfect for two-phase crawling patterns
- **🔒 Security Fixes**: Hooks disabled by default, `file://` URLs blocked on Docker API, `__import__` removed from sandbox
[Read full release notes →](../blog/release-v0.7.4.md)
[Read full release notes →](../blog/release-v0.8.0.md)
## Recent Releases
### [Crawl4AI v0.7.8 Stability & Bug Fix Release](../blog/release-v0.7.8.md)
*December 2025*
Crawl4AI v0.7.8 is a focused stability release addressing 11 bugs reported by the community. Fixes for Docker deployments, LLM extraction, URL handling, and dependency compatibility.
Key highlights:
- **🐳 Docker API Fixes**: ContentRelevanceFilter deserialization, ProxyConfig serialization, cache folder permissions
- **🤖 LLM Improvements**: Configurable rate limiter backoff, HTML input format support
- **📦 Dependencies**: Replaced deprecated PyPDF2 with pypdf, Pydantic v2 ConfigDict compatibility
[Read full release notes →](../blog/release-v0.7.8.md)
### [Crawl4AI v0.7.7 The Self-Hosting & Monitoring Update](../blog/release-v0.7.7.md)
*November 14, 2025*
Crawl4AI v0.7.7 transforms Docker into a complete self-hosting platform with enterprise-grade real-time monitoring, comprehensive observability, and full operational control.
Key highlights:
- **📊 Real-time Monitoring Dashboard**: Interactive web UI with live system metrics
- **🔌 Comprehensive Monitor API**: Complete REST API for programmatic access
- **⚡ WebSocket Streaming**: Real-time updates every 2 seconds
- **🔥 Smart Browser Pool**: 3-tier architecture with automatic promotion and cleanup
[Read full release notes →](../blog/release-v0.7.7.md)
### [Crawl4AI v0.7.6 The Webhook Infrastructure Update](../blog/release-v0.7.6.md)
*October 22, 2025*
Crawl4AI v0.7.6 introduces comprehensive webhook support for the Docker job queue API, bringing real-time notifications to both crawling and LLM extraction workflows.
Key highlights:
- **🪝 Complete Webhook Support**: Real-time notifications for both `/crawl/job` and `/llm/job` endpoints
- **🔄 Reliable Delivery**: Exponential backoff retry mechanism
- **🔐 Custom Authentication**: Add custom headers for webhook authentication
[Read full release notes →](../blog/release-v0.7.6.md)
---
## Older Releases
| Version | Date | Highlights |
|---------|------|------------|
| [v0.7.5](../blog/release-v0.7.5.md) | September 2025 | Docker Hooks System, enhanced LLM integration, HTTPS preservation |
| [v0.7.4](../blog/release-v0.7.4.md) | August 2025 | LLM-powered table extraction, performance improvements |
| [v0.7.3](../blog/release-v0.7.3.md) | July 2025 | Undetected browser, multi-URL config, memory monitoring |
| [v0.7.1](../blog/release-v0.7.1.md) | June 2025 | Bug fixes and stability improvements |
| [v0.7.0](../blog/release-v0.7.0.md) | May 2025 | Adaptive crawling, virtual scroll, link analysis |
## 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.

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# Crawl4AI v0.7.6 Release Notes
*Release Date: October 22, 2025*
I'm excited to announce Crawl4AI v0.7.6, featuring a complete webhook infrastructure for the Docker job queue API! This release eliminates polling and brings real-time notifications to both crawling and LLM extraction workflows.
## 🎯 What's New
### Webhook Support for Docker Job Queue API
The headline feature of v0.7.6 is comprehensive webhook support for asynchronous job processing. No more constant polling to check if your jobs are done - get instant notifications when they complete!
**Key Capabilities:**
-**Universal Webhook Support**: Both `/crawl/job` and `/llm/job` endpoints now support webhooks
-**Flexible Delivery Modes**: Choose notification-only or include full data in the webhook payload
-**Reliable Delivery**: Exponential backoff retry mechanism (5 attempts: 1s → 2s → 4s → 8s → 16s)
-**Custom Authentication**: Add custom headers for webhook authentication
-**Global Configuration**: Set default webhook URL in `config.yml` for all jobs
-**Task Type Identification**: Distinguish between `crawl` and `llm_extraction` tasks
### How It Works
Instead of constantly checking job status:
**OLD WAY (Polling):**
```python
# Submit job
response = requests.post("http://localhost:11235/crawl/job", json=payload)
task_id = response.json()['task_id']
# Poll until complete
while True:
status = requests.get(f"http://localhost:11235/crawl/job/{task_id}")
if status.json()['status'] == 'completed':
break
time.sleep(5) # Wait and try again
```
**NEW WAY (Webhooks):**
```python
# Submit job with webhook
payload = {
"urls": ["https://example.com"],
"webhook_config": {
"webhook_url": "https://myapp.com/webhook",
"webhook_data_in_payload": True
}
}
response = requests.post("http://localhost:11235/crawl/job", json=payload)
# Done! Webhook will notify you when complete
# Your webhook handler receives the results automatically
```
### Crawl Job Webhooks
```bash
curl -X POST http://localhost:11235/crawl/job \
-H "Content-Type: application/json" \
-d '{
"urls": ["https://example.com"],
"browser_config": {"headless": true},
"crawler_config": {"cache_mode": "bypass"},
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/crawl-complete",
"webhook_data_in_payload": false,
"webhook_headers": {
"X-Webhook-Secret": "your-secret-token"
}
}
}'
```
### LLM Extraction Job Webhooks (NEW!)
```bash
curl -X POST http://localhost:11235/llm/job \
-H "Content-Type: application/json" \
-d '{
"url": "https://example.com/article",
"q": "Extract the article title, author, and publication date",
"schema": "{\"type\":\"object\",\"properties\":{\"title\":{\"type\":\"string\"}}}",
"provider": "openai/gpt-4o-mini",
"webhook_config": {
"webhook_url": "https://myapp.com/webhooks/llm-complete",
"webhook_data_in_payload": true
}
}'
```
### Webhook Payload Structure
**Success (with data):**
```json
{
"task_id": "llm_1698765432",
"task_type": "llm_extraction",
"status": "completed",
"timestamp": "2025-10-22T10:30:00.000000+00:00",
"urls": ["https://example.com/article"],
"data": {
"extracted_content": {
"title": "Understanding Web Scraping",
"author": "John Doe",
"date": "2025-10-22"
}
}
}
```
**Failure:**
```json
{
"task_id": "crawl_abc123",
"task_type": "crawl",
"status": "failed",
"timestamp": "2025-10-22T10:30:00.000000+00:00",
"urls": ["https://example.com"],
"error": "Connection timeout after 30s"
}
```
### Simple Webhook Handler Example
```python
from flask import Flask, request, jsonify
app = Flask(__name__)
@app.route('/webhook', methods=['POST'])
def handle_webhook():
payload = request.json
task_id = payload['task_id']
task_type = payload['task_type']
status = payload['status']
if status == 'completed':
if 'data' in payload:
# Process data directly
data = payload['data']
else:
# Fetch from API
endpoint = 'crawl' if task_type == 'crawl' else 'llm'
response = requests.get(f'http://localhost:11235/{endpoint}/job/{task_id}')
data = response.json()
# Your business logic here
print(f"Job {task_id} completed!")
elif status == 'failed':
error = payload.get('error', 'Unknown error')
print(f"Job {task_id} failed: {error}")
return jsonify({"status": "received"}), 200
app.run(port=8080)
```
## 📊 Performance Improvements
- **Reduced Server Load**: Eliminates constant polling requests
- **Lower Latency**: Instant notification vs. polling interval delay
- **Better Resource Usage**: Frees up client connections while jobs run in background
- **Scalable Architecture**: Handles high-volume crawling workflows efficiently
## 🐛 Bug Fixes
- Fixed webhook configuration serialization for Pydantic HttpUrl fields
- Improved error handling in webhook delivery service
- Enhanced Redis task storage for webhook config persistence
## 🌍 Expected Real-World Impact
### For Web Scraping Workflows
- **Reduced Costs**: Less API calls = lower bandwidth and server costs
- **Better UX**: Instant notifications improve user experience
- **Scalability**: Handle 100s of concurrent jobs without polling overhead
### For LLM Extraction Pipelines
- **Async Processing**: Submit LLM extraction jobs and move on
- **Batch Processing**: Queue multiple extractions, get notified as they complete
- **Integration**: Easy integration with workflow automation tools (Zapier, n8n, etc.)
### For Microservices
- **Event-Driven**: Perfect for event-driven microservice architectures
- **Decoupling**: Decouple job submission from result processing
- **Reliability**: Automatic retries ensure webhooks are delivered
## 🔄 Breaking Changes
**None!** This release is fully backward compatible.
- Webhook configuration is optional
- Existing code continues to work without modification
- Polling is still supported for jobs without webhook config
## 📚 Documentation
### New Documentation
- **[WEBHOOK_EXAMPLES.md](../deploy/docker/WEBHOOK_EXAMPLES.md)** - Comprehensive webhook usage guide
- **[docker_webhook_example.py](../docs/examples/docker_webhook_example.py)** - Working code examples
### Updated Documentation
- **[Docker README](../deploy/docker/README.md)** - Added webhook sections
- API documentation with webhook examples
## 🛠️ Migration Guide
No migration needed! Webhooks are opt-in:
1. **To use webhooks**: Add `webhook_config` to your job payload
2. **To keep polling**: Continue using your existing code
### Quick Start
```python
# Just add webhook_config to your existing payload
payload = {
# Your existing configuration
"urls": ["https://example.com"],
"browser_config": {...},
"crawler_config": {...},
# NEW: Add webhook configuration
"webhook_config": {
"webhook_url": "https://myapp.com/webhook",
"webhook_data_in_payload": True
}
}
```
## 🔧 Configuration
### Global Webhook Configuration (config.yml)
```yaml
webhooks:
enabled: true
default_url: "https://myapp.com/webhooks/default" # Optional
data_in_payload: false
retry:
max_attempts: 5
initial_delay_ms: 1000
max_delay_ms: 32000
timeout_ms: 30000
headers:
User-Agent: "Crawl4AI-Webhook/1.0"
```
## 🚀 Upgrade Instructions
### Docker
```bash
# Pull the latest image
docker pull unclecode/crawl4ai:0.7.6
# Or use latest tag
docker pull unclecode/crawl4ai:latest
# Run with webhook support
docker run -d \
-p 11235:11235 \
--env-file .llm.env \
--name crawl4ai \
unclecode/crawl4ai:0.7.6
```
### Python Package
```bash
pip install --upgrade crawl4ai
```
## 💡 Pro Tips
1. **Use notification-only mode** for large results - fetch data separately to avoid large webhook payloads
2. **Set custom headers** for webhook authentication and request tracking
3. **Configure global default webhook** for consistent handling across all jobs
4. **Implement idempotent webhook handlers** - same webhook may be delivered multiple times on retry
5. **Use structured schemas** with LLM extraction for predictable webhook data
## 🎬 Demo
Try the release demo:
```bash
python docs/releases_review/demo_v0.7.6.py
```
This comprehensive demo showcases:
- Crawl job webhooks (notification-only and with data)
- LLM extraction webhooks (with JSON schema support)
- Custom headers for authentication
- Webhook retry mechanism
- Real-time webhook receiver
## 🙏 Acknowledgments
Thank you to the community for the feedback that shaped this feature! Special thanks to everyone who requested webhook support for asynchronous job processing.
## 📞 Support
- **Documentation**: https://docs.crawl4ai.com
- **GitHub Issues**: https://github.com/unclecode/crawl4ai/issues
- **Discord**: https://discord.gg/crawl4ai
---
**Happy crawling with webhooks!** 🕷️🪝
*- unclecode*

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# 🚀 Crawl4AI v0.7.5: The Docker Hooks & Security Update
*September 29, 2025 • 8 min read*
---
Today I'm releasing Crawl4AI v0.7.5—focused on extensibility and security. This update introduces the Docker Hooks System for pipeline customization, enhanced LLM integration, and important security improvements.
## 🎯 What's New at a Glance
- **Docker Hooks System**: Custom Python functions at key pipeline points with function-based API
- **Function-Based Hooks**: New `hooks_to_string()` utility with Docker client auto-conversion
- **Enhanced LLM Integration**: Custom providers with temperature control
- **HTTPS Preservation**: Secure internal link handling
- **Bug Fixes**: Resolved multiple community-reported issues
- **Improved Docker Error Handling**: Better debugging and reliability
## 🔧 Docker Hooks System: Pipeline Customization
Every scraping project needs custom logic—authentication, performance optimization, content processing. Traditional solutions require forking or complex workarounds. Docker Hooks let you inject custom Python functions at 8 key points in the crawling pipeline.
### Real Example: Authentication & Performance
```python
import requests
# Real working hooks for httpbin.org
hooks_config = {
"on_page_context_created": """
async def hook(page, context, **kwargs):
print("Hook: Setting up page context")
# Block images to speed up crawling
await context.route("**/*.{png,jpg,jpeg,gif,webp}", lambda route: route.abort())
print("Hook: Images blocked")
return page
""",
"before_retrieve_html": """
async def hook(page, context, **kwargs):
print("Hook: Before retrieving HTML")
# Scroll to bottom to load lazy content
await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
await page.wait_for_timeout(1000)
print("Hook: Scrolled to bottom")
return page
""",
"before_goto": """
async def hook(page, context, url, **kwargs):
print(f"Hook: About to navigate to {url}")
# Add custom headers
await page.set_extra_http_headers({
'X-Test-Header': 'crawl4ai-hooks-test'
})
return page
"""
}
# Test with Docker API
payload = {
"urls": ["https://httpbin.org/html"],
"hooks": {
"code": hooks_config,
"timeout": 30
}
}
response = requests.post("http://localhost:11235/crawl", json=payload)
result = response.json()
if result.get('success'):
print("✅ Hooks executed successfully!")
print(f"Content length: {len(result.get('markdown', ''))} characters")
```
**Available Hook Points:**
- `on_browser_created`: Browser setup
- `on_page_context_created`: Page context configuration
- `before_goto`: Pre-navigation setup
- `after_goto`: Post-navigation processing
- `on_user_agent_updated`: User agent changes
- `on_execution_started`: Crawl initialization
- `before_retrieve_html`: Pre-extraction processing
- `before_return_html`: Final HTML processing
### Function-Based Hooks API
Writing hooks as strings works, but lacks IDE support and type checking. v0.7.5 introduces a function-based approach with automatic conversion!
**Option 1: Using the `hooks_to_string()` Utility**
```python
from crawl4ai import hooks_to_string
import requests
# Define hooks as regular Python functions (with full IDE support!)
async def on_page_context_created(page, context, **kwargs):
"""Block images to speed up crawling"""
await context.route("**/*.{png,jpg,jpeg,gif,webp}", lambda route: route.abort())
await page.set_viewport_size({"width": 1920, "height": 1080})
return page
async def before_goto(page, context, url, **kwargs):
"""Add custom headers"""
await page.set_extra_http_headers({
'X-Crawl4AI': 'v0.7.5',
'X-Custom-Header': 'my-value'
})
return page
# Convert functions to strings
hooks_code = hooks_to_string({
"on_page_context_created": on_page_context_created,
"before_goto": before_goto
})
# Use with REST API
payload = {
"urls": ["https://httpbin.org/html"],
"hooks": {"code": hooks_code, "timeout": 30}
}
response = requests.post("http://localhost:11235/crawl", json=payload)
```
**Option 2: Docker Client with Automatic Conversion (Recommended!)**
```python
from crawl4ai.docker_client import Crawl4aiDockerClient
# Define hooks as functions (same as above)
async def on_page_context_created(page, context, **kwargs):
await context.route("**/*.{png,jpg,jpeg,gif,webp}", lambda route: route.abort())
return page
async def before_retrieve_html(page, context, **kwargs):
# Scroll to load lazy content
await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
await page.wait_for_timeout(1000)
return page
# Use Docker client - conversion happens automatically!
client = Crawl4aiDockerClient(base_url="http://localhost:11235")
results = await client.crawl(
urls=["https://httpbin.org/html"],
hooks={
"on_page_context_created": on_page_context_created,
"before_retrieve_html": before_retrieve_html
},
hooks_timeout=30
)
if results and results.success:
print(f"✅ Hooks executed! HTML length: {len(results.html)}")
```
**Benefits of Function-Based Hooks:**
- ✅ Full IDE support (autocomplete, syntax highlighting)
- ✅ Type checking and linting
- ✅ Easier to test and debug
- ✅ Reusable across projects
- ✅ Automatic conversion in Docker client
- ✅ No breaking changes - string hooks still work!
## 🤖 Enhanced LLM Integration
Enhanced LLM integration with custom providers, temperature control, and base URL configuration.
### Multi-Provider Support
```python
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
from crawl4ai.extraction_strategy import LLMExtractionStrategy
# Test with different providers
async def test_llm_providers():
# OpenAI with custom temperature
openai_strategy = LLMExtractionStrategy(
provider="gemini/gemini-2.5-flash-lite",
api_token="your-api-token",
temperature=0.7, # New in v0.7.5
instruction="Summarize this page in one sentence"
)
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
"https://example.com",
config=CrawlerRunConfig(extraction_strategy=openai_strategy)
)
if result.success:
print("✅ LLM extraction completed")
print(result.extracted_content)
# Docker API with enhanced LLM config
llm_payload = {
"url": "https://example.com",
"f": "llm",
"q": "Summarize this page in one sentence.",
"provider": "gemini/gemini-2.5-flash-lite",
"temperature": 0.7
}
response = requests.post("http://localhost:11235/md", json=llm_payload)
```
**New Features:**
- Custom `temperature` parameter for creativity control
- `base_url` for custom API endpoints
- Multi-provider environment variable support
- Docker API integration
## 🔒 HTTPS Preservation
**The Problem:** Modern web apps require HTTPS everywhere. When crawlers downgrade internal links from HTTPS to HTTP, authentication breaks and security warnings appear.
**Solution:** HTTPS preservation maintains secure protocols throughout crawling.
```python
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, FilterChain, URLPatternFilter, BFSDeepCrawlStrategy
async def test_https_preservation():
# Enable HTTPS preservation
url_filter = URLPatternFilter(
patterns=["^(https:\/\/)?quotes\.toscrape\.com(\/.*)?$"]
)
config = CrawlerRunConfig(
exclude_external_links=True,
preserve_https_for_internal_links=True, # New in v0.7.5
deep_crawl_strategy=BFSDeepCrawlStrategy(
max_depth=2,
max_pages=5,
filter_chain=FilterChain([url_filter])
)
)
async with AsyncWebCrawler() as crawler:
async for result in await crawler.arun(
url="https://quotes.toscrape.com",
config=config
):
# All internal links maintain HTTPS
internal_links = [link['href'] for link in result.links['internal']]
https_links = [link for link in internal_links if link.startswith('https://')]
print(f"HTTPS links preserved: {len(https_links)}/{len(internal_links)}")
for link in https_links[:3]:
print(f"{link}")
```
## 🛠️ Bug Fixes and Improvements
### Major Fixes
- **URL Processing**: Fixed '+' sign preservation in query parameters (#1332)
- **Proxy Configuration**: Enhanced proxy string parsing (old `proxy` parameter deprecated)
- **Docker Error Handling**: Comprehensive error messages with status codes
- **Memory Management**: Fixed leaks in long-running sessions
- **JWT Authentication**: Fixed Docker JWT validation issues (#1442)
- **Playwright Stealth**: Fixed stealth features for Playwright integration (#1481)
- **API Configuration**: Fixed config handling to prevent overriding user-provided settings (#1505)
- **Docker Filter Serialization**: Resolved JSON encoding errors in deep crawl strategy (#1419)
- **LLM Provider Support**: Fixed custom LLM provider integration for adaptive crawler (#1291)
- **Performance Issues**: Resolved backoff strategy failures and timeout handling (#989)
### Community-Reported Issues Fixed
This release addresses multiple issues reported by the community through GitHub issues and Discord discussions:
- Fixed browser configuration reference errors
- Resolved dependency conflicts with cssselect
- Improved error messaging for failed authentications
- Enhanced compatibility with various proxy configurations
- Fixed edge cases in URL normalization
### Configuration Updates
```python
# Old proxy config (deprecated)
# browser_config = BrowserConfig(proxy="http://proxy:8080")
# New enhanced proxy config
browser_config = BrowserConfig(
proxy_config={
"server": "http://proxy:8080",
"username": "optional-user",
"password": "optional-pass"
}
)
```
## 🔄 Breaking Changes
1. **Python 3.10+ Required**: Upgrade from Python 3.9
2. **Proxy Parameter Deprecated**: Use new `proxy_config` structure
3. **New Dependency**: Added `cssselect` for better CSS handling
## 🚀 Get Started
```bash
# Install latest version
pip install crawl4ai==0.7.5
# Docker deployment
docker pull unclecode/crawl4ai:latest
docker run -p 11235:11235 unclecode/crawl4ai:latest
```
**Try the Demo:**
```bash
# Run working examples
python docs/releases_review/demo_v0.7.5.py
```
**Resources:**
- 📖 Documentation: [docs.crawl4ai.com](https://docs.crawl4ai.com)
- 🐙 GitHub: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
- 💬 Discord: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
- 🐦 Twitter: [@unclecode](https://x.com/unclecode)
Happy crawling! 🕷️

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# 🚀 Crawl4AI v0.7.7: The Self-Hosting & Monitoring Update
*November 14, 2025 • 10 min read*
---
Today I'm releasing Crawl4AI v0.7.7—the Self-Hosting & Monitoring Update. This release transforms Crawl4AI Docker from a simple containerized crawler into a complete self-hosting platform with enterprise-grade real-time monitoring, full operational transparency, and production-ready observability.
## 🎯 What's New at a Glance
- **📊 Real-time Monitoring Dashboard**: Interactive web UI with live system metrics and browser pool status
- **🔌 Comprehensive Monitor API**: Complete REST API for programmatic access to all monitoring data
- **⚡ WebSocket Streaming**: Real-time updates every 2 seconds for custom dashboards
- **🎮 Control Actions**: Manual browser management (kill, restart, cleanup)
- **🔥 Smart Browser Pool**: 3-tier architecture (permanent/hot/cold) with automatic promotion
- **🧹 Janitor Cleanup System**: Automatic resource management with event logging
- **📈 Production Metrics**: 6 critical metrics for operational excellence
- **🏭 Integration Ready**: Prometheus, alerting, and log aggregation examples
- **🐛 Critical Bug Fixes**: Async LLM extraction, DFS crawling, viewport config, and more
## 📊 Real-time Monitoring Dashboard: Complete Visibility
**The Problem:** Running Crawl4AI in Docker was like flying blind. Users had no visibility into what was happening inside the container—memory usage, active requests, browser pools, or errors. Troubleshooting required checking logs, and there was no way to monitor performance or manually intervene when issues occurred.
**My Solution:** I built a complete real-time monitoring system with an interactive dashboard, comprehensive REST API, WebSocket streaming, and manual control actions. Now you have full transparency and control over your crawling infrastructure.
### The Self-Hosting Value Proposition
Before v0.7.7, Docker was just a containerized crawler. After v0.7.7, it's a complete self-hosting platform that gives you:
- **🔒 Data Privacy**: Your data never leaves your infrastructure
- **💰 Cost Control**: No per-request pricing or rate limits
- **🎯 Full Customization**: Complete control over configurations and strategies
- **📊 Complete Transparency**: Real-time visibility into every aspect
- **⚡ Performance**: Direct access without network overhead
- **🛡️ Enterprise Security**: Keep workflows behind your firewall
### Interactive Monitoring Dashboard
Access the dashboard at `http://localhost:11235/dashboard` to see:
- **System Health Overview**: CPU, memory, network, and uptime in real-time
- **Live Request Tracking**: Active and completed requests with full details
- **Browser Pool Management**: Interactive table with permanent/hot/cold browsers
- **Janitor Events Log**: Automatic cleanup activities
- **Error Monitoring**: Full context error logs
The dashboard updates every 2 seconds via WebSocket, giving you live visibility into your crawling operations.
## 🔌 Monitor API: Programmatic Access
**The Problem:** Monitoring dashboards are great for humans, but automation and integration require programmatic access.
**My Solution:** A comprehensive REST API that exposes all monitoring data for integration with your existing infrastructure.
### System Health Endpoint
```python
import httpx
import asyncio
async def monitor_system_health():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/health")
health = response.json()
print(f"Container Metrics:")
print(f" CPU: {health['container']['cpu_percent']:.1f}%")
print(f" Memory: {health['container']['memory_percent']:.1f}%")
print(f" Uptime: {health['container']['uptime_seconds']}s")
print(f"\nBrowser Pool:")
print(f" Permanent: {health['pool']['permanent']['active']} active")
print(f" Hot Pool: {health['pool']['hot']['count']} browsers")
print(f" Cold Pool: {health['pool']['cold']['count']} browsers")
print(f"\nStatistics:")
print(f" Total Requests: {health['stats']['total_requests']}")
print(f" Success Rate: {health['stats']['success_rate_percent']:.1f}%")
print(f" Avg Latency: {health['stats']['avg_latency_ms']:.0f}ms")
asyncio.run(monitor_system_health())
```
### Request Tracking
```python
async def track_requests():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/requests")
requests_data = response.json()
print(f"Active Requests: {len(requests_data['active'])}")
print(f"Completed Requests: {len(requests_data['completed'])}")
# See details of recent requests
for req in requests_data['completed'][:5]:
status_icon = "" if req['success'] else ""
print(f"{status_icon} {req['endpoint']} - {req['latency_ms']:.0f}ms")
```
### Browser Pool Management
```python
async def monitor_browser_pool():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/browsers")
browsers = response.json()
print(f"Pool Summary:")
print(f" Total Browsers: {browsers['summary']['total_count']}")
print(f" Total Memory: {browsers['summary']['total_memory_mb']} MB")
print(f" Reuse Rate: {browsers['summary']['reuse_rate_percent']:.1f}%")
# List all browsers
for browser in browsers['permanent']:
print(f"🔥 Permanent: {browser['browser_id'][:8]}... | "
f"Requests: {browser['request_count']} | "
f"Memory: {browser['memory_mb']:.0f} MB")
```
### Endpoint Performance Statistics
```python
async def get_endpoint_stats():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/endpoints/stats")
stats = response.json()
print("Endpoint Analytics:")
for endpoint, data in stats.items():
print(f" {endpoint}:")
print(f" Requests: {data['count']}")
print(f" Avg Latency: {data['avg_latency_ms']:.0f}ms")
print(f" Success Rate: {data['success_rate_percent']:.1f}%")
```
### Complete API Reference
The Monitor API includes these endpoints:
- `GET /monitor/health` - System health with pool statistics
- `GET /monitor/requests` - Active and completed request tracking
- `GET /monitor/browsers` - Browser pool details and efficiency
- `GET /monitor/endpoints/stats` - Per-endpoint performance analytics
- `GET /monitor/timeline?minutes=5` - Time-series data for charts
- `GET /monitor/logs/janitor?limit=10` - Cleanup activity logs
- `GET /monitor/logs/errors?limit=10` - Error logs with context
- `POST /monitor/actions/cleanup` - Force immediate cleanup
- `POST /monitor/actions/kill_browser` - Kill specific browser
- `POST /monitor/actions/restart_browser` - Restart browser
- `POST /monitor/stats/reset` - Reset accumulated statistics
## ⚡ WebSocket Streaming: Real-time Updates
**The Problem:** Polling the API every few seconds wastes resources and adds latency. Real-time dashboards need instant updates.
**My Solution:** WebSocket streaming with 2-second update intervals for building custom real-time dashboards.
### WebSocket Integration Example
```python
import websockets
import json
import asyncio
async def monitor_realtime():
uri = "ws://localhost:11235/monitor/ws"
async with websockets.connect(uri) as websocket:
print("Connected to real-time monitoring stream")
while True:
# Receive update every 2 seconds
data = await websocket.recv()
update = json.loads(data)
# Access all monitoring data
print(f"\n--- Update at {update['timestamp']} ---")
print(f"Memory: {update['health']['container']['memory_percent']:.1f}%")
print(f"Active Requests: {len(update['requests']['active'])}")
print(f"Total Browsers: {update['browsers']['summary']['total_count']}")
if update['errors']:
print(f"⚠️ Recent Errors: {len(update['errors'])}")
asyncio.run(monitor_realtime())
```
**Expected Real-World Impact:**
- **Custom Dashboards**: Build tailored monitoring UIs for your team
- **Real-time Alerting**: Trigger alerts instantly when metrics exceed thresholds
- **Integration**: Feed live data into monitoring tools like Grafana
- **Automation**: React to events in real-time without polling
## 🔥 Smart Browser Pool: 3-Tier Architecture
**The Problem:** Creating a new browser for every request is slow and memory-intensive. Traditional browser pools are static and inefficient.
**My Solution:** A smart 3-tier browser pool that automatically adapts to usage patterns.
### How It Works
```python
import httpx
async def demonstrate_browser_pool():
async with httpx.AsyncClient() as client:
# Request 1-3: Default config → Uses permanent browser
print("Phase 1: Using permanent browser")
for i in range(3):
await client.post(
"http://localhost:11235/crawl",
json={"urls": [f"https://httpbin.org/html?req={i}"]}
)
print(f" Request {i+1}: Reused permanent browser")
# Request 4-6: Custom viewport → Cold pool (first use)
print("\nPhase 2: Custom config creates cold pool browser")
viewport_config = {"viewport": {"width": 1280, "height": 720}}
for i in range(4):
await client.post(
"http://localhost:11235/crawl",
json={
"urls": [f"https://httpbin.org/json?v={i}"],
"browser_config": viewport_config
}
)
if i < 2:
print(f" Request {i+1}: Cold pool browser")
else:
print(f" Request {i+1}: Promoted to hot pool! (after 3 uses)")
# Check pool status
response = await client.get("http://localhost:11235/monitor/browsers")
browsers = response.json()
print(f"\nPool Status:")
print(f" Permanent: {len(browsers['permanent'])} (always active)")
print(f" Hot: {len(browsers['hot'])} (frequently used configs)")
print(f" Cold: {len(browsers['cold'])} (on-demand)")
print(f" Reuse Rate: {browsers['summary']['reuse_rate_percent']:.1f}%")
asyncio.run(demonstrate_browser_pool())
```
**Pool Tiers:**
- **🔥 Permanent Browser**: Always-on, default configuration, instant response
- **♨️ Hot Pool**: Browsers promoted after 3+ uses, kept warm for quick access
- **❄️ Cold Pool**: On-demand browsers for variant configs, cleaned up when idle
**Expected Real-World Impact:**
- **Memory Efficiency**: 10x reduction in memory usage vs creating browsers per request
- **Performance**: Instant access to frequently-used configurations
- **Automatic Optimization**: Pool adapts to your usage patterns
- **Resource Management**: Janitor automatically cleans up idle browsers
## 🧹 Janitor System: Automatic Cleanup
**The Problem:** Long-running crawlers accumulate idle browsers and consume memory over time.
**My Solution:** An automatic janitor system that monitors and cleans up idle resources.
```python
async def monitor_janitor_activity():
async with httpx.AsyncClient() as client:
response = await client.get("http://localhost:11235/monitor/logs/janitor?limit=5")
logs = response.json()
print("Recent Cleanup Activities:")
for log in logs:
print(f" {log['timestamp']}: {log['message']}")
# Example output:
# 2025-11-14 10:30:00: Cleaned up 2 cold pool browsers (idle > 5min)
# 2025-11-14 10:25:00: Browser reuse rate: 85.3%
# 2025-11-14 10:20:00: Hot pool browser promoted (10 requests)
```
## 🎮 Control Actions: Manual Management
**The Problem:** Sometimes you need to manually intervene—kill a stuck browser, force cleanup, or restart resources.
**My Solution:** Manual control actions via the API for operational troubleshooting.
### Force Cleanup
```python
async def force_cleanup():
async with httpx.AsyncClient() as client:
response = await client.post("http://localhost:11235/monitor/actions/cleanup")
result = response.json()
print(f"Cleanup completed:")
print(f" Browsers cleaned: {result.get('cleaned_count', 0)}")
print(f" Memory freed: {result.get('memory_freed_mb', 0):.1f} MB")
```
### Kill Specific Browser
```python
async def kill_stuck_browser(browser_id: str):
async with httpx.AsyncClient() as client:
response = await client.post(
"http://localhost:11235/monitor/actions/kill_browser",
json={"browser_id": browser_id}
)
if response.status_code == 200:
print(f"✅ Browser {browser_id} killed successfully")
```
### Reset Statistics
```python
async def reset_stats():
async with httpx.AsyncClient() as client:
response = await client.post("http://localhost:11235/monitor/stats/reset")
print("📊 Statistics reset for fresh monitoring")
```
## 📈 Production Integration Patterns
### Prometheus Integration
```python
# Export metrics for Prometheus scraping
async def export_prometheus_metrics():
async with httpx.AsyncClient() as client:
health = await client.get("http://localhost:11235/monitor/health")
data = health.json()
# Export in Prometheus format
metrics = f"""
# HELP crawl4ai_memory_usage_percent Memory usage percentage
# TYPE crawl4ai_memory_usage_percent gauge
crawl4ai_memory_usage_percent {data['container']['memory_percent']}
# HELP crawl4ai_request_success_rate Request success rate
# TYPE crawl4ai_request_success_rate gauge
crawl4ai_request_success_rate {data['stats']['success_rate_percent']}
# HELP crawl4ai_browser_pool_count Total browsers in pool
# TYPE crawl4ai_browser_pool_count gauge
crawl4ai_browser_pool_count {data['pool']['permanent']['active'] + data['pool']['hot']['count'] + data['pool']['cold']['count']}
"""
return metrics
```
### Alerting Example
```python
async def check_alerts():
async with httpx.AsyncClient() as client:
health = await client.get("http://localhost:11235/monitor/health")
data = health.json()
# Memory alert
if data['container']['memory_percent'] > 80:
print("🚨 ALERT: Memory usage above 80%")
# Trigger cleanup
await client.post("http://localhost:11235/monitor/actions/cleanup")
# Success rate alert
if data['stats']['success_rate_percent'] < 90:
print("🚨 ALERT: Success rate below 90%")
# Check error logs
errors = await client.get("http://localhost:11235/monitor/logs/errors")
print(f"Recent errors: {len(errors.json())}")
# Latency alert
if data['stats']['avg_latency_ms'] > 5000:
print("🚨 ALERT: Average latency above 5s")
```
### Key Metrics to Track
```python
CRITICAL_METRICS = {
"memory_usage": {
"current": "container.memory_percent",
"target": "<80%",
"alert_threshold": ">80%",
"action": "Force cleanup or scale"
},
"success_rate": {
"current": "stats.success_rate_percent",
"target": ">95%",
"alert_threshold": "<90%",
"action": "Check error logs"
},
"avg_latency": {
"current": "stats.avg_latency_ms",
"target": "<2000ms",
"alert_threshold": ">5000ms",
"action": "Investigate slow requests"
},
"browser_reuse_rate": {
"current": "browsers.summary.reuse_rate_percent",
"target": ">80%",
"alert_threshold": "<60%",
"action": "Check pool configuration"
},
"total_browsers": {
"current": "browsers.summary.total_count",
"target": "<15",
"alert_threshold": ">20",
"action": "Check for browser leaks"
},
"error_frequency": {
"current": "len(errors)",
"target": "<5/hour",
"alert_threshold": ">10/hour",
"action": "Review error patterns"
}
}
```
## 🐛 Critical Bug Fixes
This release includes significant bug fixes that improve stability and performance:
### Async LLM Extraction (#1590)
**The Problem:** LLM extraction was blocking async execution, causing URLs to be processed sequentially instead of in parallel (issue #1055).
**The Fix:** Resolved the blocking issue to enable true parallel processing for LLM extraction.
```python
# Before v0.7.7: Sequential processing
# After v0.7.7: True parallel processing
async with AsyncWebCrawler() as crawler:
urls = ["url1", "url2", "url3", "url4"]
# Now processes truly in parallel with LLM extraction
results = await crawler.arun_many(
urls,
config=CrawlerRunConfig(
extraction_strategy=LLMExtractionStrategy(...)
)
)
# 4x faster for parallel LLM extraction!
```
**Expected Impact:** Major performance improvement for batch LLM extraction workflows.
### DFS Deep Crawling (#1607)
**The Problem:** DFS (Depth-First Search) deep crawl strategy had implementation issues.
**The Fix:** Enhanced DFSDeepCrawlStrategy with proper seen URL tracking and improved documentation.
### Browser & Crawler Config Documentation (#1609)
**The Problem:** Documentation didn't match the actual `async_configs.py` implementation.
**The Fix:** Updated all configuration documentation to accurately reflect the current implementation.
### Sitemap Seeder (#1598)
**The Problem:** Sitemap parsing and URL normalization issues in AsyncUrlSeeder (issue #1559).
**The Fix:** Added comprehensive tests and fixes for sitemap namespace parsing and URL normalization.
### Remove Overlay Elements (#1529)
**The Problem:** The `remove_overlay_elements` functionality wasn't working (issue #1396).
**The Fix:** Fixed by properly calling the injected JavaScript function.
### Viewport Configuration (#1495)
**The Problem:** Viewport configuration wasn't working in managed browsers (issue #1490).
**The Fix:** Added proper viewport size configuration support for browser launch.
### Managed Browser CDP Timing (#1528)
**The Problem:** CDP (Chrome DevTools Protocol) endpoint verification had timing issues causing connection failures (issue #1445).
**The Fix:** Added exponential backoff for CDP endpoint verification to handle timing variations.
### Security Updates
- **pyOpenSSL**: Updated from >=24.3.0 to >=25.3.0 to address security vulnerability
- Added verification tests for the security update
### Docker Fixes
- **Port Standardization**: Fixed inconsistent port usage (11234 vs 11235) - now standardized to 11235
- **LLM Environment**: Fixed LLM API key handling for multi-provider support (PR #1537)
- **Error Handling**: Improved Docker API error messages with comprehensive status codes
- **Serialization**: Fixed `fit_html` property serialization in `/crawl` and `/crawl/stream` endpoints
### Other Important Fixes
- **arun_many Returns**: Fixed function to always return a list, even on exception (PR #1530)
- **Webhook Serialization**: Properly serialize Pydantic HttpUrl in webhook config
- **LLMConfig Documentation**: Fixed casing and variable name consistency (issue #1551)
- **Python Version**: Dropped Python 3.9 support, now requires Python >=3.10
## 📊 Expected Real-World Impact
### For DevOps & Infrastructure Teams
- **Full Visibility**: Know exactly what's happening inside your crawling infrastructure
- **Proactive Monitoring**: Catch issues before they become problems
- **Resource Optimization**: Identify memory leaks and performance bottlenecks
- **Operational Control**: Manual intervention when automated systems need help
### For Production Deployments
- **Enterprise Observability**: Prometheus, Grafana, and alerting integration
- **Debugging**: Real-time logs and error tracking
- **Capacity Planning**: Historical metrics for scaling decisions
- **SLA Monitoring**: Track success rates and latency against targets
### For Development Teams
- **Local Monitoring**: Understand crawler behavior during development
- **Performance Testing**: Measure impact of configuration changes
- **Troubleshooting**: Quickly identify and fix issues
- **Learning**: See exactly how the browser pool works
## 🔄 Breaking Changes
**None!** This release is fully backward compatible.
- All existing Docker configurations continue to work
- No API changes to existing endpoints
- Monitoring is additive functionality
- No migration required
## 🚀 Upgrade Instructions
### Docker
```bash
# Pull the latest version
docker pull unclecode/crawl4ai:0.7.7
# Or use the latest tag
docker pull unclecode/crawl4ai:latest
# Run with monitoring enabled (default)
docker run -d \
-p 11235:11235 \
--shm-size=1g \
--name crawl4ai \
unclecode/crawl4ai:0.7.7
# Access the monitoring dashboard
open http://localhost:11235/dashboard
```
### Python Package
```bash
# Upgrade to latest version
pip install --upgrade crawl4ai
# Or install specific version
pip install crawl4ai==0.7.7
```
## 🎬 Try the Demo
Run the comprehensive demo that showcases all monitoring features:
```bash
python docs/releases_review/demo_v0.7.7.py
```
**The demo includes:**
1. System health overview with live metrics
2. Request tracking with active/completed monitoring
3. Browser pool management (permanent/hot/cold)
4. Complete Monitor API endpoint examples
5. WebSocket streaming demonstration
6. Control actions (cleanup, kill, restart)
7. Production metrics and alerting patterns
8. Self-hosting value proposition
## 📚 Documentation
### New Documentation
- **[Self-Hosting Guide](https://docs.crawl4ai.com/core/self-hosting/)** - Complete self-hosting documentation with monitoring
- **Demo Script**: `docs/releases_review/demo_v0.7.7.py` - Working examples
### Updated Documentation
- **Docker Deployment** → **Self-Hosting** (renamed for better positioning)
- Added comprehensive monitoring sections
- Production integration patterns
- WebSocket streaming examples
## 💡 Pro Tips
1. **Start with the dashboard** - Visit `/dashboard` to get familiar with the monitoring system
2. **Track the 6 key metrics** - Memory, success rate, latency, reuse rate, browser count, errors
3. **Set up alerting early** - Use the Monitor API to build alerts before issues occur
4. **Monitor browser pool efficiency** - Aim for >80% reuse rate for optimal performance
5. **Use WebSocket for custom dashboards** - Build tailored monitoring UIs for your team
6. **Leverage Prometheus integration** - Export metrics for long-term storage and analysis
7. **Check janitor logs** - Understand automatic cleanup patterns
8. **Use control actions judiciously** - Manual interventions are for exceptional cases
## 🙏 Acknowledgments
Thank you to our community for the feedback, bug reports, and feature requests that shaped this release. Special thanks to everyone who contributed to the issues that were fixed in this version.
The monitoring system was built based on real user needs for production deployments, and your input made it comprehensive and practical.
## 📞 Support & Resources
- **📖 Documentation**: [docs.crawl4ai.com](https://docs.crawl4ai.com)
- **🐙 GitHub**: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
- **💬 Discord**: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
- **🐦 Twitter**: [@unclecode](https://x.com/unclecode)
- **📊 Dashboard**: `http://localhost:11235/dashboard` (when running)
---
**Crawl4AI v0.7.7 delivers complete self-hosting with enterprise-grade monitoring. You now have full visibility and control over your web crawling infrastructure. The monitoring dashboard, comprehensive API, and WebSocket streaming give you everything needed for production deployments. Try the self-hosting platform—it's a game changer for operational excellence!**
**Happy crawling with full visibility!** 🕷️📊
*- unclecode*

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# Crawl4AI v0.7.8: Stability & Bug Fix Release
*December 2025*
---
I'm releasing Crawl4AI v0.7.8—a focused stability release that addresses 11 bugs reported by the community. While there are no new features in this release, these fixes resolve important issues affecting Docker deployments, LLM extraction, URL handling, and dependency compatibility.
## What's Fixed at a Glance
- **Docker API**: Fixed ContentRelevanceFilter deserialization, ProxyConfig serialization, and cache folder permissions
- **LLM Extraction**: Configurable rate limiter backoff, HTML input format support, and proper URL handling for raw HTML
- **URL Handling**: Correct relative URL resolution after JavaScript redirects
- **Dependencies**: Replaced deprecated PyPDF2 with pypdf, Pydantic v2 ConfigDict compatibility
- **AdaptiveCrawler**: Fixed query expansion to actually use LLM instead of hardcoded mock data
## Bug Fixes
### Docker & API Fixes
#### ContentRelevanceFilter Deserialization (#1642)
**The Problem:** When sending deep crawl requests to the Docker API with `ContentRelevanceFilter`, the server failed to deserialize the filter, causing requests to fail.
**The Fix:** I added `ContentRelevanceFilter` to the public exports and enhanced the deserialization logic with dynamic imports.
```python
# This now works correctly in Docker API
import httpx
request = {
"urls": ["https://docs.example.com"],
"crawler_config": {
"deep_crawl_strategy": {
"type": "BFSDeepCrawlStrategy",
"max_depth": 2,
"filter_chain": [
{
"type": "ContentRelevanceFilter",
"query": "API documentation",
"threshold": 0.3
}
]
}
}
}
async with httpx.AsyncClient() as client:
response = await client.post("http://localhost:11235/crawl", json=request)
# Previously failed, now works!
```
#### ProxyConfig JSON Serialization (#1629)
**The Problem:** `BrowserConfig.to_dict()` failed when `proxy_config` was set because `ProxyConfig` wasn't being serialized to a dictionary.
**The Fix:** `ProxyConfig.to_dict()` is now called during serialization.
```python
from crawl4ai import BrowserConfig
from crawl4ai.async_configs import ProxyConfig
proxy = ProxyConfig(
server="http://proxy.example.com:8080",
username="user",
password="pass"
)
config = BrowserConfig(headless=True, proxy_config=proxy)
# Previously raised TypeError, now works
config_dict = config.to_dict()
json.dumps(config_dict) # Valid JSON
```
#### Docker Cache Folder Permissions (#1638)
**The Problem:** The `.cache` folder in the Docker image had incorrect permissions, causing crawling to fail when caching was enabled.
**The Fix:** Corrected ownership and permissions during image build.
```bash
# Cache now works correctly in Docker
docker run -d -p 11235:11235 \
--shm-size=1g \
-v ./my-cache:/app/.cache \
unclecode/crawl4ai:0.7.8
```
---
### LLM & Extraction Fixes
#### Configurable Rate Limiter Backoff (#1269)
**The Problem:** The LLM rate limiting backoff parameters were hardcoded, making it impossible to adjust retry behavior for different API rate limits.
**The Fix:** `LLMConfig` now accepts three new parameters for complete control over retry behavior.
```python
from crawl4ai import LLMConfig
# Default behavior (unchanged)
default_config = LLMConfig(provider="openai/gpt-4o-mini")
# backoff_base_delay=2, backoff_max_attempts=3, backoff_exponential_factor=2
# Custom configuration for APIs with strict rate limits
custom_config = LLMConfig(
provider="openai/gpt-4o-mini",
backoff_base_delay=5, # Wait 5 seconds on first retry
backoff_max_attempts=5, # Try up to 5 times
backoff_exponential_factor=3 # Multiply delay by 3 each attempt
)
# Retry sequence: 5s -> 15s -> 45s -> 135s -> 405s
```
#### LLM Strategy HTML Input Support (#1178)
**The Problem:** `LLMExtractionStrategy` always sent markdown to the LLM, but some extraction tasks work better with HTML structure preserved.
**The Fix:** Added `input_format` parameter supporting `"markdown"`, `"html"`, `"fit_markdown"`, `"cleaned_html"`, and `"fit_html"`.
```python
from crawl4ai import LLMExtractionStrategy, LLMConfig
# Default: markdown input (unchanged)
markdown_strategy = LLMExtractionStrategy(
llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
instruction="Extract product information"
)
# NEW: HTML input - preserves table/list structure
html_strategy = LLMExtractionStrategy(
llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
instruction="Extract the data table preserving structure",
input_format="html"
)
# NEW: Filtered markdown - only relevant content
fit_strategy = LLMExtractionStrategy(
llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
instruction="Summarize the main content",
input_format="fit_markdown"
)
```
#### Raw HTML URL Variable (#1116)
**The Problem:** When using `url="raw:<html>..."`, the entire HTML content was being passed to extraction strategies as the URL parameter, polluting LLM prompts.
**The Fix:** The URL is now correctly set to `"Raw HTML"` for raw HTML inputs.
```python
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
html = "<html><body><h1>Test</h1></body></html>"
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(
url=f"raw:{html}",
config=CrawlerRunConfig(extraction_strategy=my_strategy)
)
# extraction_strategy receives url="Raw HTML" instead of the HTML blob
```
---
### URL Handling Fix
#### Relative URLs After Redirects (#1268)
**The Problem:** When JavaScript caused a page redirect, relative links were resolved against the original URL instead of the final URL.
**The Fix:** `redirected_url` now captures the actual page URL after all JavaScript execution completes.
```python
from crawl4ai import AsyncWebCrawler
async with AsyncWebCrawler() as crawler:
# Page at /old-page redirects via JS to /new-page
result = await crawler.arun(url="https://example.com/old-page")
# BEFORE: redirected_url = "https://example.com/old-page"
# AFTER: redirected_url = "https://example.com/new-page"
# Links are now correctly resolved against the final URL
for link in result.links['internal']:
print(link['href']) # Relative links resolved correctly
```
---
### Dependency & Compatibility Fixes
#### PyPDF2 Replaced with pypdf (#1412)
**The Problem:** PyPDF2 was deprecated in 2022 and is no longer maintained.
**The Fix:** Replaced with the actively maintained `pypdf` library.
```python
# Installation (unchanged)
pip install crawl4ai[pdf]
# The PDF processor now uses pypdf internally
# No code changes required - API remains the same
```
#### Pydantic v2 ConfigDict Compatibility (#678)
**The Problem:** Using the deprecated `class Config` syntax caused deprecation warnings with Pydantic v2.
**The Fix:** Migrated to `model_config = ConfigDict(...)` syntax.
```python
# No more deprecation warnings when importing crawl4ai models
from crawl4ai.models import CrawlResult
from crawl4ai import CrawlerRunConfig, BrowserConfig
# All models are now Pydantic v2 compatible
```
---
### AdaptiveCrawler Fix
#### Query Expansion Using LLM (#1621)
**The Problem:** The `EmbeddingStrategy` in AdaptiveCrawler had commented-out LLM code and was using hardcoded mock query variations instead.
**The Fix:** Uncommented and activated the LLM call for actual query expansion.
```python
# AdaptiveCrawler query expansion now actually uses the LLM
# Instead of hardcoded variations like:
# variations = {'queries': ['what are the best vegetables...']}
# The LLM generates relevant query variations based on your actual query
```
---
### Code Formatting Fix
#### Import Statement Formatting (#1181)
**The Problem:** When extracting code from web pages, import statements were sometimes concatenated without proper line separation.
**The Fix:** Import statements now maintain proper newline separation.
```python
# BEFORE: "import osimport sysfrom pathlib import Path"
# AFTER:
# import os
# import sys
# from pathlib import Path
```
---
## Breaking Changes
**None!** This release is fully backward compatible.
- All existing code continues to work without modification
- New parameters have sensible defaults matching previous behavior
- No API changes to existing functionality
---
## Upgrade Instructions
### Python Package
```bash
pip install --upgrade crawl4ai
# or
pip install crawl4ai==0.7.8
```
### Docker
```bash
# Pull the latest version
docker pull unclecode/crawl4ai:0.7.8
# Run
docker run -d -p 11235:11235 --shm-size=1g unclecode/crawl4ai:0.7.8
```
---
## Verification
Run the verification tests to confirm all fixes are working:
```bash
python docs/releases_review/demo_v0.7.8.py
```
This runs actual tests that verify each bug fix is properly implemented.
---
## Acknowledgments
Thank you to everyone who reported these issues and provided detailed reproduction steps. Your bug reports make Crawl4AI better for everyone.
Issues fixed: #1642, #1638, #1629, #1621, #1412, #1269, #1268, #1181, #1178, #1116, #678
---
## Support & Resources
- **Documentation**: [docs.crawl4ai.com](https://docs.crawl4ai.com)
- **GitHub**: [github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)
- **Discord**: [discord.gg/crawl4ai](https://discord.gg/jP8KfhDhyN)
- **Twitter**: [@unclecode](https://x.com/unclecode)
---
**This stability release ensures Crawl4AI works reliably across Docker deployments, LLM extraction workflows, and various edge cases. Thank you for your continued support and feedback!**
**Happy crawling!**
*- unclecode*

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@@ -0,0 +1,243 @@
# Crawl4AI v0.8.0 Release Notes
**Release Date**: January 2026
**Previous Version**: v0.7.6
**Status**: Release Candidate
---
## Highlights
- **Critical Security Fixes** for Docker API deployment
- **11 New Features** including crash recovery, prefetch mode, and proxy improvements
- **Breaking Changes** - see migration guide below
---
## Breaking Changes
### 1. Docker API: Hooks Disabled by Default
**What changed**: Hooks are now disabled by default on the Docker API.
**Why**: Security fix for Remote Code Execution (RCE) vulnerability.
**Who is affected**: Users of the Docker API who use the `hooks` parameter in `/crawl` requests.
**Migration**:
```bash
# To re-enable hooks (only if you trust all API users):
export CRAWL4AI_HOOKS_ENABLED=true
```
### 2. Docker API: file:// URLs Blocked
**What changed**: The endpoints `/execute_js`, `/screenshot`, `/pdf`, and `/html` now reject `file://` URLs.
**Why**: Security fix for Local File Inclusion (LFI) vulnerability.
**Who is affected**: Users who were reading local files via the Docker API.
**Migration**: Use the Python library directly for local file processing:
```python
# Instead of API call with file:// URL, use library:
from crawl4ai import AsyncWebCrawler
async with AsyncWebCrawler() as crawler:
result = await crawler.arun(url="file:///path/to/file.html")
```
---
## Security Fixes
### Critical: Remote Code Execution via Hooks (CVE Pending)
**Severity**: CRITICAL (CVSS 10.0)
**Affected**: Docker API deployment (all versions before v0.8.0)
**Vector**: `POST /crawl` with malicious `hooks` parameter
**Details**: The `__import__` builtin was available in hook code, allowing attackers to import `os`, `subprocess`, etc. and execute arbitrary commands.
**Fix**:
1. Removed `__import__` from allowed builtins
2. Hooks disabled by default (`CRAWL4AI_HOOKS_ENABLED=false`)
### High: Local File Inclusion via file:// URLs (CVE Pending)
**Severity**: HIGH (CVSS 8.6)
**Affected**: Docker API deployment (all versions before v0.8.0)
**Vector**: `POST /execute_js` (and other endpoints) with `file:///etc/passwd`
**Details**: API endpoints accepted `file://` URLs, allowing attackers to read arbitrary files from the server.
**Fix**: URL scheme validation now only allows `http://`, `https://`, and `raw:` URLs.
### Credits
Discovered by **Neo by ProjectDiscovery** ([projectdiscovery.io](https://projectdiscovery.io)) - December 2025
---
## New Features
### 1. init_scripts Support for BrowserConfig
Pre-page-load JavaScript injection for stealth evasions.
```python
config = BrowserConfig(
init_scripts=[
"Object.defineProperty(navigator, 'webdriver', {get: () => false})"
]
)
```
### 2. CDP Connection Improvements
- WebSocket URL support (`ws://`, `wss://`)
- Proper cleanup with `cdp_cleanup_on_close=True`
- Browser reuse across multiple connections
### 3. Crash Recovery for Deep Crawl Strategies
All deep crawl strategies (BFS, DFS, Best-First) now support crash recovery:
```python
from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
strategy = BFSDeepCrawlStrategy(
max_depth=3,
resume_state=saved_state, # Resume from checkpoint
on_state_change=save_callback # Persist state in real-time
)
```
### 4. PDF and MHTML for raw:/file:// URLs
Generate PDFs and MHTML from cached HTML content.
### 5. Screenshots for raw:/file:// URLs
Render cached HTML and capture screenshots.
### 6. base_url Parameter for CrawlerRunConfig
Proper URL resolution for raw: HTML processing:
```python
config = CrawlerRunConfig(base_url='https://example.com')
result = await crawler.arun(url='raw:{html}', config=config)
```
### 7. Prefetch Mode for Two-Phase Deep Crawling
Fast link extraction without full page processing:
```python
config = CrawlerRunConfig(prefetch=True)
```
### 8. Proxy Rotation and Configuration
Enhanced proxy rotation with sticky sessions support.
### 9. Proxy Support for HTTP Strategy
Non-browser crawler now supports proxies.
### 10. Browser Pipeline for raw:/file:// URLs
New `process_in_browser` parameter for browser operations on local content:
```python
config = CrawlerRunConfig(
process_in_browser=True, # Force browser processing
screenshot=True
)
result = await crawler.arun(url='raw:<html>...</html>', config=config)
```
### 11. Smart TTL Cache for Sitemap URL Seeder
Intelligent cache invalidation for sitemaps:
```python
config = SeedingConfig(
cache_ttl_hours=24,
validate_sitemap_lastmod=True
)
```
---
## Bug Fixes
### raw: URL Parsing Truncates at # Character
**Problem**: CSS color codes like `#eee` were being truncated.
**Before**: `raw:body{background:#eee}``body{background:`
**After**: `raw:body{background:#eee}``body{background:#eee}`
### Caching System Improvements
Various fixes to cache validation and persistence.
---
## Documentation Updates
- Multi-sample schema generation documentation
- URL seeder smart TTL cache parameters
- Security documentation (SECURITY.md)
---
## Upgrade Guide
### From v0.7.x to v0.8.0
1. **Update the package**:
```bash
pip install --upgrade crawl4ai
```
2. **Docker API users**:
- Hooks are now disabled by default
- If you need hooks: `export CRAWL4AI_HOOKS_ENABLED=true`
- `file://` URLs no longer work on API (use library directly)
3. **Review security settings**:
```yaml
# config.yml - recommended for production
security:
enabled: true
jwt_enabled: true
```
4. **Test your integration** before deploying to production
### Breaking Change Checklist
- [ ] Check if you use `hooks` parameter in API calls
- [ ] Check if you use `file://` URLs via the API
- [ ] Update environment variables if needed
- [ ] Review security configuration
---
## Full Changelog
See [CHANGELOG.md](../CHANGELOG.md) for complete version history.
---
## Contributors
Thanks to all contributors who made this release possible.
Special thanks to **Neo by ProjectDiscovery** for responsible security disclosure.
---
*For questions or issues, please open a [GitHub Issue](https://github.com/unclecode/crawl4ai/issues).*

File diff suppressed because it is too large Load Diff

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@@ -17,6 +17,11 @@ class BrowserConfig:
def __init__(
browser_type="chromium",
headless=True,
browser_mode="dedicated",
use_managed_browser=False,
cdp_url=None,
debugging_port=9222,
host="localhost",
proxy_config=None,
viewport_width=1080,
viewport_height=600,
@@ -25,7 +30,13 @@ class BrowserConfig:
user_data_dir=None,
cookies=None,
headers=None,
user_agent=None,
user_agent=(
# "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:109.0) AppleWebKit/537.36 "
# "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 "
# "(KHTML, like Gecko) Chrome/116.0.5845.187 Safari/604.1 Edg/117.0.2045.47"
"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 Chrome/116.0.0.0 Safari/537.36"
),
user_agent_mode="",
text_mode=False,
light_mode=False,
extra_args=None,
@@ -37,17 +48,33 @@ class BrowserConfig:
### Key Fields to Note
1. **`browser_type`**
- Options: `"chromium"`, `"firefox"`, or `"webkit"`.
- Defaults to `"chromium"`.
- If you need a different engine, specify it here.
1.**`browser_type`**
- Options: `"chromium"`, `"firefox"`, or `"webkit"`.
- Defaults to `"chromium"`.
- If you need a different engine, specify it here.
2. **`headless`**
2.**`headless`**
- `True`: Runs the browser in headless mode (invisible browser).
- `False`: Runs the browser in visible mode, which helps with debugging.
3. **`proxy_config`**
- A dictionary with fields like:
3.**`browser_mode`**
- Determines how the browser should be initialized:
- `"dedicated"` (default): Creates a new browser instance each time
- `"builtin"`: Uses the builtin CDP browser running in background
- `"custom"`: Uses explicit CDP settings provided in `cdp_url`
- `"docker"`: Runs browser in Docker container with isolation
4.**`use_managed_browser`** & **`cdp_url`**
- `use_managed_browser=True`: Launch browser using Chrome DevTools Protocol (CDP) for advanced control
- `cdp_url`: URL for CDP endpoint (e.g., `"ws://localhost:9222/devtools/browser/"`)
- Automatically set based on `browser_mode`
5.**`debugging_port`** & **`host`**
- `debugging_port`: Port for browser debugging protocol (default: 9222)
- `host`: Host for browser connection (default: "localhost")
6.**`proxy_config`**
- A `ProxyConfig` object or dictionary with fields like:
```json
{
"server": "http://proxy.example.com:8080",
@@ -57,35 +84,35 @@ class BrowserConfig:
```
- Leave as `None` if a proxy is not required.
4. **`viewport_width` & `viewport_height`**:
7.**`viewport_width` & `viewport_height`**
- The initial window size.
- Some sites behave differently with smaller or bigger viewports.
5. **`verbose`**:
8.**`verbose`**
- If `True`, prints extra logs.
- Handy for debugging.
6. **`use_persistent_context`**:
9.**`use_persistent_context`**
- If `True`, uses a **persistent** browser profile, storing cookies/local storage across runs.
- Typically also set `user_data_dir` to point to a folder.
7. **`cookies`** & **`headers`**:
- If you want to start with specific cookies or add universal HTTP headers, set them here.
- E.g. `cookies=[{"name": "session", "value": "abc123", "domain": "example.com"}]`.
10.**`cookies`** & **`headers`**
- If you want to start with specific cookies or add universal HTTP headers to the browser context, set them here.
- E.g. `cookies=[{"name": "session", "value": "abc123", "domain": "example.com"}]`.
8. **`user_agent`**:
- Custom User-Agent string. If `None`, a default is used.
- You can also set `user_agent_mode="random"` for randomization (if you want to fight bot detection).
11.**`user_agent`** & **`user_agent_mode`**
- `user_agent`: Custom User-Agent string. If `None`, a default is used.
- `user_agent_mode`: Set to `"random"` for randomization (helps fight bot detection).
9. **`text_mode`** & **`light_mode`**:
- `text_mode=True` disables images, possibly speeding up text-only crawls.
- `light_mode=True` turns off certain background features for performance.
12.**`text_mode`** & **`light_mode`**
- `text_mode=True` disables images, possibly speeding up text-only crawls.
- `light_mode=True` turns off certain background features for performance.
10. **`extra_args`**:
13.**`extra_args`**
- Additional flags for the underlying browser.
- E.g. `["--disable-extensions"]`.
11. **`enable_stealth`**:
14.**`enable_stealth`**
- If `True`, enables stealth mode using playwright-stealth.
- Modifies browser fingerprints to avoid basic bot detection.
- Default is `False`. Recommended for sites with bot protection.
@@ -134,9 +161,11 @@ class CrawlerRunConfig:
def __init__(
word_count_threshold=200,
extraction_strategy=None,
chunking_strategy=RegexChunking(),
markdown_generator=None,
cache_mode=None,
cache_mode=CacheMode.BYPASS,
js_code=None,
c4a_script=None,
wait_for=None,
screenshot=False,
pdf=False,
@@ -145,13 +174,18 @@ class CrawlerRunConfig:
locale=None, # e.g. "en-US", "fr-FR"
timezone_id=None, # e.g. "America/New_York"
geolocation=None, # GeolocationConfig object
# Resource Management
enable_rate_limiting=False,
rate_limit_config=None,
memory_threshold_percent=70.0,
check_interval=1.0,
max_session_permit=20,
display_mode=None,
# Proxy Configuration
proxy_config=None,
proxy_rotation_strategy=None,
# Page Interaction Parameters
scan_full_page=False,
scroll_delay=0.2,
wait_until="domcontentloaded",
page_timeout=60000,
delay_before_return_html=0.1,
# URL Matching Parameters
url_matcher=None, # For URL-specific configurations
match_mode=MatchMode.OR,
verbose=True,
stream=False, # Enable streaming for arun_many()
# ... other advanced parameters omitted
@@ -161,69 +195,68 @@ class CrawlerRunConfig:
### Key Fields to Note
1. **`word_count_threshold`**:
1.**`word_count_threshold`**:
- The minimum word count before a block is considered.
- If your site has lots of short paragraphs or items, you can lower it.
2. **`extraction_strategy`**:
2.**`extraction_strategy`**:
- Where you plug in JSON-based extraction (CSS, LLM, etc.).
- If `None`, no structured extraction is done (only raw/cleaned HTML + markdown).
3. **`markdown_generator`**:
3.**`chunking_strategy`**:
- Strategy to chunk content before extraction.
- Defaults to `RegexChunking()`. Can be customized for different chunking approaches.
4.**`markdown_generator`**:
- E.g., `DefaultMarkdownGenerator(...)`, controlling how HTML→Markdown conversion is done.
- If `None`, a default approach is used.
4. **`cache_mode`**:
5.**`cache_mode`**:
- Controls caching behavior (`ENABLED`, `BYPASS`, `DISABLED`, etc.).
- If `None`, defaults to some level of caching or you can specify `CacheMode.ENABLED`.
- Defaults to `CacheMode.BYPASS`.
5. **`js_code`**:
- A string or list of JS strings to execute.
6.**`js_code`** & **`c4a_script`**:
- `js_code`: A string or list of JavaScript strings to execute.
- `c4a_script`: C4A script that compiles to JavaScript.
- Great for "Load More" buttons or user interactions.
6. **`wait_for`**:
7.**`wait_for`**:
- A CSS or JS expression to wait for before extracting content.
- Common usage: `wait_for="css:.main-loaded"` or `wait_for="js:() => window.loaded === true"`.
7. **`screenshot`**, **`pdf`**, & **`capture_mhtml`**:
8.**`screenshot`**, **`pdf`**, & **`capture_mhtml`**:
- If `True`, captures a screenshot, PDF, or MHTML snapshot after the page is fully loaded.
- The results go to `result.screenshot` (base64), `result.pdf` (bytes), or `result.mhtml` (string).
8. **Location Parameters**:
9.**Location Parameters**:
- **`locale`**: Browser's locale (e.g., `"en-US"`, `"fr-FR"`) for language preferences
- **`timezone_id`**: Browser's timezone (e.g., `"America/New_York"`, `"Europe/Paris"`)
- **`geolocation`**: GPS coordinates via `GeolocationConfig(latitude=48.8566, longitude=2.3522)`
- See [Identity Based Crawling](../advanced/identity-based-crawling.md#7-locale-timezone-and-geolocation-control)
9. **`verbose`**:
- Logs additional runtime details.
- Overlaps with the browser's verbosity if also set to `True` in `BrowserConfig`.
10.**Proxy Configuration**:
- **`proxy_config`**: Proxy server configuration (ProxyConfig object or dict) e.g. {"server": "...", "username": "...", "password"}
- **`proxy_rotation_strategy`**: Strategy for rotating proxies during crawls
10. **`enable_rate_limiting`**:
- If `True`, enables rate limiting for batch processing.
- Requires `rate_limit_config` to be set.
11.**Page Interaction Parameters**:
- **`scan_full_page`**: If `True`, scroll through the entire page to load all content
- **`wait_until`**: Condition to wait for when navigating (e.g., "domcontentloaded", "networkidle")
- **`page_timeout`**: Timeout in milliseconds for page operations (default: 60000)
- **`delay_before_return_html`**: Delay in seconds before retrieving final HTML.
11. **`memory_threshold_percent`**:
- The memory threshold (as a percentage) to monitor.
- If exceeded, the crawler will pause or slow down.
12. **`check_interval`**:
- The interval (in seconds) to check system resources.
- Affects how often memory and CPU usage are monitored.
13. **`max_session_permit`**:
- The maximum number of concurrent crawl sessions.
- Helps prevent overwhelming the system.
14. **`url_matcher`** & **`match_mode`**:
12.**`url_matcher`** & **`match_mode`**:
- Enable URL-specific configurations when used with `arun_many()`.
- Set `url_matcher` to patterns (glob, function, or list) to match specific URLs.
- Use `match_mode` (OR/AND) to control how multiple patterns combine.
- See [URL-Specific Configurations](../api/arun_many.md#url-specific-configurations) for examples.
15. **`display_mode`**:
- The display mode for progress information (`DETAILED`, `BRIEF`, etc.).
- Affects how much information is printed during the crawl.
13.**`verbose`**:
- Logs additional runtime details.
- Overlaps with the browser's verbosity if also set to `True` in `BrowserConfig`.
14.**`stream`**:
- If `True`, enables streaming mode for `arun_many()` to process URLs as they complete.
- Allows handling results incrementally instead of waiting for all URLs to finish.
### Helper Methods
@@ -263,20 +296,32 @@ The `clone()` method:
### Key fields to note
1. **`provider`**:
1.**`provider`**:
- Which LLM provider to use.
- Possible values are `"ollama/llama3","groq/llama3-70b-8192","groq/llama3-8b-8192", "openai/gpt-4o-mini" ,"openai/gpt-4o","openai/o1-mini","openai/o1-preview","openai/o3-mini","openai/o3-mini-high","anthropic/claude-3-haiku-20240307","anthropic/claude-3-opus-20240229","anthropic/claude-3-sonnet-20240229","anthropic/claude-3-5-sonnet-20240620","gemini/gemini-pro","gemini/gemini-1.5-pro","gemini/gemini-2.0-flash","gemini/gemini-2.0-flash-exp","gemini/gemini-2.0-flash-lite-preview-02-05","deepseek/deepseek-chat"`<br/>*(default: `"openai/gpt-4o-mini"`)*
2. **`api_token`**:
2.**`api_token`**:
- Optional. When not provided explicitly, api_token will be read from environment variables based on provider. For example: If a gemini model is passed as provider then,`"GEMINI_API_KEY"` will be read from environment variables
- API token of LLM provider <br/> eg: `api_token = "gsk_1ClHGGJ7Lpn4WGybR7vNWGdyb3FY7zXEw3SCiy0BAVM9lL8CQv"`
- Environment variable - use with prefix "env:" <br/> eg:`api_token = "env: GROQ_API_KEY"`
3. **`base_url`**:
3.**`base_url`**:
- If your provider has a custom endpoint
4.**Retry/backoff controls** *(optional)*:
- `backoff_base_delay` *(default `2` seconds)* base delay inserted before the first retry when the provider returns a rate-limit response.
- `backoff_max_attempts` *(default `3`)* total number of attempts (initial call plus retries) before the request is surfaced as an error.
- `backoff_exponential_factor` *(default `2`)* growth rate for the retry delay (`delay = base_delay * factor^attempt`).
- These values are forwarded to the shared `perform_completion_with_backoff` helper, ensuring every strategy that consumes your `LLMConfig` honors the same throttling policy.
```python
llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
llm_config = LLMConfig(
provider="openai/gpt-4o-mini",
api_token=os.getenv("OPENAI_API_KEY"),
backoff_base_delay=1, # optional
backoff_max_attempts=5, # optional
backoff_exponential_factor=3, #optional
)
```
## 4. Putting It All Together

View File

@@ -69,12 +69,12 @@ The tutorial includes a Flask-based web interface with:
cd docs/examples/c4a_script/tutorial/
# Install dependencies
pip install flask
pip install -r requirements.txt
# Launch the tutorial server
python app.py
python server.py
# Open http://localhost:5000 in your browser
# Open http://localhost:8000 in your browser
```
## Core Concepts
@@ -111,8 +111,8 @@ CLICK `.submit-btn`
# By attribute
CLICK `button[type="submit"]`
# By text content
CLICK `button:contains("Sign In")`
# By accessible attributes
CLICK `button[aria-label="Search"][title="Search"]`
# Complex selectors
CLICK `.form-container input[name="email"]`

View File

@@ -4,11 +4,13 @@ One of Crawl4AI's most powerful features is its ability to perform **configurabl
In this tutorial, you'll learn:
1. How to set up a **Basic Deep Crawler** with BFS strategy
2. Understanding the difference between **streamed and non-streamed** output
3. Implementing **filters and scorers** to target specific content
4. Creating **advanced filtering chains** for sophisticated crawls
5. Using **BestFirstCrawling** for intelligent exploration prioritization
1. How to set up a **Basic Deep Crawler** with BFS strategy
2. Understanding the difference between **streamed and non-streamed** output
3. Implementing **filters and scorers** to target specific content
4. Creating **advanced filtering chains** for sophisticated crawls
5. Using **BestFirstCrawling** for intelligent exploration prioritization
6. **Crash recovery** for long-running production crawls
7. **Prefetch mode** for fast URL discovery
> **Prerequisites**
> - Youve completed or read [AsyncWebCrawler Basics](../core/simple-crawling.md) to understand how to run a simple crawl.
@@ -485,7 +487,249 @@ This is especially useful for security-conscious crawling or when dealing with s
---
## 10. Summary & Next Steps
## 10. Crash Recovery for Long-Running Crawls
For production deployments, especially in cloud environments where instances can be terminated unexpectedly, Crawl4AI provides built-in crash recovery support for all deep crawl strategies.
### 10.1 Enabling State Persistence
All deep crawl strategies (BFS, DFS, Best-First) support two optional parameters:
- **`resume_state`**: Pass a previously saved state to resume from a checkpoint
- **`on_state_change`**: Async callback fired after each URL is processed
```python
from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
import json
# Callback to save state after each URL
async def save_state_to_redis(state: dict):
await redis.set("crawl_state", json.dumps(state))
strategy = BFSDeepCrawlStrategy(
max_depth=3,
on_state_change=save_state_to_redis, # Called after each URL
)
```
### 10.2 State Structure
The state dictionary is JSON-serializable and contains:
```python
{
"strategy_type": "bfs", # or "dfs", "best_first"
"visited": ["url1", "url2", ...], # Already crawled URLs
"pending": [{"url": "...", "parent_url": "..."}], # Queue/stack
"depths": {"url1": 0, "url2": 1}, # Depth tracking
"pages_crawled": 42 # Counter
}
```
### 10.3 Resuming from a Checkpoint
```python
import json
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
# Load saved state (e.g., from Redis, database, or file)
saved_state = json.loads(await redis.get("crawl_state"))
# Resume crawling from where we left off
strategy = BFSDeepCrawlStrategy(
max_depth=3,
resume_state=saved_state, # Continue from checkpoint
on_state_change=save_state_to_redis, # Keep saving progress
)
config = CrawlerRunConfig(deep_crawl_strategy=strategy)
async with AsyncWebCrawler() as crawler:
# Will skip already-visited URLs and continue from pending queue
results = await crawler.arun(start_url, config=config)
```
### 10.4 Manual State Export
You can export the last captured state using `export_state()`. Note that this requires `on_state_change` to be set (state is captured in the callback):
```python
import json
captured_state = None
async def capture_state(state: dict):
global captured_state
captured_state = state
strategy = BFSDeepCrawlStrategy(
max_depth=2,
on_state_change=capture_state, # Required for state capture
)
config = CrawlerRunConfig(deep_crawl_strategy=strategy)
async with AsyncWebCrawler() as crawler:
results = await crawler.arun(start_url, config=config)
# Get the last captured state
state = strategy.export_state()
if state:
# Save to your preferred storage
with open("crawl_checkpoint.json", "w") as f:
json.dump(state, f)
```
### 10.5 Complete Example: Redis-Based Recovery
```python
import asyncio
import json
import redis.asyncio as redis
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
from crawl4ai.deep_crawling import BFSDeepCrawlStrategy
REDIS_KEY = "crawl4ai:crawl_state"
async def main():
redis_client = redis.Redis(host='localhost', port=6379, db=0)
# Check for existing state
saved_state = None
existing = await redis_client.get(REDIS_KEY)
if existing:
saved_state = json.loads(existing)
print(f"Resuming from checkpoint: {saved_state['pages_crawled']} pages already crawled")
# State persistence callback
async def persist_state(state: dict):
await redis_client.set(REDIS_KEY, json.dumps(state))
# Create strategy with recovery support
strategy = BFSDeepCrawlStrategy(
max_depth=3,
max_pages=100,
resume_state=saved_state,
on_state_change=persist_state,
)
config = CrawlerRunConfig(deep_crawl_strategy=strategy, stream=True)
try:
async with AsyncWebCrawler() as crawler:
async for result in await crawler.arun("https://example.com", config=config):
print(f"Crawled: {result.url}")
except Exception as e:
print(f"Crawl interrupted: {e}")
print("State saved - restart to resume")
finally:
await redis_client.close()
if __name__ == "__main__":
asyncio.run(main())
```
### 10.6 Zero Overhead
When `resume_state=None` and `on_state_change=None` (the defaults), there is no performance impact. State tracking only activates when you enable these features.
---
## 11. Prefetch Mode for Fast URL Discovery
When you need to quickly discover URLs without full page processing, use **prefetch mode**. This is ideal for two-phase crawling where you first map the site, then selectively process specific pages.
### 11.1 Enabling Prefetch Mode
```python
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
config = CrawlerRunConfig(prefetch=True)
async with AsyncWebCrawler() as crawler:
result = await crawler.arun("https://example.com", config=config)
# Result contains only HTML and links - no markdown, no extraction
print(f"Found {len(result.links['internal'])} internal links")
print(f"Found {len(result.links['external'])} external links")
```
### 11.2 What Gets Skipped
Prefetch mode uses a fast path that bypasses heavy processing:
| Processing Step | Normal Mode | Prefetch Mode |
|----------------|-------------|---------------|
| Fetch HTML | ✅ | ✅ |
| Extract links | ✅ | ✅ (fast `quick_extract_links()`) |
| Generate markdown | ✅ | ❌ Skipped |
| Content scraping | ✅ | ❌ Skipped |
| Media extraction | ✅ | ❌ Skipped |
| LLM extraction | ✅ | ❌ Skipped |
### 11.3 Performance Benefit
- **Normal mode**: Full pipeline (~2-5 seconds per page)
- **Prefetch mode**: HTML + links only (~200-500ms per page)
This makes prefetch mode **5-10x faster** for URL discovery.
### 11.4 Two-Phase Crawling Pattern
The most common use case is two-phase crawling:
```python
import asyncio
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
async def two_phase_crawl(start_url: str):
async with AsyncWebCrawler() as crawler:
# ═══════════════════════════════════════════════
# Phase 1: Fast discovery (prefetch mode)
# ═══════════════════════════════════════════════
prefetch_config = CrawlerRunConfig(prefetch=True)
discovery = await crawler.arun(start_url, config=prefetch_config)
all_urls = [link["href"] for link in discovery.links.get("internal", [])]
print(f"Discovered {len(all_urls)} URLs")
# Filter to URLs you care about
blog_urls = [url for url in all_urls if "/blog/" in url]
print(f"Found {len(blog_urls)} blog posts to process")
# ═══════════════════════════════════════════════
# Phase 2: Full processing on selected URLs only
# ═══════════════════════════════════════════════
full_config = CrawlerRunConfig(
# Your normal extraction settings
word_count_threshold=100,
remove_overlay_elements=True,
)
results = []
for url in blog_urls:
result = await crawler.arun(url, config=full_config)
if result.success:
results.append(result)
print(f"Processed: {url}")
return results
if __name__ == "__main__":
results = asyncio.run(two_phase_crawl("https://example.com"))
print(f"Fully processed {len(results)} pages")
```
### 11.5 Use Cases
- **Site mapping**: Quickly discover all URLs before deciding what to process
- **Link validation**: Check which pages exist without heavy processing
- **Selective deep crawl**: Prefetch to find URLs, filter by pattern, then full crawl
- **Crawl planning**: Estimate crawl size before committing resources
---
## 12. Summary & Next Steps
In this **Deep Crawling with Crawl4AI** tutorial, you learned to:
@@ -495,5 +739,7 @@ In this **Deep Crawling with Crawl4AI** tutorial, you learned to:
- Use scorers to prioritize the most relevant pages
- Limit crawls with `max_pages` and `score_threshold` parameters
- Build a complete advanced crawler with combined techniques
- **Implement crash recovery** with `resume_state` and `on_state_change` for production deployments
- **Use prefetch mode** for fast URL discovery and two-phase crawling
With these tools, you can efficiently extract structured data from websites at scale, focusing precisely on the content you need for your specific use case.

View File

@@ -11,6 +11,12 @@ This page provides a comprehensive list of example scripts that demonstrate vari
| Quickstart Set 1 | Basic examples for getting started with Crawl4AI. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/quickstart_examples_set_1.py) |
| Quickstart Set 2 | More advanced examples for working with Crawl4AI. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/quickstart_examples_set_2.py) |
## Proxies
| Example | Description | Link |
|----------|--------------|------|
| **NSTProxy** | [NSTProxy](https://www.nstproxy.com/?utm_source=crawl4ai) Seamlessly integrates with crawl4ai — no setup required. Access high-performance residential, datacenter, ISP, and IPv6 proxies with smart rotation and anti-blocking technology. Starts from $0.1/GB. Use code crawl4ai for 10% off. | [View Code](https://github.com/unclecode/crawl4ai/tree/main/docs/examples/proxy) |
## Browser & Crawling Features
| Example | Description | Link |
@@ -56,13 +62,14 @@ This page provides a comprehensive list of example scripts that demonstrate vari
## Anti-Bot & Stealth Features
| Example | Description | Link |
|---------|-------------|------|
| Stealth Mode Quick Start | Five practical examples showing how to use stealth mode for bypassing basic bot detection. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/stealth_mode_quick_start.py) |
| Example | Description | Link |
|----------------------------|-------------|------|
| Stealth Mode Quick Start | Five practical examples showing how to use stealth mode for bypassing basic bot detection. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/stealth_mode_quick_start.py) |
| Stealth Mode Comprehensive | Comprehensive demonstration of stealth mode features with bot detection testing and comparisons. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/stealth_mode_example.py) |
| Undetected Browser | Simple example showing how to use the undetected browser adapter. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/hello_world_undetected.py) |
| Undetected Browser Demo | Basic demo comparing regular and undetected browser modes. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/undetected_simple_demo.py) |
| Undetected Tests | Advanced tests comparing regular vs undetected browsers on various bot detection services. | [View Folder](https://github.com/unclecode/crawl4ai/tree/main/docs/examples/undetectability/) |
| Undetected Browser | Simple example showing how to use the undetected browser adapter. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/hello_world_undetected.py) |
| Undetected Browser Demo | Basic demo comparing regular and undetected browser modes. | [View Code](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/undetected_simple_demo.py) |
| Undetected Tests | Advanced tests comparing regular vs undetected browsers on various bot detection services. | [View Folder](https://github.com/unclecode/crawl4ai/tree/main/docs/examples/undetectability/) |
| CapSolver Captcha Solver | Seamlessly integrate with [CapSolver](https://www.capsolver.com/?utm_source=crawl4ai&utm_medium=github_pr&utm_campaign=crawl4ai_integration) to automatically solve reCAPTCHA v2/v3, Cloudflare Turnstile / Challenges, AWS WAF and more for uninterrupted scraping and automation. | [View Folder](https://github.com/unclecode/crawl4ai/tree/main/docs/examples/capsolver_captcha_solver/) |
## Customization & Security

View File

@@ -22,18 +22,6 @@ When you self-host, you can scale from a single container to a full browser infr
- [Option 1: Using Pre-built Docker Hub Images (Recommended)](#option-1-using-pre-built-docker-hub-images-recommended)
- [Option 2: Using Docker Compose](#option-2-using-docker-compose)
- [Option 3: Manual Local Build & Run](#option-3-manual-local-build--run)
- [Dockerfile Parameters](#dockerfile-parameters)
- [Using the API](#using-the-api)
- [Playground Interface](#playground-interface)
- [Python SDK](#python-sdk)
- [Understanding Request Schema](#understanding-request-schema)
- [REST API Examples](#rest-api-examples)
- [Additional API Endpoints](#additional-api-endpoints)
- [HTML Extraction Endpoint](#html-extraction-endpoint)
- [Screenshot Endpoint](#screenshot-endpoint)
- [PDF Export Endpoint](#pdf-export-endpoint)
- [JavaScript Execution Endpoint](#javascript-execution-endpoint)
- [Library Context Endpoint](#library-context-endpoint)
- [MCP (Model Context Protocol) Support](#mcp-model-context-protocol-support)
- [What is MCP?](#what-is-mcp)
- [Connecting via MCP](#connecting-via-mcp)
@@ -79,13 +67,13 @@ Pull and run images directly from Docker Hub without building locally.
#### 1. Pull the Image
Our latest release is `0.7.3`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
Our latest release is `0.8.0`. Images are built with multi-arch manifests, so Docker automatically pulls the correct version for your system.
> 💡 **Note**: The `latest` tag points to the stable `0.7.3` version.
> 💡 **Note**: The `latest` tag points to the stable `0.8.0` version.
```bash
# Pull the latest version
docker pull unclecode/crawl4ai:0.7.3
docker pull unclecode/crawl4ai:0.8.0
# Or pull using the latest tag
docker pull unclecode/crawl4ai:latest
@@ -157,7 +145,7 @@ docker stop crawl4ai && docker rm crawl4ai
#### Docker Hub Versioning Explained
* **Image Name:** `unclecode/crawl4ai`
* **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.7.3`)
* **Tag Format:** `LIBRARY_VERSION[-SUFFIX]` (e.g., `0.8.0`)
* `LIBRARY_VERSION`: The semantic version of the core `crawl4ai` Python library
* `SUFFIX`: Optional tag for release candidates (``) and revisions (`r1`)
* **`latest` Tag:** Points to the most recent stable version
@@ -853,6 +841,733 @@ else:
> 💡 **Remember**: Always test your hooks on safe, known websites first before using them on production sites. Never crawl sites that you don't have permission to access or that might be malicious.
### Hooks Utility: Function-Based Approach (Python)
For Python developers, Crawl4AI provides a more convenient way to work with hooks using the `hooks_to_string()` utility function and Docker client integration.
#### Why Use Function-Based Hooks?
**String-Based Approach (shown above)**:
```python
hooks_code = {
"on_page_context_created": """
async def hook(page, context, **kwargs):
await page.set_viewport_size({"width": 1920, "height": 1080})
return page
"""
}
```
**Function-Based Approach (recommended for Python)**:
```python
from crawl4ai import Crawl4aiDockerClient
async def my_hook(page, context, **kwargs):
await page.set_viewport_size({"width": 1920, "height": 1080})
return page
async with Crawl4aiDockerClient(base_url="http://localhost:11235") as client:
result = await client.crawl(
["https://example.com"],
hooks={"on_page_context_created": my_hook}
)
```
**Benefits**:
- ✅ Write hooks as regular Python functions
- ✅ Full IDE support (autocomplete, syntax highlighting, type checking)
- ✅ Easy to test and debug
- ✅ Reusable hook libraries
- ✅ Automatic conversion to API format
#### Using the Hooks Utility
The `hooks_to_string()` utility converts Python function objects to the string format required by the API:
```python
from crawl4ai import hooks_to_string
# Define your hooks as functions
async def setup_hook(page, context, **kwargs):
await page.set_viewport_size({"width": 1920, "height": 1080})
await context.add_cookies([{
"name": "session",
"value": "token",
"domain": ".example.com"
}])
return page
async def scroll_hook(page, context, **kwargs):
await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
return page
# Convert to string format
hooks_dict = {
"on_page_context_created": setup_hook,
"before_retrieve_html": scroll_hook
}
hooks_string = hooks_to_string(hooks_dict)
# Now use with REST API or Docker client
# hooks_string contains the string representations
```
#### Docker Client with Automatic Conversion
The Docker client automatically detects and converts function objects:
```python
from crawl4ai import Crawl4aiDockerClient
async def auth_hook(page, context, **kwargs):
"""Add authentication cookies"""
await context.add_cookies([{
"name": "auth_token",
"value": "your_token",
"domain": ".example.com"
}])
return page
async def performance_hook(page, context, **kwargs):
"""Block unnecessary resources"""
await context.route("**/*.{png,jpg,gif}", lambda r: r.abort())
await context.route("**/analytics/*", lambda r: r.abort())
return page
async with Crawl4aiDockerClient(base_url="http://localhost:11235") as client:
# Pass functions directly - automatic conversion!
result = await client.crawl(
["https://example.com"],
hooks={
"on_page_context_created": performance_hook,
"before_goto": auth_hook
},
hooks_timeout=30 # Optional timeout in seconds (1-120)
)
print(f"Success: {result.success}")
print(f"HTML: {len(result.html)} chars")
```
#### Creating Reusable Hook Libraries
Build collections of reusable hooks:
```python
# hooks_library.py
class CrawlHooks:
"""Reusable hook collection for common crawling tasks"""
@staticmethod
async def block_images(page, context, **kwargs):
"""Block all images to speed up crawling"""
await context.route("**/*.{png,jpg,jpeg,gif,webp}", lambda r: r.abort())
return page
@staticmethod
async def block_analytics(page, context, **kwargs):
"""Block analytics and tracking scripts"""
tracking_domains = [
"**/google-analytics.com/*",
"**/googletagmanager.com/*",
"**/facebook.com/tr/*",
"**/doubleclick.net/*"
]
for domain in tracking_domains:
await context.route(domain, lambda r: r.abort())
return page
@staticmethod
async def scroll_infinite(page, context, **kwargs):
"""Handle infinite scroll to load more content"""
previous_height = 0
for i in range(5): # Max 5 scrolls
current_height = await page.evaluate("document.body.scrollHeight")
if current_height == previous_height:
break
await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
await page.wait_for_timeout(1000)
previous_height = current_height
return page
@staticmethod
async def wait_for_dynamic_content(page, context, url, response, **kwargs):
"""Wait for dynamic content to load"""
await page.wait_for_timeout(2000)
try:
# Click "Load More" if present
load_more = await page.query_selector('[class*="load-more"]')
if load_more:
await load_more.click()
await page.wait_for_timeout(1000)
except:
pass
return page
# Use in your application
from hooks_library import CrawlHooks
from crawl4ai import Crawl4aiDockerClient
async def crawl_with_optimizations(url):
async with Crawl4aiDockerClient() as client:
result = await client.crawl(
[url],
hooks={
"on_page_context_created": CrawlHooks.block_images,
"before_retrieve_html": CrawlHooks.scroll_infinite
}
)
return result
```
#### Choosing the Right Approach
| Approach | Best For | IDE Support | Language |
|----------|----------|-------------|----------|
| **String-based** | Non-Python clients, REST APIs, other languages | ❌ None | Any |
| **Function-based** | Python applications, local development | ✅ Full | Python only |
| **Docker Client** | Python apps with automatic conversion | ✅ Full | Python only |
**Recommendation**:
- **Python applications**: Use Docker client with function objects (easiest)
- **Non-Python or REST API**: Use string-based hooks (most flexible)
- **Manual control**: Use `hooks_to_string()` utility (middle ground)
#### Complete Example with Function Hooks
```python
from crawl4ai import Crawl4aiDockerClient, BrowserConfig, CrawlerRunConfig, CacheMode
# Define hooks as regular Python functions
async def setup_environment(page, context, **kwargs):
"""Setup crawling environment"""
# Set viewport
await page.set_viewport_size({"width": 1920, "height": 1080})
# Block resources for speed
await context.route("**/*.{png,jpg,gif}", lambda r: r.abort())
# Add custom headers
await page.set_extra_http_headers({
"Accept-Language": "en-US",
"X-Custom-Header": "Crawl4AI"
})
print("[HOOK] Environment configured")
return page
async def extract_content(page, context, **kwargs):
"""Extract and prepare content"""
# Scroll to load lazy content
await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
await page.wait_for_timeout(1000)
# Extract metadata
metadata = await page.evaluate('''() => ({
title: document.title,
links: document.links.length,
images: document.images.length
})''')
print(f"[HOOK] Page metadata: {metadata}")
return page
async def main():
async with Crawl4aiDockerClient(base_url="http://localhost:11235", verbose=True) as client:
# Configure crawl
browser_config = BrowserConfig(headless=True)
crawler_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
# Crawl with hooks
result = await client.crawl(
["https://httpbin.org/html"],
browser_config=browser_config,
crawler_config=crawler_config,
hooks={
"on_page_context_created": setup_environment,
"before_retrieve_html": extract_content
},
hooks_timeout=30
)
if result.success:
print(f"✅ Crawl successful!")
print(f" URL: {result.url}")
print(f" HTML: {len(result.html)} chars")
print(f" Markdown: {len(result.markdown)} chars")
else:
print(f"❌ Crawl failed: {result.error_message}")
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
#### Additional Resources
- **Comprehensive Examples**: See `/docs/examples/hooks_docker_client_example.py` for Python function-based examples
- **REST API Examples**: See `/docs/examples/hooks_rest_api_example.py` for string-based examples
- **Comparison Guide**: See `/docs/examples/README_HOOKS.md` for detailed comparison
- **Utility Documentation**: See `/docs/hooks-utility-guide.md` for complete guide
---
## Job Queue & Webhook API
The Docker deployment includes a powerful asynchronous job queue system with webhook support for both crawling and LLM extraction tasks. Instead of waiting for long-running operations to complete, submit jobs and receive real-time notifications via webhooks when they finish.
### Why Use the Job Queue API?
**Traditional Synchronous API (`/crawl`):**
- Client waits for entire crawl to complete
- Timeout issues with long-running crawls
- Resource blocking during execution
- Constant polling required for status updates
**Asynchronous Job Queue API (`/crawl/job`, `/llm/job`):**
- ✅ Submit job and continue immediately
- ✅ No timeout concerns for long operations
- ✅ Real-time webhook notifications on completion
- ✅ Better resource utilization
- ✅ Perfect for batch processing
- ✅ Ideal for microservice architectures
### Available Endpoints
#### 1. Crawl Job Endpoint
```
POST /crawl/job
```
Submit an asynchronous crawl job with optional webhook notification.
**Request Body:**
```json
{
"urls": ["https://example.com"],
"cache_mode": "bypass",
"extraction_strategy": {
"type": "JsonCssExtractionStrategy",
"schema": {
"title": "h1",
"content": ".article-body"
}
},
"webhook_config": {
"webhook_url": "https://your-app.com/webhook/crawl-complete",
"webhook_data_in_payload": true,
"webhook_headers": {
"X-Webhook-Secret": "your-secret-token",
"X-Custom-Header": "value"
}
}
}
```
**Response:**
```json
{
"task_id": "crawl_1698765432",
"message": "Crawl job submitted"
}
```
#### 2. LLM Extraction Job Endpoint
```
POST /llm/job
```
Submit an asynchronous LLM extraction job with optional webhook notification.
**Request Body:**
```json
{
"url": "https://example.com/article",
"q": "Extract the article title, author, publication date, and main points",
"provider": "openai/gpt-4o-mini",
"schema": "{\"title\": \"string\", \"author\": \"string\", \"date\": \"string\", \"points\": [\"string\"]}",
"cache": false,
"webhook_config": {
"webhook_url": "https://your-app.com/webhook/llm-complete",
"webhook_data_in_payload": true,
"webhook_headers": {
"X-Webhook-Secret": "your-secret-token"
}
}
}
```
**Response:**
```json
{
"task_id": "llm_1698765432",
"message": "LLM job submitted"
}
```
#### 3. Job Status Endpoint
```
GET /job/{task_id}
```
Check the status and retrieve results of a submitted job.
**Response (In Progress):**
```json
{
"task_id": "crawl_1698765432",
"status": "processing",
"message": "Job is being processed"
}
```
**Response (Completed):**
```json
{
"task_id": "crawl_1698765432",
"status": "completed",
"result": {
"markdown": "# Page Title\n\nContent...",
"extracted_content": {...},
"links": {...}
}
}
```
### Webhook Configuration
Webhooks provide real-time notifications when your jobs complete, eliminating the need for constant polling.
#### Webhook Config Parameters
| Parameter | Type | Required | Description |
|-----------|------|----------|-------------|
| `webhook_url` | string | Yes | Your HTTP(S) endpoint to receive notifications |
| `webhook_data_in_payload` | boolean | No | Include full result data in webhook payload (default: false) |
| `webhook_headers` | object | No | Custom headers for authentication/identification |
#### Webhook Payload Format
**Success Notification (Crawl Job):**
```json
{
"task_id": "crawl_1698765432",
"task_type": "crawl",
"status": "completed",
"timestamp": "2025-10-22T12:30:00.000000+00:00",
"urls": ["https://example.com"],
"data": {
"markdown": "# Page content...",
"extracted_content": {...},
"links": {...}
}
}
```
**Success Notification (LLM Job):**
```json
{
"task_id": "llm_1698765432",
"task_type": "llm_extraction",
"status": "completed",
"timestamp": "2025-10-22T12:30:00.000000+00:00",
"urls": ["https://example.com/article"],
"data": {
"extracted_content": {
"title": "Understanding Web Scraping",
"author": "John Doe",
"date": "2025-10-22",
"points": ["Point 1", "Point 2"]
}
}
}
```
**Failure Notification:**
```json
{
"task_id": "crawl_1698765432",
"task_type": "crawl",
"status": "failed",
"timestamp": "2025-10-22T12:30:00.000000+00:00",
"urls": ["https://example.com"],
"error": "Connection timeout after 30 seconds"
}
```
#### Webhook Delivery & Retry
- **Delivery Method:** HTTP POST to your `webhook_url`
- **Content-Type:** `application/json`
- **Retry Policy:** Exponential backoff with 5 attempts
- Attempt 1: Immediate
- Attempt 2: 1 second delay
- Attempt 3: 2 seconds delay
- Attempt 4: 4 seconds delay
- Attempt 5: 8 seconds delay
- **Success Status Codes:** 200-299
- **Custom Headers:** Your `webhook_headers` are included in every request
### Usage Examples
#### Example 1: Python with Webhook Handler (Flask)
```python
from flask import Flask, request, jsonify
import requests
app = Flask(__name__)
# Webhook handler
@app.route('/webhook/crawl-complete', methods=['POST'])
def handle_crawl_webhook():
payload = request.json
if payload['status'] == 'completed':
print(f"✅ Job {payload['task_id']} completed!")
print(f"Task type: {payload['task_type']}")
# Access the crawl results
if 'data' in payload:
markdown = payload['data'].get('markdown', '')
extracted = payload['data'].get('extracted_content', {})
print(f"Extracted {len(markdown)} characters")
print(f"Structured data: {extracted}")
else:
print(f"❌ Job {payload['task_id']} failed: {payload.get('error')}")
return jsonify({"status": "received"}), 200
# Submit a crawl job with webhook
def submit_crawl_job():
response = requests.post(
"http://localhost:11235/crawl/job",
json={
"urls": ["https://example.com"],
"extraction_strategy": {
"type": "JsonCssExtractionStrategy",
"schema": {
"name": "Example Schema",
"baseSelector": "body",
"fields": [
{"name": "title", "selector": "h1", "type": "text"},
{"name": "description", "selector": "meta[name='description']", "type": "attribute", "attribute": "content"}
]
}
},
"webhook_config": {
"webhook_url": "https://your-app.com/webhook/crawl-complete",
"webhook_data_in_payload": True,
"webhook_headers": {
"X-Webhook-Secret": "your-secret-token"
}
}
}
)
task_id = response.json()['task_id']
print(f"Job submitted: {task_id}")
return task_id
if __name__ == '__main__':
app.run(port=5000)
```
#### Example 2: LLM Extraction with Webhooks
```python
import requests
def submit_llm_job_with_webhook():
response = requests.post(
"http://localhost:11235/llm/job",
json={
"url": "https://example.com/article",
"q": "Extract the article title, author, and main points",
"provider": "openai/gpt-4o-mini",
"webhook_config": {
"webhook_url": "https://your-app.com/webhook/llm-complete",
"webhook_data_in_payload": True,
"webhook_headers": {
"X-Webhook-Secret": "your-secret-token"
}
}
}
)
task_id = response.json()['task_id']
print(f"LLM job submitted: {task_id}")
return task_id
# Webhook handler for LLM jobs
@app.route('/webhook/llm-complete', methods=['POST'])
def handle_llm_webhook():
payload = request.json
if payload['status'] == 'completed':
extracted = payload['data']['extracted_content']
print(f"✅ LLM extraction completed!")
print(f"Results: {extracted}")
else:
print(f"❌ LLM extraction failed: {payload.get('error')}")
return jsonify({"status": "received"}), 200
```
#### Example 3: Without Webhooks (Polling)
If you don't use webhooks, you can poll for results:
```python
import requests
import time
# Submit job
response = requests.post(
"http://localhost:11235/crawl/job",
json={"urls": ["https://example.com"]}
)
task_id = response.json()['task_id']
# Poll for results
while True:
result = requests.get(f"http://localhost:11235/job/{task_id}")
data = result.json()
if data['status'] == 'completed':
print("Job completed!")
print(data['result'])
break
elif data['status'] == 'failed':
print(f"Job failed: {data.get('error')}")
break
print("Still processing...")
time.sleep(2)
```
#### Example 4: Global Webhook Configuration
Set a default webhook URL in your `config.yml` to avoid repeating it in every request:
```yaml
# config.yml
api:
crawler:
# ... other settings ...
webhook:
default_url: "https://your-app.com/webhook/default"
default_headers:
X-Webhook-Secret: "your-secret-token"
```
Then submit jobs without webhook config:
```python
# Uses the global webhook configuration
response = requests.post(
"http://localhost:11235/crawl/job",
json={"urls": ["https://example.com"]}
)
```
### Webhook Best Practices
1. **Authentication:** Always use custom headers for webhook authentication
```json
"webhook_headers": {
"X-Webhook-Secret": "your-secret-token"
}
```
2. **Idempotency:** Design your webhook handler to be idempotent (safe to receive duplicate notifications)
3. **Fast Response:** Return HTTP 200 quickly; process data asynchronously if needed
```python
@app.route('/webhook', methods=['POST'])
def webhook():
payload = request.json
# Queue for background processing
queue.enqueue(process_webhook, payload)
return jsonify({"status": "received"}), 200
```
4. **Error Handling:** Handle both success and failure notifications
```python
if payload['status'] == 'completed':
# Process success
elif payload['status'] == 'failed':
# Log error, retry, or alert
```
5. **Validation:** Verify webhook authenticity using custom headers
```python
secret = request.headers.get('X-Webhook-Secret')
if secret != os.environ['EXPECTED_SECRET']:
return jsonify({"error": "Unauthorized"}), 401
```
6. **Logging:** Log webhook deliveries for debugging
```python
logger.info(f"Webhook received: {payload['task_id']} - {payload['status']}")
```
### Use Cases
**1. Batch Processing**
Submit hundreds of URLs and get notified as each completes:
```python
urls = ["https://site1.com", "https://site2.com", ...]
for url in urls:
submit_crawl_job(url, webhook_url="https://app.com/webhook")
```
**2. Microservice Integration**
Integrate with event-driven architectures:
```python
# Service A submits job
task_id = submit_crawl_job(url)
# Service B receives webhook and triggers next step
@app.route('/webhook')
def webhook():
process_result(request.json)
trigger_next_service()
return "OK", 200
```
**3. Long-Running Extractions**
Handle complex LLM extractions without timeouts:
```python
submit_llm_job(
url="https://long-article.com",
q="Comprehensive summary with key points and analysis",
webhook_url="https://app.com/webhook/llm"
)
```
### Troubleshooting
**Webhook not receiving notifications?**
- Check your webhook URL is publicly accessible
- Verify firewall/security group settings
- Use webhook testing tools like webhook.site for debugging
- Check server logs for delivery attempts
- Ensure your handler returns 200-299 status code
**Job stuck in processing?**
- Check Redis connection: `docker logs <container_name> | grep redis`
- Verify worker processes: `docker exec <container_name> ps aux | grep worker`
- Check server logs: `docker logs <container_name>`
**Need to cancel a job?**
Jobs are processed asynchronously. If you need to cancel:
- Delete the task from Redis (requires Redis CLI access)
- Or implement a cancellation endpoint in your webhook handler
---
## Dockerfile Parameters
@@ -913,10 +1628,12 @@ This is the easiest way to translate Python configuration to JSON requests when
Install the SDK: `pip install crawl4ai`
The Python SDK provides a convenient way to interact with the Docker API, including **automatic hook conversion** when using function objects.
```python
import asyncio
from crawl4ai.docker_client import Crawl4aiDockerClient
from crawl4ai import BrowserConfig, CrawlerRunConfig, CacheMode # Assuming you have crawl4ai installed
from crawl4ai import BrowserConfig, CrawlerRunConfig, CacheMode
async def main():
# Point to the correct server port
@@ -928,23 +1645,22 @@ async def main():
print("--- Running Non-Streaming Crawl ---")
results = await client.crawl(
["https://httpbin.org/html"],
browser_config=BrowserConfig(headless=True), # Use library classes for config aid
browser_config=BrowserConfig(headless=True),
crawler_config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
)
if results: # client.crawl returns None on failure
print(f"Non-streaming results success: {results.success}")
if results.success:
for result in results: # Iterate through the CrawlResultContainer
print(f"URL: {result.url}, Success: {result.success}")
if results:
print(f"Non-streaming results success: {results.success}")
if results.success:
for result in results:
print(f"URL: {result.url}, Success: {result.success}")
else:
print("Non-streaming crawl failed.")
# Example Streaming crawl
print("\n--- Running Streaming Crawl ---")
stream_config = CrawlerRunConfig(stream=True, cache_mode=CacheMode.BYPASS)
try:
async for result in await client.crawl( # client.crawl returns an async generator for streaming
async for result in await client.crawl(
["https://httpbin.org/html", "https://httpbin.org/links/5/0"],
browser_config=BrowserConfig(headless=True),
crawler_config=stream_config
@@ -953,17 +1669,56 @@ async def main():
except Exception as e:
print(f"Streaming crawl failed: {e}")
# Example with hooks (Python function objects)
print("\n--- Crawl with Hooks ---")
async def my_hook(page, context, **kwargs):
"""Custom hook to optimize performance"""
await page.set_viewport_size({"width": 1920, "height": 1080})
await context.route("**/*.{png,jpg}", lambda r: r.abort())
print("[HOOK] Page optimized")
return page
result = await client.crawl(
["https://httpbin.org/html"],
browser_config=BrowserConfig(headless=True),
crawler_config=CrawlerRunConfig(cache_mode=CacheMode.BYPASS),
hooks={"on_page_context_created": my_hook}, # Pass function directly!
hooks_timeout=30
)
print(f"Crawl with hooks success: {result.success}")
# Example Get schema
print("\n--- Getting Schema ---")
schema = await client.get_schema()
print(f"Schema received: {bool(schema)}") # Print whether schema was received
print(f"Schema received: {bool(schema)}")
if __name__ == "__main__":
asyncio.run(main())
```
*(SDK parameters like timeout, verify_ssl etc. remain the same)*
#### SDK Parameters
The Docker client supports the following parameters:
**Client Initialization**:
- `base_url` (str): URL of the Docker server (default: `http://localhost:8000`)
- `timeout` (float): Request timeout in seconds (default: 30.0)
- `verify_ssl` (bool): Verify SSL certificates (default: True)
- `verbose` (bool): Enable verbose logging (default: True)
- `log_file` (Optional[str]): Path to log file (default: None)
**crawl() Method**:
- `urls` (List[str]): List of URLs to crawl
- `browser_config` (Optional[BrowserConfig]): Browser configuration
- `crawler_config` (Optional[CrawlerRunConfig]): Crawler configuration
- `hooks` (Optional[Dict]): Hook functions or strings - **automatically converts function objects!**
- `hooks_timeout` (int): Timeout for each hook execution in seconds (default: 30)
**Returns**:
- Single URL: `CrawlResult` object
- Multiple URLs: `List[CrawlResult]`
- Streaming: `AsyncGenerator[CrawlResult]`
### Second Approach: Direct API Calls

View File

@@ -255,6 +255,8 @@ The `SeedingConfig` object is your control panel. Here's everything you can conf
| `scoring_method` | str | None | Scoring method (currently "bm25") |
| `score_threshold` | float | None | Minimum score to include URL |
| `filter_nonsense_urls` | bool | True | Filter out utility URLs (robots.txt, etc.) |
| `cache_ttl_hours` | int | 24 | Hours before sitemap cache expires (0 = no TTL) |
| `validate_sitemap_lastmod` | bool | True | Check sitemap's lastmod and refetch if newer |
#### Pattern Matching Examples
@@ -968,10 +970,49 @@ config = SeedingConfig(
The seeder automatically caches results to speed up repeated operations:
- **Common Crawl cache**: `~/.crawl4ai/seeder_cache/[index]_[domain]_[hash].jsonl`
- **Sitemap cache**: `~/.crawl4ai/seeder_cache/sitemap_[domain]_[hash].jsonl`
- **Sitemap cache**: `~/.crawl4ai/seeder_cache/sitemap_[domain]_[hash].json`
- **HEAD data cache**: `~/.cache/url_seeder/head/[hash].json`
Cache expires after 7 days by default. Use `force=True` to refresh.
#### Smart TTL Cache for Sitemaps
Sitemap caches now include intelligent validation:
```python
# Default: 24-hour TTL with lastmod validation
config = SeedingConfig(
source="sitemap",
cache_ttl_hours=24, # Cache expires after 24 hours
validate_sitemap_lastmod=True # Also check if sitemap was updated
)
# Aggressive caching (1 week, no lastmod check)
config = SeedingConfig(
source="sitemap",
cache_ttl_hours=168, # 7 days
validate_sitemap_lastmod=False # Trust TTL only
)
# Always validate (no TTL, only lastmod)
config = SeedingConfig(
source="sitemap",
cache_ttl_hours=0, # Disable TTL
validate_sitemap_lastmod=True # Refetch if sitemap has newer lastmod
)
# Always fresh (bypass cache completely)
config = SeedingConfig(
source="sitemap",
force=True # Ignore all caching
)
```
**Cache validation priority:**
1. `force=True` → Always refetch
2. Cache doesn't exist → Fetch fresh
3. `validate_sitemap_lastmod=True` and sitemap has newer `<lastmod>` → Refetch
4. `cache_ttl_hours > 0` and cache is older than TTL → Refetch
5. Cache corrupted → Refetch (automatic recovery)
6. Otherwise → Use cache
### Pattern Matching Strategies
@@ -1060,6 +1101,9 @@ config = SeedingConfig(
| Rate limit errors | Reduce `hits_per_sec` and `concurrency` |
| Memory issues with large sites | Use `max_urls` to limit results, reduce `concurrency` |
| Connection not closed | Use context manager or call `await seeder.close()` |
| Stale/outdated URLs | Set `cache_ttl_hours=0` or use `force=True` |
| Cache not updating | Check `validate_sitemap_lastmod=True`, or use `force=True` |
| Incomplete URL list | Delete cache file and refetch, or use `force=True` |
### Performance Benchmarks
@@ -1119,6 +1163,7 @@ config = SeedingConfig(
3. **Context Manager Support**: Automatic cleanup with `async with` statement
4. **URL-Based Scoring**: Smart filtering even without head extraction
5. **Smart URL Filtering**: Automatically excludes utility/nonsense URLs
6. **Dual Caching**: Separate caches for URL lists and metadata
6. **Smart TTL Cache**: Sitemap caches with TTL expiry and lastmod validation
7. **Automatic Cache Recovery**: Corrupted or incomplete caches are automatically refreshed
Now go forth and seed intelligently! 🌱🚀
Now go forth and seed intelligently!

View File

@@ -20,10 +20,10 @@ In some cases, you need to extract **complex or unstructured** information from
## 2. Provider-Agnostic via LiteLLM
You can use LlmConfig, to quickly configure multiple variations of LLMs and experiment with them to find the optimal one for your use case. You can read more about LlmConfig [here](/api/parameters).
You can use LLMConfig, to quickly configure multiple variations of LLMs and experiment with them to find the optimal one for your use case. You can read more about LLMConfig [here](/api/parameters).
```python
llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
```
Crawl4AI uses a “provider string” (e.g., `"openai/gpt-4o"`, `"ollama/llama2.0"`, `"aws/titan"`) to identify your LLM. **Any** model that LiteLLM supports is fair game. You just provide:
@@ -58,7 +58,7 @@ For structured data, `"schema"` is recommended. You provide `schema=YourPydantic
Below is an overview of important LLM extraction parameters. All are typically set inside `LLMExtractionStrategy(...)`. You then put that strategy in your `CrawlerRunConfig(..., extraction_strategy=...)`.
1. **`llmConfig`** (LlmConfig): e.g., `"openai/gpt-4"`, `"ollama/llama2"`.
1. **`llm_config`** (LLMConfig): e.g., `"openai/gpt-4"`, `"ollama/llama2"`.
2. **`schema`** (dict): A JSON schema describing the fields you want. Usually generated by `YourModel.model_json_schema()`.
3. **`extraction_type`** (str): `"schema"` or `"block"`.
4. **`instruction`** (str): Prompt text telling the LLM what you want extracted. E.g., “Extract these fields as a JSON array.”
@@ -112,7 +112,7 @@ async def main():
# 1. Define the LLM extraction strategy
llm_strategy = LLMExtractionStrategy(
llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv('OPENAI_API_KEY')),
schema=Product.schema_json(), # Or use model_json_schema()
schema=Product.model_json_schema(), # Or use model_json_schema()
extraction_type="schema",
instruction="Extract all product objects with 'name' and 'price' from the content.",
chunk_token_threshold=1000,
@@ -238,7 +238,7 @@ class KnowledgeGraph(BaseModel):
async def main():
# LLM extraction strategy
llm_strat = LLMExtractionStrategy(
llmConfig = LLMConfig(provider="openai/gpt-4", api_token=os.getenv('OPENAI_API_KEY')),
llm_config = LLMConfig(provider="openai/gpt-4", api_token=os.getenv('OPENAI_API_KEY')),
schema=KnowledgeGraph.model_json_schema(),
extraction_type="schema",
instruction="Extract entities and relationships from the content. Return valid JSON.",

View File

@@ -712,10 +712,106 @@ strategy = JsonCssExtractionStrategy(css_schema)
3. **Consider Both CSS and XPath**: Try both schema types and choose the one that works best for your specific case.
4. **Cache Generated Schemas**: Since generation uses LLM, save successful schemas for reuse.
5. **API Token Security**: Never hardcode API tokens. Use environment variables or secure configuration management.
6. **Choose Provider Wisely**:
6. **Choose Provider Wisely**:
- Use OpenAI for production-quality schemas
- Use Ollama for development, testing, or when you need a self-hosted solution
### Multi-Sample Schema Generation
When scraping multiple pages with varying DOM structures (e.g., product pages where table rows appear in different positions), single-sample schema generation may produce **fragile selectors** like `tr:nth-child(6)` that break on other pages.
**The Problem:**
```
Page A: Manufacturer is in row 6 → selector: tr:nth-child(6) td a
Page B: Manufacturer is in row 5 → selector FAILS
Page C: Manufacturer is in row 7 → selector FAILS
```
**The Solution:** Provide multiple HTML samples so the LLM identifies stable patterns that work across all pages.
```python
from crawl4ai import JsonCssExtractionStrategy, LLMConfig
# Collect HTML samples from different pages
html_sample_1 = """
<table class="specs">
<tr><td>Brand</td><td>Apple</td></tr>
<tr><td>Manufacturer</td><td><a href="/m/apple">Apple Inc</a></td></tr>
</table>
"""
html_sample_2 = """
<table class="specs">
<tr><td>Manufacturer</td><td><a href="/m/samsung">Samsung</a></td></tr>
<tr><td>Brand</td><td>Galaxy</td></tr>
</table>
"""
html_sample_3 = """
<table class="specs">
<tr><td>Model</td><td>Pixel 8</td></tr>
<tr><td>Brand</td><td>Google</td></tr>
<tr><td>Manufacturer</td><td><a href="/m/google">Google LLC</a></td></tr>
</table>
"""
# Combine samples with labels
combined_html = """
## HTML Sample 1 (Product A):
```html
""" + html_sample_1 + """
```
## HTML Sample 2 (Product B):
```html
""" + html_sample_2 + """
```
## HTML Sample 3 (Product C):
```html
""" + html_sample_3 + """
```
"""
# Provide instructions for stable selectors
query = """
IMPORTANT: I'm providing 3 HTML samples from different product pages.
The manufacturer field appears in different row positions across pages.
Generate selectors using stable attributes like href patterns (e.g., a[href*='/m/'])
instead of fragile positional selectors like nth-child().
Extract: manufacturer name and link.
"""
# Generate schema with multi-sample awareness
schema = JsonCssExtractionStrategy.generate_schema(
html=combined_html,
query=query,
schema_type="CSS",
llm_config=LLMConfig(provider="openai/gpt-4o", api_token="your-token")
)
# The generated schema will use stable selectors like:
# a[href*="/m/"] instead of tr:nth-child(6) td a
print(schema)
```
**Key Points for Multi-Sample Queries:**
1. **Format samples clearly** - Use markdown headers and code blocks to separate samples
2. **State the number of samples** - "I'm providing 3 HTML samples..."
3. **Explain the variation** - "...the manufacturer field appears in different row positions"
4. **Request stable selectors** - "Use href patterns, data attributes, or class names instead of nth-child"
**Stable vs Fragile Selectors:**
| Fragile (single sample) | Stable (multi-sample) |
|------------------------|----------------------|
| `tr:nth-child(6) td a` | `a[href*="/m/"]` |
| `div:nth-child(3) .price` | `.price, [data-price]` |
| `ul li:first-child` | `li[data-featured="true"]` |
This approach lets you generate schemas once that work reliably across hundreds of similar pages with varying structures.
---
## 10. Conclusion

View File

@@ -55,9 +55,40 @@
</div>
---
#### 🚀 Crawl4AI Cloud API — Closed Beta (Launching Soon)
Reliable, large-scale web extraction, now built to be _**drastically more cost-effective**_ than any of the existing solutions.
👉 **Apply [here](https://forms.gle/E9MyPaNXACnAMaqG7) for early access**
_Well be onboarding in phases and working closely with early users.
Limited slots._
---
Crawl4AI is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for large language models, AI agents, and data pipelines. Fully open source, flexible, and built for real-time performance, **Crawl4AI** empowers developers with unmatched speed, precision, and deployment ease.
> **Note**: If you're looking for the old documentation, you can access it [here](https://old.docs.crawl4ai.com).
> Enjoy using Crawl4AI? Consider **[becoming a sponsor](https://github.com/sponsors/unclecode)** to support ongoing development and community growth!
## 🆕 AI Assistant Skill Now Available!
<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 20px; border-radius: 10px; margin: 20px 0; box-shadow: 0 4px 6px rgba(0,0,0,0.1);">
<h3 style="color: white; margin: 0 0 10px 0;">🤖 Crawl4AI Skill for Claude & AI Assistants</h3>
<p style="color: white; margin: 10px 0;">Supercharge your AI coding assistant with complete Crawl4AI knowledge! Download our comprehensive skill package that includes:</p>
<ul style="color: white; margin: 10px 0;">
<li>📚 Complete SDK reference (23K+ words)</li>
<li>🚀 Ready-to-use extraction scripts</li>
<li>⚡ Schema generation for efficient scraping</li>
<li>🔧 Version 0.7.4 compatible</li>
</ul>
<div style="text-align: center; margin-top: 15px;">
<a href="assets/crawl4ai-skill.zip" download style="background: white; color: #667eea; padding: 12px 30px; border-radius: 5px; text-decoration: none; font-weight: bold; display: inline-block; transition: transform 0.2s;">
📦 Download Skill Package
</a>
</div>
<p style="color: white; margin: 15px 0 0 0; font-size: 0.9em; text-align: center;">
Works with Claude, Cursor, Windsurf, and other AI coding assistants. Import the .zip file into your AI assistant's skill/knowledge system.
</p>
</div>
## 🎯 New: Adaptive Web Crawling

View File

@@ -529,8 +529,19 @@ class AdminDashboard {
</label>
</div>
<div class="form-group full-width">
<label>Integration Guide</label>
<textarea id="form-integration" rows="10">${app?.integration_guide || ''}</textarea>
<label>Long Description (Markdown - Overview tab)</label>
<textarea id="form-long-description" rows="10" placeholder="Enter detailed description with markdown formatting...">${app?.long_description || ''}</textarea>
<small>Markdown support: **bold**, *italic*, [links](url), # headers, code blocks, lists</small>
</div>
<div class="form-group full-width">
<label>Integration Guide (Markdown - Integration tab)</label>
<textarea id="form-integration" rows="20" placeholder="Enter integration guide with installation, examples, and code snippets using markdown...">${app?.integration_guide || ''}</textarea>
<small>Single markdown field with installation, examples, and complete guide. Code blocks get auto copy buttons.</small>
</div>
<div class="form-group full-width">
<label>Documentation (Markdown - Documentation tab)</label>
<textarea id="form-documentation" rows="20" placeholder="Enter documentation with API reference, examples, and best practices using markdown...">${app?.documentation || ''}</textarea>
<small>Full documentation with API reference, examples, best practices, etc.</small>
</div>
</div>
`;
@@ -712,7 +723,9 @@ class AdminDashboard {
data.contact_email = document.getElementById('form-email').value;
data.featured = document.getElementById('form-featured').checked ? 1 : 0;
data.sponsored = document.getElementById('form-sponsored').checked ? 1 : 0;
data.long_description = document.getElementById('form-long-description').value;
data.integration_guide = document.getElementById('form-integration').value;
data.documentation = document.getElementById('form-documentation').value;
} else if (type === 'articles') {
data.title = document.getElementById('form-title').value;
data.slug = this.generateSlug(data.title);

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