Files
crawl4ai/docs/md_v2/api/parameters.md
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

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1. BrowserConfig Controlling the Browser

BrowserConfig focuses on how the browser is launched and behaves. This includes headless mode, proxies, user agents, and other environment tweaks.

from crawl4ai import AsyncWebCrawler, BrowserConfig

browser_cfg = BrowserConfig(
    browser_type="chromium",
    headless=True,
    viewport_width=1280,
    viewport_height=720,
    proxy="http://user:pass@proxy:8080",
    user_agent="Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 Chrome/116.0.0.0 Safari/537.36",
)

1.1 Parameter Highlights

Parameter Type / Default What It Does
browser_type "chromium", "firefox", "webkit"
(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.
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).
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": "...", ...}.
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.
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.
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.
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.
extra_args list (default: []) Additional flags for the underlying browser process, e.g. ["--disable-extensions"].

Tips:

  • Set headless=False to visually debug how pages load or how interactions proceed.
  • If you need authentication storage or repeated sessions, consider use_persistent_context=True and specify user_data_dir.
  • For large pages, you might need a bigger viewport_width and viewport_height to handle dynamic content.

2. CrawlerRunConfig Controlling Each Crawl

While BrowserConfig sets up the environment, CrawlerRunConfig details how each crawl operation should behave: caching, content filtering, link or domain blocking, timeouts, JavaScript code, etc.

from crawl4ai import AsyncWebCrawler, CrawlerRunConfig

run_cfg = CrawlerRunConfig(
    wait_for="css:.main-content",
    word_count_threshold=15,
    excluded_tags=["nav", "footer"],
    exclude_external_links=True,
    stream=True,  # Enable streaming for arun_many()
)

2.1 Parameter Highlights

We group them by category.

A) Content Processing

Parameter Type / Default What It Does
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.).
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.
excluded_tags list (None) Removes entire tags (e.g. ["script", "style"]).
excluded_selector str (None) Like css_selector but to exclude. E.g. "#ads, .tracker".
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.
remove_forms bool (False) If True, remove all <form> elements.

B) 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).

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

Parameter Type / Default What It Does
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_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.
mean_delay and max_range float (0.1, 0.3) If you call arun_many(), these define random delay intervals between crawls, helping avoid detection or rate limits.
semaphore_count int (5) Max concurrency for arun_many(). Increase if you have resources for parallel crawls.

D) 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.
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.
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.
override_navigator bool (False) Override navigator properties in JS for stealth.
magic bool (False) Automatic handling of popups/consent banners. Experimental.
adjust_viewport_to_content bool (False) Resizes viewport to match page content height.

If your page is a single-page app with repeated JS updates, set js_only=True in subsequent calls, plus a session_id for reusing the same tab.


E) Media Handling

Parameter Type / Default What It Does
screenshot bool (False) Capture a screenshot (base64) in result.screenshot.
screenshot_wait_for float or None Extra wait time before the screenshot.
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_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.

F) Link/Domain Handling

Parameter Type / Default What It Does
exclude_social_media_domains list (e.g. Facebook/Twitter) A default list can be extended. Any link to these domains is removed from final output.
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"]).
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

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.

H) Virtual Scroll Configuration

Parameter Type / Default What It Does
virtual_scroll_config VirtualScrollConfig or dict (None) Configuration for handling virtualized scrolling on sites like Twitter/Instagram where content is replaced rather than appended.

When sites use virtual scrolling (content replaced as you scroll), use VirtualScrollConfig:

from crawl4ai import VirtualScrollConfig

virtual_config = VirtualScrollConfig(
    container_selector="#timeline",    # CSS selector for scrollable container
    scroll_count=30,                   # Number of times to scroll
    scroll_by="container_height",      # How much to scroll: "container_height", "page_height", or pixels (e.g. 500)
    wait_after_scroll=0.5             # Seconds to wait after each scroll for content to load
)

config = CrawlerRunConfig(
    virtual_scroll_config=virtual_config
)

VirtualScrollConfig Parameters:

Parameter Type / Default What It Does
container_selector str (required) CSS selector for the scrollable container (e.g., "#feed", ".timeline")
scroll_count int (10) Maximum number of scrolls to perform
scroll_by str or int ("container_height") Scroll amount: "container_height", "page_height", or pixels (e.g., 500)
wait_after_scroll float (0.5) Time in seconds to wait after each scroll for new content to load

When to use Virtual Scroll vs scan_full_page:

  • Use virtual_scroll_config when content is replaced during scroll (Twitter, Instagram)
  • Use scan_full_page when content is appended during scroll (traditional infinite scroll)

See Virtual Scroll documentation for detailed examples.


I) URL Matching Configuration

Parameter Type / Default What It Does
url_matcher UrlMatcher (None) Pattern(s) to match URLs against. Can be: string (glob), function, or list of mixed types. None means match ALL URLs
match_mode MatchMode (MatchMode.OR) How to combine multiple matchers in a list: MatchMode.OR (any match) or MatchMode.AND (all must match)

The url_matcher parameter enables URL-specific configurations when used with arun_many():

from crawl4ai import CrawlerRunConfig, MatchMode
from crawl4ai.processors.pdf import PDFContentScrapingStrategy
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy

# Simple string pattern (glob-style)
pdf_config = CrawlerRunConfig(
    url_matcher="*.pdf",
    scraping_strategy=PDFContentScrapingStrategy()
)

# Multiple patterns with OR logic (default)
blog_config = CrawlerRunConfig(
    url_matcher=["*/blog/*", "*/article/*", "*/news/*"],
    match_mode=MatchMode.OR  # Any pattern matches
)

# Function matcher
api_config = CrawlerRunConfig(
    url_matcher=lambda url: 'api' in url or url.endswith('.json'),
    # Other settings like extraction_strategy
)

# Mixed: String + Function with AND logic
complex_config = CrawlerRunConfig(
    url_matcher=[
        lambda url: url.startswith('https://'),  # Must be HTTPS
        "*.org/*",                               # Must be .org domain
        lambda url: 'docs' in url                # Must contain 'docs'
    ],
    match_mode=MatchMode.AND  # ALL conditions must match
)

# Combined patterns and functions with AND logic
secure_docs = CrawlerRunConfig(
    url_matcher=["https://*", lambda url: '.doc' in url],
    match_mode=MatchMode.AND  # Must be HTTPS AND contain .doc
)

# Default config - matches ALL URLs
default_config = CrawlerRunConfig()  # No url_matcher = matches everything

UrlMatcher Types:

  • None (default): When url_matcher is None or not set, the config matches ALL URLs
  • String patterns: Glob-style patterns like "*.pdf", "*/api/*", "https://*.example.com/*"
  • Functions: lambda url: bool - Custom logic for complex matching
  • Lists: Mix strings and functions, combined with MatchMode.OR or MatchMode.AND

Important Behavior:

  • When passing a list of configs to arun_many(), URLs are matched against each config's url_matcher in order. First match wins!
  • 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

Both BrowserConfig and CrawlerRunConfig provide a clone() method to create modified copies:

# Create a base configuration
base_config = CrawlerRunConfig(
    cache_mode=CacheMode.ENABLED,
    word_count_threshold=200
)

# Create variations using clone()
stream_config = base_config.clone(stream=True)
no_cache_config = base_config.clone(
    cache_mode=CacheMode.BYPASS,
    stream=True
)

The clone() method is particularly useful when you need slightly different configurations for different use cases, without modifying the original config.

2.3 Example Usage

import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode

async def main():
    # Configure the browser
    browser_cfg = BrowserConfig(
        headless=False,
        viewport_width=1280,
        viewport_height=720,
        proxy="http://user:pass@myproxy:8080",
        text_mode=True
    )

    # Configure the run
    run_cfg = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        session_id="my_session",
        css_selector="main.article",
        excluded_tags=["script", "style"],
        exclude_external_links=True,
        wait_for="css:.article-loaded",
        screenshot=True,
        stream=True
    )

    async with AsyncWebCrawler(config=browser_cfg) as crawler:
        result = await crawler.arun(
            url="https://example.com/news",
            config=run_cfg
        )
        if result.success:
            print("Final cleaned_html length:", len(result.cleaned_html))
            if result.screenshot:
                print("Screenshot captured (base64, length):", len(result.screenshot))
        else:
            print("Crawl failed:", result.error_message)

if __name__ == "__main__":
    asyncio.run(main())

2.4 Compliance & Ethics

Parameter Type / Default What It Does
check_robots_txt bool (False) When True, checks and respects robots.txt rules before crawling. Uses efficient caching with SQLite backend.
user_agent str (None) User agent string to identify your crawler. Used for robots.txt checking when enabled.
run_config = CrawlerRunConfig(
    check_robots_txt=True,  # Enable robots.txt compliance
    user_agent="MyBot/1.0"  # Identify your crawler
)

3. LLMConfig - Setting up LLM providers

LLMConfig is useful to pass LLM provider config to strategies and functions that rely on LLMs to do extraction, filtering, schema generation etc. Currently it can be used in the following -

  1. LLMExtractionStrategy
  2. LLMContentFilter
  3. JsonCssExtractionStrategy.generate_schema
  4. JsonXPathExtractionStrategy.generate_schema

3.1 Parameters

Parameter Type / Default What It Does
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"
(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
2. API token of LLM provider
eg: api_token = "gsk_1ClHGGJ7Lpn4WGybR7vNWGdyb3FY7zXEw3SCiy0BAVM9lL8CQv"
3. Environment variable - use with prefix "env:"
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

3.2 Example Usage

llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))

4. Putting It All Together

  • Use BrowserConfig for global browser settings: engine, headless, proxy, user agent.
  • Use CrawlerRunConfig for each crawls context: how to filter content, handle caching, wait for dynamic elements, or run JS.
  • Pass both configs to AsyncWebCrawler (the BrowserConfig) and then to arun() (the CrawlerRunConfig).
  • Use LLMConfig for LLM provider configurations that can be used across all extraction, filtering, schema generation tasks. Can be used in - LLMExtractionStrategy, LLMContentFilter, JsonCssExtractionStrategy.generate_schema & JsonXPathExtractionStrategy.generate_schema
# Create a modified copy with the clone() method
stream_cfg = run_cfg.clone(
    stream=True,
    cache_mode=CacheMode.BYPASS
)