Crawl4AI: Open-source web crawler and scraper for LLMs

Reliable, large-scale web extraction, now built to be * drastically more cost-effective* than any of the existing solutions.

👉 Apply here for early access

We’ll be onboarding in phases and working closely with early users. Limited slots.

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 v0.9.1

✨ New in v0.9.1: Patch release with 12 bug fixes across Docker, browser, and core. Adds preserve_classes

/preserve_tags

whitelist for PruningContentFilter, fixes Windows browser crash, Docker auth gate UI, HTTP timeout unit mismatch, and more. Release notes →

✨ Recent v0.9.0: Major secure-by-default release of the Docker API server. Auth is on by default, the server binds loopback unless given a token, and the request body is now an untrusted trust boundary. Release notes →

✨ Recent v0.8.7: Security-hardening release. Fixes critical Docker API vulnerabilities (RCE, SSRF, auth bypass, file write, XSS, hardcoded JWT secret), adds DomainMapper, and ships scraping, deep-crawl, and LLM fixes. Release notes →

✨ Previous 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. Release notes →

✨ Previous 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 →

🤓 My Personal Story #

I grew up on an Amstrad, thanks to my dad, and never stopped building. In grad school I specialized in NLP and built crawlers for research. That’s where I learned how much extraction matters.

In 2023, I needed web-to-Markdown. The “open source” option wanted an account, API token, and $16, and still under-delivered. I went turbo anger mode, built Crawl4AI in days, and it went viral. Now it’s the most-starred crawler on GitHub.

I made it open source for availability, anyone can use it without a gate. Now I’m building the platform for affordability, anyone can run serious crawls without breaking the bank. If that resonates, join in, send feedback, or just crawl something amazing.

Why developers pick Crawl4AI #

LLM ready output, smart Markdown with headings, tables, code, citation hints** Fast in practice**, async browser pool, caching, minimal hops** Full control**, sessions, proxies, cookies, user scripts, hooks** Adaptive intelligence**, learns site patterns, explores only what matters** Deploy anywhere**, zero keys, CLI and Docker, cloud friendly

• Install Crawl4AI:

pip install -U crawl4ai

pip install crawl4ai --pre

crawl4ai-setup

crawl4ai-doctor

If you encounter any browser-related issues, you can install them manually:

python -m playwright install --with-deps chromium

• Run a simple web crawl with Python:

import asyncio
from crawl4ai import *

async def main():
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url="https://www.nbcnews.com/business",
        )
        print(result.markdown)

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

• Or use the new command-line interface:

crwl https://www.nbcnews.com/business -o markdown

crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10

crwl https://www.example.com/products -q "Extract all product prices"

🎉

Sponsorship Program Now Open!After powering 51K+ developers and 1 year of growth, Crawl4AI is launching dedicated support forstartupsandenterprises. Be among the first 50Founding Sponsorsfor permanent recognition in our Hall of Fame.

Crawl4AI is the #1 trending open-source web crawler on GitHub. Your support keeps it independent, innovative, and free for the community — while giving you direct access to premium benefits.

🌱 Believer ($5/mo)— Join the movement for data democratization🚀 Builder ($50/mo)— Priority support & early access to features💼 Growing Team ($500/mo)— Bi-weekly syncs & optimization help🏢 Data Infrastructure Partner ($2000/mo)— Full partnership with dedicated support

Custom arrangements available - seeSPONSORS.mdfor details & contact

Why sponsor?

No rate-limited APIs. No lock-in. Build and own your data pipeline with direct guidance from the creator of Crawl4AI.

📝 Markdown Generation #

• 🧹 Clean Markdown: Generates clean, structured Markdown with accurate formatting. - 🎯 Fit Markdown: Heuristic-based filtering to remove noise and irrelevant parts for AI-friendly processing. - 🔗 Citations and References: Converts page links into a numbered reference list with clean citations. - 🛠️ Custom Strategies: Users can create their own Markdown generation strategies tailored to specific needs. - 📚 BM25 Algorithm: Employs BM25-based filtering for extracting core information and removing irrelevant content.

📊 Structured Data Extraction #

• 🤖 LLM-Driven Extraction: Supports all LLMs (open-source and proprietary) for structured data extraction. - 🧱 Chunking Strategies: Implements chunking (topic-based, regex, sentence-level) for targeted content processing. - 🌌 Cosine Similarity: Find relevant content chunks based on user queries for semantic extraction. - 🔎 CSS-Based Extraction: Fast schema-based data extraction using XPath and CSS selectors. - 🔧 Schema Definition: Define custom schemas for extracting structured JSON from repetitive patterns.

🌐 Browser Integration #

• 🖥️ Managed Browser: Use user-owned browsers with full control, avoiding bot detection. - 🔄 Remote Browser Control: Connect to Chrome Developer Tools Protocol for remote, large-scale data extraction. - 👤 Browser Profiler: Create and manage persistent profiles with saved authentication states, cookies, and settings. - 🔒 Session Management: Preserve browser states and reuse them for multi-step crawling. - 🧩 Proxy Support: Seamlessly connect to proxies with authentication for secure access. - ⚙️ Full Browser Control: Modify headers, cookies, user agents, and more for tailored crawling setups. - 🌍 Multi-Browser Support: Compatible with Chromium, Firefox, and WebKit. - 📐 Dynamic Viewport Adjustment: Automatically adjusts the browser viewport to match page content, ensuring complete rendering and capturing of all elements.

🔎 Crawling & Scraping #

• 🖼️ Media Support: Extract images, audio, videos, and responsive image formats likesrcset

andpicture

. - 🚀 Dynamic Crawling: Execute JS and wait for async or sync for dynamic content extraction. - 📸 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 (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. - 🕵️ Lazy Load Handling: Waits for images to fully load, ensuring no content is missed due to lazy . - 🔄 Full-Page Scanning: Simulates scrolling to load and capture all dynamic content, perfect for infinite scroll pages.

🚀 Deployment #

• 🐳 Dockerized Setup: Optimized Docker image with FastAPI server for easy deployment. - 🔑 Secure Authentication: Built-in JWT token authentication for API security. - 🔄 API Gateway: One-click deployment with secure token authentication for API-based workflows. - 🌐 Scalable Architecture: Designed for mass-scale production and optimized server performance. - ☁️ Cloud Deployment: Ready-to-deploy configurations for major cloud platforms.

🎯 Additional Features #

• 🕶️ Stealth Mode: Avoid bot detection by mimicking real users. - 🏷️ Tag-Based Content Extraction: Refine crawling based on custom tags, headers, or metadata. - 🔗 Link Analysis: Extract and analyze all links for detailed data exploration. - 🛡️ Error Handling: Robust error management for seamless execution. - 🔐 CORS & Static Serving: Supports filesystem-based caching and cross-origin requests. - 📖 Clear Documentation: Simplified and updated guides for onboarding and advanced usage. - 🙌 Community Recognition: Acknowledges contributors and pull requests for transparency.

✨ Visit our Documentation Website

Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.

🐍 Using pip #

Choose the installation option that best fits your needs:

For basic web crawling and scraping tasks:

pip install crawl4ai
crawl4ai-setup # Setup the browser

By default, this will install the asynchronous version of Crawl4AI, using Playwright for web crawling.

👉 Note: When you install Crawl4AI, the crawl4ai-setup

should automatically install and set up Playwright. However, if you encounter any Playwright-related errors, you can manually install it using one of these methods:

Through the command line:

playwright install

If the above doesn't work, try this more specific command:

python -m playwright install chromium

This second method has proven to be more reliable in some cases.

The sync version is deprecated and will be removed in future versions. If you need the synchronous version using Selenium:

pip install crawl4ai[sync]

For contributors who plan to modify the source code:

git clone https://github.com/unclecode/crawl4ai.git
cd crawl4ai
pip install -e .                    # Basic installation in editable mode

Install optional features:

pip install -e ".[torch]"           # With PyTorch features
pip install -e ".[transformer]"     # With Transformer features
pip install -e ".[cosine]"          # With cosine similarity features
pip install -e ".[sync]"            # With synchronous crawling (Selenium)
pip install -e ".[all]"             # Install all optional features

🐳 Docker Deployment #

🚀

Now Available!Our completely redesigned Docker implementation is here! This new solution makes deployment more efficient and seamless than ever.

The new Docker implementation includes:

Real-time Monitoring Dashboard with live system metrics and browser pool visibilityBrowser pooling with page pre-warming for faster response timesInteractive playground to test and generate request codeMCP integration for direct connection to AI tools like Claude CodeComprehensive API endpoints including HTML extraction, screenshots, PDF generation, and JavaScript executionMulti-architecture support with automatic detection (AMD64/ARM64)Optimized resources with improved memory management

docker pull unclecode/crawl4ai:latest
docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:latest

Run a quick test (works for both Docker options):

import requests

response = requests.post(
    "http://localhost:11235/crawl",
    json={"urls": ["https://example.com"], "priority": 10}
)
if response.status_code == 200:
    print("Crawl job submitted successfully.")
    
if "results" in response.json():
    results = response.json()["results"]
    print("Crawl job completed. Results:")
    for result in results:
        print(result)
else:
    task_id = response.json()["task_id"]
    print(f"Crawl job submitted. Task ID:: {task_id}")
    result = requests.get(f"http://localhost:11235/task/{task_id}")

For more examples, see our Docker Examples. For advanced configuration, monitoring features, and production deployment, see our Self-Hosting Guide.

You can check the project structure in the directory docs/examples. Over there, you can find a variety of examples; here, some popular examples are shared.

📝 Heuristic Markdown Generation with Clean and Fit Markdown #

import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai.content_filter_strategy import PruningContentFilter, BM25ContentFilter
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator

async def main():
    browser_config = BrowserConfig(
        headless=True,  
        verbose=True,
    )
    run_config = CrawlerRunConfig(
        cache_mode=CacheMode.ENABLED,
        markdown_generator=DefaultMarkdownGenerator(
            content_filter=PruningContentFilter(threshold=0.48, threshold_type="fixed", min_word_threshold=0)
        ),
    )
    
    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(
            url="https://docs.micronaut.io/4.9.9/guide/",
            config=run_config
        )
        print(len(result.markdown.raw_markdown))
        print(len(result.markdown.fit_markdown))

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

🖥️ Executing JavaScript & Extract Structured Data without LLMs #

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

async def main():
    schema = {
    "name": "KidoCode Courses",
    "baseSelector": "section.charge-methodology .w-tab-content > div",
    "fields": [
        {
            "name": "section_title",
            "selector": "h3.heading-50",
            "type": "text",
        },
        {
            "name": "section_description",
            "selector": ".charge-content",
            "type": "text",
        },
        {
            "name": "course_name",
            "selector": ".text-block-93",
            "type": "text",
        },
        {
            "name": "course_description",
            "selector": ".course-content-text",
            "type": "text",
        },
        {
            "name": "course_icon",
            "selector": ".image-92",
            "type": "attribute",
            "attribute": "src"
        }
    ]
}

    extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)

    browser_config = BrowserConfig(
        headless=False,
        verbose=True
    )
    run_config = CrawlerRunConfig(
        extraction_strategy=extraction_strategy,
        js_code=["""(async () => {const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");for(let tab of tabs) {tab.scrollIntoView();tab.click();await new Promise(r => setTimeout(r, 500));}})();"""],
        cache_mode=CacheMode.BYPASS
    )
        
    async with AsyncWebCrawler(config=browser_config) as crawler:
        
        result = await crawler.arun(
            url="https://www.kidocode.com/degrees/technology",
            config=run_config
        )

        companies = json.loads(result.extracted_content)
        print(f"Successfully extracted {len(companies)} companies")
        print(json.dumps(companies[0], indent=2))

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

📚 Extracting Structured Data with LLMs #

import os
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
from crawl4ai import LLMExtractionStrategy
from pydantic import BaseModel, Field

class OpenAIModelFee(BaseModel):
    model_name: str = Field(..., description="Name of the OpenAI model.")
    input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
    output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")

async def main():
    browser_config = BrowserConfig(verbose=True)
    run_config = CrawlerRunConfig(
        word_count_threshold=1,
        extraction_strategy=LLMExtractionStrategy(
            llm_config = LLMConfig(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY')), 
            schema=OpenAIModelFee.schema(),
            extraction_type="schema",
            instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
            Do not miss any models in the entire content. One extracted model JSON format should look like this: 
            {"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}."""
        ),            
        cache_mode=CacheMode.BYPASS,
    )
    
    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(
            url='https://openai.com/api/pricing/',
            config=run_config
        )
        print(result.extracted_content)

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

🤖 Using Your own Browser with Custom User Profile #

import os, sys
from pathlib import Path
import asyncio, time
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode

async def test_news_crawl():
    user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
    os.makedirs(user_data_dir, exist_ok=True)

    browser_config = BrowserConfig(
        verbose=True,
        headless=True,
        user_data_dir=user_data_dir,
        use_persistent_context=True,
    )
    run_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS
    )
    
    async with AsyncWebCrawler(config=browser_config) as crawler:
        url = "ADDRESS_OF_A_CHALLENGING_WEBSITE"
        
        result = await crawler.arun(
            url,
            config=run_config,
            magic=True,
        )
        
        print(f"Successfully crawled {url}")
        print(f"Content length: {len(result.markdown)}")

Version 0.9.1 Release Highlights - Bug Fixes & PruningContentFilter Whitelist

A patch release with 12 bug fixes and one new feature. The new preserve_classes

/ preserve_tags

parameters for PruningContentFilter

let you whitelist CSS classes or HTML tags that should never be pruned — useful for protecting short metadata elements like author names and timestamps.

Bug fixes span Docker (auth gate UI, supervisord/redis dirs, FastAPI compatibility, redis auth), browser (Windows channel crash, context snapshot leak), core (HTTP timeout unit mismatch, best-first ordering), and extraction (html2text table attributes).

pip install -U crawl4ai

Version 0.9.0 Release Highlights - Secure-by-Default Docker Server

A major, secure-by-default release of the Docker API server. The out-of-the-box deployment is hardened with defense in depth: authentication is on by default, the server binds loopback unless you give it a token, and the network request body is treated as an untrusted trust boundary.

pip install -U crawl4ai

Version 0.8.7 Release Highlights - Security Hardening, DomainMapper & Community Fixes

A security-hardening release. Fixes critical Docker API vulnerabilities (AST sandbox escape RCE, hook sandbox RCE, hardcoded JWT secret, SSRF on webhook and crawl endpoints, arbitrary file write, monitor auth bypass, stored XSS, and unauthenticated JS execution), adds the DomainMapper feature, and ships a batch of scraping, deep-crawl, and LLM fixes. If you self-host the Docker API, upgrade immediately.

pip install -U crawl4ai

Version 0.8.6 - Security Hotfix: litellm Supply Chain Fix

Replaced litellm

dependency with unclecode-litellm

due to a PyPI supply chain compromise affecting the original package. If you're on v0.8.5 or earlier, upgrade immediately.

pip install -U crawl4ai

Version 0.8.5 Release Highlights - Anti-Bot Detection, Shadow DOM & 60+ Bug Fixes

Our biggest release since v0.8.0. Anti-bot detection with proxy escalation, Shadow DOM flattening, deep crawl cancellation, and over 60 bug fixes.

🛡️ Anti-Bot Detection & Proxy Escalation:- 3-tier detection: known vendors, generic block indicators, structural integrity checks

• Automatic retry with proxy chain and fallback fetch function

from crawl4ai import CrawlerRunConfig
from crawl4ai.async_configs import ProxyConfig

config = CrawlerRunConfig(
    proxy_config=[ProxyConfig.DIRECT, ProxyConfig(server="http://my-proxy:8080")],
    max_retries=2,
    fallback_fetch_function=my_web_unlocker,
)

🌑 Shadow DOM Flattening:- Extract content hidden inside shadow DOM components

config = CrawlerRunConfig(flatten_shadow_dom=True)

🛑 Deep Crawl Cancellation:- Stop long crawls gracefully with cancel()

orshould_cancel

callback - Works with BFS, DFS, and BestFirst strategies

• Stop long crawls gracefully with

⚙️ Config Defaults API:set_defaults()

/get_defaults()

/reset_defaults()

on BrowserConfig and CrawlerRunConfig

🔒 Critical Security Fixes:- RCE via deserialization in Docker /crawl

endpoint — removedeval()

, added allowlist - Redis CVE-2025-49844 (CVSS 10.0) — upgraded to 7.2.7

• RCE via deserialization in Docker

60+ Bug Fixes across browser management, proxy, deep crawling, extraction, CLI, and Docker

Version 0.8.0 Release Highlights - Crash Recovery & Prefetch Mode

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 persistenceresume_state

parameter to continue from a saved checkpoint- JSON-serializable state for Redis/database storage

• Works with BFS, DFS, and Best-First strategies

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

config = CrawlerRunConfig(prefetch=True)
result = await crawler.arun("https://example.com", config=config)

🔒 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

• Hooks disabled by default (

Version 0.7.8 Release Highlights - Stability & Bug Fix Release

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 inBrowserConfig.to_dict()

(#1629) - Fixed .cache

folder permissions in Docker image (#1638)

• Fixed

🤖 LLM Extraction Improvements:- Configurable rate limiter backoff with new LLMConfig

parameters (#1269):

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):

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)

• Configurable rate limiter backoff with new

🔗 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)

Version 0.7.7 Release Highlights - The Self-Hosting & Monitoring Update

📊 Real-time Monitoring Dashboard: Interactive web UI with live system metrics and browser pool visibility

🔌 Comprehensive Monitor API: Complete REST API for programmatic access to all monitoring data

import httpx

async with httpx.AsyncClient() as client:
    health = await client.get("http://localhost:11235/monitor/health")

    requests = await client.get("http://localhost:11235/monitor/requests")

    browsers = await client.get("http://localhost:11235/monitor/browsers")

    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)

Version 0.7.5 Release Highlights - The Docker Hooks & Security Update

🔧 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:

from crawl4ai import hooks_to_string
from crawl4ai.docker_client import Crawl4aiDockerClient

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

hooks_code = hooks_to_string({
    "on_page_context_created": on_page_context_created,
    "before_goto": before_goto
})

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
    }
)

🤖 Enhanced LLM Integration: Custom providers with temperature control and base_url configuration - 🔒 HTTPS Preservation: Secure internal link handling withpreserve_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

Version 0.7.4 Release Highlights - The Intelligent Table Extraction & Performance Update

🚀 LLMTableExtraction: Revolutionary table extraction with intelligent chunking for massive tables:

from crawl4ai import LLMTableExtraction, LLMConfig

table_strategy = LLMTableExtraction(
    llm_config=LLMConfig(provider="openai/gpt-4.1-mini"),
    enable_chunking=True,           # Handle massive tables
    chunk_token_threshold=5000,     # Smart chunking threshold
    overlap_threshold=100,          # Maintain context between chunks
    extraction_type="structured"    # Get structured data output
)

config = CrawlerRunConfig(table_extraction_strategy=table_strategy)
result = await crawler.arun("https://complex-tables-site.com", config=config)

for table in result.tables:
    print(f"Extracted table: {len(table['data'])} rows")

⚡ Dispatcher Bug Fix: Fixed sequential processing bottleneck in arun_many for fast-completing tasks - 🧹 Memory Management Refactor: Consolidated memory utilities into main utils module for cleaner architecture - 🔧 Browser Manager Fixes: Resolved race conditions in concurrent page creation with thread-safe locking - 🔗 Advanced URL Processing: Better handling of raw:// URLs and base tag link resolution - 🛡️ Enhanced Proxy Support: Flexible proxy configuration supporting both dict and string formats

Version 0.7.3 Release Highlights - The Multi-Config Intelligence Update

🕵️ Undetected Browser Support: Bypass sophisticated bot detection systems:

from crawl4ai import AsyncWebCrawler, BrowserConfig

browser_config = BrowserConfig(
    browser_type="undetected",  # Use undetected Chrome
    headless=True,              # Can run headless with stealth
    extra_args=[
        "--disable-blink-features=AutomationControlled",
        "--disable-web-security"
    ]
)

async with AsyncWebCrawler(config=browser_config) as crawler:
    result = await crawler.arun("https://protected-site.com")

🎨 Multi-URL Configuration: Different strategies for different URL patterns in one batch:

from crawl4ai import CrawlerRunConfig, MatchMode, CacheMode
  
  configs = [
      CrawlerRunConfig(
          url_matcher=["*docs*", "*documentation*"],
          cache_mode=CacheMode.WRITE_ONLY,
          markdown_generator_options={"include_links": True}
      ),
      
      CrawlerRunConfig(
          url_matcher=lambda url: 'blog' in url or 'news' in url,
          cache_mode=CacheMode.BYPASS
      ),
      
      CrawlerRunConfig()
  ]
  
  results = await crawler.arun_many(urls, config=configs)

🧠 Memory Monitoring: Track and optimize memory usage during crawling:

from crawl4ai.memory_utils import MemoryMonitor

monitor = MemoryMonitor()
monitor.start_monitoring()

results = await crawler.arun_many(large_url_list)

report = monitor.get_report()
print(f"Peak memory: {report['peak_mb']:.1f} MB")
print(f"Efficiency: {report['efficiency']:.1f}%")

📊 Enhanced Table Extraction: Direct DataFrame conversion from web tables:

result = await crawler.arun("https://site-with-tables.com")

if result.tables:
    import pandas as pd
    for table in result.tables:
        df = pd.DataFrame(table['data'])
        print(f"Table: {df.shape[0]} rows × {df.shape[1]} columns")

💰 GitHub Sponsors: 4-tier sponsorship system for project sustainability - 🐳 Docker LLM Flexibility: Configure providers via environment variables

Version 0.7.0 Release Highlights - The Adaptive Intelligence Update

🧠 Adaptive Crawling: Your crawler now learns and adapts to website patterns automatically:

config = AdaptiveConfig(
    confidence_threshold=0.7, # Min confidence to stop crawling
    max_depth=5, # Maximum crawl depth
    max_pages=20, # Maximum number of pages to crawl
    strategy="statistical"
)

async with AsyncWebCrawler() as crawler:
    adaptive_crawler = AdaptiveCrawler(crawler, config)
    state = await adaptive_crawler.digest(
        start_url="https://news.example.com",
        query="latest news content"
    )

🌊 Virtual Scroll Support: Complete content extraction from infinite scroll pages:

scroll_config = VirtualScrollConfig(
    container_selector="[data-testid='feed']",
    scroll_count=20,
    scroll_by="container_height",
    wait_after_scroll=1.0
)

result = await crawler.arun(url, config=CrawlerRunConfig(
    virtual_scroll_config=scroll_config
))

🔗 Intelligent Link Analysis: 3-layer scoring system for smart link prioritization:

link_config = LinkPreviewConfig(
    query="machine learning tutorials",
    score_threshold=0.3,
    concurrent_requests=10
)

result = await crawler.arun(url, config=CrawlerRunConfig(
    link_preview_config=link_config,
    score_links=True
))

🎣 Async URL Seeder: Discover thousands of URLs in seconds:

seeder = AsyncUrlSeeder(SeedingConfig(
    source="sitemap+cc",
    pattern="*/blog/*",
    query="python tutorials",
    score_threshold=0.4
))

urls = await seeder.discover("https://example.com")

⚡ Performance Boost: Up to 3x faster with optimized resource handling and memory efficiency

Read the full details in our 0.7.0 Release Notes or check the CHANGELOG.

Crawl4AI follows standard Python version numbering conventions (PEP 440) to help users understand the stability and features of each release.

📈 Version Numbers Explained #

Our version numbers follow this pattern: MAJOR.MINOR.PATCH

(e.g., 0.4.3)

We use different suffixes to indicate development stages:

dev

(0.4.3dev1): Development versions, unstablea

(0.4.3a1): Alpha releases, experimental featuresb

(0.4.3b1): Beta releases, feature complete but needs testingrc

(0.4.3): Release candidates, potential final version

Regular installation (stable version):

pip install -U crawl4ai

Install pre-release versions:

pip install crawl4ai --pre

Install specific version:

pip install crawl4ai==0.4.3b1

We use pre-releases to:

• Test new features in real-world scenarios
• Gather feedback before final releases
• Ensure stability for production users
• Allow early adopters to try new features

For production environments, we recommend using the stable version. For testing new features, you can opt-in to pre-releases using the --pre

flag.

🚨

Documentation Update Alert: We're undertaking a major documentation overhaul next week to reflect recent updates and improvements. Stay tuned for a more comprehensive and up-to-date guide!

For current documentation, including installation instructions, advanced features, and API reference, visit our Documentation Website.

To check our development plans and upcoming features, visit our Roadmap.

📈 Development TODOs #

• 0. Graph Crawler: Smart website traversal using graph search algorithms for comprehensive nested page extraction
• 1. Question-Based Crawler: Natural language driven web discovery and content extraction
• 2. Knowledge-Optimal Crawler: Smart crawling that maximizes knowledge while minimizing data extraction
• 3. Agentic Crawler: Autonomous system for complex multi-step crawling operations
• 4. Automated Schema Generator: Convert natural language to extraction schemas
• 5. Domain-Specific Scrapers: Pre-configured extractors for common platforms (academic, e-commerce)
• 6. Web Embedding Index: Semantic search infrastructure for crawled content
• 7. Interactive Playground: Web UI for testing, comparing strategies with AI assistance
• 8. Performance Monitor: Real-time insights into crawler operations
• 9. Cloud Integration: One-click deployment solutions across cloud providers
• 10. Sponsorship Program: Structured support system with tiered benefits
• 11. Educational Content: "How to Crawl" video series and interactive tutorials

We welcome contributions from the open-source community. Check out our contribution guidelines for more information.

I'll help modify the license section with badges. For the halftone effect, here's a version with it:

Here's the updated license section:

This project is licensed under the Apache License 2.0, attribution is recommended via the badges below. See the Apache 2.0 License file for details.

When using Crawl4AI, you must include one of the following attribution methods:

📈 1. Badge Attribution (Recommended) #

Add one of these badges to your README, documentation, or website:

Theme	Badge
Disco Theme (Animated)
Night Theme (Dark with Neon)
Dark Theme (Classic)
Light Theme (Classic)

HTML code for adding the badges:

<!-- Disco Theme (Animated) -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-disco.svg" alt="Powered by Crawl4AI" width="200"/>
</a>

<!-- Night Theme (Dark with Neon) -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-night.svg" alt="Powered by Crawl4AI" width="200"/>
</a>

<!-- Dark Theme (Classic) -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-dark.svg" alt="Powered by Crawl4AI" width="200"/>
</a>

<!-- Light Theme (Classic) -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://raw.githubusercontent.com/unclecode/crawl4ai/main/docs/assets/powered-by-light.svg" alt="Powered by Crawl4AI" width="200"/>
</a>

<!-- Simple Shield Badge -->
<a href="https://github.com/unclecode/crawl4ai">
  <img src="https://img.shields.io/badge/Powered%20by-Crawl4AI-blue?style=flat-square" alt="Powered by Crawl4AI"/>
</a>

📖 2. Text Attribution #

Add this line to your documentation:

This project uses Crawl4AI ([https://github.com/unclecode/crawl4ai](https://github.com/unclecode/crawl4ai)) for web data extraction. ```

If you use Crawl4AI in your research or project, please cite:

@software{crawl4ai2024, author = {UncleCode}, title = {Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper}, year = {2024}, publisher = {GitHub}, journal = {GitHub Repository}, howpublished = {\url{https://github.com/unclecode/crawl4ai}}, commit = {Please use the commit hash you're working with} }

Text citation format:

UncleCode. (2024). Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper [Computer software]. GitHub. https://github.com/unclecode/crawl4ai

For questions, suggestions, or feedback, feel free to reach out:

- GitHub:
[unclecode](https://github.com/unclecode) - Twitter:
[@unclecode](https://twitter.com/unclecode) - Website:
[crawl4ai.com](https://crawl4ai.com)

Happy Crawling! 🕸️🚀

Our mission is to unlock the value of personal and enterprise data by transforming digital footprints into structured, tradeable assets. Crawl4AI empowers individuals and organizations with open-source tools to extract and structure data, fostering a shared data economy.

We envision a future where AI is powered by real human knowledge, ensuring data creators directly benefit from their contributions. By democratizing data and enabling ethical sharing, we are laying the foundation for authentic AI advancement.

## 🔑 **Key Opportunities**

**Data Capitalization**: Transform digital footprints into measurable, valuable assets.** Authentic AI Data**: Provide AI systems with real human insights.** Shared Economy**: Create a fair data marketplace that benefits data creators.

## 🚀 **Development Pathway**

**Open-Source Tools**: Community-driven platforms for transparent data extraction.** Digital Asset Structuring**: Tools to organize and value digital knowledge.** Ethical Data Marketplace**: A secure, fair platform for exchanging structured data.

For more details, see our [full mission statement](/unclecode/crawl4ai/blob/main/MISSION.md).

These companies provide core infrastructure and technology that power Crawl4AI’s capabilities — from web access and proxy networks to AI tooling and data pipelines.

Our enterprise sponsors support Crawl4AI and help scale it to power production-grade data pipelines.

Interested in partnering with Crawl4AI?

Whether you’re a proxy provider, AI infrastructure company, cloud platform, or an organization looking to support the Crawl4AI ecosystem, we’d love to hear from you.

📩 Contact: [hello@crawl4ai.com](mailto:hello@crawl4ai.com)

A heartfelt thanks to our individual supporters! Every contribution helps us keep our opensource mission alive and thriving!

Want to join them?

[Sponsor Crawl4AI →]

source & further reading

github.com — original article