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.

That platform is live now: Crawl4AI Cloud.

• LLM-ready output: smart Markdown with headings, tables, code and 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: no keys needed, CLI and Docker, or the hosted cloud

• 🧹 Clean Markdown: headings, lists, tables and code blocks, in a structure an LLM reads well.
• 🎯 Fit Markdown: filters remove menus, footers and boilerplate: PruningContentFilterLXML, BM25ContentFilter (for a query) and LLMContentFilter.
• 🔗 Citations: page links become a numbered reference list.
• 🛠️ Your own strategy: plug in a custom Markdown generator.

☁️ Same in the cloud: POST /scrape returns this Markdown, with no browser to run. Docs →

• 🔎 CSS and XPath schemas: fast extraction with no LLM (JsonCssExtractionStrategy, JsonXPathExtractionStrategy, RegexExtractionStrategy).
• 🪄 Schema generator: describe what you want once; generate_schema writes a reusable schema.
• 🤖 LLM extraction: any LLM provider, open-source or hosted, into a typed JSON schema (LLMExtractionStrategy).
• 🧱 Chunking: topic, regex and sentence chunking for long pages.
• 🌌 Cosine similarity: find the chunks that match a query (CosineStrategy).

☁️ Same in the cloud: POST /extract, with no LLM key of your own. Docs →

• 🖥️ Your own browser: persistent profiles with saved logins, cookies and settings.
• 🔄 Remote browsers: connect over the Chrome DevTools Protocol (CDP).
• 🔒 Sessions: keep a browser state across multi-step crawls.
• 🧩 Proxies: with authentication and rotation.
• 🕶️ Stealth mode: enable_stealth, and an undetected-browser adapter for sites that detect automation.
• ⚙️ Full control: headers, cookies, user agents, viewport.
• 🌍 Chromium, Firefox and WebKit.

• 🕸️ Deep crawl: BFS, DFS and best-first strategies, with crash recovery (resume_state) for long crawls.
• 🧠 Adaptive crawling: AdaptiveCrawler stops when it has learned enough to answer your query.
• 🌱 URL discovery: AsyncUrlSeeder (sitemaps, Common Crawl) and DomainMapper; prefetch=True finds URLs 5 to 10 times faster.
• 🚀 Dynamic pages: run JavaScript, wait for elements, scroll the full page (scan_full_page) for infinite scroll and lazy images.
• 📸 Screenshots and PDFs of any page.
• 🖼️ Media and links: images, audio, video, srcset, internal and external links, iframes, metadata.
• 📂 Raw HTML and local files: raw: and file://.
• 🛠️ Hooks at every step of a crawl.
• 💾 Caching to skip repeated fetches.
• ⚡ Many URLs at once: arun_many with a memory-adaptive dispatcher.

☁️ Same in the cloud: up to 50 URLs in one streamed call, or 10,000 in a background job. Docs →

• 🔐 Secure by default: every endpoint needs your CRAWL4AI_API_TOKEN.
• 🧰 REST API: /md, /html, /crawl, /crawl/stream, /screenshot, /pdf, /execute_js.
• 🤖 MCP: connect Claude Code and other agents to your own server.
• 📊 Monitoring dashboard and playground, a browser pool with pre-warmed pages.
• 🏗️ AMD64 and ARM64 images.

☁️ Rather not run a server? The cloud is the same idea, hosted. Get a key →

• 🔍 Web search API: GET /search, browser-free, ranked and cleaned. Docs →
• 💬 Answers: GET /answer gives a direct answer to a question (experimental). Docs →
• 🧪 Extraction without your own LLM key: POST /extract. Docs →
• 🧗 JS-heavy pages and bot walls: handled automatically; you never pick an engine. Docs →
• 🤝 MCP for your agent: one line in Claude Code, Codex, Cursor or OpenCode. Docs →

pip install -U crawl4ai
crawl4ai-setup      # installs and sets up the browser
crawl4ai-doctor     # checks the installation

If the browser setup fails, install it by hand:

python -m playwright install --with-deps chromium

Pre-release versions: pip install crawl4ai --pre

Development install, for contributors:

git clone https://github.com/unclecode/crawl4ai.git
cd crawl4ai
pip install -e ".[all]"     # or: pip install -e .   (the core only)

The server needs a token. Without one it answers only inside its container.

export CRAWL4AI_API_TOKEN="$(openssl rand -hex 32)"
docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g \
  -e CRAWL4AI_API_TOKEN="$CRAWL4AI_API_TOKEN" \
  unclecode/crawl4ai:latest

Test it (allow about 10 seconds for the start):

curl -s http://localhost:11235/md \
  -H "Authorization: Bearer $CRAWL4AI_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"url": "https://news.ycombinator.com"}' | jq -r .markdown

The dashboard is at http://localhost:11235/dashboard, the playground at http://localhost:11235/playground. LLM keys, MCP and every setting: Self-hosting guide.

# A page as Markdown
crwl https://news.ycombinator.com -o markdown

# Deep crawl, breadth first, at most 10 pages
crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10

# Ask a question about a page (needs an LLM key: crwl config)
crwl https://www.example.com/products -q "Extract all product prices"

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

async def main():
    run_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        markdown_generator=DefaultMarkdownGenerator(
            content_filter=PruningContentFilterLXML(threshold=0.48, threshold_type="fixed", min_word_threshold=0)
        ),
    )
    async with AsyncWebCrawler(config=BrowserConfig(headless=True)) as crawler:
        result = await crawler.arun(url="https://en.wikipedia.org/wiki/Web_crawler", config=run_config)
        print(len(result.markdown.raw_markdown), "characters of raw Markdown")
        print(len(result.markdown.fit_markdown), "characters after the filter")

asyncio.run(main())

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

schema = {
    "name": "Quotes",
    "baseSelector": "div.quote",
    "fields": [
        {"name": "text", "selector": "span.text", "type": "text"},
        {"name": "author", "selector": "small.author", "type": "text"},
        {"name": "tags", "selector": "a.tag", "type": "list", "fields": [{"name": "tag", "type": "text"}]},
    ],
}

async def main():
    run_config = CrawlerRunConfig(
        extraction_strategy=JsonCssExtractionStrategy(schema),
        scan_full_page=True,   # scroll to the end, so the page loads every quote
        scroll_delay=0.5,
        cache_mode=CacheMode.BYPASS,
    )
    async with AsyncWebCrawler(config=BrowserConfig(headless=True)) as crawler:
        result = await crawler.arun(url="https://quotes.toscrape.com/scroll", config=run_config)
        quotes = json.loads(result.extracted_content)
        print(f"Extracted {len(quotes)} quotes")
        print(json.dumps(quotes[0], indent=2))

asyncio.run(main())

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

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

async def main():
    run_config = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        extraction_strategy=LLMExtractionStrategy(
            # any provider LiteLLM supports, e.g. "ollama/llama3.3" with api_token="no-token"
            llm_config=LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY")),
            schema=ModelFee.model_json_schema(),
            extraction_type="schema",
            instruction="Extract every model name with its input and output token fee.",
        ),
    )
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(url="https://openai.com/api/pricing/", config=run_config)
        print(result.extracted_content)

asyncio.run(main())

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

async def main():
    user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
    os.makedirs(user_data_dir, exist_ok=True)
    browser_config = BrowserConfig(headless=True, user_data_dir=user_data_dir, use_persistent_context=True)
    run_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS, magic=True)
    async with AsyncWebCrawler(config=browser_config) as crawler:
        result = await crawler.arun(url="ADDRESS_OF_A_CHALLENGING_WEBSITE", config=run_config)
        print(result.success, len(result.markdown))

asyncio.run(main())

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