Crawl4AI

Overview

Crawl4AI provides comprehensive web crawling and data extraction capabilities. This skill supports both CLI (recommended for quick tasks) and Python SDK (for programmatic control).

Choose your interface:

• CLI (crwl) - Quick, scriptable commands: CLI Guide
• Python SDK - Full programmatic control: SDK Guide

Quick Start

Installation

pip install crawl4ai
crawl4ai-setup

# Verify installation
crawl4ai-doctor

CLI (Recommended)

# Basic crawling - returns markdown
crwl https://example.com

# Get markdown output
crwl https://example.com -o markdown

# JSON output with cache bypass
crwl https://example.com -o json -v --bypass-cache

# See more examples
crwl --example

Python SDK

import asyncio
from crawl4ai import AsyncWebCrawler

async def main():
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun("https://example.com")
        print(result.markdown[:500])

asyncio.run(main())

For SDK configuration details: SDK Guide - Configuration (lines 61-150)

Core Concepts

Configuration Layers

Both CLI and SDK use the same underlying configuration:

Key Parameters

Browser Configuration:

• headless: Run with/without GUI
• viewport_width/height: Browser dimensions
• user_agent: Custom user agent
• proxy_config: Proxy settings

Crawler Configuration:

• page_timeout: Max page load time (ms)
• wait_for: CSS selector or JS condition to wait for
• cache_mode: bypass, enabled, disabled
• js_code: JavaScript to execute
• css_selector: Focus on specific element

For complete parameters: CLI Config | SDK Config

Output Content

Every crawl returns:

• markdown - Clean, formatted markdown
• html - Raw HTML
• links - Internal and external links discovered
• media - Images, videos, audio found
• extracted_content - Structured data (if extraction configured)

Markdown Generation (Primary Use Case)

Crawl4AI excels at generating clean, well-formatted markdown:

CLI

# Basic markdown
crwl https://docs.example.com -o markdown

# Filtered markdown (removes noise)
crwl https://docs.example.com -o markdown-fit

# With content filter
crwl https://docs.example.com -f filter_bm25.yml -o markdown-fit

Filter configuration:

# filter_bm25.yml (relevance-based)
type: "bm25"
query: "machine learning tutorials"
threshold: 1.0

Python SDK

from crawl4ai.content_filter_strategy import BM25ContentFilter
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator

bm25_filter = BM25ContentFilter(user_query="machine learning", bm25_threshold=1.0)
md_generator = DefaultMarkdownGenerator(content_filter=bm25_filter)

config = CrawlerRunConfig(markdown_generator=md_generator)
result = await crawler.arun(url, config=config)

print(result.markdown.fit_markdown)  # Filtered
print(result.markdown.raw_markdown)  # Original

For content filters: Content Processing (lines 2481-3101)

Data Extraction

1. Schema-Based CSS Extraction (Most Efficient)

No LLM required - fast, deterministic, cost-free.

CLI:

# Generate schema once (uses LLM)
python scripts/extraction_pipeline.py --generate-schema https://shop.com "extract products"

# Use schema for extraction (no LLM)
crwl https://shop.com -e extract_css.yml -s product_schema.json -o json

Schema format:

{
  "name": "products",
  "baseSelector": ".product-card",
  "fields": [
    {"name": "title", "selector": "h2", "type": "text"},
    {"name": "price", "selector": ".price", "type": "text"},
    {"name": "link", "selector": "a", "type": "attribute", "attribute": "href"}
  ]
}

2. LLM-Based Extraction

For complex or irregular content:

CLI:

# extract_llm.yml
type: "llm"
provider: "openai/gpt-4o-mini"
instruction: "Extract product names and prices"
api_token: "your-token"

crwl https://shop.com -e extract_llm.yml -o json

For extraction details: Extraction Strategies (lines 4522-5429)

Advanced Patterns

Dynamic Content (JavaScript-Heavy Sites)

CLI:

crwl https://example.com -c "wait_for=css:.ajax-content,scan_full_page=true,page_timeout=60000"

Crawler config:

# crawler.yml
wait_for: "css:.ajax-content"
scan_full_page: true
page_timeout: 60000
delay_before_return_html: 2.0

Multi-URL Processing

CLI (sequential):

for url in url1 url2 url3; do crwl "$url" -o markdown; done

Python SDK (concurrent):

urls = ["https://site1.com", "https://site2.com", "https://site3.com"]
results = await crawler.arun_many(urls, config=config)

For batch processing: arun_many() Reference (lines 1057-1224)

Session & Authentication

CLI:

# login_crawler.yml
session_id: "user_session"
js_code: |
  document.querySelector('#username').value = 'user';
  document.querySelector('#password').value = 'pass';
  document.querySelector('#submit').click();
wait_for: "css:.dashboard"

# Login
crwl https://site.com/login -C login_crawler.yml

# Access protected content (session reused)
crwl https://site.com/protected -c "session_id=user_session"

For session management: Advanced Features (lines 5429-5940)

Anti-Detection & Proxies

CLI:

# browser.yml
headless: true
proxy_config:
  server: "http://proxy:8080"
  username: "user"
  password: "pass"
user_agent_mode: "random"

crwl https://example.com -B browser.yml

Common Use Cases

Documentation to Markdown

crwl https://docs.example.com -o markdown > docs.md

E-commerce Product Monitoring

# Generate schema once
python scripts/extraction_pipeline.py --generate-schema https://shop.com "extract products"

# Monitor (no LLM costs)
crwl https://shop.com -e extract_css.yml -s schema.json -o json

News Aggregation

# Multiple sources with filtering
for url in news1.com news2.com news3.com; do
  crwl "https://$url" -f filter_bm25.yml -o markdown-fit
done

Interactive Q&A

# First view content
crwl https://example.com -o markdown

# Then ask questions
crwl https://example.com -q "What are the main conclusions?"
crwl https://example.com -q "Summarize the key points"

Resources

Provided Scripts

• scripts/extraction_pipeline.py - Schema generation and extraction
• scripts/basic_crawler.py - Simple markdown extraction
• scripts/batch_crawler.py - Multi-URL processing

Reference Documentation

Best Practices

1. Start with CLI for quick tasks, SDK for automation
2. Use schema-based extraction - 10-100x more efficient than LLM
3. Enable caching during development - --bypass-cache only when needed
4. Set appropriate timeouts - 30s normal, 60s+ for JS-heavy sites
5. **Use con