ayrisdev/crawl4ai
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge branch 'next' - Update README, and quickstart examples

Browse Source
This commit is contained in:
UncleCode
2024-11-28 16:43:16 +08:00
parent e4acd18429 efe93a5f57
commit 0cbd594512
2 changed files with 393 additions and 355 deletions
Download Patch File Download Diff File Expand all files Collapse all files

611
README.md
View File

@@ -1,4 +1,4 @@
# 🔥🕷️ Crawl4AI: LLM Friendly Web Crawler & Scraper
# 🔥🕷️ Crawl4AI: Crawl Smarter, Faster, Freely. For AI.
<a href="https://trendshift.io/repositories/11716" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11716" alt="unclecode%2Fcrawl4ai | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
@@ -9,17 +9,114 @@
[![GitHub Pull Requests](https://img.shields.io/github/issues-pr/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/pulls)
[![License](https://img.shields.io/github/license/unclecode/crawl4ai)](https://github.com/unclecode/crawl4ai/blob/main/LICENSE)
Crawl4AI simplifies asynchronous web crawling and data extraction, making it accessible for large language models (LLMs) and AI applications. 🆓🌐
Crawl4AI is the #1 trending GitHub repository, actively maintained by a vibrant community. It delivers blazing-fast, AI-ready web crawling tailored for LLMs, AI agents, and data pipelines. Open source, flexible, and built for real-time performance, Crawl4AI empowers developers with unmatched speed, precision, and deployment ease.
## New in 0.3.743 ✨
[✨ Check out what's new in the latest update!](#recent-updates)
## 🧐 Why Crawl4AI?
1. **Built for LLMs**: Creates smart, concise Markdown optimized for RAG and fine-tuning applications.
2. **Lightning Fast**: Delivers results 6x faster with real-time, cost-efficient performance.
3. **Flexible Browser Control**: Offers session management, proxies, and custom hooks for seamless data access.
4. **Heuristic Intelligence**: Uses advanced algorithms for efficient extraction, reducing reliance on costly models.
5. **Open Source & Deployable**: Fully open-source with no API keys—ready for Docker and cloud integration.
6. **Thriving Community**: Actively maintained by a vibrant community and the #1 trending GitHub repository.
## 🚀 Quick Start
1. Install Crawl4AI:
```bash
pip install crawl4ai
```
2. Run a simple web crawl:
```python
import asyncio
from crawl4ai import AsyncWebCrawler, CacheMode
async def main():
async with AsyncWebCrawler(verbose=True) as crawler:
result = await crawler.arun(url="https://www.nbcnews.com/business")
# Soone will be change to result.markdown
print(result.markdown_v2.raw_markdown)
if __name__ == "__main__":
asyncio.run(main())
```
## ✨ Features
<details>
<summary>📝 <strong>Markdown Generation</strong></summary>
- 🧹 **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.
</details>
<details>
<summary>📊 <strong>Structured Data Extraction</strong></summary>
- 🤖 **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.
</details>
<details>
<summary>🌐 <strong>Browser Integration</strong></summary>
- 🖥️ **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.
- 🔒 **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.
</details>
<details>
<summary>🔎 <strong>Crawling & Scraping</strong></summary>
- 🖼️ **Media Support**: Extract images, audio, videos, and responsive image formats like `srcset` and `picture`.
- 🚀 **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.
- 💾 **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.
</details>
<details>
<summary>🚀 <strong>Deployment</strong></summary>
- 🐳 **Dockerized Setup**: Optimized Docker image with API server for easy deployment.
- 🔄 **API Gateway**: One-click deployment with secure token authentication for API-based workflows.
- 🌐 **Scalable Architecture**: Designed for mass-scale production and optimized server performance.
- ⚙️ **DigitalOcean Deployment**: Ready-to-deploy configurations for DigitalOcean and similar platforms.
</details>
<details>
<summary>🎯 <strong>Additional Features</strong></summary>
- 🕶️ **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.
</details>
🚀 **Improved ManagedBrowser Configuration**: Dynamic host and port support for more flexible browser management.
📝 **Enhanced Markdown Generation**: New generator class for better formatting and customization.
⚡ **Fast HTML Formatting**: Significantly optimized HTML formatting in the web crawler.
🛠️ **Utility & Sanitization Upgrades**: Improved sanitization and expanded utility functions for streamlined workflows.
👥 **Acknowledgments**: Added contributor details and pull request acknowledgments for better transparency.
📖 **Documentation Updates**: Clearer usage scenarios and updated guidance for better user onboarding.
🧪 **Test Adjustments**: Refined tests to align with recent class name changes.
## Try it Now!
@@ -30,95 +127,42 @@ Crawl4AI simplifies asynchronous web crawling and data extraction, making it acc
## Features ✨
<details open>
<summary>🚀 <strong>Performance & Scalability</strong></summary>
- ⚡ **Blazing Fast Scraping**: Outperforms many paid services with cutting-edge optimization.
- 🔄 **Asynchronous Architecture**: Enhanced performance for complex multi-page crawling.
- ⚡ **Dynamic HTML Formatting**: New, fast HTML formatting for streamlined workflows.
- 🗂️ **Large Dataset Optimization**: Improved caching for handling massive content sets.
</details>
<details>
<summary>🔎 <strong>Extraction Capabilities</strong></summary>
- 🖼️ **Comprehensive Media Support**: Extracts images, audio, video, and responsive image formats like `srcset` and `picture`.
- 📚 **Advanced Content Chunking**: Topic-based, regex, sentence-level, and cosine clustering strategies.
- 🎯 **Precise Data Extraction**: Supports CSS selectors and keyword-based refinements.
- 🔗 **All-Inclusive Link Crawling**: Extracts internal and external links.
- 📝 **Markdown Generation**: Enhanced markdown generator class for custom, clean, LLM-friendly outputs.
- 🏷️ **Metadata Extraction**: Fetches metadata directly from pages.
</details>
<details>
<summary>🌐 <strong>Browser Integration</strong></summary>
- 🌍 **Multi-Browser Support**: Works with Chromium, Firefox, and WebKit.
- 🖥️ **ManagedBrowser with Dynamic Config**: Flexible host/port control for tailored setups.
- ⚙️ **Custom Browser Hooks**: Authentication, headers, and page modifications.
- 🕶️ **Stealth Mode**: Bypasses bot detection with advanced techniques.
- 📸 **Screenshots & JavaScript Execution**: Takes screenshots and executes custom JavaScript before crawling.
</details>
<details>
<summary>📁 <strong>Input/Output Flexibility</strong></summary>
- 📂 **Local & Raw HTML Crawling**: Directly processes `file://` paths and raw HTML.
- 🌐 **Custom Headers for LLM**: Tailored headers for enhanced AI interactions.
- 🛠️ **Structured Output Options**: Supports JSON, cleaned HTML, and markdown outputs.
</details>
<details>
<summary>🔧 <strong>Utility & Debugging</strong></summary>
- 🛡️ **Error Handling**: Robust error management for seamless execution.
- 🔐 **Session Management**: Handles complex, multi-page interactions.
- 🧹 **Utility Functions**: Enhanced sanitization and flexible extraction helpers.
- 🕰️ **Delayed Content Loading**: Improved handling of lazy-loading and dynamic content.
</details>
<details>
<summary>🔐 <strong>Security & Accessibility</strong></summary>
- 🕵️ **Proxy Support**: Enables authenticated access for restricted pages.
- 🚪 **API Gateway**: Deploy as an API service with secure token authentication.
- 🌐 **CORS & Static Serving**: Enhanced support for filesystem-based caching and cross-origin requests.
</details>
<details>
<summary>🌟 <strong>Community & Documentation</strong></summary>
- 🙌 **Contributor Acknowledgments**: Recognition for pull requests and contributions.
- 📖 **Clear Documentation**: Simplified and updated for better onboarding and usage.
</details>
<details>
<summary>🎯 <strong>Cutting-Edge Features</strong></summary>
- 🛠️ **BM25-Based Markdown Filtering**: Extracts cleaner, context-relevant markdown.
- 📚 **LLM-Friendly Citations**: Auto-converts links to numbered citations with reference lists.
- 📡 **IFrame Content Extraction**: Comprehensive analysis for embedded content.
- 🕰️ **Flexible Content Retrieval**: Combines timing-based strategies for reliable extractions.
</details>
- 🆓 Completely free and open-source
- 🚀 Blazing fast performance, outperforming many paid services
- 🤖 LLM-friendly output formats (JSON, cleaned HTML, markdown)
- 🌐 Multi-browser support (Chromium, Firefox, WebKit)
- 🌍 Supports crawling multiple URLs simultaneously
- 🎨 Extracts and returns all media tags (Images, Audio, and Video)
- 🔗 Extracts all external and internal links
- 📚 Extracts metadata from the page
- 🔄 Custom hooks for authentication, headers, and page modifications
- 🕵️ User-agent customization
- 🖼️ Takes screenshots of pages with enhanced error handling
- 📜 Executes multiple custom JavaScripts before crawling
- 📊 Generates structured output without LLM using JsonCssExtractionStrategy
- 📚 Various chunking strategies: topic-based, regex, sentence, and more
- 🧠 Advanced extraction strategies: cosine clustering, LLM, and more
- 🎯 CSS selector support for precise data extraction
- 📝 Passes instructions/keywords to refine extraction
- 🔒 Proxy support with authentication for enhanced access
- 🔄 Session management for complex multi-page crawling
- 🌐 Asynchronous architecture for improved performance
- 🖼️ Improved image processing with lazy-loading detection
- 🕰️ Enhanced handling of delayed content loading
- 🔑 Custom headers support for LLM interactions
- 🖼️ iframe content extraction for comprehensive analysis
- ⏱️ Flexible timeout and delayed content retrieval options
## Installation 🛠️
Crawl4AI offers flexible installation options to suit various use cases. You can install it as a Python package or use Docker.
### Using pip 🐍
<details>
<summary>🐍 <strong>Using pip</strong></summary>
Choose the installation option that best fits your needs:
#### Basic Installation
### Basic Installation
For basic web crawling and scraping tasks:
@@ -128,7 +172,7 @@ pip install crawl4ai
By default, this will install the asynchronous version of Crawl4AI, using Playwright for web crawling.
👉 Note: When you install Crawl4AI, the setup script 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:
👉 **Note**: When you install Crawl4AI, the setup script 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:
1. Through the command line:
@@ -144,15 +188,19 @@ By default, this will install the asynchronous version of Crawl4AI, using Playwr
This second method has proven to be more reliable in some cases.
#### Installation with Synchronous Version
---
If you need the synchronous version using Selenium:
### Installation with Synchronous Version
The sync version is deprecated and will be removed in future versions. If you need the synchronous version using Selenium:
```bash
pip install crawl4ai[sync]
```
#### Development Installation
---
### Development Installation
For contributors who plan to modify the source code:
@@ -161,7 +209,9 @@ git clone https://github.com/unclecode/crawl4ai.git
cd crawl4ai
pip install -e . # Basic installation in editable mode
```
Install optional features:
```bash
pip install -e ".[torch]" # With PyTorch features
pip install -e ".[transformer]" # With Transformer features
@@ -170,7 +220,10 @@ pip install -e ".[sync]" # With synchronous crawling (Selenium)
pip install -e ".[all]" # Install all optional features
```
## One-Click Deployment 🚀
</details>
<details>
<summary>🚀 <strong>One-Click Deployment</strong></summary>
Deploy your own instance of Crawl4AI with one click:
@@ -181,14 +234,19 @@ Deploy your own instance of Crawl4AI with one click:
The deploy will:
- Set up a Docker container with Crawl4AI
- Configure Playwright and all dependencies
- Start the FastAPI server on port 11235
- Start the FastAPI server on port `11235`
- Set up health checks and auto-deployment
### Using Docker 🐳
</details>
<details>
<summary>🐳 <strong>Using Docker</strong></summary>
Crawl4AI is available as Docker images for easy deployment. You can either pull directly from Docker Hub (recommended) or build from the repository.
#### Option 1: Docker Hub (Recommended)
---
### Option 1: Docker Hub (Recommended)
```bash
# Pull and run from Docker Hub (choose one):
@@ -206,7 +264,9 @@ docker run --platform linux/arm64 -p 11235:11235 unclecode/crawl4ai:basic
docker run --shm-size=2gb -p 11235:11235 unclecode/crawl4ai:basic
```
#### Option 2: Build from Repository
---
### Option 2: Build from Repository
```bash
# Clone the repository
@@ -228,7 +288,12 @@ docker build -t crawl4ai:local \
docker run -p 11235:11235 crawl4ai:local
```
Quick test (works for both options):
---
### Quick Test
Run a quick test (works for both Docker options):
```python
import requests
@@ -245,143 +310,134 @@ result = requests.get(f"http://localhost:11235/task/{task_id}")
For advanced configuration, environment variables, and usage examples, see our [Docker Deployment Guide](https://crawl4ai.com/mkdocs/basic/docker-deployment/).
</details>
## Quick Start 🚀
## 🔬 Advanced Usage Examples 🔬
You can check the project structure in the directory [https://github.com/unclecode/crawl4ai/docs/examples](docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.
<details>
<summary>📝 <strong>Heuristic Markdown Generation with Clean and Fit Markdown</strong></summary>
```python
import asyncio
from crawl4ai import AsyncWebCrawler
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.content_filter_strategy import BM25ContentFilter
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
async def main():
async with AsyncWebCrawler(verbose=True) as crawler:
result = await crawler.arun(url="https://www.nbcnews.com/business")
print(result.markdown)
if __name__ == "__main__":
asyncio.run(main())
```
## Advanced Usage 🔬
### Executing JavaScript and Using CSS Selectors
```python
import asyncio
from crawl4ai import AsyncWebCrawler
async def main():
async with AsyncWebCrawler(verbose=True) as crawler:
js_code = ["const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"]
async with AsyncWebCrawler(
headless=True,
verbose=True,
) as crawler:
result = await crawler.arun(
url="https://www.nbcnews.com/business",
js_code=js_code,
css_selector=".wide-tease-item__description",
bypass_cache=True
url="https://docs.micronaut.io/4.7.6/guide/",
cache_mode=CacheMode.ENABLED,
markdown_generator=DefaultMarkdownGenerator(
content_filter=BM25ContentFilter(user_query=None, bm25_threshold=1.0)
),
)
print(result.extracted_content)
print(len(result.markdown))
print(len(result.fit_markdown))
print(len(result.markdown_v2.fit_markdown))
if __name__ == "__main__":
asyncio.run(main())
```
### Using a Proxy
</details>
<details>
<summary>🖥️ <strong>Executing JavaScript & Extract Structured Data without LLMs</strong></summary>
```python
import asyncio
from crawl4ai import AsyncWebCrawler
async def main():
async with AsyncWebCrawler(verbose=True, proxy="http://127.0.0.1:7890") as crawler:
result = await crawler.arun(
url="https://www.nbcnews.com/business",
bypass_cache=True
)
print(result.markdown)
if __name__ == "__main__":
asyncio.run(main())
```
### Extracting Structured Data without LLM
The `JsonCssExtractionStrategy` allows for precise extraction of structured data from web pages using CSS selectors.
```python
import asyncio
import json
from crawl4ai import AsyncWebCrawler
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
import json
async def extract_news_teasers():
async def main():
schema = {
"name": "News Teaser Extractor",
"baseSelector": ".wide-tease-item__wrapper",
"name": "KidoCode Courses",
"baseSelector": "section.charge-methodology .w-tab-content > div",
"fields": [
{
"name": "category",
"selector": ".unibrow span[data-testid='unibrow-text']",
"name": "section_title",
"selector": "h3.heading-50",
"type": "text",
},
{
"name": "headline",
"selector": ".wide-tease-item__headline",
"name": "section_description",
"selector": ".charge-content",
"type": "text",
},
{
"name": "summary",
"selector": ".wide-tease-item__description",
"name": "course_name",
"selector": ".text-block-93",
"type": "text",
},
{
"name": "time",
"selector": "[data-testid='wide-tease-date']",
"name": "course_description",
"selector": ".course-content-text",
"type": "text",
},
{
"name": "image",
"type": "nested",
"selector": "picture.teasePicture img",
"fields": [
{"name": "src", "type": "attribute", "attribute": "src"},
{"name": "alt", "type": "attribute", "attribute": "alt"},
],
},
{
"name": "link",
"selector": "a[href]",
"name": "course_icon",
"selector": ".image-92",
"type": "attribute",
"attribute": "href",
},
],
"attribute": "src"
}
]
}
extraction_strategy = JsonCssExtractionStrategy(schema, verbose=True)
async with AsyncWebCrawler(verbose=True) as crawler:
async with AsyncWebCrawler(
headless=False,
verbose=True
) as crawler:
# Create the JavaScript that handles clicking multiple times
js_click_tabs = """
(async () => {
const tabs = document.querySelectorAll("section.charge-methodology .tabs-menu-3 > div");
for(let tab of tabs) {
// scroll to the tab
tab.scrollIntoView();
tab.click();
// Wait for content to load and animations to complete
await new Promise(r => setTimeout(r, 500));
}
})();
"""
result = await crawler.arun(
url="https://www.nbcnews.com/business",
extraction_strategy=extraction_strategy,
bypass_cache=True,
url="https://www.kidocode.com/degrees/technology",
extraction_strategy=JsonCssExtractionStrategy(schema, verbose=True),
js_code=[js_click_tabs],
cache_mode=CacheMode.BYPASS
)
assert result.success, "Failed to crawl the page"
companies = json.loads(result.extracted_content)
print(f"Successfully extracted {len(companies)} companies")
print(json.dumps(companies[0], indent=2))
news_teasers = json.loads(result.extracted_content)
print(f"Successfully extracted {len(news_teasers)} news teasers")
print(json.dumps(news_teasers[0], indent=2))
if __name__ == "__main__":
asyncio.run(extract_news_teasers())
asyncio.run(main())
```
For more advanced usage examples, check out our [Examples](https://crawl4ai.com/mkdocs/extraction/css-advanced/) section in the documentation.
</details>
### Extracting Structured Data with OpenAI
<details>
<summary>📚 <strong>Extracting Structured Data with LLMs</strong></summary>
```python
import os
import asyncio
from crawl4ai import AsyncWebCrawler
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.extraction_strategy import LLMExtractionStrategy
from pydantic import BaseModel, Field
@@ -396,6 +452,8 @@ async def main():
url='https://openai.com/api/pricing/',
word_count_threshold=1,
extraction_strategy=LLMExtractionStrategy(
# Here you can use any provider that Litellm library supports, for instance: ollama/qwen2
# provider="ollama/qwen2", api_token="no-token",
provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'),
schema=OpenAIModelFee.schema(),
extraction_type="schema",
@@ -403,7 +461,7 @@ async def main():
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"}."""
),
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
)
print(result.extracted_content)
@@ -411,143 +469,98 @@ if __name__ == "__main__":
asyncio.run(main())
```
### Session Management and Dynamic Content Crawling
</details>
Crawl4AI excels at handling complex scenarios, such as crawling multiple pages with dynamic content loaded via JavaScript. Here's an example of crawling GitHub commits across multiple pages:
<details>
<summary>🤖 <strong>Using You own Browswer with Custome User Profile</strong></summary>
```python
import asyncio
import re
from bs4 import BeautifulSoup
import os, sys
from pathlib import Path
import asyncio, time
from crawl4ai import AsyncWebCrawler
async def crawl_typescript_commits():
first_commit = ""
async def on_execution_started(page):
nonlocal first_commit
try:
while True:
await page.wait_for_selector('li.Box-sc-g0xbh4-0 h4')
commit = await page.query_selector('li.Box-sc-g0xbh4-0 h4')
commit = await commit.evaluate('(element) => element.textContent')
commit = re.sub(r'\s+', '', commit)
if commit and commit != first_commit:
first_commit = commit
break
await asyncio.sleep(0.5)
except Exception as e:
print(f"Warning: New content didn't appear after JavaScript execution: {e}")
async def test_news_crawl():
# Create a persistent user data directory
user_data_dir = os.path.join(Path.home(), ".crawl4ai", "browser_profile")
os.makedirs(user_data_dir, exist_ok=True)
async with AsyncWebCrawler(verbose=True) as crawler:
crawler.crawler_strategy.set_hook('on_execution_started', on_execution_started)
async with AsyncWebCrawler(
verbose=True,
headless=True,
user_data_dir=user_data_dir,
use_persistent_context=True,
headers={
"Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
"Accept-Language": "en-US,en;q=0.5",
"Accept-Encoding": "gzip, deflate, br",
"DNT": "1",
"Connection": "keep-alive",
"Upgrade-Insecure-Requests": "1",
"Sec-Fetch-Dest": "document",
"Sec-Fetch-Mode": "navigate",
"Sec-Fetch-Site": "none",
"Sec-Fetch-User": "?1",
"Cache-Control": "max-age=0",
}
) as crawler:
url = "ADDRESS_OF_A_CHALLENGING_WEBSITE"
url = "https://github.com/microsoft/TypeScript/commits/main"
session_id = "typescript_commits_session"
all_commits = []
js_next_page = """
const button = document.querySelector('a[data-testid="pagination-next-button"]');
if (button) button.click();
"""
for page in range(3): # Crawl 3 pages
result = await crawler.arun(
url=url,
session_id=session_id,
css_selector="li.Box-sc-g0xbh4-0",
js=js_next_page if page > 0 else None,
bypass_cache=True,
js_only=page > 0
url,
cache_mode=CacheMode.BYPASS,
magic=True,
)
assert result.success, f"Failed to crawl page {page + 1}"
soup = BeautifulSoup(result.cleaned_html, 'html.parser')
commits = soup.select("li")
all_commits.extend(commits)
print(f"Page {page + 1}: Found {len(commits)} commits")
await crawler.crawler_strategy.kill_session(session_id)
print(f"Successfully crawled {len(all_commits)} commits across 3 pages")
if __name__ == "__main__":
asyncio.run(crawl_typescript_commits())
print(f"Successfully crawled {url}")
print(f"Content length: {len(result.markdown)}")
```
This example demonstrates Crawl4AI's ability to handle complex scenarios where content is loaded asynchronously. It crawls multiple pages of GitHub commits, executing JavaScript to load new content and using custom hooks to ensure data is loaded before proceeding.
For more advanced usage examples, check out our [Examples](https://crawl4ai.com/mkdocs/tutorial/episode_12_Session-Based_Crawling_for_Dynamic_Websites/) section in the documentation.
</details>
## Speed Comparison 🚀
## ✨ Recent Updates
Crawl4AI is designed with speed as a primary focus. Our goal is to provide the fastest possible response with high-quality data extraction, minimizing abstractions between the data and the user.
- 🚀 **Improved ManagedBrowser Configuration**: Dynamic host and port support for more flexible browser management.
- 📝 **Enhanced Markdown Generation**: New generator class for better formatting and customization.
- ⚡ **Fast HTML Formatting**: Significantly optimized HTML formatting in the web crawler.
- 🛠️ **Utility & Sanitization Upgrades**: Improved sanitization and expanded utility functions for streamlined workflows.
- 👥 **Acknowledgments**: Added contributor details and pull request acknowledgments for better transparency.
We've conducted a speed comparison between Crawl4AI and Firecrawl, a paid service. The results demonstrate Crawl4AI's superior performance:
```bash
Firecrawl:
Time taken: 7.02 seconds
Content length: 42074 characters
Images found: 49
Crawl4AI (simple crawl):
Time taken: 1.60 seconds
Content length: 18238 characters
Images found: 49
Crawl4AI (with JavaScript execution):
Time taken: 4.64 seconds
Content length: 40869 characters
Images found: 89
```
As you can see, Crawl4AI outperforms Firecrawl significantly:
- Simple crawl: Crawl4AI is over 4 times faster than Firecrawl.
- With JavaScript execution: Even when executing JavaScript to load more content (doubling the number of images found), Crawl4AI is still faster than Firecrawl's simple crawl.
You can find the full comparison code in our repository at `docs/examples/crawl4ai_vs_firecrawl.py`.
## Documentation 📚
## 📖 Documentation & Roadmap
For detailed documentation, including installation instructions, advanced features, and API reference, visit our [Documentation Website](https://crawl4ai.com/mkdocs/).
## Crawl4AI Roadmap 🗺️
Moreover to check our development plans and upcoming features, check out our [Roadmap](https://github.com/unclecode/crawl4ai/blob/main/ROADMAP.md).
For detailed information on our development plans and upcoming features, check out our [Roadmap](https://github.com/unclecode/crawl4ai/blob/main/ROADMAP.md).
<details>
<summary>📈 <strong>Development TODOs</strong></summary>
### Advanced Crawling Systems 🔧
- [x] 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
### Specialized Features 🛠️
- [ ] 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
### Development Tools 🔨
- [ ] 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
### Community & Growth 🌱
- [ ] 10. Sponsorship Program: Structured support system with tiered benefits
- [ ] 11. Educational Content: "How to Crawl" video series and interactive tutorials
## Contributing 🤝
</details>
## 🤝 Contributing
We welcome contributions from the open-source community. Check out our [contribution guidelines](https://github.com/unclecode/crawl4ai/blob/main/CONTRIBUTING.md) for more information.
## License 📄
## 📄 License
Crawl4AI is released under the [Apache 2.0 License](https://github.com/unclecode/crawl4ai/blob/main/LICENSE).
## Contact 📧
## 📧 Contact
For questions, suggestions, or feedback, feel free to reach out:
@@ -557,32 +570,32 @@ For questions, suggestions, or feedback, feel free to reach out:
Happy Crawling! 🕸️🚀
## 🗾 Mission
# Mission
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.
Our mission is to unlock the untapped potential of personal and enterprise data in the digital age. In today's world, individuals and organizations generate vast amounts of valuable digital footprints, yet this data remains largely uncapitalized as a true asset.
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.
Our open-source solution empowers developers and innovators to build tools for data extraction and structuring, laying the foundation for a new era of data ownership. By transforming personal and enterprise data into structured, tradeable assets, we're creating opportunities for individuals to capitalize on their digital footprints and for organizations to unlock the value of their collective knowledge.
<details>
<summary>🔑 <strong>Key Opportunities</strong></summary>
This democratization of data represents the first step toward a shared data economy, where willing participation in data sharing drives AI advancement while ensuring the benefits flow back to data creators. Through this approach, we're building a future where AI development is powered by authentic human knowledge rather than synthetic alternatives.
- **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.
![Mission Diagram](./docs/assets/pitch-dark.svg)
</details>
For a detailed exploration of our vision, opportunities, and pathway forward, please see our [full mission statement](./MISSION.md).
<details>
<summary>🚀 <strong>Development Pathway</strong></summary>
## Key Opportunities
1. **Open-Source Tools**: Community-driven platforms for transparent data extraction.
2. **Digital Asset Structuring**: Tools to organize and value digital knowledge.
3. **Ethical Data Marketplace**: A secure, fair platform for exchanging structured data.
- **Data Capitalization**: Transform digital footprints into valuable assets that can appear on personal and enterprise balance sheets
- **Authentic Data**: Unlock the vast reservoir of real human insights and knowledge for AI advancement
- **Shared Economy**: Create new value streams where data creators directly benefit from their contributions
For more details, see our [full mission statement](./MISSION.md).
</details>
## Development Pathway
1. **Open-Source Foundation**: Building transparent, community-driven data extraction tools
2. **Data Capitalization Platform**: Creating tools to structure and value digital assets
3. **Shared Data Marketplace**: Establishing an economic platform for ethical data exchange
For a detailed exploration of our vision, challenges, and solutions, please see our [full mission statement](./MISSION.md).
## Star History

95
docs/examples/quickstart_async.py
View File

@@ -13,7 +13,9 @@ import re
from typing import Dict, List
from bs4 import BeautifulSoup
from pydantic import BaseModel, Field
from crawl4ai import AsyncWebCrawler
from crawl4ai import AsyncWebCrawler, CacheMode
from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
from crawl4ai.content_filter_strategy import BM25ContentFilter
from crawl4ai.extraction_strategy import (
JsonCssExtractionStrategy,
LLMExtractionStrategy,
@@ -51,7 +53,7 @@ async def simple_example_with_running_js_code():
url="https://www.nbcnews.com/business",
js_code=js_code,
# wait_for=wait_for,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
)
print(result.markdown[:500]) # Print first 500 characters
@@ -61,7 +63,7 @@ async def simple_example_with_css_selector():
result = await crawler.arun(
url="https://www.nbcnews.com/business",
css_selector=".wide-tease-item__description",
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
)
print(result.markdown[:500]) # Print first 500 characters
@@ -132,7 +134,7 @@ async def extract_structured_data_using_llm(provider: str, api_token: str = None
{"model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens"}.""",
extra_args=extra_args
),
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
)
print(result.extracted_content)
@@ -166,7 +168,7 @@ async def extract_structured_data_using_css_extractor():
result = await crawler.arun(
url="https://www.coinbase.com/explore",
extraction_strategy=extraction_strategy,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
)
assert result.success, "Failed to crawl the page"
@@ -213,7 +215,7 @@ async def crawl_dynamic_content_pages_method_1():
session_id=session_id,
css_selector="li.Box-sc-g0xbh4-0",
js=js_next_page if page > 0 else None,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
js_only=page > 0,
headless=False,
)
@@ -282,7 +284,7 @@ async def crawl_dynamic_content_pages_method_2():
extraction_strategy=extraction_strategy,
js_code=js_next_page_and_wait if page > 0 else None,
js_only=page > 0,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
headless=False,
)
@@ -343,7 +345,7 @@ async def crawl_dynamic_content_pages_method_3():
js_code=js_next_page if page > 0 else None,
wait_for=wait_for if page > 0 else None,
js_only=page > 0,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
headless=False,
)
@@ -384,7 +386,7 @@ async def crawl_with_user_simultion():
url = "YOUR-URL-HERE"
result = await crawler.arun(
url=url,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
magic = True, # Automatically detects and removes overlays, popups, and other elements that block content
# simulate_user = True,# Causes a series of random mouse movements and clicks to simulate user interaction
# override_navigator = True # Overrides the navigator object to make it look like a real user
@@ -408,7 +410,7 @@ async def speed_comparison():
params={'formats': ['markdown', 'html']}
)
end = time.time()
print("Firecrawl (simulated):")
print("Firecrawl:")
print(f"Time taken: {end - start:.2f} seconds")
print(f"Content length: {len(scrape_status['markdown'])} characters")
print(f"Images found: {scrape_status['markdown'].count('cldnry.s-nbcnews.com')}")
@@ -420,7 +422,7 @@ async def speed_comparison():
result = await crawler.arun(
url="https://www.nbcnews.com/business",
word_count_threshold=0,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
verbose=False,
)
end = time.time()
@@ -430,6 +432,25 @@ async def speed_comparison():
print(f"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}")
print()
# Crawl4AI with advanced content filtering
start = time.time()
result = await crawler.arun(
url="https://www.nbcnews.com/business",
word_count_threshold=0,
markdown_generator=DefaultMarkdownGenerator(
content_filter=BM25ContentFilter(user_query=None, bm25_threshold=1.0)
),
cache_mode=CacheMode.BYPASS,
verbose=False,
)
end = time.time()
print("Crawl4AI (Markdown Plus):")
print(f"Time taken: {end - start:.2f} seconds")
print(f"Content length: {len(result.markdown_v2.raw_markdown)} characters")
print(f"Fit Markdown: {len(result.markdown_v2.fit_markdown)} characters")
print(f"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}")
print()
# Crawl4AI with JavaScript execution
start = time.time()
result = await crawler.arun(
@@ -438,13 +459,17 @@ async def speed_comparison():
"const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
],
word_count_threshold=0,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
markdown_generator=DefaultMarkdownGenerator(
content_filter=BM25ContentFilter(user_query=None, bm25_threshold=1.0)
),
verbose=False,
)
end = time.time()
print("Crawl4AI (with JavaScript execution):")
print(f"Time taken: {end - start:.2f} seconds")
print(f"Content length: {len(result.markdown)} characters")
print(f"Fit Markdown: {len(result.markdown_v2.fit_markdown)} characters")
print(f"Images found: {result.markdown.count('cldnry.s-nbcnews.com')}")
print("\nNote on Speed Comparison:")
@@ -483,7 +508,7 @@ async def generate_knowledge_graph():
url = "https://paulgraham.com/love.html"
result = await crawler.arun(
url=url,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
extraction_strategy=extraction_strategy,
# magic=True
)
@@ -496,7 +521,7 @@ async def fit_markdown_remove_overlay():
url = "https://janineintheworld.com/places-to-visit-in-central-mexico"
result = await crawler.arun(
url=url,
bypass_cache=True,
cache_mode=CacheMode.BYPASS,
word_count_threshold = 10,
remove_overlay_elements=True,
screenshot = True
@@ -509,31 +534,31 @@ async def fit_markdown_remove_overlay():
async def main():
await simple_crawl()
await simple_example_with_running_js_code()
await simple_example_with_css_selector()
await use_proxy()
await capture_and_save_screenshot("https://www.example.com", os.path.join(__location__, "tmp/example_screenshot.jpg"))
await extract_structured_data_using_css_extractor()
# await simple_crawl()
# await simple_example_with_running_js_code()
# await simple_example_with_css_selector()
# await use_proxy()
# await capture_and_save_screenshot("https://www.example.com", os.path.join(__location__, "tmp/example_screenshot.jpg"))
# await extract_structured_data_using_css_extractor()
# LLM extraction examples
await extract_structured_data_using_llm()
await extract_structured_data_using_llm("huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct", os.getenv("HUGGINGFACE_API_KEY"))
await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
await extract_structured_data_using_llm("ollama/llama3.2")
# # LLM extraction examples
# await extract_structured_data_using_llm()
# await extract_structured_data_using_llm("huggingface/meta-llama/Meta-Llama-3.1-8B-Instruct", os.getenv("HUGGINGFACE_API_KEY"))
# await extract_structured_data_using_llm("openai/gpt-4o", os.getenv("OPENAI_API_KEY"))
# await extract_structured_data_using_llm("ollama/llama3.2")
# You always can pass custom headers to the extraction strategy
custom_headers = {
"Authorization": "Bearer your-custom-token",
"X-Custom-Header": "Some-Value"
}
await extract_structured_data_using_llm(extra_headers=custom_headers)
# # You always can pass custom headers to the extraction strategy
# custom_headers = {
# "Authorization": "Bearer your-custom-token",
# "X-Custom-Header": "Some-Value"
# }
# await extract_structured_data_using_llm(extra_headers=custom_headers)
# await crawl_dynamic_content_pages_method_1()
# await crawl_dynamic_content_pages_method_2()
await crawl_dynamic_content_pages_method_3()
# # await crawl_dynamic_content_pages_method_1()
# # await crawl_dynamic_content_pages_method_2()
# await crawl_dynamic_content_pages_method_3()
await crawl_custom_browser_type()
# await crawl_custom_browser_type()
await speed_comparison()