разбор · репозиторийтема-фронтир · H 1.37

из 10

смотреть по главам — взять две-три нужные минуты

оценка машинная и частично зависит от длины ролика — спорите, открывайте оригинал

Crawl4AI: краулер для LLM с извлечением данных и Docker API

🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper.

Crawl4AI — open-source краулер, превращающий страницы в чистый Markdown для LLM, с фильтрами, извлечением данных, deep-crawl и Docker API.

что из этого моё

Можно использовать для автоматизации сбора данных с сайтов клиентов: превращать страницы в Markdown для RAG, извлекать цены/характеристики через LLM-схемы, а также для мониторинга конкурентов. Встроенный Docker API позволяет развернуть как микросервис в пайплайне.

Что забрать

отметь, что берёшь в работу → или отбрось как не своёмоё →

поднять Crawl4AI в Docker и проверить превращение страницы в Markdown

настроить JsonCssExtractionStrategy для извлечения цен с сайта клиента

запустить deep-crawl с BFS и prefetch=True для сбора URL

расшифровка ролика ↓

# 🚀🤖 Crawl4AI: Open-source LLM Friendly Web Crawler & Scraper.

[![GitHub Stars](https://img.shields.io/github/stars/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/stargazers) [![GitHub Forks](https://img.shields.io/github/forks/unclecode/crawl4ai?style=social)](https://github.com/unclecode/crawl4ai/network/members)

[![PyPI version](https://badge.fury.io/py/crawl4ai.svg)](https://badge.fury.io/py/crawl4ai) [![Python Version](https://img.shields.io/pypi/pyversions/crawl4ai)](https://pypi.org/project/crawl4ai/) [![Downloads](https://static.pepy.tech/badge/crawl4ai/month)](https://pepy.tech/project/crawl4ai) [![GitHub Sponsors](https://img.shields.io/github/sponsors/unclecode?style=flat&logo=GitHub-Sponsors&label=Sponsors&color=pink)](https://github.com/sponsors/unclecode)

--- #### 🚀 Crawl4AI Cloud API — Closed Beta (Launching Soon) Reliable, large-scale web extraction, now built to be _**drastically more cost-effective**_ than any of the existing solutions.

👉 **Apply [here](https://forms.gle/E9MyPaNXACnAMaqG7) 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 v0.9.2](#-recent-updates)

✨ **New in v0.9.2**: Maintenance patch release. Fixes a `MemoryAdaptiveDispatcher` task/page leak when a streaming crawl is closed, Docker Playground "Advanced Config" and Monitor WebSocket auth, Playwright headless-shell packaging, and GPU (`ENABLE_GPU=true`) Docker builds. [Release notes →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.9.2.md)

✨ 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 →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.9.0.md)

✨ 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 →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.8.7.md)

✨ 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 →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.8.0.md)

✨ 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 →](https://github.com/unclecode/crawl4ai/blob/main/docs/blog/release-v0.7.8.md)

<details> <summary>🤓 My Personal Story</summary>

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. </details>

<details> <summary>Why developers pick Crawl4AI</summary>

- **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 </details>

## 🚀 Quick Start

1. Install Crawl4AI: ```bash # Install the package pip install -U crawl4ai

# For pre release versions pip install crawl4ai --pre

# Run post-installation setup crawl4ai-setup

# Verify your installation crawl4ai-doctor ```

If you encounter any browser-related issues, you can install them manually: ```bash python -m playwright install --with-deps chromium ```

2. Run a simple web crawl with Python: ```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()) ```

3. Or use the new command-line interface: ```bash # Basic crawl with markdown output crwl https://www.nbcnews.com/business -o markdown

# Deep crawl with BFS strategy, max 10 pages crwl https://docs.crawl4ai.com --deep-crawl bfs --max-pages 10

# Use LLM extraction with a specific question crwl https://www.example.com/products -q "Extract all product prices" ```

## 💖 Support Crawl4AI

> 🎉 **Sponsorship Program Now Open!** After powering 51K+ developers and 1 year of growth, Crawl4AI is launching dedicated support for **startups** and **enterprises**. Be among the first 50 **Founding Sponsors** for 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.

<div align=""> [![Become a Sponsor](https://img.shields.io/badge/Become%20a%20Sponsor-pink?style=for-the-badge&logo=github-sponsors&logoColor=white)](https://github.com/sponsors/unclecode) [![Current Sponsors](https://img.shields.io/github/sponsors/unclecode?style=for-the-badge&logo=github&label=Current%20Sponsors&color=green)](https://github.com/sponsors/unclecode)

</div>

### 🤝 Sponsorship Tiers

- **🌱 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 - see [SPONSORS.md](SPONSORS.md) for 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.

[See All Tiers & Benefits →](https://github.com/sponsors/unclecode)

## ✨ Features

<details> <summary>📝 Markdown Generation</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>📊 Structured Data Extraction</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>🌐 Browser Integration</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. - 👤 **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.

</details>

<details> <summary>🔎 Crawling & Scraping</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 (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 loading. - 🔄 **Full-Page Scanning**: Simulates scrolling to load and capture all dynamic content, perfect for infinite scroll pages.

</details>

<details> <summary>🚀 Deployment</summary>

- 🐳 **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.

</details>

<details> <summary>🎯 Additional Features</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>

## Try it Now!

✨ Play around with this [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1SgRPrByQLzjRfwoRNq1wSGE9nYY_EE8C?usp=sharing)

✨ Visit our [Documentation Website](https://docs.crawl4ai.com/)

## Installation 🛠️

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

<details> <summary>🐍 Using pip</summary>

Choose the installation option that best fits your needs:

### Basic Installation

For basic web crawling and scraping tasks:

```bash 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:

1. Through the command line:

```bash playwright install ```

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

```bash python -m playwright install chromium ```

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

---

### 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

For contributors who plan to modify the source code:

```bash 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 pip install -e ".[cosine]" # With cosine similarity features pip install -e ".[sync]" # With synchronous crawling (Selenium) pip install -e ".[all]" # Install all optional features ```

</details>

<details> <summary>🐳 Docker Deployment</summary>

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

### New Docker Features

The new Docker implementation includes: - **Real-time Monitoring Dashboard** with live system metrics and browser pool visibility - **Browser pooling** with page pre-warming for faster response times - **Interactive playground** to test and generate request code - **MCP integration** for direct connection to AI tools like Claude Code - **Comprehensive API endpoints** including HTML extraction, screenshots, PDF generation, and JavaScript execution - **Multi-architecture support** with automatic detection (AMD64/ARM64) - **Optimized resources** with improved memory management

### Getting Started

```bash # Pull and run the latest release docker pull unclecode/crawl4ai:latest docker run -d -p 11235:11235 --name crawl4ai --shm-size=1g unclecode/crawl4ai:latest

# Visit the monitoring dashboard at http://localhost:11235/dashboard # Or the playground at http://localhost:11235/playground ```

### Quick Test

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

```python import requests

# Submit a crawl job 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](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/docker_example.py). For advanced configuration, monitoring features, and production deployment, see our [Self-Hosting Guide](https://docs.crawl4ai.com/core/self-hosting/).

</details>

---

## 🔬 Advanced Usage Examples 🔬

You can check the project structure in the directory [docs/examples](https://github.com/unclecode/crawl4ai/tree/main/docs/examples). Over there, you can find a variety of examples; here, some popular examples are shared.

<details> <summary>📝 Heuristic Markdown Generation with Clean and Fit Markdown</summary>

```python 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) ), # markdown_generator=DefaultMarkdownGenerator( # content_filter=BM25ContentFilter(user_query="WHEN_WE_FOCUS_BASED_ON_A_USER_QUERY", bm25_threshold=1.0) # ), ) 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()) ```

</details>

<details> <summary>🖥️ Executing JavaScript & Extract Structured Data without LLMs</summary>

```python 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()) ```

</details>

<details> <summary>📚 Extracting Structured Data with LLMs</summary>

```python 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( # Here you can use any provider that Litellm library supports, for instance: ollama/qwen2 # provider="ollama/qwen2", api_token="no-token", 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()) ```

</details>

<details> <summary>🤖 Using Your own Browser with Custom User Profile</summary>

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

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)

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)}") ```

</details>

---

## ✨ Recent Updates

<details open> <summary>Version 0.9.2 Release Highlights - Maintenance Bug Fixes</summary>

A maintenance patch release with bug fixes across the dispatcher, Docker, and GPU builds. `MemoryAdaptiveDispatcher` no longer leaks crawl tasks and browser pages when a streaming crawl is closed. Docker fixes cover the Playground "Advanced Config" 400, the Monitor WebSocket 500 under JWT auth, and Playwright headless-shell packaging. `ENABLE_GPU=true` Docker builds no longer fail on the CUDA toolkit.

No new features, no breaking changes.