# 🕷️ Crawailer

**Browser control for robots** - Delightful web automation and content extraction

Crawailer is a modern Python library designed for AI agents, automation scripts, and MCP servers that need to interact with the web. It provides a clean, intuitive API for browser control and intelligent content extraction.

## ✨ Features

- **🎯 Intuitive API**: Simple, predictable functions that just work
- **🚀 Modern & Fast**: Built on Playwright with selectolax for 5-10x faster HTML processing  
- **🤖 AI-Friendly**: Optimized outputs for LLMs and structured data extraction
- **🔧 Flexible**: Use as a library, CLI tool, or MCP server
- **📦 Zero Config**: Sensible defaults with optional customization
- **🎨 Delightful DX**: Rich output, helpful errors, progress tracking

## 🚀 Quick Start

```python
import crawailer as web

# Simple content extraction
content = await web.get("https://example.com")
print(content.markdown)  # Clean, LLM-ready markdown
print(content.text)      # Human-readable text
print(content.title)     # Extracted title

# Batch processing  
results = await web.get_many(["url1", "url2", "url3"])
for result in results:
    print(f"{result.title}: {result.word_count} words")

# Smart discovery
research = await web.discover("AI safety papers", limit=10)
# Returns the most relevant content, not just the first 10 results
```

## 🎯 Design Philosophy

### For Robots, By Humans
- **Predictive**: Anticipates what you need and provides it
- **Forgiving**: Handles errors gracefully with helpful suggestions  
- **Efficient**: Fast by default, with smart caching and concurrency
- **Composable**: Small, focused functions that work well together

### Perfect for AI Workflows
- **LLM-Optimized**: Clean markdown, structured data, semantic chunking
- **Context-Aware**: Extracts relationships and metadata automatically
- **Quality-Focused**: Built-in content quality assessment
- **Archive-Ready**: Designed for long-term storage and retrieval

## 📖 Use Cases

### AI Agents & LLM Applications
```python
# Research assistant workflow
research = await web.discover("quantum computing breakthroughs")
for paper in research:
    summary = await llm.summarize(paper.markdown)
    insights = await llm.extract_insights(paper.content)
```

### MCP Servers
```python
# Easy MCP integration (with crawailer[mcp])
from crawailer.mcp import create_mcp_server

server = create_mcp_server()
# Automatically exposes web.get, web.discover, etc. as MCP tools
```

### Data Pipeline & Automation
```python
# Monitor competitors
competitors = ["competitor1.com", "competitor2.com"] 
changes = await web.monitor_changes(competitors, check_interval="1h")
for change in changes:
    if change.significance > 0.7:
        await notify_team(change)
```

## 🛠️ Installation

```bash
# Basic installation
pip install crawailer

# With AI features (semantic search, entity extraction)
pip install crawailer[ai]

# With MCP server capabilities  
pip install crawailer[mcp]

# Everything
pip install crawailer[all]

# Post-install setup (installs Playwright browsers)
crawailer setup
```

## 🏗️ Architecture

Crawailer is built on modern, focused libraries:

- **🎭 Playwright**: Reliable browser automation
- **⚡ selectolax**: 5-10x faster HTML parsing (C-based)
- **📝 markdownify**: Clean HTML→Markdown conversion
- **🧹 justext**: Intelligent content extraction and cleaning
- **🔄 httpx**: Modern async HTTP client

## 🤝 Perfect for MCP Projects

MCP servers love Crawailer because it provides:

- **Focused tools**: Each function does one thing well
- **Rich outputs**: Structured data ready for LLM consumption  
- **Smart defaults**: Works out of the box with minimal configuration
- **Extensible**: Easy to add domain-specific extraction logic

```python
# Example MCP server tool
@mcp_tool("web_research")
async def research_topic(topic: str, depth: str = "comprehensive"):
    results = await web.discover(topic, max_pages=20)
    return {
        "sources": len(results),
        "content": [r.summary for r in results],
        "insights": await analyze_patterns(results)
    }
```

## 🎉 What Makes It Delightful

### Predictive Intelligence
```python
content = await web.get("blog-post-url")
# Automatically detects it's a blog post
# Extracts: author, date, reading time, topics

product = await web.get("ecommerce-url") 
# Recognizes product page
# Extracts: price, reviews, availability, specs
```

### Beautiful Output
```
✨ Found 15 high-quality sources
📊 Sources: 4 arxiv, 3 journals, 2 conferences, 6 blogs  
📅 Date range: 2023-2024 (recent research)
⚡ Average quality score: 8.7/10
🔍 Key topics: transformers, safety, alignment
```

### Helpful Errors
```python
try:
    content = await web.get("problematic-site.com")
except web.CloudflareProtected:
    # "💡 Try: await web.get(url, stealth=True)"
except web.PaywallDetected as e:
    # "🔍 Found archived version: {e.archive_url}"
```

## 📚 Documentation

- **[Getting Started](docs/getting-started.md)**: Installation and first steps
- **[API Reference](docs/api.md)**: Complete function documentation  
- **[MCP Integration](docs/mcp.md)**: Building MCP servers with Crawailer
- **[Examples](examples/)**: Real-world usage patterns
- **[Architecture](docs/architecture.md)**: How Crawailer works internally

## 🤝 Contributing

We love contributions! Crawailer is designed to be:
- **Easy to extend**: Add new content extractors and browser capabilities
- **Well-tested**: Comprehensive test suite with real websites
- **Documented**: Every feature has examples and use cases

See [CONTRIBUTING.md](CONTRIBUTING.md) for details.

## 📄 License

MIT License - see [LICENSE](LICENSE) for details.

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**Built with ❤️ for the age of AI agents and automation**

*Crawailer: Because robots deserve delightful web experiences too* 🤖✨