# AsyncWebCrawler

The **`AsyncWebCrawler`** is the core class for asynchronous web crawling in Crawl4AI. You typically create it **once**, optionally customize it with a **`BrowserConfig`** (e.g., headless, user agent), then **run** multiple **`arun()`** calls with different **`CrawlerRunConfig`** objects.

**Recommended usage**:
1. **Create** a `BrowserConfig` for global browser settings.  
2. **Instantiate** `AsyncWebCrawler(config=browser_config)`.  
3. **Use** the crawler in an async context manager (`async with`) or manage start/close manually.  
4. **Call** `arun(url, config=crawler_run_config)` for each page you want.

---

## 1. Constructor Overview

```python
class AsyncWebCrawler:
    def __init__(
        self,
        crawler_strategy: Optional[AsyncCrawlerStrategy] = None,
        config: Optional[BrowserConfig] = None,
        always_bypass_cache: bool = False,           # deprecated
        always_by_pass_cache: Optional[bool] = None, # also deprecated
        base_directory: str = ...,
        thread_safe: bool = False,
        **kwargs,
    ):
        """
        Create an AsyncWebCrawler instance.

        Args:
            crawler_strategy: 
                (Advanced) Provide a custom crawler strategy if needed.
            config: 
                A BrowserConfig object specifying how the browser is set up.
            always_bypass_cache: 
                (Deprecated) Use CrawlerRunConfig.cache_mode instead.
            base_directory:     
                Folder for storing caches/logs (if relevant).
            thread_safe: 
                If True, attempts some concurrency safeguards. Usually False.
            **kwargs: 
                Additional legacy or debugging parameters.
        """
    )

### Typical Initialization

```python
from crawl4ai import AsyncWebCrawler, BrowserConfig

browser_cfg = BrowserConfig(
    browser_type="chromium",
    headless=True,
    verbose=True
)

crawler = AsyncWebCrawler(config=browser_cfg)
```

**Notes**:
- **Legacy** parameters like `always_bypass_cache` remain for backward compatibility, but prefer to set **caching** in `CrawlerRunConfig`.

---

## 2. Lifecycle: Start/Close or Context Manager

### 2.1 Context Manager (Recommended)

```python
async with AsyncWebCrawler(config=browser_cfg) as crawler:
    result = await crawler.arun("https://example.com")
    # The crawler automatically starts/closes resources
```

When the `async with` block ends, the crawler cleans up (closes the browser, etc.).

### 2.2 Manual Start & Close

```python
crawler = AsyncWebCrawler(config=browser_cfg)
await crawler.start()

result1 = await crawler.arun("https://example.com")
result2 = await crawler.arun("https://another.com")

await crawler.close()
```

Use this style if you have a **long-running** application or need full control of the crawler’s lifecycle.

---

## 3. Primary Method: `arun()`

```python
async def arun(
    self,
    url: str,
    config: Optional[CrawlerRunConfig] = None,
    # Legacy parameters for backward compatibility...
) -> CrawlResult:
    ...
```

### 3.1 New Approach

You pass a `CrawlerRunConfig` object that sets up everything about a crawl—content filtering, caching, session reuse, JS code, screenshots, etc.

```python
import asyncio
from crawl4ai import CrawlerRunConfig, CacheMode

run_cfg = CrawlerRunConfig(
    cache_mode=CacheMode.BYPASS,
    css_selector="main.article",
    word_count_threshold=10,
    screenshot=True
)

async with AsyncWebCrawler(config=browser_cfg) as crawler:
    result = await crawler.arun("https://example.com/news", config=run_cfg)
    print("Crawled HTML length:", len(result.cleaned_html))
    if result.screenshot:
        print("Screenshot base64 length:", len(result.screenshot))
```

### 3.2 Legacy Parameters Still Accepted

For **backward** compatibility, `arun()` can still accept direct arguments like `css_selector=...`, `word_count_threshold=...`, etc., but we strongly advise migrating them into a **`CrawlerRunConfig`**.

---

## 4. Batch Processing: `arun_many()`

```python
async def arun_many(
    self,
    urls: List[str],
    config: Optional[CrawlerRunConfig] = None,
    # Legacy parameters maintained for backwards compatibility...
) -> List[CrawlResult]:
    """
    Process multiple URLs with intelligent rate limiting and resource monitoring.
    """
```

### 4.1 Resource-Aware Crawling

The `arun_many()` method now uses an intelligent dispatcher that:
- Monitors system memory usage
- Implements adaptive rate limiting
- Provides detailed progress monitoring
- Manages concurrent crawls efficiently

### 4.2 Example Usage

```python
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, RateLimitConfig
from crawl4ai.dispatcher import DisplayMode

# Configure browser
browser_cfg = BrowserConfig(headless=True)

# Configure crawler with rate limiting
run_cfg = CrawlerRunConfig(
    # Enable rate limiting
    enable_rate_limiting=True,
    rate_limit_config=RateLimitConfig(
        base_delay=(1.0, 2.0),  # Random delay between 1-2 seconds
        max_delay=30.0,         # Maximum delay after rate limit hits
        max_retries=2,          # Number of retries before giving up
        rate_limit_codes=[429, 503]  # Status codes that trigger rate limiting
    ),
    # Resource monitoring
    memory_threshold_percent=70.0,  # Pause if memory exceeds this
    check_interval=0.5,            # How often to check resources
    max_session_permit=3,          # Maximum concurrent crawls
    display_mode=DisplayMode.DETAILED.value  # Show detailed progress
)

urls = [
    "https://example.com/page1",
    "https://example.com/page2",
    "https://example.com/page3"
]

async with AsyncWebCrawler(config=browser_cfg) as crawler:
    results = await crawler.arun_many(urls, config=run_cfg)
    for result in results:
        print(f"URL: {result.url}, Success: {result.success}")
```

### 4.3 Key Features

1. **Rate Limiting**
   - Automatic delay between requests
   - Exponential backoff on rate limit detection
   - Domain-specific rate limiting
   - Configurable retry strategy

2. **Resource Monitoring**
   - Memory usage tracking
   - Adaptive concurrency based on system load
   - Automatic pausing when resources are constrained

3. **Progress Monitoring**
   - Detailed or aggregated progress display
   - Real-time status updates
   - Memory usage statistics

4. **Error Handling**
   - Graceful handling of rate limits
   - Automatic retries with backoff
   - Detailed error reporting

---

## 5. `CrawlResult` Output

Each `arun()` returns a **`CrawlResult`** containing:

- `url`: Final URL (if redirected).
- `html`: Original HTML.
- `cleaned_html`: Sanitized HTML.
- `markdown_v2` (or future `markdown`): Markdown outputs (raw, fit, etc.).
- `extracted_content`: If an extraction strategy was used (JSON for CSS/LLM strategies).
- `screenshot`, `pdf`: If screenshots/PDF requested.
- `media`, `links`: Information about discovered images/links.
- `success`, `error_message`: Status info.

For details, see [CrawlResult doc](./crawl-result.md).

---

## 6. Quick Example

Below is an example hooking it all together:

```python
import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
import json

async def main():
    # 1. Browser config
    browser_cfg = BrowserConfig(
        browser_type="firefox",
        headless=False,
        verbose=True
    )

    # 2. Run config
    schema = {
        "name": "Articles",
        "baseSelector": "article.post",
        "fields": [
            {
                "name": "title", 
                "selector": "h2", 
                "type": "text"
            },
            {
                "name": "url", 
                "selector": "a", 
                "type": "attribute", 
                "attribute": "href"
            }
        ]
    }

    run_cfg = CrawlerRunConfig(
        cache_mode=CacheMode.BYPASS,
        extraction_strategy=JsonCssExtractionStrategy(schema),
        word_count_threshold=15,
        remove_overlay_elements=True,
        wait_for="css:.post"  # Wait for posts to appear
    )

    async with AsyncWebCrawler(config=browser_cfg) as crawler:
        result = await crawler.arun(
            url="https://example.com/blog",
            config=run_cfg
        )

        if result.success:
            print("Cleaned HTML length:", len(result.cleaned_html))
            if result.extracted_content:
                articles = json.loads(result.extracted_content)
                print("Extracted articles:", articles[:2])
        else:
            print("Error:", result.error_message)

asyncio.run(main())
```

**Explanation**:
- We define a **`BrowserConfig`** with Firefox, no headless, and `verbose=True`.  
- We define a **`CrawlerRunConfig`** that **bypasses cache**, uses a **CSS** extraction schema, has a `word_count_threshold=15`, etc.  
- We pass them to `AsyncWebCrawler(config=...)` and `arun(url=..., config=...)`.

---

## 7. Best Practices & Migration Notes

1. **Use** `BrowserConfig` for **global** settings about the browser’s environment.  
2. **Use** `CrawlerRunConfig` for **per-crawl** logic (caching, content filtering, extraction strategies, wait conditions).  
3. **Avoid** legacy parameters like `css_selector` or `word_count_threshold` directly in `arun()`. Instead:

   ```python
   run_cfg = CrawlerRunConfig(css_selector=".main-content", word_count_threshold=20)
   result = await crawler.arun(url="...", config=run_cfg)
   ```

4. **Context Manager** usage is simplest unless you want a persistent crawler across many calls.

---

## 8. Summary

**AsyncWebCrawler** is your entry point to asynchronous crawling:

- **Constructor** accepts **`BrowserConfig`** (or defaults).  
- **`arun(url, config=CrawlerRunConfig)`** is the main method for single-page crawls.  
- **`arun_many(urls, config=CrawlerRunConfig)`** handles concurrency across multiple URLs.  
- For advanced lifecycle control, use `start()` and `close()` explicitly.  

**Migration**:  
- If you used `AsyncWebCrawler(browser_type="chromium", css_selector="...")`, move browser settings to `BrowserConfig(...)` and content/crawl logic to `CrawlerRunConfig(...)`.

This modular approach ensures your code is **clean**, **scalable**, and **easy to maintain**. For any advanced or rarely used parameters, see the [BrowserConfig docs](../api/parameters.md).