crawl4ai/docs/md_v2/api/arun_many.md

arun_many(...) Reference

Note

: This function is very similar to arun() but focused on concurrent or batch crawling. If you’re unfamiliar with arun() usage, please read that doc first, then review this for differences.

Function Signature

async def arun_many(
    urls: Union[List[str], List[Any]],
    config: Optional[CrawlerRunConfig] = None,
    dispatcher: Optional[BaseDispatcher] = None,
    ...
) -> Union[List[CrawlResult], AsyncGenerator[CrawlResult, None]]:
    """
    Crawl multiple URLs concurrently or in batches.

    :param urls: A list of URLs (or tasks) to crawl.
    :param config: (Optional) A default `CrawlerRunConfig` applying to each crawl.
    :param dispatcher: (Optional) A concurrency controller (e.g. MemoryAdaptiveDispatcher).
    ...
    :return: Either a list of `CrawlResult` objects, or an async generator if streaming is enabled.
    """

Differences from arun()

1. Multiple URLs:

• Instead of crawling a single URL, you pass a list of them (strings or tasks). 
• The function returns either a list of CrawlResult or an async generator if streaming is enabled.

2. Concurrency & Dispatchers:

• dispatcher param allows advanced concurrency control. 
• If omitted, a default dispatcher (like MemoryAdaptiveDispatcher) is used internally. 
• Dispatchers handle concurrency, rate limiting, and memory-based adaptive throttling (see Multi-URL Crawling).

3. Streaming Support:

• Enable streaming by setting stream=True in your CrawlerRunConfig.
• When streaming, use async for to process results as they become available.
• Ideal for processing large numbers of URLs without waiting for all to complete.

4. Parallel Execution**:

• arun_many() can run multiple requests concurrently under the hood. 
• Each CrawlResult might also include a dispatch_result with concurrency details (like memory usage, start/end times).

Basic Example (Batch Mode)

# Minimal usage: The default dispatcher will be used
results = await crawler.arun_many(
    urls=["https://site1.com", "https://site2.com"],
    config=CrawlerRunConfig(stream=False)  # Default behavior
)

for res in results:
    if res.success:
        print(res.url, "crawled OK!")
    else:
        print("Failed:", res.url, "-", res.error_message)

Streaming Example

config = CrawlerRunConfig(
    stream=True,  # Enable streaming mode
    cache_mode=CacheMode.BYPASS
)

# Process results as they complete
async for result in await crawler.arun_many(
    urls=["https://site1.com", "https://site2.com", "https://site3.com"],
    config=config
):
    if result.success:
        print(f"Just completed: {result.url}")
        # Process each result immediately
        process_result(result)

With a Custom Dispatcher

dispatcher = MemoryAdaptiveDispatcher(
    memory_threshold_percent=70.0,
    max_session_permit=10
)
results = await crawler.arun_many(
    urls=["https://site1.com", "https://site2.com", "https://site3.com"],
    config=my_run_config,
    dispatcher=dispatcher
)

Key Points:

• Each URL is processed by the same or separate sessions, depending on the dispatcher’s strategy.
• dispatch_result in each CrawlResult (if using concurrency) can hold memory and timing info. 
• If you need to handle authentication or session IDs, pass them in each individual task or within your run config.

Return Value

Either a list of CrawlResult objects, or an async generator if streaming is enabled. You can iterate to check result.success or read each item’s extracted_content, markdown, or dispatch_result.

---

Dispatcher Reference

• MemoryAdaptiveDispatcher: Dynamically manages concurrency based on system memory usage. 
• SemaphoreDispatcher: Fixed concurrency limit, simpler but less adaptive. 

For advanced usage or custom settings, see Multi-URL Crawling with Dispatchers.

---

Common Pitfalls

1. Large Lists: If you pass thousands of URLs, be mindful of memory or rate-limits. A dispatcher can help. 

2. Session Reuse: If you need specialized logins or persistent contexts, ensure your dispatcher or tasks handle sessions accordingly. 

3. Error Handling: Each CrawlResult might fail for different reasons—always check result.success or the error_message before proceeding.

---

Conclusion

Use arun_many() when you want to crawl multiple URLs simultaneously or in controlled parallel tasks. If you need advanced concurrency features (like memory-based adaptive throttling or complex rate-limiting), provide a dispatcher. Each result is a standard CrawlResult, possibly augmented with concurrency stats (dispatch_result) for deeper inspection. For more details on concurrency logic and dispatchers, see the Advanced Multi-URL Crawling docs.