diff --git a/docs/examples/llm_extraction_openai_pricing.py b/docs/examples/llm_extraction_openai_pricing.py
index d05a1b6b..5ae3d4d1 100644
--- a/docs/examples/llm_extraction_openai_pricing.py
+++ b/docs/examples/llm_extraction_openai_pricing.py
@@ -1,41 +1,40 @@
-import os
-import time
-from crawl4ai.web_crawler import WebCrawler
-from crawl4ai.chunking_strategy import *
 from crawl4ai.extraction_strategy import *
 from crawl4ai.crawler_strategy import *
+import asyncio
+from pydantic import BaseModel, Field
 
 url = r'https://openai.com/api/pricing/'
 
-crawler = WebCrawler()
-crawler.warmup()
-
-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.")
 
-result = crawler.run(
-    url=url,
-    word_count_threshold=1,
-    extraction_strategy= LLMExtractionStrategy(
-        # provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'), 
-        provider= "groq/llama-3.1-70b-versatile", api_token = os.getenv('GROQ_API_KEY'), 
-        schema=OpenAIModelFee.model_json_schema(),
-        extraction_type="schema",
-        instruction="From the crawled content, extract all mentioned model names along with their "\
-            "fees for input and output tokens. Make sure not to miss anything 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,
-)
+from crawl4ai import AsyncWebCrawler
 
-model_fees = json.loads(result.extracted_content)
+async def main():
+    # Use AsyncWebCrawler
+    async with AsyncWebCrawler() as crawler:
+        result = await crawler.arun(
+            url=url,
+            word_count_threshold=1,
+            extraction_strategy= LLMExtractionStrategy(
+                # provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'),
+                provider= "groq/llama-3.1-70b-versatile", api_token = os.getenv('GROQ_API_KEY'),
+                schema=OpenAIModelFee.model_json_schema(),
+                extraction_type="schema",
+                instruction="From the crawled content, extract all mentioned model names along with their " \
+                            "fees for input and output tokens. Make sure not to miss anything 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" }'
+            ),
 
-print(len(model_fees))
+        )
+        print("Success:", result.success)
+        model_fees = json.loads(result.extracted_content)
+        print(len(model_fees))
 
-with open(".data/data.json", "w", encoding="utf-8") as f:
-    f.write(result.extracted_content)
\ No newline at end of file
+        with open(".data/data.json", "w", encoding="utf-8") as f:
+            f.write(result.extracted_content)
+
+asyncio.run(main())