diff --git a/ai_agent_tutorials/ai_voice_agent_openaisdk/README.md b/ai_agent_tutorials/ai_voice_agent_openaisdk/README.md
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diff --git a/ai_agent_tutorials/ai_voice_agent_openaisdk/ai_voice_agent_docs.py b/ai_agent_tutorials/ai_voice_agent_openaisdk/ai_voice_agent_docs.py
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+++ b/ai_agent_tutorials/ai_voice_agent_openaisdk/ai_voice_agent_docs.py
@@ -0,0 +1,360 @@
+from typing import List, Dict, Optional
+from dataclasses import dataclass
+from pathlib import Path
+import os
+from firecrawl import FirecrawlApp
+from qdrant_client import QdrantClient
+from qdrant_client.http import models
+from qdrant_client.http.models import Distance, VectorParams
+from fastembed import TextEmbedding
+from agents import Agent, ModelSettings, function_tool, Runner
+from openai import OpenAI, AsyncOpenAI
+from openai.helpers import LocalAudioPlayer
+import textwrap
+import tempfile
+import uuid
+import numpy as np
+from typing import Callable
+from urllib.parse import urlparse
+from dotenv import load_dotenv
+import asyncio
+import json
+from datetime import datetime
+import time
+
+load_dotenv()
+
+
+
+def setup_qdrant_collection(qdrant_url: str, qdrant_api_key: str, collection_name: str = "docs_embeddings"):
+    print("\n--- Step 1: Setting up Qdrant Collection ---")
+    try:
+        client = QdrantClient(url=qdrant_url, api_key=qdrant_api_key)
+        print("✓ Connected to Qdrant")
+        
+        embedding_model = TextEmbedding()
+        test_embedding = list(embedding_model.embed(["test"]))[0]
+        embedding_dim = len(test_embedding)
+        print(f"✓ Embedding model ready (dimension: {embedding_dim})")
+        
+        client.create_collection(
+            collection_name=collection_name,
+            vectors_config=VectorParams(size=embedding_dim, distance=Distance.COSINE)
+        )
+        print(f"✓ Created collection: {collection_name}")
+        
+        return client, embedding_model
+    
+    except Exception as e:
+        if "already exists" in str(e):
+            print(f"✓ Collection {collection_name} already exists")
+            return client, embedding_model
+        raise e
+
+def crawl_documentation(firecrawl_api_key: str, url: str, output_dir: Optional[str] = None):
+    print("\n--- Step 2: Crawling Documentation ---")
+    try:
+        firecrawl = FirecrawlApp(api_key=firecrawl_api_key)
+        print(f"✓ Initialized Firecrawl")
+        
+        if output_dir:
+            os.makedirs(output_dir, exist_ok=True)
+            print(f"✓ Created output directory: {output_dir}")
+        
+        print(f"Starting crawl of {url}...")
+        
+        pages = []
+        
+        response = firecrawl.crawl_url(
+            url,
+            params={
+                'limit': 5,
+                'scrapeOptions': {
+                    'formats': ['markdown', 'html']
+                }
+            }
+        )
+        
+        while True:
+            if response.get('status') == 'scraping':
+                print(f"Progress: {response.get('completed', 0)}/{response.get('total', 0)} pages")
+                print(f"Credits used: {response.get('creditsUsed', 0)}")
+            
+            for page in response.get('data', []):
+                content = page.get('markdown') or page.get('html', '')
+                metadata = page.get('metadata', {})
+                source_url = metadata.get('sourceURL', '')
+                
+                if output_dir and content:
+                    filename = f"{uuid.uuid4()}.md"
+                    filepath = os.path.join(output_dir, filename)
+                    with open(filepath, 'w', encoding='utf-8') as f:
+                        f.write(content)
+                
+                pages.append({
+                    "content": content,
+                    "url": source_url,
+                    "metadata": {
+                        "title": metadata.get('title', ''),
+                        "description": metadata.get('description', ''),
+                        "language": metadata.get('language', 'en'),
+                        "crawl_date": datetime.now().isoformat()
+                    }
+                })
+                
+                print(f"✓ Processed page: {metadata.get('title', 'Untitled')}")
+            
+            next_url = response.get('next')
+            if not next_url:
+                break
+                
+            response = firecrawl.get(next_url)
+            time.sleep(1)
+        
+        print(f"✓ Crawled {len(pages)} pages")
+        return pages
+    
+    except Exception as e:
+        print(f"Error crawling documentation: {str(e)}")
+        raise e
+
+def store_embeddings(client: QdrantClient, embedding_model: TextEmbedding, pages: List[Dict], collection_name: str):
+    print("\n--- Step 3: Generating and Storing Embeddings ---")
+    try:
+        for page in pages:
+            embedding = list(embedding_model.embed([page["content"]]))[0]
+            
+            client.upsert(
+                collection_name=collection_name,
+                points=[
+                    models.PointStruct(
+                        id=str(uuid.uuid4()),
+                        vector=embedding.tolist(),
+                        payload={
+                            "content": page["content"],
+                            "url": page["url"],
+                            **page["metadata"]
+                        }
+                    )
+                ]
+            )
+            print(f"✓ Stored embedding for: {page['metadata']['title'] or page['url']}")
+        
+        print(f"✓ Stored {len(pages)} embeddings in Qdrant")
+    
+    except Exception as e:
+        print(f"Error storing embeddings: {str(e)}")
+        raise e
+
+def setup_agents(openai_api_key: str):
+    print("\n--- Step 4: Setting up OpenAI Agents ---")
+    try:
+        # Set OpenAI API key in environment
+        os.environ["OPENAI_API_KEY"] = openai_api_key
+        print("✓ Set OpenAI API key in environment")
+        
+        processor_agent = Agent(
+            name="Documentation Processor",
+            instructions="""You are a helpful documentation assistant. Your task is to:
+            1. Analyze the provided documentation content
+            2. Answer the user's question clearly and concisely
+            3. Include relevant examples when available
+            4. Cite the source URLs when referencing specific content
+            5. Keep responses natural and conversational
+            6. Format your response in a way that's easy to speak out loud""",
+            model="gpt-4o"
+        )
+        print("✓ Set up Documentation Processor Agent")
+
+        tts_agent = Agent(
+            name="Text-to-Speech Agent",
+            instructions="""You are a text-to-speech agent. Your task is to:
+            1. Convert the processed documentation response into natural speech
+            2. Maintain proper pacing and emphasis
+            3. Handle technical terms clearly
+            4. Keep the tone professional but friendly
+            5. Use appropriate pauses for better comprehension
+            6. Ensure the speech is clear and well-articulated""",
+            model="gpt-4o-mini-tts"
+        )
+        print("✓ Set up TTS Agent")
+        
+        return processor_agent, tts_agent
+    
+    except Exception as e:
+        print(f"Error setting up agents: {str(e)}")
+        raise e
+
+async def process_query(
+    query: str,
+    client: QdrantClient,
+    embedding_model: TextEmbedding,
+    processor_agent: Agent,
+    tts_agent: Agent,
+    collection_name: str,
+    openai_api_key: str
+):
+    print("\n--- Step 5: Processing Query ---")
+    try:
+        # Generate query embedding
+        print("Generating query embedding...")
+        query_embedding = list(embedding_model.embed([query]))[0]
+        print(f"✓ Generated query embedding with shape: {len(query_embedding)}")
+        print(f"Vector sample (first 5 elements): {query_embedding[:5]}")
+        
+        # Try to get collection info first
+        print("\nVerifying collection status...")
+        try:
+            collection_info = client.get_collection(collection_name)
+            print(f"Collection exists with {collection_info.points_count} points")
+        except Exception as e:
+            print(f"Warning: Could not get collection info: {str(e)}")
+        
+        # Attempt search with query parameter (confirmed working)
+        print("\nAttempting vector search...")
+        try:
+            print("Querying with 'query' parameter...")
+            search_response = client.query_points(
+                collection_name=collection_name,
+                query=query_embedding.tolist(),
+                limit=3,
+                with_payload=True
+            )
+            print("✓ Query successful")
+            
+            # Debug search response
+            print("\nSearch Response Debug:")
+            print(f"Response type: {type(search_response)}")
+            
+            # Get points from the response
+            if hasattr(search_response, 'points'):
+                search_results = search_response.points
+            else:
+                search_results = []
+                
+            print(f"\n✓ Found {len(search_results)} relevant documents")
+            
+            if not search_results:
+                raise Exception("No relevant documents found in the vector database")
+            
+            # Build context from search results
+            context = "Based on the following documentation:\n\n"
+            for result in search_results:
+                payload = result.payload
+                if not payload:
+                    print(f"Warning: Result missing payload")
+                    continue
+                    
+                url = payload.get('url', 'Unknown URL')
+                content = payload.get('content', '')
+                score = getattr(result, 'score', 'N/A')
+                
+                print(f"\nDocument from {url}")
+                print(f"Relevance score: {score}")
+                context += f"From {url}:\n{content}\n\n"
+            
+            context += f"\nUser Question: {query}\n\n"
+            context += "Please provide a clear, concise answer that can be easily spoken out loud."
+            
+            # Process response with agents
+            print("\nProcessing with Documentation Agent...")
+            processor_result = await Runner.run(processor_agent, context)
+            processor_response = processor_result.final_output
+            print("✓ Generated text response")
+            
+            print("\nProcessing with TTS Agent...")
+            tts_result = await Runner.run(tts_agent, processor_response)
+            tts_response = tts_result.final_output
+            print("✓ Generated TTS instructions")
+            
+            # Generate and play audio
+            print("\nGenerating audio response...")
+            async_openai = AsyncOpenAI(api_key=openai_api_key)
+            async with async_openai.audio.speech.with_streaming_response.create(
+                model="tts-1",
+                voice="alloy",
+                input=processor_response,
+                instructions=tts_response,
+                response_format="pcm"
+            ) as response:
+                print("✓ Streaming audio response")
+                await LocalAudioPlayer().play(response)
+                
+            return {
+                "status": "success",
+                "text_response": processor_response,
+                "tts_instructions": tts_response,
+                "sources": [r.payload.get("url", "Unknown URL") for r in search_results if r.payload],
+                "query_details": {
+                    "vector_size": len(query_embedding),
+                    "results_found": len(search_results),
+                    "collection_name": collection_name
+                }
+            }
+            
+        except Exception as e:
+            print(f"Error during vector search: {str(e)}")
+            print("Full error details:")
+            import traceback
+            traceback.print_exc()
+            raise
+    
+    except Exception as e:
+        print(f"\nError processing query: {str(e)}")
+        print("Full error details:")
+        import traceback
+        traceback.print_exc()
+        
+        return {
+            "status": "error",
+            "error": str(e),
+            "error_details": traceback.format_exc(),
+            "query": query
+        }
+
+async def main():
+    try:
+        env_vars = get_env_vars()
+        print("✓ Loaded environment variables")
+        
+        client, embedding_model = setup_qdrant_collection(
+            env_vars["QDRANT_URL"],
+            env_vars["QDRANT_API_KEY"]
+        )
+        
+        pages = crawl_documentation(
+            env_vars["FIRECRAWL_API_KEY"],
+            "https://docs.agentmail.to/api-reference",
+            "crawled_docs"
+        )
+        
+        store_embeddings(client, embedding_model, pages, "docs_embeddings")
+        
+        processor_agent, tts_agent = setup_agents(env_vars["OPENAI_API_KEY"])
+        
+        query = "What are the required parameters for List Threads API of Agent Mail?"
+        result = await process_query(
+            query,
+            client,
+            embedding_model,
+            processor_agent,
+            tts_agent,
+            "docs_embeddings",
+            env_vars["OPENAI_API_KEY"]
+        )
+        
+        print("\n--- Final Results ---")
+        print(json.dumps(result, indent=2))
+        
+    except ValueError as e:
+        print(f"\nConfiguration Error: {str(e)}")
+        print("\nPlease ensure your .env file contains all required variables:")
+        print("FIRECRAWL_API_KEY=your_key")
+        print("QDRANT_URL=your_qdrant_url")
+        print("QDRANT_API_KEY=your_qdrant_key")
+        print("OPENAI_API_KEY=your_openai_key")
+    except Exception as e:
+        print(f"\nError: {str(e)}")
+
+if __name__ == "__main__":
+    asyncio.run(main())
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diff --git a/ai_agent_tutorials/ai_voice_agent_openaisdk/requirements.txt b/ai_agent_tutorials/ai_voice_agent_openaisdk/requirements.txt
new file mode 100644
index 0000000..e69de29