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added azure openai agent features
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‎agent-framework/user-guide/agents/agent-types/azure-openai-chat-completion-agent.md‎

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@@ -233,6 +233,84 @@ async def main():
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asyncio.run(main())
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```
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### Web Search
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Access real-time information using the hosted web search tool:
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```python
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import asyncio
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from agent_framework import ChatAgent, HostedWebSearchTool
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from agent_framework.azure import AzureOpenAIChatClient
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from azure.identity import AzureCliCredential
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async def main():
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async with ChatAgent(
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chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
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instructions="You are a helpful assistant that can search the web for current information.",
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tools=HostedWebSearchTool(
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description="Search the web for current information"
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)
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) as agent:
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result = await agent.run("What are the latest developments in artificial intelligence?")
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print(result.text)
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asyncio.run(main())
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```
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### MCP (Model Context Protocol) Tools
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Connect to external services and APIs using MCP tools:
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```python
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import asyncio
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from agent_framework import ChatAgent, HostedMCPTool
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from agent_framework.azure import AzureOpenAIChatClient
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from azure.identity import AzureCliCredential
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async def main():
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async with ChatAgent(
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chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
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instructions="You are a helpful assistant that can search Microsoft documentation.",
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tools=HostedMCPTool(
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name="Microsoft Learn MCP",
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url="https://learn.microsoft.com/api/mcp",
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approval_mode="never_require" # Auto-approve for documentation searches
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)
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) as agent:
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result = await agent.run("How do I create an Azure storage account using Azure CLI?")
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print(result.text)
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asyncio.run(main())
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```
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### Using Threads for Context Management
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Maintain conversation context across multiple interactions:
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```python
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import asyncio
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from agent_framework.azure import AzureOpenAIChatClient
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from azure.identity import AzureCliCredential
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async def main():
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agent = AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
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instructions="You are a helpful programming assistant."
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)
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# Create a new thread for conversation context
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thread = agent.get_new_thread()
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# First interaction
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result1 = await agent.run("I'm working on a Python web application.", thread=thread, store=True)
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print(f"Assistant: {result1.text}")
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# Second interaction - context is preserved
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result2 = await agent.run("What framework should I use?", thread=thread, store=True)
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print(f"Assistant: {result2.text}")
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asyncio.run(main())
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```
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### Streaming Responses
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Get responses as they are generated using streaming:

‎agent-framework/user-guide/agents/agent-types/azure-openai-responses-agent.md‎

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## Agent Features
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### Reasoning Models
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Azure OpenAI Responses agents support advanced reasoning models like o1 for complex problem-solving:
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```python
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import asyncio
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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async def main():
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agent = AzureOpenAIResponsesClient(
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deployment_name="o1-preview", # Use reasoning model
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credential=AzureCliCredential()
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).create_agent(
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instructions="You are a helpful assistant that excels at complex reasoning.",
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name="ReasoningAgent"
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)
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result = await agent.run("Solve this logic puzzle: If A > B, B > C, and C > D, and we know D = 5, B = 10, what can we determine about A?")
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print(result.text)
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asyncio.run(main())
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```
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### Structured Output
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Get structured responses from Azure OpenAI Responses agents:
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```python
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import asyncio
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from typing import Annotated
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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from pydantic import BaseModel, Field
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class WeatherForecast(BaseModel):
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location: Annotated[str, Field(description="The location")]
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temperature: Annotated[int, Field(description="Temperature in Celsius")]
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condition: Annotated[str, Field(description="Weather condition")]
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humidity: Annotated[int, Field(description="Humidity percentage")]
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async def main():
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agent = AzureOpenAIResponsesClient(credential=AzureCliCredential()).create_agent(
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instructions="You are a weather assistant that provides structured forecasts.",
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response_format=WeatherForecast
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)
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result = await agent.run("What's the weather like in Paris today?")
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weather_data = result.structured_output
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print(f"Location: {weather_data.location}")
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print(f"Temperature: {weather_data.temperature}°C")
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print(f"Condition: {weather_data.condition}")
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print(f"Humidity: {weather_data.humidity}%")
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asyncio.run(main())
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```
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### Function Tools
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You can provide custom function tools to Azure OpenAI Responses agents:
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asyncio.run(main())
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```
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### File Search
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Enable document search capabilities using the hosted file search tool:
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```python
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import asyncio
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from agent_framework import ChatAgent, HostedFileSearchTool, HostedVectorStoreContent
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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async def main():
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# Create a vector store content reference
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vector_store = HostedVectorStoreContent(vector_store_id="vs_123")
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async with ChatAgent(
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chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
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instructions="You are a helpful assistant that can search through files.",
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tools=HostedFileSearchTool(
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inputs=vector_store,
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max_results=10
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)
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) as agent:
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result = await agent.run("Find information about quarterly reports")
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print(result.text)
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asyncio.run(main())
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```
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### Web Search
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Access real-time information using the hosted web search tool:
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```python
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import asyncio
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from agent_framework import ChatAgent, HostedWebSearchTool
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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async def main():
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async with ChatAgent(
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chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
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instructions="You are a helpful assistant that can search the web for current information.",
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tools=HostedWebSearchTool(
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description="Search the web for current information"
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)
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) as agent:
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result = await agent.run("What are the latest developments in artificial intelligence?")
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print(result.text)
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asyncio.run(main())
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```
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### Image Analysis and Generation
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Azure OpenAI Responses agents support multimodal interactions including image analysis:
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```python
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import asyncio
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from agent_framework import ChatMessage, TextContent, UriContent
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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async def main():
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agent = AzureOpenAIResponsesClient(
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deployment_name="gpt-4o", # Use vision-capable model
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credential=AzureCliCredential()
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).create_agent(
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instructions="You are a helpful assistant that can analyze images."
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)
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# Create a message with both text and image content
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message = ChatMessage(
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role="user",
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contents=[
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TextContent(text="What do you see in this image?"),
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UriContent(
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uri="https://example.com/image.jpg",
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media_type="image/jpeg"
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)
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]
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)
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result = await agent.run(message)
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print(result.text)
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asyncio.run(main())
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```
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### MCP (Model Context Protocol) Tools
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Connect to external services and APIs using MCP tools:
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```python
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import asyncio
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from agent_framework import ChatAgent, HostedMCPTool
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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async def main():
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async with ChatAgent(
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chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
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instructions="You are a helpful assistant that can search Microsoft documentation.",
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tools=HostedMCPTool(
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name="Microsoft Learn MCP",
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url="https://learn.microsoft.com/api/mcp",
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approval_mode="never_require" # Auto-approve for documentation searches
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)
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) as agent:
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result = await agent.run("How do I create an Azure storage account using Azure CLI?")
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print(result.text)
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asyncio.run(main())
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```
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### Using Threads for Context Management
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Maintain conversation context across multiple interactions:
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```python
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import asyncio
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from agent_framework.azure import AzureOpenAIResponsesClient
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from azure.identity import AzureCliCredential
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async def main():
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agent = AzureOpenAIResponsesClient(credential=AzureCliCredential()).create_agent(
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instructions="You are a helpful programming assistant."
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)
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# Create a new thread for conversation context
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thread = agent.get_new_thread()
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# First interaction
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result1 = await agent.run("I'm working on a Python web application.", thread=thread, store=True)
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print(f"Assistant: {result1.text}")
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# Second interaction - context is preserved
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result2 = await agent.run("What framework should I use?", thread=thread, store=True)
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print(f"Assistant: {result2.text}")
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asyncio.run(main())
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```
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### Streaming Responses
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Get responses as they are generated using streaming:

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