Python: New Feature: Cannot Retrieve MCP Call Results via Streaming in Azure OpenAI Responses API

Description

When running the following code, I tried to capture MCP arguments and results from the streaming chunks. However, I noticed that the corresponding events are not detected.

import asyncio
import os

from dotenv import load_dotenv
from openai import AsyncAzureOpenAI
from semantic_kernel.agents import AzureResponsesAgent
from semantic_kernel.contents import ChatMessageContent

load_dotenv()

AZURE_OPENAI_RESPONSES_MODEL_ID: str = os.getenv("AZURE_OPENAI_RESPONSES_MODEL_ID", "")

client: AsyncAzureOpenAI = AzureResponsesAgent.create_client(deployment_name=AZURE_OPENAI_RESPONSES_MODEL_ID)


async def handle_intermediate_message(message: ChatMessageContent):
    for item in message.items:
        print(">> item", item)


async def main():
    agent = AzureResponsesAgent(
        ai_model_id=AZURE_OPENAI_RESPONSES_MODEL_ID,
        client=client,
        name="Agent",
        tools=[
            {
                "type": "mcp",
                "server_label": "microsoft-document",
                "server_url": "https://learn.microsoft.com/api/mcp",
                "require_approval": "never",
            }
        ],
    )

    thread = None

    for _ in range(10):
        user_prompt = input("Enter your prompt: ")
        if not user_prompt:
            user_prompt = "microsoft-document: what is azure ai agent"

        print("reasoning start: ", user_prompt)
        async for r in agent.invoke_stream(
            messages=user_prompt,
            thread=thread,
            on_intermediate_message=handle_intermediate_message,
        ):
            print(r, end="", flush=True)
            thread = r.thread
        print("")


if __name__ == "__main__":
    asyncio.run(main())

Investigation

Looking into the source, I noticed that the following events are not defined in the current implementation:

semantic-kernel/python/semantic_kernel/agents/open_ai/responses_agent_thread_actions.py

Lines 405 to 539 in 0ec81c8

    
           match event: 
        
               case ResponseCreatedEvent(): 
        
                   if store_enabled: 
        
                       thread.response_id = event.response.id 
        
                       # Ensure subsequent requests link to this response context 
        
                       previous_response_id = event.response.id 
        
               case ResponseOutputItemAddedEvent(): 
        
                   function_calls = cls._get_tool_calls_from_output([event.item])  # type: ignore 
        
                   if function_calls: 
        
                       function_call_returned = True 
        
                   msg = cls._build_streaming_msg( 
        
                       agent=agent, 
        
                       metadata=metadata, 
        
                       event=event, 
        
                       items=function_calls, 
        
                       choice_index=request_index, 
        
                   ) 
        
                   all_messages.append(msg) 
        
               case ResponseFunctionCallArgumentsDeltaEvent(): 
        
                   function_call = FunctionCallContent( 
        
                       id=event.item_id, 
        
                       index=getattr(event, "index", None), 
        
                       arguments=event.delta, 
        
                   ) 
        
                   msg = cls._build_streaming_msg( 
        
                       agent=agent, 
        
                       metadata=metadata, 
        
                       event=event, 
        
                       items=[function_call], 
        
                       choice_index=request_index, 
        
                   ) 
        
                   all_messages.append(msg) 
        
               case ResponseTextDeltaEvent(): 
        
                   text_content = StreamingTextContent( 
        
                       text=event.delta, 
        
                       choice_index=request_index, 
        
                   ) 
        
                   msg = cls._build_streaming_msg( 
        
                       agent=agent, 
        
                       metadata=metadata, 
        
                       event=event, 
        
                       items=[text_content], 
        
                       choice_index=request_index, 
        
                   ) 
        
                   yield msg 
        
               case ResponseReasoningTextDeltaEvent(): 
        
                   if on_intermediate_message: 
        
                       reasoning_content = StreamingReasoningContent( 
        
                           text=event.delta, 
        
                           choice_index=request_index, 
        
                           metadata={ 
        
                               "item_id": event.item_id, 
        
                               "output_index": event.output_index, 
        
                               "sequence_number": event.sequence_number, 
        
                               "content_index": event.content_index, 
        
                           }, 
        
                       ) 
        
                       reasoning_msg = cls._build_streaming_msg( 
        
                           agent=agent, 
        
                           metadata=metadata, 
        
                           event=event, 
        
                           items=[reasoning_content], 
        
                           choice_index=request_index, 
        
                       ) 
        
                       await on_intermediate_message(reasoning_msg) 
        
               case ResponseReasoningTextDoneEvent(): 
        
                   if on_intermediate_message: 
        
                       final_reasoning_content = ReasoningContent( 
        
                           text=event.text, 
        
                           metadata={ 
        
                               "item_id": event.item_id, 
        
                               "output_index": event.output_index, 
        
                               "sequence_number": event.sequence_number, 
        
                               "content_index": event.content_index, 
        
                           }, 
        
                       ) 
        
                       reasoning_msg = cls._build_streaming_msg( 
        
                           agent=agent, 
        
                           metadata=metadata, 
        
                           event=event, 
        
                           items=[final_reasoning_content], 
        
                           choice_index=request_index, 
        
                       ) 
        
                       await on_intermediate_message(reasoning_msg) 
        
               case ResponseReasoningSummaryTextDeltaEvent(): 
        
                   if on_intermediate_message: 
        
                       reasoning_content = StreamingReasoningContent( 
        
                           text=event.delta, 
        
                           choice_index=request_index, 
        
                           metadata={ 
        
                               "item_id": event.item_id, 
        
                               "output_index": event.output_index, 
        
                               "sequence_number": event.sequence_number, 
        
                               "summary_index": event.summary_index, 
        
                               "is_summary": True, 
        
                           }, 
        
                       ) 
        
                       reasoning_msg = cls._build_streaming_msg( 
        
                           agent=agent, 
        
                           metadata=metadata, 
        
                           event=event, 
        
                           items=[reasoning_content], 
        
                           choice_index=request_index, 
        
                       ) 
        
                       await on_intermediate_message(reasoning_msg) 
        
               case ResponseReasoningSummaryTextDoneEvent(): 
        
                   if on_intermediate_message: 
        
                       final_reasoning_summary_content = ReasoningContent( 
        
                           text=event.text, 
        
                           metadata={ 
        
                               "item_id": event.item_id, 
        
                               "output_index": event.output_index, 
        
                               "sequence_number": event.sequence_number, 
        
                               "summary_index": event.summary_index, 
        
                               "is_summary": True, 
        
                           }, 
        
                       ) 
        
                       reasoning_msg = cls._build_streaming_msg( 
        
                           agent=agent, 
        
                           metadata=metadata, 
        
                           event=event, 
        
                           items=[final_reasoning_summary_content], 
        
                           choice_index=request_index, 
        
                       ) 
        
                       await on_intermediate_message(reasoning_msg) 
        
               case ResponseOutputItemDoneEvent(): 
        
                   msg = cls._create_output_item_done(agent, event.item)  # type: ignore 
        
                   if output_messages is not None: 
        
                       output_messages.append(msg) 
        
               case ResponseErrorEvent(): 
        
                   logger.error( 
        
                       f"Error in agent invoke_stream: {event.message} " 
        
                       f"(code: {event.code if event.code else 'unknown'})" 
        
                   ) 
        
                   break

ResponseInProgressEvent
ResponseMcpListToolsInProgressEvent
ResponseMcpListToolsCompletedEvent
ResponseMcpCallInProgressEvent
ResponseMcpCallArgumentsDeltaEvent
ResponseMcpCallArgumentsDoneEvent
ResponseMcpCallCompletedEvent
ResponseContentPartAddedEvent
ResponseTextDoneEvent
ResponseContentPartDoneEvent
ResponseCompletedEvent

Question

Would it be possible to add detection support for these events as well?

Info

python
azure openai responses api

semantic-kernel/python/semantic_kernel/agents/open_ai/responses_agent_thread_actions.py

Line 402 in 0ec81c8

async for event in response_stream:

Log of the "event" variable when no events are filtered

// .venv/lib/python3.11/site-packages/semantic_kernel/agents/open_ai/responses_agent_thread_actions.py
ResponseCreatedEvent(response=Response(id='resp_68be5b4280fc8195a7690af3ed1b527908ce4c5c780e7d9e', created_at=1757305666.0, error=None, incomplete_details=None, instructions=None, metadata={}, model='gpt-4.1-mini-2025-04-14', object='response', output=[], parallel_tool_calls=True, temperature=1.0, tool_choice='auto', tools=[Mcp(server_label='microsoft-document', type='mcp', allowed_tools=None, authorization=None, connector_id=None, headers=None, require_approval='never', server_description=None, server_url='https://learn.microsoft.com/<redacted>')], top_p=1.0, background=False, conversation=None, max_output_tokens=None, max_tool_calls=None, previous_response_id=None, prompt=None, prompt_cache_key=None, reasoning=Reasoning(effort=None, generate_summary=None, summary=None), safety_identifier=None, service_tier='auto', status='in_progress', text=ResponseTextConfig(format=ResponseFormatText(type='text'), verbosity=None), top_logprobs=None, truncation='disabled', usage=None, user=None, content_filters=None, store=True), sequence_number=0, type='response.created')
ResponseInProgressEvent(response=Response(id='resp_68be5b4280fc8195a7690af3ed1b527908ce4c5c780e7d9e', created_at=1757305666.0, error=None, incomplete_details=None, instructions=None, metadata={}, model='gpt-4.1-mini-2025-04-14', object='response', output=[], parallel_tool_calls=True, temperature=1.0, tool_choice='auto', tools=[Mcp(server_label='microsoft-document', type='mcp', allowed_tools=None, authorization=None, connector_id=None, headers=None, require_approval='never', server_description=None, server_url='https://learn.microsoft.com/<redacted>')], top_p=1.0, background=False, conversation=None, max_output_tokens=None, max_tool_calls=None, previous_response_id=None, prompt=None, prompt_cache_key=None, reasoning=Reasoning(effort=None, generate_summary=None, summary=None), safety_identifier=None, service_tier='auto', status='in_progress', text=ResponseTextConfig(format=ResponseFormatText(type='text'), verbosity=None), top_logprobs=None, truncation='disabled', usage=None, user=None, content_filters=None, store=True), sequence_number=1, type='response.in_progress')
ResponseOutputItemAddedEvent(item=McpListTools(id='mcpl_68be5b4283648195b5acfffc809da7cc08ce4c5c780e7d9e', server_label='microsoft-document', tools=[], type='mcp_list_tools', error=None), output_index=0, sequence_number=2, type='response.output_item.added')
ResponseMcpListToolsInProgressEvent(item_id='mcpl_68be5b4283648195b5acfffc809da7cc08ce4c5c780e7d9e', output_index=0, sequence_number=3, type='response.mcp_list_tools.in_progress')
ResponseMcpListToolsCompletedEvent(item_id='mcpl_68be5b4283648195b5acfffc809da7cc08ce4c5c780e7d9e', output_index=0, sequence_number=4, type='response.mcp_list_tools.completed')
ResponseOutputItemDoneEvent(item=McpListTools(id='mcpl_68be5b4283648195b5acfffc809da7cc08ce4c5c780e7d9e', server_label='microsoft-document', tools=[McpListToolsTool(input_schema={'type': 'object', 'properties': {'query': {'description': 'a query or topic about Microsoft/Azure products, services, platforms, developer tools, frameworks, or APIs', 'type': 'string', 'default': None}, 'question': {'description': 'this parameter is no longer used, use query instead.', 'type': 'string', 'default': None}}}, name='microsoft_docs_search', annotations=None, description="Search official Microsoft/Azure documentation to find the most relevant and trustworthy content for a user's query. This tool returns up to 10 high-quality content chunks (each max 500 tokens), extracted from Microsoft Learn and other official sources. Each result includes the article title, URL, and a self-contained content excerpt optimized for fast retrieval and reasoning. Always use this tool to quickly ground your answers in accurate, first-party Microsoft/Azure knowledge.\n\nThe `question` parameter is no longer used, use `query` instead.\n\n## Follow-up Pattern\nTo ensure completeness, use microsoft_docs_fetch when high-value pages are identified by search. The fetch tool complements search by providing the full detail. This is a required step for comprehensive results."), McpListToolsTool(input_schema={'type': 'object', 'properties': {'url': {'description': 'URL of the Microsoft documentation page to read', 'type': 'string'}}, 'required': ['url']}, name='microsoft_docs_fetch', annotations=None, description='Fetch and convert a Microsoft Learn documentation page to markdown format. This tool retrieves the latest complete content of Microsoft documentation pages including Azure, .NET, Microsoft 365, and other Microsoft technologies.\n\n## When to Use This Tool\n- When search results provide incomplete information or truncated content\n- When you need complete step-by-step procedures or tutorials\n- When you need troubleshooting sections, prerequisites, or detailed explanations\n- When search results reference a specific page that seems highly relevant\n- For comprehensive guides that require full context\n\n## Usage Pattern\nUse this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture.\n\n## URL Requirements\n- The URL must be a valid link from the microsoft.com domain.\n\n## Output Format\nmarkdown with headings, code blocks, tables, and links preserved.')], type='mcp_list_tools', error=None), output_index=0, sequence_number=5, type='response.output_item.done')
ResponseOutputItemAddedEvent(item=McpCall(id='mcp_68be5b44526081958e21efc12f76f6dc08ce4c5c780e7d9e', arguments='', name='microsoft_docs_search', server_label='microsoft-document', type='mcp_call', error=None, output=None, approval_request_id=None), output_index=1, sequence_number=6, type='response.output_item.added')
ResponseMcpCallInProgressEvent(item_id='mcp_68be5b44526081958e21efc12f76f6dc08ce4c5c780e7d9e', output_index=1, sequence_number=7, type='response.mcp_call.in_progress')
ResponseMcpCallArgumentsDeltaEvent(delta='{"query":"azure ai agent","question":"What is Azure AI Agent?"}', item_id='mcp_68be5b44526081958e21efc12f76f6dc08ce4c5c780e7d9e', output_index=1, sequence_number=8, type='response.mcp_call_arguments.delta', obfuscation='W')
ResponseMcpCallArgumentsDoneEvent(arguments='{"query":"azure ai agent","question":"What is Azure AI Agent?"}', item_id='mcp_68be5b44526081958e21efc12f76f6dc08ce4c5c780e7d9e', output_index=1, sequence_number=9, type='response.mcp_call_arguments.done')
ResponseMcpCallCompletedEvent(item_id='mcp_68be5b44526081958e21efc12f76f6dc08ce4c5c780e7d9e', output_index=1, sequence_number=10, type='response.mcp_call.completed')
ResponseOutputItemDoneEvent(item=McpCall(id='mcp_68be5b44526081958e21efc12f76f6dc08ce4c5c780e7d9e', arguments='{"query":"azure ai agent","question":"What is Azure AI Agent?"}', name='microsoft_docs_search', server_label='microsoft-document', type='mcp_call', error=None, output='[{"title": "Transparency Note for Azure Agent Service", "content": "# Transparency Note for Azure Agent Service\\n## The basics of Azure AI Agent Service\\n### Introduction\\nAzure AI Agent Service is a fully managed service designed to empower developers to securely build, deploy, and scale high-quality and extensible AI agents without needing to manage the underlying compute and storage resources. Azure AI Agent Service provides integrated access to **models, tools, and technology** and enables you to extend the functionality of agents with knowledge from connected sources (such as Bing Search, SharePoint, Fabric, Azure Blob storage, and licensed data) and with actions using tools such as Azure Logic Apps, Azure Functions, OpenAPI 3.0 specified tools, and Code Interpreter. [Learn more](https://learn.microsoft.com/en-us/azure/ai-services/agents/overview).\\n### General Disclaimer about Agents\\nAgentic AI systems are designed to use agentic capabilities to achieve a high-level goal specified by a user. Systems should be designed to allow users to incorporate human oversight as appropriate to ensure the system is performing the actions and tasks as intended. Should an Agent exhibit unintended or undesirable behaviors, users should have the ability to intervene and take appropriate measures to mitigate potential risks.\\n### Disclaimer about Agents in sensitive domains\\nUsers should exercise caution when designing and deploying agentic AI systems in sensitive domains where Agent actions are irreversible or highly consequential. Such domains include, but are not limited to, finance and insurance, healthcare, legal, and housing. Additional precautions should also be taken when creating autonomous agentic AI as described further in our [Code of Conduct](https://aka.ms/AI-CoC). You are responsible for complying with all applicable laws and safety standards relevant to the Agents you create using any AI services and solutions, including Agents Catalog, underlying Code Samples, and similar resources and information (see below Considerations when choosing a use case).\\n### Key terms\\nThe following are key components of the Azure AI Agent Service SDK (and the [Azure AI Foundry portal](https://ai.azure.com?cid=learnDocs) experience powered by it):", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/transparency-note#the-basics-of-azure-ai-agent-service"}, {"title": "Exploring the Semantic Kernel `AzureAIAgent` (programming-language-python)", "content": "# Exploring the Semantic Kernel `AzureAIAgent` (programming-language-python)\\n## What is an `AzureAIAgent`?\\nAn `AzureAIAgent` is a specialized agent within the Semantic Kernel framework, designed to provide advanced conversational capabilities with seamless tool integration. It automates tool calling, eliminating the need for manual parsing and invocation. The agent also securely manages conversation history using threads, reducing the overhead of maintaining state. Additionally, the `AzureAIAgent` supports a variety of built-in tools, including file retrieval, code execution, and data interaction via Bing, Azure AI Search, Azure Functions, and OpenAPI.\\nTo use an `AzureAIAgent`, an Azure AI Foundry Project must be utilized. The following articles provide an overview of the Azure AI Foundry, how to create and configure a project, and the agent service:\\n1. [What is Azure AI Foundry?](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry)\\n2. [The Azure AI Foundry SDK](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/sdk-overview)\\n3. [What is Azure AI Agent Service](https://learn.microsoft.com/en-us/azure/ai-services/agents/overview)\\n4. [Quickstart: Create a new agent](https://learn.microsoft.com/en-us/azure/ai-services/agents/quickstart)\\n## Preparing Your Development Environment\\nTo proceed with developing an `AzureAIAgent`, configure your development environment with the appropriate packages.\\nInstall the `semantic-kernel` package:\\n```bash\\npip install semantic-kernel\\n```", "contentUrl": "https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-types/azure-ai-agent#what-is-an-%60azureaiagent%60"}, {"title": "Exploring the Semantic Kernel `AzureAIAgent` (programming-language-java)", "content": "# Exploring the Semantic Kernel `AzureAIAgent` (programming-language-java)\\n## What is an `AzureAIAgent`?\\nAn `AzureAIAgent` is a specialized agent within the Semantic Kernel framework, designed to provide advanced conversational capabilities with seamless tool integration. It automates tool calling, eliminating the need for manual parsing and invocation. The agent also securely manages conversation history using threads, reducing the overhead of maintaining state. Additionally, the `AzureAIAgent` supports a variety of built-in tools, including file retrieval, code execution, and data interaction via Bing, Azure AI Search, Azure Functions, and OpenAPI.\\nTo use an `AzureAIAgent`, an Azure AI Foundry Project must be utilized. The following articles provide an overview of the Azure AI Foundry, how to create and configure a project, and the agent service:\\n1. [What is Azure AI Foundry?](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry)\\n2. [The Azure AI Foundry SDK](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/sdk-overview)\\n3. [What is Azure AI Agent Service](https://learn.microsoft.com/en-us/azure/ai-services/agents/overview)\\n4. [Quickstart: Create a new agent](https://learn.microsoft.com/en-us/azure/ai-services/agents/quickstart)\\n## Preparing Your Development Environment\\nTo proceed with developing an `AzureAIAgent`, configure your development environment with the appropriate packages.\\n\\nFeature currently unavailable in Java.\\n\\n## Configuring the AI Project Client\\nAccessing an `AzureAIAgent` first requires the creation of a client that is configured for a specific Foundry Project, most commonly by providing your project endpoint ([The Azure AI Foundry SDK: Getting Started with Projects](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/sdk-overview#get-started-with-projects)).\\n\\nFeature currently unavailable in Java.\\n\\n## Creating an `AzureAIAgent`\\nTo create an `AzureAIAgent`, you start by configuring and initializing the Foundry project through the Azure Agent service and then integrate it with Semantic Kernel:\\n\\nFeature currently unavailable in Java.\\n\\n## Interacting with an `AzureAIAgent`\\nInteraction with the `AzureAIAgent` is straightforward. The agent maintains the conversation history automatically using a thread.\\nAn agent may also produce a streamed response:\\n\\nFeature currently unavailable in Java.\\n", "contentUrl": "https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-types/azure-ai-agent#what-is-an-%60azureaiagent%60"}, {"title": "Exploring the Semantic Kernel `AzureAIAgent` (programming-language-csharp)", "content": "# Exploring the Semantic Kernel `AzureAIAgent` (programming-language-csharp)\\nImportant\\nThis feature is in the experimental stage. Features at this stage are under development and subject to change before advancing to the preview or release candidate stage.\\nTip\\nDetailed API documentation related to this discussion is available at:\\n1. [`AzureAIAgent`](https://learn.microsoft.com/en-us/dotnet/api/microsoft.semantickernel.agents.azureai)\\n## What is an `AzureAIAgent`?\\nAn `AzureAIAgent` is a specialized agent within the Semantic Kernel framework, designed to provide advanced conversational capabilities with seamless tool integration. It automates tool calling, eliminating the need for manual parsing and invocation. The agent also securely manages conversation history using threads, reducing the overhead of maintaining state. Additionally, the `AzureAIAgent` supports a variety of built-in tools, including file retrieval, code execution, and data interaction via Bing, Azure AI Search, Azure Functions, and OpenAPI.\\nTo use an `AzureAIAgent`, an Azure AI Foundry Project must be utilized. The following articles provide an overview of the Azure AI Foundry, how to create and configure a project, and the agent service:\\n1. [What is Azure AI Foundry?](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry)\\n2. [The Azure AI Foundry SDK](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/sdk-overview)\\n3. [What is Azure AI Agent Service](https://learn.microsoft.com/en-us/azure/ai-services/agents/overview)\\n4. [Quickstart: Create a new agent](https://learn.microsoft.com/en-us/azure/ai-services/agents/quickstart)\\n## Preparing Your Development Environment\\nTo proceed with developing an `AzureAIAgent`, configure your development environment with the appropriate packages.\\nAdd the `Microsoft.SemanticKernel.Agents.AzureAI` package to your project:\\n```pwsh\\ndotnet add package Microsoft.SemanticKernel.Agents.AzureAI --prerelease\\n```\\nYou may also want to include the `Azure.Identity` package:\\n```pwsh\\ndotnet add package Azure.Identity\\n```", "contentUrl": "https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-types/azure-ai-agent"}, {"title": "Transparency Note for Azure Agent Service", "content": "# Transparency Note for Azure Agent Service\\n## Capabilities\\nMulti-agent systems using Azure AI Agent Service can be designed to achieve performant autonomous workflows for specific scenarios. In multi-agent systems, multiple context-aware autonomous agents, whether humans or AI systems, interact or work together to achieve individual or collective goals specified by the user. Azure AI Agent Service works out-of-the-box with multi-agent orchestration frameworks that are wireline compatible^1^ with the Assistants API, such as [**AutoGen**](https://www.microsoft.com/research/blog/autogen-enabling-next-generation-large-language-model-applications/), a state-of-the-art research SDK for Python created by Microsoft Research, and [**Semantic Kernel**](https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-architecture?pivots=programming-language-csharp), an enterprise AI SDK for Python, .NET, and Java.\\nWhen building a new multi-agent solution, start with building singleton agents with Azure AI Agent Service to get the most reliable, scalable, and secure agents. You can then orchestrate these agents together, using supported orchestration frameworks. AutoGen is constantly evolving to find the best collaboration patterns for agents (and humans) to work together. Features that show production value with AutoGen can then be moved into Semantic Kernel if you\'re looking for production support and non-breaking changes.\\n^1^*Wireline compatible* means that an API can communicate and exchange data in a way that is fully compatible with an existing protocol, existing data formats and communication standards, in this case the Assistants API protocol. It means that two systems can work together seamlessly without needing changes to their core implementation.\\n### Use cases\\n#### Intended uses\\nAzure AI Agent Service is **flexible and use-case agnostic.** This presents multiple possibilities to automate routine tasks and unlock new possibilities for knowledge work - whether it is personal productivity agents that send emails and schedule meetings, research agents that continuously monitor market trends and automate report creation, sales agents that can research leads and automatically qualify them, customer service agents that proactively follow up with personalized messages, or developer agents that can upgrade your code base or evolve a code repository interactively. Here are examples of intended uses of agents developed using Azure AI Agent Service:", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/transparency-note#capabilities"}, {"title": "Transparency Note for Azure Agent Service", "content": "# Transparency Note for Azure Agent Service\\n## The basics of Azure AI Agent Service\\n| **Term** | **Definition**| \\n|  --- | ---  |\\n| Developer | A customer of Azure AI Agent Service who builds an Agent. |\\n| User | A person who uses and/or operates an Agent that is created by a developer. |\\n| Agent | An application or a system that uses generative AI models with tools to access and interact with real-world data sources, APIs, and systems to achieve user-specified goals such as answer questions, perform actions, or completely automate workflows, with or without human supervision. |\\n| Tool | A built-in or custom-defined functionality that enables an Agent to perform simple or complex tasks or interact with information sources, applications, and/or services via the Agent Service SDK or [Azure AI Foundry portal](https://ai.azure.com?cid=learnDocs). |\\n| Knowledge Tool | A tool that enables an Agent to access and process data from internal and external sources, including information beyond its model training cut-off date, to improve the accuracy and relevance of responses to user queries. |\\n| Action Tool | A tool that enables an Agent to perform tasks and take actions on behalf of users by integrating with external systems, APIs, and services. |\\n| Thread | A conversation session between an Agent and a user. Threads store Messages and automatically handle truncation to fit content into a model\\u2019s context. |\\n| Message | A message created by an Agent or a user. Messages can include text, images, and other files. Messages are stored as a list on the Thread. |\\n| Run | The activation of an Agent to begin running based on the contents of the Thread. The Agent uses its configuration and the Thread\\u2019s Messages to perform tasks by calling models and tools. As part of a Run, the Agent appends Messages to the Thread. |\\n| Run Steps | A detailed list of steps the Agent took as part of a Run. An Agent can call tools or create Messages during its Run. Examining Run Steps allows you to understand how the Agent is getting to its final results. |\\n\\n### Relevant capability concepts", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/transparency-note#the-basics-of-azure-ai-agent-service"}, {"title": "Transparency Note for Azure Agent Service", "content": "# Transparency Note for Azure Agent Service\\n## Capabilities\\n1. **File Search** (a built-in retrieval-augmented generation (RAG) tool to process and search through private data in Azure AI Search, Azure Blob Storage, and local files)\\n2. **Grounding with Bing Search** (a web search tool that uses Bing Search to extract information from the web)\\n3. **SharePoint** (built-in tools that connect an organization\\u2019s internal documents in SharePoint for grounded responses)\\n4. **Fabric Data Agent** (a built-in tool to chat with structured data on Microsoft Fabric using generative AI)\\n5. **Bring your licensed data** (a tool that enables grounding using proprietary data accessed using a licensed API key obtained by the developer from the data provider, for example, TripAdvisor)\\nAgents simplify secure data access to SharePoint and Fabric AI Skills through on-behalf-of (OBO) authentication, allowing the Agent to access only the SharePoint or Fabric files for which the user has permissions.\\n#### Enabling autonomous actions with or without human input through Action Tools\\nDevelopers can connect an Agent to external systems, APIs, and services through Action Tools, enabling the Agent to perform tasks and take actions on behalf of users. Action Tools built into Azure AI Agent Service include:\\n1. **Code Interpreter** (a tool that can write and run Python code in a secure environment, handle diverse data formats and generate files with data and visuals)\\n2. **Azure Logic Apps** (a cloud-based PaaS tool that enables automated workflows using 1,400+ built-in connectors)\\n3. **Azure Functions** (a tool that enables an Agent to execute serverless code for synchronous, asynchronous, long-running, and event-driven actions)\\n4. **OpenAPI 3.0 specified tools** (a custom function defined with OpenAPI 3.0 specification to connect an Agent to external OpenAPI-based APIs securely)\\n5. **Model Context Protocol tools** (a custom service connected via Model Context Protocol through an existing remote MCP server to an Agent).\\n6. **Deep Research tool**: (a tool that enables multi-step web-based research with the o3-deep-research model and Grounding with Bing Search.).\\n7. **Browser Automation Tool** (a tool that can perform real-world browser tasks through natural language prompts, enabling automated browsing activities without human intervention in the middle)\\n#### Orchestrating multi-agent systems", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/transparency-note#capabilities"}, {"title": "Data, privacy, and security for Azure AI Agent Service", "content": "# Data, privacy, and security for Azure AI Agent Service\\nImportant\\nNon-English translations are provided for convenience only. Please consult the `EN-US` version of this document for the binding version.\\nAzure AI Agent Service is a fully managed service designed to empower developers to securely build, deploy, and scale high-quality, and extensible AI agents without needing to manage the underlying compute and storage resources. Azure AI Agent Service integrates **models, tools and technology** and enables you to extend agents with knowledge from connected sources (such as Bing Search, SharePoint, Fabric, Azure Blob storage, and licensed data) and actions using tools such as Azure Logic Apps, Azure Functions, OpenAPI 3.0 specified tools and Code Interpreter\\nNote\\nThis article provides details regarding how data provided by you to the Azure AI Agent service is processed, used, and stored. Please also see the [Microsoft Products and Services Data Protection Addendum](https://aka.ms/DPA), which governs data processing by the Azure AI Agent Service (but may not necessarily apply to external tools or services with which Azure AI Agent Service interacts, which are subject to their own data processing terms).\\nImportant\\nYour prompts (inputs) and completions (outputs) and your data:\\n1. are NOT available to other customers.\\n2. are NOT available to OpenAI, Meta, Cohere, or Mistral.\\n3. are NOT used to improve OpenAI, Meta, Cohere, or Mistral models.\\nWhen you use Azure AI Agent Service with tools that retrieve data from external sources or services (such as the Grounding with Bing Search tool), the terms (including data processing terms) for those services apply to any data processed by those services. For example, the Grounding with Bing Search tool is subject to separate data collection and privacy terms (see Terms of Use for Grounding with Bing Search in Azure AI Agents Service), and the services are Microsoft-as-controller services and thereby excluded from the Microsoft Products and Services Data Protection Addendum.", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/data-privacy-security"}, {"title": "Data, privacy, and security for Azure AI Agent Service", "content": "# Data, privacy, and security for Azure AI Agent Service\\n## Model inferencing\\nAzure AI Agent Service interacts with the configured model inferencing endpoints you designate. Models (base or fine-tuned) process your input prompts and generate output responses which may be further used or processed by your agent. Data is processed for model inferencing in accordance with the terms that apply to the relevant model. Learn more at [Data, privacy, and security for Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/openai/data-privacy) and [Data, privacy, and security for use of models through the model catalog in AI Foundry portal](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/concept-data-privacy).\\n## Data storage for Azure AI Agent Service features\\nSome Azure AI Agent Service features store data in the service. This data is either uploaded by the customer or is automatically stored in connection with certain stateful entities such as Messages, Threads, and Runs. Data stored for Azure AI Agent Service:\\n1. Is stored at rest in the Azure OpenAI resource which is created when you configure Azure AI Agent Service in the customer\'s Azure tenant, within the same geography as the Azure OpenAI resource.\\n2. Can be double encrypted at rest, by default with Microsoft\'s AES-256 encryption and optionally with a customer managed key (except preview features may not support customer managed keys).\\n3. Can be deleted by the customer at any time.\\n## Location of data processing\\nTo use the Azure AI Agent Service, you must create an Azure OpenAI Service resource in which the Azure AI Agent Service will be hosted. If you use features of the Azure AI Agent Service that store data at rest, that data will be stored in the geography where your Azure OpenAI resource is located. When the Azure AI Agent Service interacts with models and tools, the location of processing of data will depend on the configuration of the model inferencing endpoints and the hosting location of the tools.\\n## Preventing harmful content generation\\nTo reduce the risk of harmful use of the Azure AI Agent Service, the Service includes [content filtering](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/content-filter) support. The outputs processed by the Azure AI Agent Service will be filtered in accordance with any content filtering that has been applied to the model deployment used by your agent.", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/data-privacy-security#model-inferencing"}, {"title": "Data, privacy, and security for Azure AI Agent Service", "content": "# Data, privacy, and security for Azure AI Agent Service\\n## What data does the Azure AI Agent Service process?\\nAzure AI Agent Service processes the following types of data:\\n1. **Prompts and generated content**. Prompts are submitted by the user, and content is generated by the service, via the completions, chat completions, images and audio operations of models with which the Azure AI Agent Service interacts.\\n2. **Uploaded data**. You can provide your own data for use with certain tools in Azure AI Agent service (e.g., File Search, Code Interpreter, Azure AI Search) using your own Azure Storage account or a configured data store.\\n3. **External Data**. When you use the Grounding with Bing Search tool or tools that support function calling, the service processes and stores the outputs of these tools.\\n**Data for stateful entities**. When you use Threads, Messages and Runs, the service will create a data store to persist message history and other content, in accordance with how you configure the feature and in keeping with applicable privacy, security, and compliance commitments. When you use the MCP tool, the data you pass in headers is only available for the current Run and will not persist.\\n**Augmented data included with or via prompts**. When using data associated with stateful entities, the service retrieves relevant data from your configured data store and augments the prompt to produce generations that are grounded with your data. Prompts may also be augmented with data retrieved from a source included in the prompt itself, such as a URL.\\n## How does the Azure AI Agent Service process data?\\nThe diagram below illustrates how your data is processed. This diagram covers several types of processing:\\n1. How the Azure AI Agent Service processes your prompts via inferencing to generate content (including when additional data from a designated data source is added to a prompt using File Search, Code Interpreter, or other tools).\\n2. How the Azure AI Agent Service stores data in connection with Messages, Threads, and Runs.\\n3. How the Azure AI Agent Service processes data ingested into the service from external tools.\\n[![A diagram showing how Azure AI Agent Service processes data.](https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/media/agents-diagram.png)](media/agents-diagram.png#lightbox)", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/data-privacy-security#what-data-does-the-azure-ai-agent-service-process"}]', approval_request_id=None), output_index=1, sequence_number=11, type='response.output_item.done')
ResponseOutputItemAddedEvent(item=ResponseOutputMessage(id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', content=[], role='assistant', status='in_progress', type='message'), output_index=2, sequence_number=12, type='response.output_item.added')
ResponseContentPartAddedEvent(content_index=0, item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', output_index=2, part=ResponseOutputText(annotations=[], text='', type='output_text', logprobs=None), sequence_number=13, type='response.content_part.added')
ResponseTextDeltaEvent(content_index=0, delta='検索', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=14, type='response.output_text.delta', obfuscation='jr9rvEuhEtCa4M')
ResponseTextDeltaEvent(content_index=0, delta='結果', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=15, type='response.output_text.delta', obfuscation='pITlQbKPZQTEz0')
ResponseTextDeltaEvent(content_index=0, delta='の', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=16, type='response.output_text.delta', obfuscation='o4WEc8rsfKHINhk')
ResponseTextDeltaEvent(content_index=0, delta='最', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=17, type='response.output_text.delta', obfuscation='vQCbdiP9iNv9jn2')
ResponseTextDeltaEvent(content_index=0, delta='初', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=18, type='response.output_text.delta', obfuscation='s4RKkBvdt9mzKUo')
ResponseTextDeltaEvent(content_index=0, delta='の', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=19, type='response.output_text.delta', obfuscation='QeL9iyCY6fPOBMd')
ResponseTextDeltaEvent(content_index=0, delta='100', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=20, type='response.output_text.delta', obfuscation='VQnDSkaMXe5j5')
ResponseTextDeltaEvent(content_index=0, delta='文字', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=21, type='response.output_text.delta', obfuscation='rDOaF32uipC0ou')
ResponseTextDeltaEvent(content_index=0, delta='は', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=22, type='response.output_text.delta', obfuscation='0jnYGVmM3W3IY8X')
ResponseTextDeltaEvent(content_index=0, delta='以下', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=23, type='response.output_text.delta', obfuscation='I1zEJIYk5MnNwO')
ResponseTextDeltaEvent(content_index=0, delta='の', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=24, type='response.output_text.delta', obfuscation='nUepSj5uCYjKzoH')
ResponseTextDeltaEvent(content_index=0, delta='通', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=25, type='response.output_text.delta', obfuscation='HAr3nC0ZhcbbdSW')
ResponseTextDeltaEvent(content_index=0, delta='り', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=26, type='response.output_text.delta', obfuscation='2uRClX80tkYa7we')
ResponseTextDeltaEvent(content_index=0, delta='です', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=27, type='response.output_text.delta', obfuscation='iiIVvRoIeOSKMZ')
ResponseTextDeltaEvent(content_index=0, delta='。\n\n', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=28, type='response.output_text.delta', obfuscation='F1ArMZbG0dWgK')
ResponseTextDeltaEvent(content_index=0, delta='「', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=29, type='response.output_text.delta', obfuscation='1xSSra4flLeUKHc')
ResponseTextDeltaEvent(content_index=0, delta='#', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=30, type='response.output_text.delta', obfuscation='8AhrKP8QkS5m43f')
ResponseTextDeltaEvent(content_index=0, delta=' Transparency', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=31, type='response.output_text.delta', obfuscation='q0D')
ResponseTextDeltaEvent(content_index=0, delta=' Note', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=32, type='response.output_text.delta', obfuscation='80GdLXiePsX')
ResponseTextDeltaEvent(content_index=0, delta=' for', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=33, type='response.output_text.delta', obfuscation='YuCjfDvYRJ0c')
ResponseTextDeltaEvent(content_index=0, delta=' Azure', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=34, type='response.output_text.delta', obfuscation='qzvGjdKjUu')
ResponseTextDeltaEvent(content_index=0, delta=' Agent', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=35, type='response.output_text.delta', obfuscation='OgnN7xrfdP')
ResponseTextDeltaEvent(content_index=0, delta=' Service', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=36, type='response.output_text.delta', obfuscation='hsXdwtg3')
ResponseTextDeltaEvent(content_index=0, delta='\n', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=37, type='response.output_text.delta', obfuscation='tK3zhtZ64FbAhSf')
ResponseTextDeltaEvent(content_index=0, delta='##', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=38, type='response.output_text.delta', obfuscation='UaY32DJg26fHAl')
ResponseTextDeltaEvent(content_index=0, delta=' The', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=39, type='response.output_text.delta', obfuscation='KzomL2eaEz07')
ResponseTextDeltaEvent(content_index=0, delta=' basics', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=40, type='response.output_text.delta', obfuscation='53EnunjvU')
ResponseTextDeltaEvent(content_index=0, delta=' of', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=41, type='response.output_text.delta', obfuscation='CQjnEGns46W1t')
ResponseTextDeltaEvent(content_index=0, delta=' Azure', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=42, type='response.output_text.delta', obfuscation='8NT9TnHVh8')
ResponseTextDeltaEvent(content_index=0, delta=' AI', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=43, type='response.output_text.delta', obfuscation='15ZPp59UWbzil')
ResponseTextDeltaEvent(content_index=0, delta=' Agent', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=44, type='response.output_text.delta', obfuscation='nC5pFzTr0E')
ResponseTextDeltaEvent(content_index=0, delta=' Service', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=45, type='response.output_text.delta', obfuscation='s0dPPzF0')
ResponseTextDeltaEvent(content_index=0, delta='\n', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=46, type='response.output_text.delta', obfuscation='jC2nYUXwSuda1zo')
ResponseTextDeltaEvent(content_index=0, delta='###', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=47, type='response.output_text.delta', obfuscation='EtzRjVMNC9q8d')
ResponseTextDeltaEvent(content_index=0, delta=' Introduction', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=48, type='response.output_text.delta', obfuscation='eju')
ResponseTextDeltaEvent(content_index=0, delta='\n', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=49, type='response.output_text.delta', obfuscation='dAlqa6WE8rMhAuH')
ResponseTextDeltaEvent(content_index=0, delta='Azure', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=50, type='response.output_text.delta', obfuscation='DMbidIyixLb')
ResponseTextDeltaEvent(content_index=0, delta=' AI', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=51, type='response.output_text.delta', obfuscation='3IDy6wNbPLGFi')
ResponseTextDeltaEvent(content_index=0, delta=' Agent', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=52, type='response.output_text.delta', obfuscation='0iPELZBLVf')
ResponseTextDeltaEvent(content_index=0, delta=' Service', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=53, type='response.output_text.delta', obfuscation='po9a8upx')
ResponseTextDeltaEvent(content_index=0, delta=' is', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=54, type='response.output_text.delta', obfuscation='ToI4gEWJCOjJw')
ResponseTextDeltaEvent(content_index=0, delta=' a', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=55, type='response.output_text.delta', obfuscation='jB1uwBF9A7kO3t')
ResponseTextDeltaEvent(content_index=0, delta=' fully', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=56, type='response.output_text.delta', obfuscation='6LOsaNPx8P')
ResponseTextDeltaEvent(content_index=0, delta=' managed', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=57, type='response.output_text.delta', obfuscation='rkWOZfbl')
ResponseTextDeltaEvent(content_index=0, delta=' service', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=58, type='response.output_text.delta', obfuscation='iUmVIkqY')
ResponseTextDeltaEvent(content_index=0, delta=' designed', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=59, type='response.output_text.delta', obfuscation='LqF8Q0h')
ResponseTextDeltaEvent(content_index=0, delta=' to', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=60, type='response.output_text.delta', obfuscation='i4hNwTQIXkASN')
ResponseTextDeltaEvent(content_index=0, delta=' empower', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=61, type='response.output_text.delta', obfuscation='7yTMy4bS')
ResponseTextDeltaEvent(content_index=0, delta=' developers', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=62, type='response.output_text.delta', obfuscation='pmkUA')
ResponseTextDeltaEvent(content_index=0, delta=' to', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=63, type='response.output_text.delta', obfuscation='krEZOdfEy1wSE')
ResponseTextDeltaEvent(content_index=0, delta=' securely', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=64, type='response.output_text.delta', obfuscation='KD2mzgR')
ResponseTextDeltaEvent(content_index=0, delta=' buil', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=65, type='response.output_text.delta', obfuscation='LUrcOmcfMNI')
ResponseTextDeltaEvent(content_index=0, delta='」', item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=66, type='response.output_text.delta', obfuscation='frQy7EqAGBKG1tu')
ResponseTextDoneEvent(content_index=0, item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', logprobs=None, output_index=2, sequence_number=67, text='検索結果の最初の100文字は以下の通りです。\n\n「# Transparency Note for Azure Agent Service\n## The basics of Azure AI Agent Service\n### Introduction\nAzure AI Agent Service is a fully managed service designed to empower developers to securely buil」', type='response.output_text.done')
ResponseContentPartDoneEvent(content_index=0, item_id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', output_index=2, part=ResponseOutputText(annotations=[], text='検索結果の最初の100文字は以下の通りです。\n\n「# Transparency Note for Azure Agent Service\n## The basics of Azure AI Agent Service\n### Introduction\nAzure AI Agent Service is a fully managed service designed to empower developers to securely buil」', type='output_text', logprobs=None), sequence_number=68, type='response.content_part.done')
ResponseOutputItemDoneEvent(item=ResponseOutputMessage(id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', content=[ResponseOutputText(annotations=[], text='検索結果の最初の100文字は以下の通りです。\n\n「# Transparency Note for Azure Agent Service\n## The basics of Azure AI Agent Service\n### Introduction\nAzure AI Agent Service is a fully managed service designed to empower developers to securely buil」', type='output_text', logprobs=None)], role='assistant', status='completed', type='message'), output_index=2, sequence_number=69, type='response.output_item.done')
ResponseCompletedEvent(response=Response(id='resp_68be5b4280fc8195a7690af3ed1b527908ce4c5c780e7d9e', created_at=1757305666.0, error=None, incomplete_details=None, instructions=None, metadata={}, model='gpt-4.1-mini-2025-04-14', object='response', output=[McpListTools(id='mcpl_68be5b4283648195b5acfffc809da7cc08ce4c5c780e7d9e', server_label='microsoft-document', tools=[McpListToolsTool(input_schema={'type': 'object', 'properties': {'query': {'description': 'a query or topic about Microsoft/Azure products, services, platforms, developer tools, frameworks, or APIs', 'type': 'string', 'default': None}, 'question': {'description': 'this parameter is no longer used, use query instead.', 'type': 'string', 'default': None}}}, name='microsoft_docs_search', annotations=None, description="Search official Microsoft/Azure documentation to find the most relevant and trustworthy content for a user's query. This tool returns up to 10 high-quality content chunks (each max 500 tokens), extracted from Microsoft Learn and other official sources. Each result includes the article title, URL, and a self-contained content excerpt optimized for fast retrieval and reasoning. Always use this tool to quickly ground your answers in accurate, first-party Microsoft/Azure knowledge.\n\nThe `question` parameter is no longer used, use `query` instead.\n\n## Follow-up Pattern\nTo ensure completeness, use microsoft_docs_fetch when high-value pages are identified by search. The fetch tool complements search by providing the full detail. This is a required step for comprehensive results."), McpListToolsTool(input_schema={'type': 'object', 'properties': {'url': {'description': 'URL of the Microsoft documentation page to read', 'type': 'string'}}, 'required': ['url']}, name='microsoft_docs_fetch', annotations=None, description='Fetch and convert a Microsoft Learn documentation page to markdown format. This tool retrieves the latest complete content of Microsoft documentation pages including Azure, .NET, Microsoft 365, and other Microsoft technologies.\n\n## When to Use This Tool\n- When search results provide incomplete information or truncated content\n- When you need complete step-by-step procedures or tutorials\n- When you need troubleshooting sections, prerequisites, or detailed explanations\n- When search results reference a specific page that seems highly relevant\n- For comprehensive guides that require full context\n\n## Usage Pattern\nUse this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture.\n\n## URL Requirements\n- The URL must be a valid link from the microsoft.com domain.\n\n## Output Format\nmarkdown with headings, code blocks, tables, and links preserved.')], type='mcp_list_tools', error=None), McpCall(id='mcp_68be5b44526081958e21efc12f76f6dc08ce4c5c780e7d9e', arguments='{"query":"azure ai agent","question":"What is Azure AI Agent?"}', name='microsoft_docs_search', server_label='microsoft-document', type='mcp_call', error=None, output='[{"title": "Transparency Note for Azure Agent Service", "content": "# Transparency Note for Azure Agent Service\\n## The basics of Azure AI Agent Service\\n### Introduction\\nAzure AI Agent Service is a fully managed service designed to empower developers to securely build, deploy, and scale high-quality and extensible AI agents without needing to manage the underlying compute and storage resources. Azure AI Agent Service provides integrated access to **models, tools, and technology** and enables you to extend the functionality of agents with knowledge from connected sources (such as Bing Search, SharePoint, Fabric, Azure Blob storage, and licensed data) and with actions using tools such as Azure Logic Apps, Azure Functions, OpenAPI 3.0 specified tools, and Code Interpreter. [Learn more](https://learn.microsoft.com/en-us/azure/ai-services/agents/overview).\\n### General Disclaimer about Agents\\nAgentic AI systems are designed to use agentic capabilities to achieve a high-level goal specified by a user. Systems should be designed to allow users to incorporate human oversight as appropriate to ensure the system is performing the actions and tasks as intended. Should an Agent exhibit unintended or undesirable behaviors, users should have the ability to intervene and take appropriate measures to mitigate potential risks.\\n### Disclaimer about Agents in sensitive domains\\nUsers should exercise caution when designing and deploying agentic AI systems in sensitive domains where Agent actions are irreversible or highly consequential. Such domains include, but are not limited to, finance and insurance, healthcare, legal, and housing. Additional precautions should also be taken when creating autonomous agentic AI as described further in our [Code of Conduct](https://aka.ms/AI-CoC). You are responsible for complying with all applicable laws and safety standards relevant to the Agents you create using any AI services and solutions, including Agents Catalog, underlying Code Samples, and similar resources and information (see below Considerations when choosing a use case).\\n### Key terms\\nThe following are key components of the Azure AI Agent Service SDK (and the [Azure AI Foundry portal](https://ai.azure.com?cid=learnDocs) experience powered by it):", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/transparency-note#the-basics-of-azure-ai-agent-service"}, {"title": "Exploring the Semantic Kernel `AzureAIAgent` (programming-language-python)", "content": "# Exploring the Semantic Kernel `AzureAIAgent` (programming-language-python)\\n## What is an `AzureAIAgent`?\\nAn `AzureAIAgent` is a specialized agent within the Semantic Kernel framework, designed to provide advanced conversational capabilities with seamless tool integration. It automates tool calling, eliminating the need for manual parsing and invocation. The agent also securely manages conversation history using threads, reducing the overhead of maintaining state. Additionally, the `AzureAIAgent` supports a variety of built-in tools, including file retrieval, code execution, and data interaction via Bing, Azure AI Search, Azure Functions, and OpenAPI.\\nTo use an `AzureAIAgent`, an Azure AI Foundry Project must be utilized. The following articles provide an overview of the Azure AI Foundry, how to create and configure a project, and the agent service:\\n1. [What is Azure AI Foundry?](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry)\\n2. [The Azure AI Foundry SDK](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/sdk-overview)\\n3. [What is Azure AI Agent Service](https://learn.microsoft.com/en-us/azure/ai-services/agents/overview)\\n4. [Quickstart: Create a new agent](https://learn.microsoft.com/en-us/azure/ai-services/agents/quickstart)\\n## Preparing Your Development Environment\\nTo proceed with developing an `AzureAIAgent`, configure your development environment with the appropriate packages.\\nInstall the `semantic-kernel` package:\\n```bash\\npip install semantic-kernel\\n```", "contentUrl": "https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-types/azure-ai-agent#what-is-an-%60azureaiagent%60"}, {"title": "Exploring the Semantic Kernel `AzureAIAgent` (programming-language-java)", "content": "# Exploring the Semantic Kernel `AzureAIAgent` (programming-language-java)\\n## What is an `AzureAIAgent`?\\nAn `AzureAIAgent` is a specialized agent within the Semantic Kernel framework, designed to provide advanced conversational capabilities with seamless tool integration. It automates tool calling, eliminating the need for manual parsing and invocation. The agent also securely manages conversation history using threads, reducing the overhead of maintaining state. Additionally, the `AzureAIAgent` supports a variety of built-in tools, including file retrieval, code execution, and data interaction via Bing, Azure AI Search, Azure Functions, and OpenAPI.\\nTo use an `AzureAIAgent`, an Azure AI Foundry Project must be utilized. The following articles provide an overview of the Azure AI Foundry, how to create and configure a project, and the agent service:\\n1. [What is Azure AI Foundry?](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry)\\n2. [The Azure AI Foundry SDK](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/sdk-overview)\\n3. [What is Azure AI Agent Service](https://learn.microsoft.com/en-us/azure/ai-services/agents/overview)\\n4. [Quickstart: Create a new agent](https://learn.microsoft.com/en-us/azure/ai-services/agents/quickstart)\\n## Preparing Your Development Environment\\nTo proceed with developing an `AzureAIAgent`, configure your development environment with the appropriate packages.\\n\\nFeature currently unavailable in Java.\\n\\n## Configuring the AI Project Client\\nAccessing an `AzureAIAgent` first requires the creation of a client that is configured for a specific Foundry Project, most commonly by providing your project endpoint ([The Azure AI Foundry SDK: Getting Started with Projects](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/sdk-overview#get-started-with-projects)).\\n\\nFeature currently unavailable in Java.\\n\\n## Creating an `AzureAIAgent`\\nTo create an `AzureAIAgent`, you start by configuring and initializing the Foundry project through the Azure Agent service and then integrate it with Semantic Kernel:\\n\\nFeature currently unavailable in Java.\\n\\n## Interacting with an `AzureAIAgent`\\nInteraction with the `AzureAIAgent` is straightforward. The agent maintains the conversation history automatically using a thread.\\nAn agent may also produce a streamed response:\\n\\nFeature currently unavailable in Java.\\n", "contentUrl": "https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-types/azure-ai-agent#what-is-an-%60azureaiagent%60"}, {"title": "Exploring the Semantic Kernel `AzureAIAgent` (programming-language-csharp)", "content": "# Exploring the Semantic Kernel `AzureAIAgent` (programming-language-csharp)\\nImportant\\nThis feature is in the experimental stage. Features at this stage are under development and subject to change before advancing to the preview or release candidate stage.\\nTip\\nDetailed API documentation related to this discussion is available at:\\n1. [`AzureAIAgent`](https://learn.microsoft.com/en-us/dotnet/api/microsoft.semantickernel.agents.azureai)\\n## What is an `AzureAIAgent`?\\nAn `AzureAIAgent` is a specialized agent within the Semantic Kernel framework, designed to provide advanced conversational capabilities with seamless tool integration. It automates tool calling, eliminating the need for manual parsing and invocation. The agent also securely manages conversation history using threads, reducing the overhead of maintaining state. Additionally, the `AzureAIAgent` supports a variety of built-in tools, including file retrieval, code execution, and data interaction via Bing, Azure AI Search, Azure Functions, and OpenAPI.\\nTo use an `AzureAIAgent`, an Azure AI Foundry Project must be utilized. The following articles provide an overview of the Azure AI Foundry, how to create and configure a project, and the agent service:\\n1. [What is Azure AI Foundry?](https://learn.microsoft.com/en-us/azure/ai-foundry/what-is-ai-foundry)\\n2. [The Azure AI Foundry SDK](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/develop/sdk-overview)\\n3. [What is Azure AI Agent Service](https://learn.microsoft.com/en-us/azure/ai-services/agents/overview)\\n4. [Quickstart: Create a new agent](https://learn.microsoft.com/en-us/azure/ai-services/agents/quickstart)\\n## Preparing Your Development Environment\\nTo proceed with developing an `AzureAIAgent`, configure your development environment with the appropriate packages.\\nAdd the `Microsoft.SemanticKernel.Agents.AzureAI` package to your project:\\n```pwsh\\ndotnet add package Microsoft.SemanticKernel.Agents.AzureAI --prerelease\\n```\\nYou may also want to include the `Azure.Identity` package:\\n```pwsh\\ndotnet add package Azure.Identity\\n```", "contentUrl": "https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-types/azure-ai-agent"}, {"title": "Transparency Note for Azure Agent Service", "content": "# Transparency Note for Azure Agent Service\\n## Capabilities\\nMulti-agent systems using Azure AI Agent Service can be designed to achieve performant autonomous workflows for specific scenarios. In multi-agent systems, multiple context-aware autonomous agents, whether humans or AI systems, interact or work together to achieve individual or collective goals specified by the user. Azure AI Agent Service works out-of-the-box with multi-agent orchestration frameworks that are wireline compatible^1^ with the Assistants API, such as [**AutoGen**](https://www.microsoft.com/research/blog/autogen-enabling-next-generation-large-language-model-applications/), a state-of-the-art research SDK for Python created by Microsoft Research, and [**Semantic Kernel**](https://learn.microsoft.com/en-us/semantic-kernel/frameworks/agent/agent-architecture?pivots=programming-language-csharp), an enterprise AI SDK for Python, .NET, and Java.\\nWhen building a new multi-agent solution, start with building singleton agents with Azure AI Agent Service to get the most reliable, scalable, and secure agents. You can then orchestrate these agents together, using supported orchestration frameworks. AutoGen is constantly evolving to find the best collaboration patterns for agents (and humans) to work together. Features that show production value with AutoGen can then be moved into Semantic Kernel if you\'re looking for production support and non-breaking changes.\\n^1^*Wireline compatible* means that an API can communicate and exchange data in a way that is fully compatible with an existing protocol, existing data formats and communication standards, in this case the Assistants API protocol. It means that two systems can work together seamlessly without needing changes to their core implementation.\\n### Use cases\\n#### Intended uses\\nAzure AI Agent Service is **flexible and use-case agnostic.** This presents multiple possibilities to automate routine tasks and unlock new possibilities for knowledge work - whether it is personal productivity agents that send emails and schedule meetings, research agents that continuously monitor market trends and automate report creation, sales agents that can research leads and automatically qualify them, customer service agents that proactively follow up with personalized messages, or developer agents that can upgrade your code base or evolve a code repository interactively. Here are examples of intended uses of agents developed using Azure AI Agent Service:", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/transparency-note#capabilities"}, {"title": "Transparency Note for Azure Agent Service", "content": "# Transparency Note for Azure Agent Service\\n## The basics of Azure AI Agent Service\\n| **Term** | **Definition**| \\n|  --- | ---  |\\n| Developer | A customer of Azure AI Agent Service who builds an Agent. |\\n| User | A person who uses and/or operates an Agent that is created by a developer. |\\n| Agent | An application or a system that uses generative AI models with tools to access and interact with real-world data sources, APIs, and systems to achieve user-specified goals such as answer questions, perform actions, or completely automate workflows, with or without human supervision. |\\n| Tool | A built-in or custom-defined functionality that enables an Agent to perform simple or complex tasks or interact with information sources, applications, and/or services via the Agent Service SDK or [Azure AI Foundry portal](https://ai.azure.com?cid=learnDocs). |\\n| Knowledge Tool | A tool that enables an Agent to access and process data from internal and external sources, including information beyond its model training cut-off date, to improve the accuracy and relevance of responses to user queries. |\\n| Action Tool | A tool that enables an Agent to perform tasks and take actions on behalf of users by integrating with external systems, APIs, and services. |\\n| Thread | A conversation session between an Agent and a user. Threads store Messages and automatically handle truncation to fit content into a model\\u2019s context. |\\n| Message | A message created by an Agent or a user. Messages can include text, images, and other files. Messages are stored as a list on the Thread. |\\n| Run | The activation of an Agent to begin running based on the contents of the Thread. The Agent uses its configuration and the Thread\\u2019s Messages to perform tasks by calling models and tools. As part of a Run, the Agent appends Messages to the Thread. |\\n| Run Steps | A detailed list of steps the Agent took as part of a Run. An Agent can call tools or create Messages during its Run. Examining Run Steps allows you to understand how the Agent is getting to its final results. |\\n\\n### Relevant capability concepts", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/transparency-note#the-basics-of-azure-ai-agent-service"}, {"title": "Transparency Note for Azure Agent Service", "content": "# Transparency Note for Azure Agent Service\\n## Capabilities\\n1. **File Search** (a built-in retrieval-augmented generation (RAG) tool to process and search through private data in Azure AI Search, Azure Blob Storage, and local files)\\n2. **Grounding with Bing Search** (a web search tool that uses Bing Search to extract information from the web)\\n3. **SharePoint** (built-in tools that connect an organization\\u2019s internal documents in SharePoint for grounded responses)\\n4. **Fabric Data Agent** (a built-in tool to chat with structured data on Microsoft Fabric using generative AI)\\n5. **Bring your licensed data** (a tool that enables grounding using proprietary data accessed using a licensed API key obtained by the developer from the data provider, for example, TripAdvisor)\\nAgents simplify secure data access to SharePoint and Fabric AI Skills through on-behalf-of (OBO) authentication, allowing the Agent to access only the SharePoint or Fabric files for which the user has permissions.\\n#### Enabling autonomous actions with or without human input through Action Tools\\nDevelopers can connect an Agent to external systems, APIs, and services through Action Tools, enabling the Agent to perform tasks and take actions on behalf of users. Action Tools built into Azure AI Agent Service include:\\n1. **Code Interpreter** (a tool that can write and run Python code in a secure environment, handle diverse data formats and generate files with data and visuals)\\n2. **Azure Logic Apps** (a cloud-based PaaS tool that enables automated workflows using 1,400+ built-in connectors)\\n3. **Azure Functions** (a tool that enables an Agent to execute serverless code for synchronous, asynchronous, long-running, and event-driven actions)\\n4. **OpenAPI 3.0 specified tools** (a custom function defined with OpenAPI 3.0 specification to connect an Agent to external OpenAPI-based APIs securely)\\n5. **Model Context Protocol tools** (a custom service connected via Model Context Protocol through an existing remote MCP server to an Agent).\\n6. **Deep Research tool**: (a tool that enables multi-step web-based research with the o3-deep-research model and Grounding with Bing Search.).\\n7. **Browser Automation Tool** (a tool that can perform real-world browser tasks through natural language prompts, enabling automated browsing activities without human intervention in the middle)\\n#### Orchestrating multi-agent systems", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/transparency-note#capabilities"}, {"title": "Data, privacy, and security for Azure AI Agent Service", "content": "# Data, privacy, and security for Azure AI Agent Service\\nImportant\\nNon-English translations are provided for convenience only. Please consult the `EN-US` version of this document for the binding version.\\nAzure AI Agent Service is a fully managed service designed to empower developers to securely build, deploy, and scale high-quality, and extensible AI agents without needing to manage the underlying compute and storage resources. Azure AI Agent Service integrates **models, tools and technology** and enables you to extend agents with knowledge from connected sources (such as Bing Search, SharePoint, Fabric, Azure Blob storage, and licensed data) and actions using tools such as Azure Logic Apps, Azure Functions, OpenAPI 3.0 specified tools and Code Interpreter\\nNote\\nThis article provides details regarding how data provided by you to the Azure AI Agent service is processed, used, and stored. Please also see the [Microsoft Products and Services Data Protection Addendum](https://aka.ms/DPA), which governs data processing by the Azure AI Agent Service (but may not necessarily apply to external tools or services with which Azure AI Agent Service interacts, which are subject to their own data processing terms).\\nImportant\\nYour prompts (inputs) and completions (outputs) and your data:\\n1. are NOT available to other customers.\\n2. are NOT available to OpenAI, Meta, Cohere, or Mistral.\\n3. are NOT used to improve OpenAI, Meta, Cohere, or Mistral models.\\nWhen you use Azure AI Agent Service with tools that retrieve data from external sources or services (such as the Grounding with Bing Search tool), the terms (including data processing terms) for those services apply to any data processed by those services. For example, the Grounding with Bing Search tool is subject to separate data collection and privacy terms (see Terms of Use for Grounding with Bing Search in Azure AI Agents Service), and the services are Microsoft-as-controller services and thereby excluded from the Microsoft Products and Services Data Protection Addendum.", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/data-privacy-security"}, {"title": "Data, privacy, and security for Azure AI Agent Service", "content": "# Data, privacy, and security for Azure AI Agent Service\\n## Model inferencing\\nAzure AI Agent Service interacts with the configured model inferencing endpoints you designate. Models (base or fine-tuned) process your input prompts and generate output responses which may be further used or processed by your agent. Data is processed for model inferencing in accordance with the terms that apply to the relevant model. Learn more at [Data, privacy, and security for Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/openai/data-privacy) and [Data, privacy, and security for use of models through the model catalog in AI Foundry portal](https://learn.microsoft.com/en-us/azure/ai-studio/how-to/concept-data-privacy).\\n## Data storage for Azure AI Agent Service features\\nSome Azure AI Agent Service features store data in the service. This data is either uploaded by the customer or is automatically stored in connection with certain stateful entities such as Messages, Threads, and Runs. Data stored for Azure AI Agent Service:\\n1. Is stored at rest in the Azure OpenAI resource which is created when you configure Azure AI Agent Service in the customer\'s Azure tenant, within the same geography as the Azure OpenAI resource.\\n2. Can be double encrypted at rest, by default with Microsoft\'s AES-256 encryption and optionally with a customer managed key (except preview features may not support customer managed keys).\\n3. Can be deleted by the customer at any time.\\n## Location of data processing\\nTo use the Azure AI Agent Service, you must create an Azure OpenAI Service resource in which the Azure AI Agent Service will be hosted. If you use features of the Azure AI Agent Service that store data at rest, that data will be stored in the geography where your Azure OpenAI resource is located. When the Azure AI Agent Service interacts with models and tools, the location of processing of data will depend on the configuration of the model inferencing endpoints and the hosting location of the tools.\\n## Preventing harmful content generation\\nTo reduce the risk of harmful use of the Azure AI Agent Service, the Service includes [content filtering](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/concepts/content-filter) support. The outputs processed by the Azure AI Agent Service will be filtered in accordance with any content filtering that has been applied to the model deployment used by your agent.", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/data-privacy-security#model-inferencing"}, {"title": "Data, privacy, and security for Azure AI Agent Service", "content": "# Data, privacy, and security for Azure AI Agent Service\\n## What data does the Azure AI Agent Service process?\\nAzure AI Agent Service processes the following types of data:\\n1. **Prompts and generated content**. Prompts are submitted by the user, and content is generated by the service, via the completions, chat completions, images and audio operations of models with which the Azure AI Agent Service interacts.\\n2. **Uploaded data**. You can provide your own data for use with certain tools in Azure AI Agent service (e.g., File Search, Code Interpreter, Azure AI Search) using your own Azure Storage account or a configured data store.\\n3. **External Data**. When you use the Grounding with Bing Search tool or tools that support function calling, the service processes and stores the outputs of these tools.\\n**Data for stateful entities**. When you use Threads, Messages and Runs, the service will create a data store to persist message history and other content, in accordance with how you configure the feature and in keeping with applicable privacy, security, and compliance commitments. When you use the MCP tool, the data you pass in headers is only available for the current Run and will not persist.\\n**Augmented data included with or via prompts**. When using data associated with stateful entities, the service retrieves relevant data from your configured data store and augments the prompt to produce generations that are grounded with your data. Prompts may also be augmented with data retrieved from a source included in the prompt itself, such as a URL.\\n## How does the Azure AI Agent Service process data?\\nThe diagram below illustrates how your data is processed. This diagram covers several types of processing:\\n1. How the Azure AI Agent Service processes your prompts via inferencing to generate content (including when additional data from a designated data source is added to a prompt using File Search, Code Interpreter, or other tools).\\n2. How the Azure AI Agent Service stores data in connection with Messages, Threads, and Runs.\\n3. How the Azure AI Agent Service processes data ingested into the service from external tools.\\n[![A diagram showing how Azure AI Agent Service processes data.](https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/media/agents-diagram.png)](media/agents-diagram.png#lightbox)", "contentUrl": "https://learn.microsoft.com/en-us/azure/ai-foundry/responsible-ai/agents/data-privacy-security#what-data-does-the-azure-ai-agent-service-process"}]', approval_request_id=None), ResponseOutputMessage(id='msg_68be5b45d8408195b0b251f55042f28308ce4c5c780e7d9e', content=[ResponseOutputText(annotations=[], text='検索結果の最初の100文字は以下の通りです。\n\n「# Transparency Note for Azure Agent Service\n## The basics of Azure AI Agent Service\n### Introduction\nAzure AI Agent Service is a fully managed service designed to empower developers to securely buil」', type='output_text', logprobs=None)], role='assistant', status='completed', type='message')], parallel_tool_calls=True, temperature=1.0, tool_choice='auto', tools=[Mcp(server_label='microsoft-document', type='mcp', allowed_tools=None, authorization=None, connector_id=None, headers=None, require_approval='never', server_description=None, server_url='https://learn.microsoft.com/<redacted>')], top_p=1.0, background=False, conversation=None, max_output_tokens=None, max_tool_calls=None, previous_response_id=None, prompt=None, prompt_cache_key=None, reasoning=Reasoning(effort=None, generate_summary=None, summary=None), safety_identifier=None, service_tier='default', status='completed', text=ResponseTextConfig(format=ResponseFormatText(type='text'), verbosity=None), top_logprobs=None, truncation='disabled', usage=ResponseUsage(input_tokens=6114, input_tokens_details=InputTokensDetails(cached_tokens=0), output_tokens=85, output_tokens_details=OutputTokensDetails(reasoning_tokens=0), total_tokens=6199), user=None, content_filters=None, store=True), sequence_number=70, type='response.completed')

ふぁー (@ymuichiro)

Hi ふぁー (@ymuichiro), this is simply a gap in implementation. We don't have MCP tool support with the responses agent. Do you want to open a PR to fix this?

Evan Mattson (@moonbox3)

Evan Mattson (@moonbox3)
I've created a draft of the changes now. After some testing, I'll create a pull request.

ymuichiro@1a23568

Evan Mattson (@moonbox3)

Evan Mattson (@moonbox3) I have created a pr for this issue.
#13112

This issue is stale because it has been open for 90 days with no activity.

This issue was closed because it has been inactive for 14 days since being marked as stale.

Navigation Menu

Repository navigation

Python: New Feature: Cannot Retrieve MCP Call Results via Streaming in Azure OpenAI Responses API #13085

Description

Investigation

Question

Info

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

	match event:
	case ResponseCreatedEvent():
	if store_enabled:
	thread.response_id = event.response.id
	# Ensure subsequent requests link to this response context
	previous_response_id = event.response.id
	case ResponseOutputItemAddedEvent():
	function_calls = cls._get_tool_calls_from_output([event.item]) # type: ignore
	if function_calls:
	function_call_returned = True
	msg = cls._build_streaming_msg(
	agent=agent,
	metadata=metadata,
	event=event,
	items=function_calls,
	choice_index=request_index,
	)
	all_messages.append(msg)
	case ResponseFunctionCallArgumentsDeltaEvent():
	function_call = FunctionCallContent(
	id=event.item_id,
	index=getattr(event, "index", None),
	arguments=event.delta,
	)
	msg = cls._build_streaming_msg(
	agent=agent,
	metadata=metadata,
	event=event,
	items=[function_call],
	choice_index=request_index,
	)
	all_messages.append(msg)
	case ResponseTextDeltaEvent():
	text_content = StreamingTextContent(
	text=event.delta,
	choice_index=request_index,
	)
	msg = cls._build_streaming_msg(
	agent=agent,
	metadata=metadata,
	event=event,
	items=[text_content],
	choice_index=request_index,
	)
	yield msg
	case ResponseReasoningTextDeltaEvent():
	if on_intermediate_message:
	reasoning_content = StreamingReasoningContent(
	text=event.delta,
	choice_index=request_index,
	metadata={
	"item_id": event.item_id,
	"output_index": event.output_index,
	"sequence_number": event.sequence_number,
	"content_index": event.content_index,
	},
	)
	reasoning_msg = cls._build_streaming_msg(
	agent=agent,
	metadata=metadata,
	event=event,
	items=[reasoning_content],
	choice_index=request_index,
	)
	await on_intermediate_message(reasoning_msg)
	case ResponseReasoningTextDoneEvent():
	if on_intermediate_message:
	final_reasoning_content = ReasoningContent(
	text=event.text,
	metadata={
	"item_id": event.item_id,
	"output_index": event.output_index,
	"sequence_number": event.sequence_number,
	"content_index": event.content_index,
	},
	)
	reasoning_msg = cls._build_streaming_msg(
	agent=agent,
	metadata=metadata,
	event=event,
	items=[final_reasoning_content],
	choice_index=request_index,
	)
	await on_intermediate_message(reasoning_msg)
	case ResponseReasoningSummaryTextDeltaEvent():
	if on_intermediate_message:
	reasoning_content = StreamingReasoningContent(
	text=event.delta,
	choice_index=request_index,
	metadata={
	"item_id": event.item_id,
	"output_index": event.output_index,
	"sequence_number": event.sequence_number,
	"summary_index": event.summary_index,
	"is_summary": True,
	},
	)
	reasoning_msg = cls._build_streaming_msg(
	agent=agent,
	metadata=metadata,
	event=event,
	items=[reasoning_content],
	choice_index=request_index,
	)
	await on_intermediate_message(reasoning_msg)
	case ResponseReasoningSummaryTextDoneEvent():
	if on_intermediate_message:
	final_reasoning_summary_content = ReasoningContent(
	text=event.text,
	metadata={
	"item_id": event.item_id,
	"output_index": event.output_index,
	"sequence_number": event.sequence_number,
	"summary_index": event.summary_index,
	"is_summary": True,
	},
	)
	reasoning_msg = cls._build_streaming_msg(
	agent=agent,
	metadata=metadata,
	event=event,
	items=[final_reasoning_summary_content],
	choice_index=request_index,
	)
	await on_intermediate_message(reasoning_msg)
	case ResponseOutputItemDoneEvent():
	msg = cls._create_output_item_done(agent, event.item) # type: ignore
	if output_messages is not None:
	output_messages.append(msg)
	case ResponseErrorEvent():
	logger.error(
	f"Error in agent invoke_stream: {event.message} "
	f"(code: {event.code if event.code else 'unknown'})"
	)
	break

Python: New Feature: Cannot Retrieve MCP Call Results via Streaming in Azure OpenAI Responses API #13085

Description

Description

Investigation

Question

Info

Activity

yuichiromukaiyama commented on Sep 8, 2025

moonbox3 commented on Sep 9, 2025

ymuichiro commented on Sep 9, 2025

ymuichiro commented on Sep 12, 2025

github-actions commented on Dec 12, 2025

github-actions commented on Dec 26, 2025

Metadata

Metadata

Assignees

Labels

Type

Projects

Milestone

Relationships

Development

Issue actions