Python: Fix runtime response format for responses client (#2440)

* Fix runtime response format for responses client

* Handle run time schema for Azure AI Client.

python/samples/getting_started/agents/azure_ai/README.md

@@ -20,6 +20,7 @@ This folder contains examples demonstrating different ways to create and use age
 | [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use the `HostedFileSearchTool` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. |
 | [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent. |
 | [`azure_ai_with_response_format.py`](azure_ai_with_response_format.py) | Shows how to use structured outputs (response format) with Azure AI agents using Pydantic models to enforce specific response schemas. |
+| [`azure_ai_with_runtime_json_schema.py`](azure_ai_with_runtime_json_schema.py) | Shows how to use structured outputs (response format) with Azure AI agents using a JSON schema to enforce specific response schemas. |
 | [`azure_ai_with_search_context_agentic.py`](../../context_providers/azure_ai_search/azure_ai_with_search_context_agentic.py) | Shows how to use AzureAISearchContextProvider with agentic mode. Uses Knowledge Bases for multi-hop reasoning across documents with query planning. Recommended for most scenarios - slightly slower with more token consumption for query planning, but more accurate results. |
 | [`azure_ai_with_search_context_semantic.py`](../../context_providers/azure_ai_search/azure_ai_with_search_context_semantic.py) | Shows how to use AzureAISearchContextProvider with semantic mode. Fast hybrid search with vector + keyword search and semantic ranking for RAG. Best for simple queries where speed is critical. |
 | [`azure_ai_with_sharepoint.py`](azure_ai_with_sharepoint.py) | Shows how to use SharePoint grounding with Azure AI agents to search through SharePoint content and answer user questions with proper citations. Requires a SharePoint connection configured in your Azure AI project. |

python/samples/getting_started/agents/azure_ai/azure_ai_with_runtime_json_schema.py

@@ -0,0 +1,65 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework.azure import AzureAIClient
+from azure.identity.aio import AzureCliCredential
+
+"""
+Azure AI Agent Response Format Example with Runtime JSON Schema
+
+This sample demonstrates basic usage of AzureAIClient with response format,
+also known as structured outputs.
+"""
+
+
+runtime_schema = {
+    "title": "WeatherDigest",
+    "type": "object",
+    "properties": {
+        "location": {"type": "string"},
+        "conditions": {"type": "string"},
+        "temperature_c": {"type": "number"},
+        "advisory": {"type": "string"},
+    },
+    # OpenAI strict mode requires every property to appear in required.
+    "required": ["location", "conditions", "temperature_c", "advisory"],
+    "additionalProperties": False,
+}
+
+
+async def main() -> None:
+    """Example of using response_format property."""
+
+    # Since no Agent ID is provided, the agent will be automatically created.
+    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
+    # authentication option.
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIClient(async_credential=credential).create_agent(
+            name="ProductMarketerAgent",
+            instructions="Return launch briefs as structured JSON.",
+        ) as agent,
+    ):
+        query = "Draft a launch brief for the Contoso Note app."
+        print(f"User: {query}")
+        result = await agent.run(
+            query,
+            # Specify type to use as response
+            additional_chat_options={
+                "response_format": {
+                    "type": "json_schema",
+                    "json_schema": {
+                        "name": runtime_schema["title"],
+                        "strict": True,
+                        "schema": runtime_schema,
+                    },
+                },
+            },
+        )
+
+        print(result.text)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/getting_started/agents/openai/README.md

@@ -31,6 +31,7 @@ This folder contains examples demonstrating different ways to create and use age
 | [`openai_responses_client_with_function_tools.py`](openai_responses_client_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and run-level tools (provided with specific queries). |
 | [`openai_responses_client_with_hosted_mcp.py`](openai_responses_client_with_hosted_mcp.py) | Shows how to integrate OpenAI agents with hosted Model Context Protocol (MCP) servers, including approval workflows and tool management for remote MCP services. |
 | [`openai_responses_client_with_local_mcp.py`](openai_responses_client_with_local_mcp.py) | Shows how to integrate OpenAI agents with local Model Context Protocol (MCP) servers for enhanced functionality and tool integration. |
+| [`openai_responses_client_with_runtime_json_schema.py`](openai_responses_client_with_runtime_json_schema.py) | Shows how to supply a runtime JSON Schema via `additional_chat_options` for structured output without defining a Pydantic model. |
 | [`openai_responses_client_with_structured_output.py`](openai_responses_client_with_structured_output.py) | Demonstrates how to use structured outputs with OpenAI agents to get structured data responses in predefined formats. |
 | [`openai_responses_client_with_thread.py`](openai_responses_client_with_thread.py) | Demonstrates thread management with OpenAI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
 | [`openai_responses_client_with_web_search.py`](openai_responses_client_with_web_search.py) | Shows how to use web search capabilities with OpenAI agents to retrieve and use information from the internet in responses. |

python/samples/getting_started/agents/openai/openai_chat_client_with_runtime_json_schema.py

@@ -73,7 +73,7 @@ async def streaming_example() -> None:
     query = "Give a brief weather digest for Portland."
     print(f"User: {query}")
 
-    chunks = []
+    chunks: list[str] = []
     async for chunk in agent.run_stream(
         query,
         additional_chat_options={

python/samples/getting_started/agents/openai/openai_responses_client_with_runtime_json_schema.py

@@ -0,0 +1,110 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import json
+
+from agent_framework.openai import OpenAIResponsesClient
+
+"""
+OpenAI Chat Client Runtime JSON Schema Example
+
+Demonstrates structured outputs when the schema is only known at runtime.
+Uses additional_chat_options to pass a JSON Schema payload directly to OpenAI
+without defining a Pydantic model up front.
+"""
+
+
+runtime_schema = {
+    "title": "WeatherDigest",
+    "type": "object",
+    "properties": {
+        "location": {"type": "string"},
+        "conditions": {"type": "string"},
+        "temperature_c": {"type": "number"},
+        "advisory": {"type": "string"},
+    },
+    # OpenAI strict mode requires every property to appear in required.
+    "required": ["location", "conditions", "temperature_c", "advisory"],
+    "additionalProperties": False,
+}
+
+
+async def non_streaming_example() -> None:
+    print("=== Non-streaming runtime JSON schema example ===")
+
+    agent = OpenAIResponsesClient().create_agent(
+        name="RuntimeSchemaAgent",
+        instructions="Return only JSON that matches the provided schema. Do not add commentary.",
+    )
+
+    query = "Give a brief weather digest for Seattle."
+    print(f"User: {query}")
+
+    response = await agent.run(
+        query,
+        additional_chat_options={
+            "response_format": {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": runtime_schema["title"],
+                    "strict": True,
+                    "schema": runtime_schema,
+                },
+            },
+        },
+    )
+
+    print("Model output:")
+    print(response.text)
+
+    parsed = json.loads(response.text)
+    print("Parsed dict:")
+    print(parsed)
+
+
+async def streaming_example() -> None:
+    print("=== Streaming runtime JSON schema example ===")
+
+    agent = OpenAIResponsesClient().create_agent(
+        name="RuntimeSchemaAgent",
+        instructions="Return only JSON that matches the provided schema. Do not add commentary.",
+    )
+
+    query = "Give a brief weather digest for Portland."
+    print(f"User: {query}")
+
+    chunks: list[str] = []
+    async for chunk in agent.run_stream(
+        query,
+        additional_chat_options={
+            "response_format": {
+                "type": "json_schema",
+                "json_schema": {
+                    "name": runtime_schema["title"],
+                    "strict": True,
+                    "schema": runtime_schema,
+                },
+            },
+        },
+    ):
+        if chunk.text:
+            chunks.append(chunk.text)
+
+    raw_text = "".join(chunks)
+    print("Model output:")
+    print(raw_text)
+
+    parsed = json.loads(raw_text)
+    print("Parsed dict:")
+    print(parsed)
+
+
+async def main() -> None:
+    print("=== OpenAI Chat Client with runtime JSON Schema ===")
+
+    await non_streaming_example()
+    await streaming_example()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())