Create/Get Agent API - fixes and example improvements (microsoft#3246)

Author: dmytrostruk

python/packages/azure-ai/agent_framework_azure_ai/_project_provider.py

@@ -358,6 +358,7 @@ def _to_chat_agent_from_details(
             agent_name=details.name,
             agent_version=details.version,
             agent_description=details.description,
+            model_deployment_name=details.definition.model,
         )
 
         # Merge tools: hosted tools from definition + user-provided function tools

python/packages/azure-ai/agent_framework_azure_ai/_shared.py

@@ -501,6 +501,7 @@ def create_text_format_config(
         return ResponseTextFormatConfigurationJsonSchema(
             name=response_format.__name__,
             schema=schema,
+            strict=True,
         )
 
     if isinstance(response_format, Mapping):

python/packages/azure-ai/tests/test_provider.py

@@ -317,11 +317,21 @@ def test_provider_as_agent(mock_project_client: MagicMock) -> None:
     mock_agent_version.definition.top_p = 0.9
     mock_agent_version.definition.tools = []
 
-    agent = provider.as_agent(mock_agent_version)
+    with patch("agent_framework_azure_ai._project_provider.AzureAIClient") as mock_azure_ai_client:
+        agent = provider.as_agent(mock_agent_version)
 
-    assert isinstance(agent, ChatAgent)
-    assert agent.name == "test-agent"
-    assert agent.description == "Test Agent"
+        assert isinstance(agent, ChatAgent)
+        assert agent.name == "test-agent"
+        assert agent.description == "Test Agent"
+
+        # Verify AzureAIClient was called with correct parameters
+        mock_azure_ai_client.assert_called_once()
+        call_kwargs = mock_azure_ai_client.call_args[1]
+        assert call_kwargs["project_client"] is mock_project_client
+        assert call_kwargs["agent_name"] == "test-agent"
+        assert call_kwargs["agent_version"] == "1.0"
+        assert call_kwargs["agent_description"] == "Test Agent"
+        assert call_kwargs["model_deployment_name"] == "gpt-4"
 
 
 async def test_provider_context_manager(mock_project_client: MagicMock) -> None:
@@ -370,6 +380,24 @@ async def test_provider_close_method(mock_project_client: MagicMock) -> None:
             mock_client.close.assert_called_once()
 
 
+def test_create_text_format_config_sets_strict_for_pydantic_models() -> None:
+    """Test that create_text_format_config sets strict=True for Pydantic models."""
+    from pydantic import BaseModel
+
+    from agent_framework_azure_ai._shared import create_text_format_config
+
+    class TestSchema(BaseModel):
+        subject: str
+        summary: str
+
+    result = create_text_format_config(TestSchema)
+
+    # Verify strict=True is set
+    assert result["strict"] is True
+    assert result["name"] == "TestSchema"
+    assert "schema" in result
+
+
 @pytest.mark.flaky
 @skip_if_azure_ai_integration_tests_disabled
 async def test_provider_create_and_get_agent_integration() -> None:

python/packages/core/agent_framework/openai/_assistants_client.py

@@ -660,6 +660,7 @@ def _prepare_options(
                     "json_schema": {
                         "name": response_format.__name__,
                         "schema": response_format.model_json_schema(),
+                        "strict": True,
                     },
                 }
 

python/packages/core/tests/openai/test_openai_assistants_client.py

@@ -784,6 +784,27 @@ def test_prepare_options_with_mapping_tool(mock_async_openai: MagicMock) -> None
     assert run_options["tool_choice"] == "auto"
 
 
+def test_prepare_options_with_pydantic_response_format(mock_async_openai: MagicMock) -> None:
+    """Test _prepare_options sets strict=True for Pydantic response_format."""
+    from pydantic import BaseModel, ConfigDict
+
+    class TestResponse(BaseModel):
+        name: str
+        value: int
+        model_config = ConfigDict(extra="forbid")
+
+    chat_client = create_test_openai_assistants_client(mock_async_openai)
+    messages = [ChatMessage(role=Role.USER, text="Test")]
+    options = {"response_format": TestResponse}
+
+    run_options, _ = chat_client._prepare_options(messages, options)  # type: ignore
+
+    assert "response_format" in run_options
+    assert run_options["response_format"]["type"] == "json_schema"
+    assert run_options["response_format"]["json_schema"]["name"] == "TestResponse"
+    assert run_options["response_format"]["json_schema"]["strict"] is True
+
+
 def test_prepare_options_with_system_message(mock_async_openai: MagicMock) -> None:
     """Test _prepare_options with system message converted to instructions."""
     chat_client = create_test_openai_assistants_client(mock_async_openai)

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

@@ -33,7 +33,7 @@ async def main() -> None:
         agent = await provider.create_agent(
             name="ProductMarketerAgent",
             instructions="Return launch briefs as structured JSON.",
-            # Specify type to use as response
+            # Specify Pydantic model for structured output via default_options
             default_options={"response_format": ReleaseBrief},
         )
 

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

@@ -37,17 +37,19 @@ async def main() -> None:
         AzureCliCredential() as credential,
         AzureAIProjectAgentProvider(credential=credential) as provider,
     ):
-        # Pass response_format at agent creation time using dict schema format
+        # Pass response_format via default_options using dict schema format
         agent = await provider.create_agent(
             name="WeatherDigestAgent",
             instructions="Return sample weather digest as structured JSON.",
-            response_format={
-                "type": "json_schema",
-                "json_schema": {
-                    "name": runtime_schema["title"],
-                    "strict": True,
-                    "schema": runtime_schema,
-                },
+            default_options={
+                "response_format": {
+                    "type": "json_schema",
+                    "json_schema": {
+                        "name": runtime_schema["title"],
+                        "strict": True,
+                        "schema": runtime_schema,
+                    },
+                }
             },
         )
 

python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_response_format.py

@@ -9,8 +9,10 @@
 """
 Azure AI Agent Provider Response Format Example
 
-This sample demonstrates using AzureAIAgentsProvider with default_options
-containing response_format for structured outputs.
+This sample demonstrates using AzureAIAgentsProvider with response_format
+for structured outputs in two ways:
+1. Setting default response_format at agent creation time (default_options)
+2. Overriding response_format at runtime (options parameter in agent.run)
 """
 
 
@@ -24,31 +26,57 @@ class WeatherInfo(BaseModel):
     model_config = ConfigDict(extra="forbid")
 
 
+class CityInfo(BaseModel):
+    """Structured city information."""
+
+    city_name: str
+    population: int
+    country: str
+    model_config = ConfigDict(extra="forbid")
+
+
 async def main() -> None:
-    """Example of using default_options with response_format in AzureAIAgentsProvider."""
+    """Example of using response_format at creation time and runtime."""
 
     async with (
         AzureCliCredential() as credential,
         AzureAIAgentsProvider(credential=credential) as provider,
     ):
+        # Create agent with default response_format (WeatherInfo)
         agent = await provider.create_agent(
-            name="WeatherReporter",
-            instructions="You provide weather reports in structured JSON format.",
+            name="StructuredReporter",
+            instructions="Return structured JSON based on the requested format.",
             default_options={"response_format": WeatherInfo},
         )
 
-        query = "What's the weather like in Paris today?"
-        print(f"User: {query}")
+        # Request 1: Uses default response_format from agent creation
+        print("--- Request 1: Using default response_format (WeatherInfo) ---")
+        query1 = "What's the weather like in Paris today?"
+        print(f"User: {query1}")
+
+        result1 = await agent.run(query1)
+
+        if isinstance(result1.value, WeatherInfo):
+            weather = result1.value
+            print("Agent:")
+            print(f"  Location: {weather.location}")
+            print(f"  Temperature: {weather.temperature}")
+            print(f"  Conditions: {weather.conditions}")
+            print(f"  Recommendation: {weather.recommendation}")
+
+        # Request 2: Override response_format at runtime with CityInfo
+        print("\n--- Request 2: Runtime override with CityInfo ---")
+        query2 = "Tell me about Tokyo."
+        print(f"User: {query2}")
 
-        result = await agent.run(query)
+        result2 = await agent.run(query2, options={"response_format": CityInfo})
 
-        if isinstance(result.value, WeatherInfo):
-            weather = result.value
+        if isinstance(result2.value, CityInfo):
+            city = result2.value
             print("Agent:")
-            print(f"Location: {weather.location}")
-            print(f"Temperature: {weather.temperature}")
-            print(f"Conditions: {weather.conditions}")
-            print(f"Recommendation: {weather.recommendation}")
+            print(f"  City: {city.city_name}")
+            print(f"  Population: {city.population}")
+            print(f"  Country: {city.country}")
 
 
 if __name__ == "__main__":

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

@@ -10,8 +10,10 @@
 """
 OpenAI Assistant Provider Response Format Example
 
-This sample demonstrates using OpenAIAssistantProvider with default_options
-containing response_format for structured outputs.
+This sample demonstrates using OpenAIAssistantProvider with response_format
+for structured outputs in two ways:
+1. Setting default response_format at agent creation time (default_options)
+2. Overriding response_format at runtime (options parameter in agent.run)
 """
 
 
@@ -25,33 +27,59 @@ class WeatherInfo(BaseModel):
     model_config = ConfigDict(extra="forbid")
 
 
+class CityInfo(BaseModel):
+    """Structured city information."""
+
+    city_name: str
+    population: int
+    country: str
+    model_config = ConfigDict(extra="forbid")
+
+
 async def main() -> None:
-    """Example of using default_options with response_format in OpenAIAssistantProvider."""
+    """Example of using response_format at creation time and runtime."""
 
     async with (
         AsyncOpenAI() as client,
         OpenAIAssistantProvider(client) as provider,
     ):
+        # Create agent with default response_format (WeatherInfo)
         agent = await provider.create_agent(
-            name="WeatherReporter",
+            name="StructuredReporter",
             model=os.environ.get("OPENAI_CHAT_MODEL_ID", "gpt-4"),
-            instructions="You provide weather reports in structured JSON format.",
+            instructions="Return structured JSON based on the requested format.",
             default_options={"response_format": WeatherInfo},
         )
 
         try:
-            query = "What's the weather like in Paris today?"
-            print(f"User: {query}")
+            # Request 1: Uses default response_format from agent creation
+            print("--- Request 1: Using default response_format (WeatherInfo) ---")
+            query1 = "What's the weather like in Paris today?"
+            print(f"User: {query1}")
 
-            result = await agent.run(query)
+            result1 = await agent.run(query1)
 
-            if isinstance(result.value, WeatherInfo):
-                weather = result.value
+            if isinstance(result1.value, WeatherInfo):
+                weather = result1.value
                 print("Agent:")
                 print(f"  Location: {weather.location}")
                 print(f"  Temperature: {weather.temperature}")
                 print(f"  Conditions: {weather.conditions}")
                 print(f"  Recommendation: {weather.recommendation}")
+
+            # Request 2: Override response_format at runtime with CityInfo
+            print("\n--- Request 2: Runtime override with CityInfo ---")
+            query2 = "Tell me about Tokyo."
+            print(f"User: {query2}")
+
+            result2 = await agent.run(query2, options={"response_format": CityInfo})
+
+            if isinstance(result2.value, CityInfo):
+                city = result2.value
+                print("Agent:")
+                print(f"  City: {city.city_name}")
+                print(f"  Population: {city.population}")
+                print(f"  Country: {city.country}")
         finally:
             await client.beta.assistants.delete(agent.id)