Python: Add kwargs to create_agent method

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

@@ -164,6 +164,7 @@ async def create_agent(
         default_options: TOptions_co | None = None,
         middleware: Sequence[Middleware] | None = None,
         context_provider: ContextProvider | None = None,
+        **kwargs: Any,
     ) -> "ChatAgent[TOptions_co]":
         """Create a new agent on the Azure AI service and return a local ChatAgent wrapper.
 
@@ -178,6 +179,7 @@ async def create_agent(
                 These options are applied to every run unless overridden.
             middleware: List of middleware to intercept agent and function invocations.
             context_provider: Context provider to include during agent invocation.
+            **kwargs: Additional parameters passed to PromptAgentDefinition.
 
         Returns:
             ChatAgent: A ChatAgent instance configured with the created agent.
@@ -211,6 +213,9 @@ async def create_agent(
         if normalized_tools:
             args["tools"] = to_azure_ai_tools(normalized_tools)
 
+        # Merge any additional kwargs into args
+        args.update(kwargs)
+
         created_agent = await self._project_client.agents.create_version(
             agent_name=name,
             definition=PromptAgentDefinition(**args),

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

@@ -207,6 +207,49 @@ async def test_provider_create_agent_missing_model(mock_project_client: MagicMoc
             await provider.create_agent(name="test-agent")
 
 
+async def test_provider_create_agent_with_kwargs(
+    mock_project_client: MagicMock,
+    azure_ai_unit_test_env: dict[str, str],
+) -> None:
+    """Test AzureAIProjectAgentProvider.create_agent passes kwargs to PromptAgentDefinition."""
+    with patch("agent_framework_azure_ai._project_provider.AzureAISettings") as mock_settings:
+        mock_settings.return_value.project_endpoint = azure_ai_unit_test_env["AZURE_AI_PROJECT_ENDPOINT"]
+        mock_settings.return_value.model_deployment_name = azure_ai_unit_test_env["AZURE_AI_MODEL_DEPLOYMENT_NAME"]
+
+        provider = AzureAIProjectAgentProvider(project_client=mock_project_client)
+
+        # Mock agent creation response
+        mock_agent_version = MagicMock(spec=AgentVersionDetails)
+        mock_agent_version.id = "agent-id"
+        mock_agent_version.name = "test-agent"
+        mock_agent_version.version = "1.0"
+        mock_agent_version.description = None
+        mock_agent_version.definition = MagicMock(spec=PromptAgentDefinition)
+        mock_agent_version.definition.model = "gpt-4"
+        mock_agent_version.definition.instructions = None
+        mock_agent_version.definition.temperature = None
+        mock_agent_version.definition.top_p = None
+        mock_agent_version.definition.tools = []
+
+        mock_project_client.agents.create_version = AsyncMock(return_value=mock_agent_version)
+
+        # Create a mock RaiConfig-like object
+        mock_rai_config = MagicMock()
+        mock_rai_config.rai_policy_name = "policy-name"
+
+        # Call create_agent with additional kwargs
+        await provider.create_agent(
+            name="test-agent",
+            model="gpt-4",
+            rai_config=mock_rai_config,
+        )
+
+        # Verify kwargs were passed to PromptAgentDefinition
+        call_args = mock_project_client.agents.create_version.call_args
+        definition = call_args[1]["definition"]
+        assert definition.rai_config is mock_rai_config
+
+
 async def test_provider_get_agent_with_name(mock_project_client: MagicMock) -> None:
     """Test AzureAIProjectAgentProvider.get_agent with name parameter."""
     provider = AzureAIProjectAgentProvider(project_client=mock_project_client)

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

@@ -17,6 +17,7 @@ This folder contains examples demonstrating different ways to create and use age
 | [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the `HostedCodeInterpreterTool` with Azure AI agents to write and execute Python code for mathematical problem solving and data analysis. |
 | [`azure_ai_with_code_interpreter_file_generation.py`](azure_ai_with_code_interpreter_file_generation.py) | Shows how to retrieve file IDs from code interpreter generated files using both streaming and non-streaming approaches. |
 | [`azure_ai_with_code_interpreter_file_download.py`](azure_ai_with_code_interpreter_file_download.py) | Shows how to download files generated by code interpreter using the OpenAI containers API. |
+| [`azure_ai_with_content_filtering.py`](azure_ai_with_content_filtering.py) | Shows how to enable content filtering (RAI policy) on Azure AI agents using `RaiConfig`. Requires creating an RAI policy in Azure AI Foundry portal first. |
 | [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent name and version to the Azure AI client. Demonstrates agent reuse patterns for production scenarios. |
 | [`azure_ai_with_existing_conversation.py`](azure_ai_with_existing_conversation.py) | Demonstrates how to use an existing conversation created on the service side with Azure AI agents. Shows two approaches: specifying conversation ID at the client level and using AgentThread with an existing conversation ID. |
 | [`azure_ai_with_application_endpoint.py`](azure_ai_with_application_endpoint.py) | Demonstrates calling the Azure AI application-scoped endpoint. |

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

@@ -0,0 +1,66 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework.azure import AzureAIProjectAgentProvider
+from azure.ai.projects.models import RaiConfig
+from azure.identity.aio import AzureCliCredential
+
+"""
+Azure AI Agent with Content Filtering (RAI Policy) Example
+
+This sample demonstrates how to enable content filtering on Azure AI agents using RaiConfig.
+
+Prerequisites:
+1. Create an RAI Policy in Azure AI Foundry portal:
+   - Go to Azure AI Foundry > Your Project > Guardrails + Controls > Content Filters
+   - Create a new content filter or use an existing one
+   - Note the policy name
+
+2. Set environment variables:
+   - AZURE_AI_PROJECT_ENDPOINT: Your Azure AI Foundry project endpoint
+   - AZURE_AI_MODEL_DEPLOYMENT_NAME: Your model deployment name
+
+3. Run `az login` to authenticate
+"""
+
+
+async def main() -> None:
+    print("=== Azure AI Agent with Content Filtering ===\n")
+
+    # Replace with your RAI policy from Azure AI Foundry portal
+    rai_policy_name = (
+        "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroup}/providers/"
+        "Microsoft.CognitiveServices/accounts/{accountName}/raiPolicies/{policyName}"
+    )
+
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIProjectAgentProvider(credential=credential) as provider,
+    ):
+        # Create agent with content filtering enabled via rai_config
+        agent = await provider.create_agent(
+            name="ContentFilteredAgent",
+            instructions="You are a helpful assistant.",
+            rai_config=RaiConfig(rai_policy_name=rai_policy_name),
+        )
+
+        # Test with a normal query
+        query = "What is the capital of France?"
+        print(f"User: {query}")
+        result = await agent.run(query)
+        print(f"Agent: {result}\n")
+
+        # Test with a query that might trigger content filtering
+        # (depending on your RAI policy configuration)
+        query2 = "Tell me something inappropriate."
+        print(f"User: {query2}")
+        try:
+            result2 = await agent.run(query2)
+            print(f"Agent: {result2}\n")
+        except Exception as e:
+            print(f"Content filter triggered: {e}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())