Python: Added rai_config to Azure AI agent creation (#3265)

* Add kwargs to create_agent method

* Added test for kwargs

* Addressed comment

* Added doc string

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

@@ -4,7 +4,7 @@ import importlib.metadata
 
 from ._agent_provider import AzureAIAgentsProvider
 from ._chat_client import AzureAIAgentClient, AzureAIAgentOptions
-from ._client import AzureAIClient
+from ._client import AzureAIClient, AzureAIProjectAgentOptions
 from ._project_provider import AzureAIProjectAgentProvider
 from ._shared import AzureAISettings
 
@@ -18,6 +18,7 @@ __all__ = [
     "AzureAIAgentOptions",
     "AzureAIAgentsProvider",
     "AzureAIClient",
+    "AzureAIProjectAgentOptions",
     "AzureAIProjectAgentProvider",
     "AzureAISettings",
     "__version__",

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

@@ -2,7 +2,7 @@
 
 import sys
 from collections.abc import Callable, Mapping, MutableMapping, MutableSequence, Sequence
-from typing import TYPE_CHECKING, Any, ClassVar, Generic, TypedDict, TypeVar, cast
+from typing import Any, ClassVar, Generic, TypedDict, TypeVar, cast
 
 from agent_framework import (
     AGENT_FRAMEWORK_USER_AGENT,
@@ -20,12 +20,14 @@ from agent_framework import (
 )
 from agent_framework.exceptions import ServiceInitializationError
 from agent_framework.observability import use_instrumentation
+from agent_framework.openai import OpenAIResponsesOptions
 from agent_framework.openai._responses_client import OpenAIBaseResponsesClient
 from azure.ai.projects.aio import AIProjectClient
 from azure.ai.projects.models import (
     MCPTool,
     PromptAgentDefinition,
     PromptAgentDefinitionText,
+    RaiConfig,
 )
 from azure.core.credentials_async import AsyncTokenCredential
 from azure.core.exceptions import ResourceNotFoundError
@@ -33,9 +35,6 @@ from pydantic import ValidationError
 
 from ._shared import AzureAISettings, create_text_format_config
 
-if TYPE_CHECKING:
-    from agent_framework.openai import OpenAIResponsesOptions
-
 if sys.version_info >= (3, 13):
     from typing import TypeVar  # type: ignore # pragma: no cover
 else:
@@ -52,10 +51,18 @@ else:
 
 logger = get_logger("agent_framework.azure")
 
+
+class AzureAIProjectAgentOptions(OpenAIResponsesOptions):
+    """Azure AI Project Agent options."""
+
+    rai_config: RaiConfig
+    """Configuration for Responsible AI (RAI) content filtering and safety features."""
+
+
 TAzureAIClientOptions = TypeVar(
     "TAzureAIClientOptions",
     bound=TypedDict,  # type: ignore[valid-type]
-    default="OpenAIResponsesOptions",
+    default="AzureAIProjectAgentOptions",
     covariant=True,
 )
 
@@ -397,6 +404,7 @@ class AzureAIClient(OpenAIBaseResponsesClient[TAzureAIClientOptions], Generic[TA
             "model",
             "tools",
             "response_format",
+            "rai_config",
             "temperature",
             "top_p",
             "text",

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

@@ -2,7 +2,7 @@
 
 import sys
 from collections.abc import Callable, MutableMapping, Sequence
-from typing import TYPE_CHECKING, Any, Generic, TypedDict
+from typing import Any, Generic, TypedDict
 
 from agent_framework import (
     AGENT_FRAMEWORK_USER_AGENT,
@@ -26,12 +26,9 @@ from azure.ai.projects.models import (
 from azure.core.credentials_async import AsyncTokenCredential
 from pydantic import ValidationError
 
-from ._client import AzureAIClient
+from ._client import AzureAIClient, AzureAIProjectAgentOptions
 from ._shared import AzureAISettings, create_text_format_config, from_azure_ai_tools, to_azure_ai_tools
 
-if TYPE_CHECKING:
-    from agent_framework.openai import OpenAIResponsesOptions
-
 if sys.version_info >= (3, 13):
     from typing import Self, TypeVar  # pragma: no cover
 else:
@@ -46,7 +43,7 @@ logger = get_logger("agent_framework.azure")
 TOptions_co = TypeVar(
     "TOptions_co",
     bound=TypedDict,  # type: ignore[valid-type]
-    default="OpenAIResponsesOptions",
+    default="AzureAIProjectAgentOptions",
     covariant=True,
 )
 
@@ -193,9 +190,10 @@ class AzureAIProjectAgentProvider(Generic[TOptions_co]):
                 "or set 'AZURE_AI_MODEL_DEPLOYMENT_NAME' environment variable."
             )
 
-        # Extract response_format from default_options if present
+        # Extract options from default_options if present
         opts = dict(default_options) if default_options else {}
         response_format = opts.get("response_format")
+        rai_config = opts.get("rai_config")
 
         args: dict[str, Any] = {"model": resolved_model}
 
@@ -205,6 +203,8 @@ class AzureAIProjectAgentProvider(Generic[TOptions_co]):
             args["text"] = PromptAgentDefinitionText(
                 format=create_text_format_config(response_format)  # type: ignore[arg-type]
             )
+        if rai_config:
+            args["rai_config"] = rai_config
 
         # Normalize tools once and reuse for both Azure AI API and ChatAgent
         normalized_tools = normalize_tools(tools)

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_rai_config(
+    mock_project_client: MagicMock,
+    azure_ai_unit_test_env: dict[str, str],
+) -> None:
+    """Test AzureAIProjectAgentProvider.create_agent passes rai_config from default_options."""
+    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 rai_config in default_options
+        await provider.create_agent(
+            name="test-agent",
+            model="gpt-4",
+            default_options={"rai_config": mock_rai_config},
+        )
+
+        # Verify rai_config was 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/packages/core/agent_framework/azure/__init__.py

@@ -9,6 +9,7 @@ _IMPORTS: dict[str, tuple[str, str]] = {
     "AgentResponseCallbackProtocol": ("agent_framework_azurefunctions", "agent-framework-azurefunctions"),
     "AzureAIAgentClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
     "AzureAIAgentOptions": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
+    "AzureAIProjectAgentOptions": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
     "AzureAIClient": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
     "AzureAIProjectAgentProvider": ("agent_framework_azure_ai", "agent-framework-azure-ai"),
     "AzureAISearchContextProvider": ("agent_framework_azure_ai_search", "agent-framework-azure-ai-search"),

python/packages/core/agent_framework/azure/__init__.pyi

@@ -4,6 +4,7 @@ from agent_framework_azure_ai import (
     AzureAIAgentClient,
     AzureAIAgentsProvider,
     AzureAIClient,
+    AzureAIProjectAgentOptions,
     AzureAIProjectAgentProvider,
     AzureAISettings,
 )
@@ -28,6 +29,7 @@ __all__ = [
     "AzureAIAgentClient",
     "AzureAIAgentsProvider",
     "AzureAIClient",
+    "AzureAIProjectAgentOptions",
     "AzureAIProjectAgentProvider",
     "AzureAISearchContextProvider",
     "AzureAISearchSettings",

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 default_options
+        agent = await provider.create_agent(
+            name="ContentFilteredAgent",
+            instructions="You are a helpful assistant.",
+            default_options={"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())