Python: Integration tests for Azure AI client and fixes in samples

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

@@ -1,25 +1,67 @@
 # Copyright (c) Microsoft. All rights reserved.
 
+import os
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+from typing import Annotated
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 from agent_framework import (
+    AgentRunResponse,
+    AgentRunResponseUpdate,
+    ChatAgent,
     ChatClientProtocol,
     ChatMessage,
     ChatOptions,
     Role,
     TextContent,
 )
 from agent_framework.exceptions import ServiceInitializationError
+from azure.ai.projects.aio import AIProjectClient
 from azure.ai.projects.models import (
     ResponseTextFormatConfigurationJsonSchema,
 )
+from azure.identity.aio import AzureCliCredential
 from openai.types.responses.parsed_response import ParsedResponse
 from openai.types.responses.response import Response as OpenAIResponse
-from pydantic import BaseModel, ConfigDict, ValidationError
+from pydantic import BaseModel, ConfigDict, Field, ValidationError
 
 from agent_framework_azure_ai import AzureAIClient, AzureAISettings
 
+skip_if_azure_ai_integration_tests_disabled = pytest.mark.skipif(
+    os.getenv("RUN_INTEGRATION_TESTS", "false").lower() != "true"
+    or os.getenv("AZURE_AI_PROJECT_ENDPOINT", "") in ("", "https://test-project.cognitiveservices.azure.com/")
+    or os.getenv("AZURE_AI_MODEL_DEPLOYMENT_NAME", "") == "",
+    reason=(
+        "No real AZURE_AI_PROJECT_ENDPOINT or AZURE_AI_MODEL_DEPLOYMENT_NAME provided; skipping integration tests."
+        if os.getenv("RUN_INTEGRATION_TESTS", "false").lower() == "true"
+        else "Integration tests are disabled."
+    ),
+)
+
+
+@asynccontextmanager
+async def temporary_chat_client(agent_name: str) -> AsyncIterator[AzureAIClient]:
+    """Async context manager that creates an Azure AI agent and yields an `AzureAIClient`.
+
+    The underlying agent version is cleaned up automatically after use.
+    Tests can construct their own `ChatAgent` instances from the yielded client.
+    """
+    endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
+    async with (
+        AzureCliCredential() as credential,
+        AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
+    ):
+        chat_client = AzureAIClient(
+            project_client=project_client,
+            agent_name=agent_name,
+        )
+        try:
+            yield chat_client
+        finally:
+            await project_client.agents.delete(agent_name=agent_name)
+
 
 def create_test_azure_ai_client(
     mock_project_client: MagicMock,
@@ -751,3 +793,64 @@ def mock_project_client() -> MagicMock:
     mock_client.close = AsyncMock()
 
     return mock_client
+
+
+def get_weather(
+    location: Annotated[str, Field(description="The location to get the weather for.")],
+) -> str:
+    """Get the weather for a given location."""
+    return f"The weather in {location} is sunny with a high of 25°C."
+
+
+@pytest.mark.flaky
+@skip_if_azure_ai_integration_tests_disabled
+async def test_azure_ai_chat_client_agent_basic_run() -> None:
+    """Test ChatAgent basic run functionality with AzureAIClient."""
+    async with (
+        temporary_chat_client(agent_name="BasicRunAgent") as chat_client,
+        ChatAgent(chat_client=chat_client) as agent,
+    ):
+        response = await agent.run("Hello! Please respond with 'Hello World' exactly.")
+
+        # Validate response
+        assert isinstance(response, AgentRunResponse)
+        assert response.text is not None
+        assert len(response.text) > 0
+        assert "Hello World" in response.text
+
+
+@pytest.mark.flaky
+@skip_if_azure_ai_integration_tests_disabled
+async def test_azure_ai_chat_client_agent_basic_run_streaming() -> None:
+    """Test ChatAgent basic streaming functionality with AzureAIClient."""
+    async with (
+        temporary_chat_client(agent_name="BasicRunStreamingAgent") as chat_client,
+        ChatAgent(chat_client=chat_client) as agent,
+    ):
+        full_message: str = ""
+        async for chunk in agent.run_stream("Please respond with exactly: 'This is a streaming response test.'"):
+            assert chunk is not None
+            assert isinstance(chunk, AgentRunResponseUpdate)
+            if chunk.text:
+                full_message += chunk.text
+
+        # Validate streaming response
+        assert len(full_message) > 0
+        assert "streaming response test" in full_message.lower()
+
+
+@pytest.mark.flaky
+@skip_if_azure_ai_integration_tests_disabled
+async def test_azure_ai_chat_client_agent_with_tools() -> None:
+    """Test ChatAgent tools with AzureAIClient."""
+    async with (
+        temporary_chat_client(agent_name="RunToolsAgent") as chat_client,
+        ChatAgent(chat_client=chat_client, tools=[get_weather]) as agent,
+    ):
+        response = await agent.run("What's the weather like in Seattle?")
+
+        # Validate response
+        assert isinstance(response, AgentRunResponse)
+        assert response.text is not None
+        assert len(response.text) > 0
+        assert any(word in response.text.lower() for word in ["sunny", "25"])

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

@@ -20,8 +20,6 @@ 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_search_context_agentic.py`](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`](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. |
 | [`azure_ai_with_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
 | [`azure_ai_with_image_generation.py`](azure_ai_with_image_generation.py) | Shows how to use the `ImageGenTool` with Azure AI agents to generate images based on text prompts. |

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

@@ -32,7 +32,7 @@ async def example_with_client() -> None:
         AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
     ):
         # Create a conversation using OpenAI client
-        openai_client = await project_client.get_openai_client()
+        openai_client = project_client.get_openai_client()
         conversation = await openai_client.conversations.create()
         conversation_id = conversation.id
         print(f"Conversation ID: {conversation_id}")
@@ -70,7 +70,7 @@ async def example_with_thread() -> None:
         ) as agent,
     ):
         # Create a conversation using OpenAI client
-        openai_client = await project_client.get_openai_client()
+        openai_client = project_client.get_openai_client()
         conversation = await openai_client.conversations.create()
         conversation_id = conversation.id
         print(f"Conversation ID: {conversation_id}")

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

@@ -20,6 +20,8 @@ This folder contains examples demonstrating different ways to create and use age
 | [`azure_ai_with_local_mcp.py`](azure_ai_with_local_mcp.py) | Shows how to integrate Azure AI agents with local Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates both agent-level and run-level tool configuration. |
 | [`azure_ai_with_multiple_tools.py`](azure_ai_with_multiple_tools.py) | Demonstrates how to use multiple tools together with Azure AI agents, including web search, MCP servers, and function tools. Shows coordinated multi-tool interactions and approval workflows. |
 | [`azure_ai_with_openapi_tools.py`](azure_ai_with_openapi_tools.py) | Demonstrates how to use OpenAPI tools with Azure AI agents to integrate external REST APIs. Shows OpenAPI specification loading, anonymous authentication, thread context management, and coordinated multi-API conversations using weather and countries APIs. |
+| [`azure_ai_with_search_context_agentic.py`](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`](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_thread.py`](azure_ai_with_thread.py) | Demonstrates thread management with Azure AI agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
 
 ## Environment Variables

python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_agentic.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_search_context_agentic.py

@@ -3,12 +3,11 @@
 import asyncio
 import os
 
-from dotenv import load_dotenv
-
 from agent_framework import ChatAgent
 from agent_framework_aisearch import AzureAISearchContextProvider
 from agent_framework_azure_ai import AzureAIAgentClient
-from azure.identity.aio import DefaultAzureCredential
+from azure.identity.aio import AzureCliCredential
+from dotenv import load_dotenv
 
 # Load environment variables from .env file
 load_dotenv()
@@ -68,7 +67,7 @@ async def main() -> None:
         endpoint=search_endpoint,
         index_name=index_name,
         api_key=search_key,  # Use api_key for API key auth, or credential for managed identity
-        credential=DefaultAzureCredential() if not search_key else None,
+        credential=AzureCliCredential() if not search_key else None,
         mode="agentic",  # Advanced mode for multi-hop reasoning
         # Agentic mode configuration
         azure_ai_project_endpoint=project_endpoint,
@@ -87,7 +86,7 @@ async def main() -> None:
         AzureAIAgentClient(
             project_endpoint=project_endpoint,
             model_deployment_name=model_deployment,
-            async_credential=DefaultAzureCredential(),
+            async_credential=AzureCliCredential(),
         ) as client,
         ChatAgent(
             chat_client=client,

python/samples/getting_started/agents/azure_ai/azure_ai_with_search_context_semantic.py → python/samples/getting_started/agents/azure_ai_agent/azure_ai_with_search_context_semantic.py

@@ -3,12 +3,11 @@
 import asyncio
 import os
 
-from dotenv import load_dotenv
-
 from agent_framework import ChatAgent
 from agent_framework_aisearch import AzureAISearchContextProvider
 from agent_framework_azure_ai import AzureAIAgentClient
-from azure.identity.aio import DefaultAzureCredential
+from azure.identity.aio import AzureCliCredential
+from dotenv import load_dotenv
 
 # Load environment variables from .env file
 load_dotenv()
@@ -58,7 +57,7 @@ async def main() -> None:
         endpoint=search_endpoint,
         index_name=index_name,
         api_key=search_key,  # Use api_key for API key auth, or credential for managed identity
-        credential=DefaultAzureCredential() if not search_key else None,
+        credential=AzureCliCredential() if not search_key else None,
         mode="semantic",  # Default mode
         top_k=3,  # Retrieve top 3 most relevant documents
     )
@@ -69,7 +68,7 @@ async def main() -> None:
         AzureAIAgentClient(
             project_endpoint=project_endpoint,
             model_deployment_name=model_deployment,
-            async_credential=DefaultAzureCredential(),
+            async_credential=AzureCliCredential(),
         ) as client,
         ChatAgent(
             chat_client=client,