Python: [BREAKING] updated structure and samples (#875)

* updated structure and samples * updated names and removed cross tests * updated projects etc * updated tests * updated test * test fixes * removed devui for now * updated all-tests task * removed old style configs * remove coverage from tests * updated to unit tests with all-tests * updated foundry everywhere * fix azure ai tests * fix merge tests * fix mypy
2026-06-16 21:04:09 +08:00 · 2025-09-25 09:02:53 +02:00
parent 366a7f7d47
commit 9355329dfd
169 changed files with 1159 additions and 1761 deletions
@@ -8,7 +8,7 @@ This folder contains examples demonstrating how to create and use agents with di

 | Folder | Description |
 |--------|-------------|
-| **[`foundry/`](foundry/)** | Create agents using Azure AI Foundry |
+| **[`azure_ai/`](azure_ai/)** | Create agents using Azure AI Foundry Agent Service |

 ### Microsoft Copilot Studio Examples

@@ -20,14 +20,17 @@ This folder contains examples demonstrating how to create and use agents with di

 | Folder | Description |
 |--------|-------------|
-| **[`azure_assistants_client/`](azure_assistants_client/)** | Create agents using Azure OpenAI Assistants API |
-| **[`azure_chat_client/`](azure_chat_client/)** | Create agents using Azure OpenAI Chat Completions API |
-| **[`azure_responses_client/`](azure_responses_client/)** | Create agents using Azure OpenAI Responses API |
+| **[`azure_openai/`](azure_openai/)** | Create agents using Azure OpenAI APIs |

 ### OpenAI Examples

 | Folder | Description |
 |--------|-------------|
-| **[`openai_assistants_client/`](openai_assistants_client/)** | Create agents using OpenAI Assistants API |
-| **[`openai_chat_client/`](openai_chat_client/)** | Create agents using OpenAI Chat Completions API |
-| **[`openai_responses_client/`](openai_responses_client/)** | Create agents using OpenAI Responses API |
+| **[`openai/`](openai/)** | Create agents using OpenAI APIs |
+
+### Custom Client Examples
+
+| Folder | Description |
+|--------|-------------|
+| **[`custom_client/`](custom_client/)** | Create agents using a custom chat client or a custom agent |
+| **[`anthropic/`](anthropic/)** | Create agents using Anthropic APIs |
@@ -0,0 +1,25 @@
+# Azure AI Agent Examples
+
+This folder contains examples demonstrating different ways to create and use agents with the Azure AI chat client from the `agent_framework.azure` package.
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| [`azure_ai_basic.py`](azure_ai_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureAIAgentClient`. It automatically handles all configuration using environment variables. |
+| [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIAgentClient` settings, including project endpoint, model deployment, credentials, and agent name. |
+| [`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 ID to the Azure AI chat client. This example also demonstrates proper cleanup of manually created agents. |
+| [`azure_ai_with_function_tools.py`](azure_ai_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
+| [`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. Includes helper methods for accessing code interpreter data from response chunks. |
+| [`azure_ai_with_local_mcp.py`](azure_ai_with_local_mcp.py) | Shows how to integrate Azure AI agents with Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates both agent-level and run-level tool configuration. |
+| [`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
+
+Make sure to set the following environment variables before running the examples:
+
+- `AZURE_AZURE_FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI project endpoint
+- `AZURE_AZURE_FOUNDRY_MODEL_DEPLOYMENT_NAME`: The name of your model deployment
+
+Optionally, you can set:
+- `AZURE_AZURE_FOUNDRY_AGENT_NAME`: The name of your agent, this can also be set programmatically when creating the agent.
@@ -4,7 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated

-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -27,7 +27,7 @@ async def non_streaming_example() -> None:
    # authentication option.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="WeatherAgent",
            instructions="You are a helpful weather agent.",
            tools=get_weather,
@@ -49,7 +49,7 @@ async def streaming_example() -> None:
    # authentication option.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="WeatherAgent",
            instructions="You are a helpful weather agent.",
            tools=get_weather,
@@ -65,7 +65,7 @@ async def streaming_example() -> None:


 async def main() -> None:
-    print("=== Basic Foundry Chat Client Agent Example ===")
+    print("=== Basic Azure AI Chat Client Agent Example ===")

    await non_streaming_example()
    await streaming_example()
@@ -2,20 +2,16 @@

 import asyncio

-from agent_framework import (
-    AgentRunResponse,
-    HostedCodeInterpreterTool,
+from agent_framework import AgentRunResponse, ChatResponseUpdate, HostedCodeInterpreterTool
+from agent_framework.azure import AzureAIAgentClient
+from azure.ai.agents.models import (
+    RunStepDeltaCodeInterpreterDetailItemObject,
 )
-from agent_framework.foundry import FoundryChatClient
 from azure.identity.aio import AzureCliCredential


 def print_code_interpreter_inputs(response: AgentRunResponse) -> None:
    """Helper method to access code interpreter data."""
-    from agent_framework import ChatResponseUpdate
-    from azure.ai.agents.models import (
-        RunStepDeltaCodeInterpreterDetailItemObject,
-    )

    print("\nCode Interpreter Inputs during the run:")
    if response.raw_representation is None:
@@ -29,14 +25,14 @@ def print_code_interpreter_inputs(response: AgentRunResponse) -> None:


 async def main() -> None:
-    """Example showing how to use the HostedCodeInterpreterTool with Foundry."""
-    print("=== Foundry Agent with Code Interpreter Example ===")
+    """Example showing how to use the HostedCodeInterpreterTool with Azure AI."""
+    print("=== Azure AI Agent with Code Interpreter Example ===")

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential) as chat_client,
+        AzureAIAgentClient(async_credential=credential) as chat_client,
    ):
        agent = chat_client.create_agent(
            name="CodingAgent",
@@ -6,7 +6,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import ChatAgent
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.ai.projects.aio import AIProjectClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field
@@ -21,23 +21,23 @@ def get_weather(


 async def main() -> None:
-    print("=== Foundry Chat Client with Existing Agent ===")
+    print("=== Azure AI Chat Client with Existing Agent ===")

    # Create the client
    async with (
        AzureCliCredential() as credential,
-        AIProjectClient(endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"], credential=credential) as client,
+        AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as client,
    ):
        # Create an agent that will persist
        created_agent = await client.agents.create_agent(
-            model=os.environ["FOUNDRY_MODEL_DEPLOYMENT_NAME"], name="WeatherAgent"
+            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"], name="WeatherAgent"
        )

        try:
            async with ChatAgent(
                # passing in the client is optional here, so if you take the agent_id from the portal
                # you can use it directly without the two lines above.
-                chat_client=FoundryChatClient(client=client, agent_id=created_agent.id),
+                chat_client=AzureAIAgentClient(client=client, agent_id=created_agent.id),
                instructions="You are a helpful weather agent.",
                tools=get_weather,
            ) as agent:
@@ -6,7 +6,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import ChatAgent
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -20,7 +20,7 @@ def get_weather(


 async def main() -> None:
-    print("=== Foundry Chat Client with Explicit Settings ===")
+    print("=== Azure AI Chat Client with Explicit Settings ===")

    # Since no Agent ID is provided, the agent will be automatically created
    # and deleted after getting a response
@@ -29,9 +29,9 @@ async def main() -> None:
    async with (
        AzureCliCredential() as credential,
        ChatAgent(
-            chat_client=FoundryChatClient(
-                project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-                model_deployment_name=os.environ["FOUNDRY_MODEL_DEPLOYMENT_NAME"],
+            chat_client=AzureAIAgentClient(
+                project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+                model_deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
                async_credential=credential,
                agent_name="WeatherAgent",
            ),
@@ -6,7 +6,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import ChatAgent
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -36,7 +36,7 @@ async def tools_on_agent_level() -> None:
    async with (
        AzureCliCredential() as credential,
        ChatAgent(
-            chat_client=FoundryChatClient(async_credential=credential),
+            chat_client=AzureAIAgentClient(async_credential=credential),
            instructions="You are a helpful assistant that can provide weather and time information.",
            tools=[get_weather, get_time],  # Tools defined at agent creation
        ) as agent,
@@ -70,7 +70,7 @@ async def tools_on_run_level() -> None:
    async with (
        AzureCliCredential() as credential,
        ChatAgent(
-            chat_client=FoundryChatClient(async_credential=credential),
+            chat_client=AzureAIAgentClient(async_credential=credential),
            instructions="You are a helpful assistant.",
            # No tools defined here
        ) as agent,
@@ -104,7 +104,7 @@ async def mixed_tools_example() -> None:
    async with (
        AzureCliCredential() as credential,
        ChatAgent(
-            chat_client=FoundryChatClient(async_credential=credential),
+            chat_client=AzureAIAgentClient(async_credential=credential),
            instructions="You are a comprehensive assistant that can help with various information requests.",
            tools=[get_weather],  # Base tool available for all queries
        ) as agent,
@@ -122,7 +122,7 @@ async def mixed_tools_example() -> None:


 async def main() -> None:
-    print("=== Foundry Chat Client Agent with Function Tools Examples ===\n")
+    print("=== Azure AI Chat Client Agent with Function Tools Examples ===\n")

    await tools_on_agent_level()
    await tools_on_run_level()
@@ -4,7 +4,7 @@ import asyncio
 from typing import Any

 from agent_framework import AgentProtocol, AgentThread, HostedMCPTool
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential


@@ -32,13 +32,13 @@ async def handle_approvals_with_thread(query: str, agent: "AgentProtocol", threa


 async def main() -> None:
-    """Example showing Hosted MCP tools for a Foundry Agent."""
+    """Example showing Hosted MCP tools for a Azure AI Agent."""
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential) as chat_client,
+        AzureAIAgentClient(async_credential=credential) as chat_client,
    ):
-        # enable foundry observability
-        await chat_client.setup_foundry_observability()
+        # enable azure-ai observability
+        await chat_client.setup_observability()
        agent = chat_client.create_agent(
            name="DocsAgent",
            instructions="You are a helpful assistant that can help with microsoft documentation questions.",
@@ -3,7 +3,7 @@
 import asyncio

 from agent_framework import ChatAgent, MCPStreamableHTTPTool
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential


@@ -21,7 +21,7 @@ async def mcp_tools_on_run_level() -> None:
            url="https://learn.microsoft.com/api/mcp",
        ) as mcp_server,
        ChatAgent(
-            chat_client=FoundryChatClient(async_credential=credential),
+            chat_client=AzureAIAgentClient(async_credential=credential),
            name="DocsAgent",
            instructions="You are a helpful assistant that can help with microsoft documentation questions.",
        ) as agent,
@@ -48,7 +48,7 @@ async def mcp_tools_on_agent_level() -> None:
    # The agent will connect to the MCP server through its context manager.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="DocsAgent",
            instructions="You are a helpful assistant that can help with microsoft documentation questions.",
            tools=MCPStreamableHTTPTool(  # Tools defined at agent creation
@@ -71,7 +71,7 @@ async def mcp_tools_on_agent_level() -> None:


 async def main() -> None:
-    print("=== Foundry Chat Client Agent with MCP Tools Examples ===\n")
+    print("=== Azure AI Chat Client Agent with MCP Tools Examples ===\n")

    await mcp_tools_on_agent_level()
    await mcp_tools_on_run_level()
@@ -10,7 +10,7 @@ from agent_framework import (
    HostedMCPTool,
    HostedWebSearchTool,
 )
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential


@@ -44,13 +44,13 @@ async def handle_approvals_with_thread(query: str, agent: "AgentProtocol", threa


 async def main() -> None:
-    """Example showing Hosted MCP tools for a Foundry Agent."""
+    """Example showing Hosted MCP tools for a Azure AI Agent."""
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential) as chat_client,
+        AzureAIAgentClient(async_credential=credential) as chat_client,
    ):
-        # enable foundry observability
-        await chat_client.setup_foundry_observability()
+        # enable azure-ai observability
+        await chat_client.setup_observability()
        agent = chat_client.create_agent(
            name="DocsAgent",
            instructions="You are a helpful assistant that can help with microsoft documentation questions.",
@@ -5,7 +5,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import AgentThread, ChatAgent
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -27,7 +27,7 @@ async def example_with_automatic_thread_creation() -> None:
    async with (
        AzureCliCredential() as credential,
        ChatAgent(
-            chat_client=FoundryChatClient(async_credential=credential),
+            chat_client=AzureAIAgentClient(async_credential=credential),
            instructions="You are a helpful weather agent.",
            tools=get_weather,
        ) as agent,
@@ -56,7 +56,7 @@ async def example_with_thread_persistence() -> None:
    async with (
        AzureCliCredential() as credential,
        ChatAgent(
-            chat_client=FoundryChatClient(async_credential=credential),
+            chat_client=AzureAIAgentClient(async_credential=credential),
            instructions="You are a helpful weather agent.",
            tools=get_weather,
        ) as agent,
@@ -97,7 +97,7 @@ async def example_with_existing_thread_id() -> None:
    async with (
        AzureCliCredential() as credential,
        ChatAgent(
-            chat_client=FoundryChatClient(async_credential=credential),
+            chat_client=AzureAIAgentClient(async_credential=credential),
            instructions="You are a helpful weather agent.",
            tools=get_weather,
        ) as agent,
@@ -120,7 +120,7 @@ async def example_with_existing_thread_id() -> None:
        async with (
            AzureCliCredential() as credential,
            ChatAgent(
-                chat_client=FoundryChatClient(thread_id=existing_thread_id, async_credential=credential),
+                chat_client=AzureAIAgentClient(thread_id=existing_thread_id, async_credential=credential),
                instructions="You are a helpful weather agent.",
                tools=get_weather,
            ) as agent,
@@ -136,7 +136,7 @@ async def example_with_existing_thread_id() -> None:


 async def main() -> None:
-    print("=== Foundry Chat Client Agent Thread Management Examples ===\n")
+    print("=== Azure AI Chat Client Agent Thread Management Examples ===\n")

    await example_with_automatic_thread_creation()
    await example_with_thread_persistence()
@@ -1,36 +0,0 @@
-# Azure Assistants Agent Examples
-
-This folder contains examples demonstrating different ways to create and use agents with the Azure Assistants client from the `agent_framework.azure` package.
-
-## Examples
-
-| File | Description |
-|------|-------------|
-| [`azure_assistants_basic.py`](azure_assistants_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureAssistantsClient`. Shows both streaming and non-streaming responses with automatic assistant creation and cleanup. |
-| [`azure_assistants_with_existing_assistant.py`](azure_assistants_with_existing_assistant.py) | Shows how to work with a pre-existing assistant by providing the assistant ID to the Azure Assistants client. Demonstrates proper cleanup of manually created assistants. |
-| [`azure_assistants_with_explicit_settings.py`](azure_assistants_with_explicit_settings.py) | Shows how to initialize an agent with a specific assistants client, configuring settings explicitly including endpoint and deployment name. |
-| [`azure_assistants_with_function_tools.py`](azure_assistants_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
-| [`azure_assistants_with_code_interpreter.py`](azure_assistants_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
-| [`azure_assistants_with_thread.py`](azure_assistants_with_thread.py) | Demonstrates thread management with Azure agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
-
-## Environment Variables
-
-Make sure to set the following environment variables before running the examples:
-
- `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
- `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`: The name of your Azure OpenAI deployment
-
-## Authentication
-
-All examples use `AzureCliCredential` for authentication. Run `az login` in your terminal before running the examples, or replace `AzureCliCredential` with your preferred authentication method.
-
-## Required role-based access control (RBAC) roles
-
-To access the Azure OpenAI API, your Azure account or service principal needs one of the following RBAC roles assigned to the Azure OpenAI resource:
-
- **Cognitive Services OpenAI User**: Provides read access to Azure OpenAI resources and the ability to call the inference APIs. This is the minimum role required for running these examples.
- **Cognitive Services OpenAI Contributor**: Provides full access to Azure OpenAI resources, including the ability to create, update, and delete deployments and models.
-
-For most scenarios, the **Cognitive Services OpenAI User** role is sufficient. You can assign this role through the Azure portal under the Azure OpenAI resource's "Access control (IAM)" section.
-
-For more detailed information about Azure OpenAI RBAC roles, see: [Role-based access control for Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control)
@@ -1,34 +0,0 @@
-# Azure Chat Agent Examples
-
-This folder contains examples demonstrating different ways to create and use agents with the Azure Chat client from the `agent_framework.azure` package.
-
-## Examples
-
-| File | Description |
-|------|-------------|
-| [`azure_chat_client_basic.py`](azure_chat_client_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureChatClient`. Shows both streaming and non-streaming responses for chat-based interactions with Azure OpenAI models. |
-| [`azure_chat_client_with_explicit_settings.py`](azure_chat_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific chat client, configuring settings explicitly including endpoint and deployment name. |
-| [`azure_chat_client_with_function_tools.py`](azure_chat_client_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
-| [`azure_chat_client_with_thread.py`](azure_chat_client_with_thread.py) | Demonstrates thread management with Azure agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
-
-## Environment Variables
-
-Make sure to set the following environment variables before running the examples:
-
- `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
- `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`: The name of your Azure OpenAI deployment
-
-## Authentication
-
-All examples use `AzureCliCredential` for authentication. Run `az login` in your terminal before running the examples, or replace `AzureCliCredential` with your preferred authentication method.
-
-## Required role-based access control (RBAC) roles
-
-To access the Azure OpenAI API, your Azure account or service principal needs one of the following RBAC roles assigned to the Azure OpenAI resource:
-
- **Cognitive Services OpenAI User**: Provides read access to Azure OpenAI resources and the ability to call the inference APIs. This is the minimum role required for running these examples.
- **Cognitive Services OpenAI Contributor**: Provides full access to Azure OpenAI resources, including the ability to create, update, and delete deployments and models.
-
-For most scenarios, the **Cognitive Services OpenAI User** role is sufficient. You can assign this role through the Azure portal under the Azure OpenAI resource's "Access control (IAM)" section.
-
-For more detailed information about Azure OpenAI RBAC roles, see: [Role-based access control for Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control)
@@ -0,0 +1,51 @@
+# Azure OpenAI Agent Examples
+
+This folder contains examples demonstrating different ways to create and use agents with the different Azure OpenAI chat client from the `agent_framework.azure` package.
+
+## Examples
+
+| File | Description |
+|------|-------------|
+| [`azure_assistants_basic.py`](azure_assistants_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureOpenAIAssistantsClient`. Shows both streaming and non-streaming responses with automatic assistant creation and cleanup. |
+| [`azure_assistants_with_existing_assistant.py`](azure_assistants_with_existing_assistant.py) | Shows how to work with a pre-existing assistant by providing the assistant ID to the Azure Assistants client. Demonstrates proper cleanup of manually created assistants. |
+| [`azure_assistants_with_explicit_settings.py`](azure_assistants_with_explicit_settings.py) | Shows how to initialize an agent with a specific assistants client, configuring settings explicitly including endpoint and deployment name. |
+| [`azure_assistants_with_function_tools.py`](azure_assistants_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
+| [`azure_assistants_with_code_interpreter.py`](azure_assistants_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
+| [`azure_assistants_with_thread.py`](azure_assistants_with_thread.py) | Demonstrates thread management with Azure agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
+| [`azure_chat_client_basic.py`](azure_chat_client_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureOpenAIChatClient`. Shows both streaming and non-streaming responses for chat-based interactions with Azure OpenAI models. |
+| [`azure_chat_client_with_explicit_settings.py`](azure_chat_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific chat client, configuring settings explicitly including endpoint and deployment name. |
+| [`azure_chat_client_with_function_tools.py`](azure_chat_client_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
+| [`azure_chat_client_with_thread.py`](azure_chat_client_with_thread.py) | Demonstrates thread management with Azure agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
+| [`azure_responses_client_basic.py`](azure_responses_client_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureOpenAIResponsesClient`. Shows both streaming and non-streaming responses for structured response generation with Azure OpenAI models. |
+| [`azure_responses_client_with_explicit_settings.py`](azure_responses_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific responses client, configuring settings explicitly including endpoint and deployment name. |
+| [`azure_responses_client_with_function_tools.py`](azure_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 query-level tools (provided with specific queries). |
+| [`azure_responses_client_with_code_interpreter.py`](azure_responses_client_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
+| [`azure_responses_client_with_thread.py`](azure_responses_client_with_thread.py) | Demonstrates thread management with Azure agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
+
+## Environment Variables
+
+Make sure to set the following environment variables before running the examples:
+
+- `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
+- `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`: The name of your Azure OpenAI chat model deployment
+- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your Azure OpenAI Responses deployment
+
+Optionally, you can set:
+- `AZURE_OPENAI_API_VERSION`: The API version to use (default is `2024-02-15-preview`)
+- `AZURE_OPENAI_API_KEY`: Your Azure OpenAI API key (if not using `AzureCliCredential`)
+- `AZURE_OPENAI_BASE_URL`: Your Azure OpenAI base URL (if different from the endpoint)
+
+## Authentication
+
+All examples use `AzureCliCredential` for authentication. Run `az login` in your terminal before running the examples, or replace `AzureCliCredential` with your preferred authentication method.
+
+## Required role-based access control (RBAC) roles
+
+To access the Azure OpenAI API, your Azure account or service principal needs one of the following RBAC roles assigned to the Azure OpenAI resource:
+
+- **Cognitive Services OpenAI User**: Provides read access to Azure OpenAI resources and the ability to call the inference APIs. This is the minimum role required for running these examples.
+- **Cognitive Services OpenAI Contributor**: Provides full access to Azure OpenAI resources, including the ability to create, update, and delete deployments and models.
+
+For most scenarios, the **Cognitive Services OpenAI User** role is sufficient. You can assign this role through the Azure portal under the Azure OpenAI resource's "Access control (IAM)" section.
+
+For more detailed information about Azure OpenAI RBAC roles, see: [Role-based access control for Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control)
@@ -4,7 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated

-from agent_framework.azure import AzureAssistantsClient
+from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -25,7 +25,7 @@ async def non_streaming_example() -> None:
    # and deleted after getting a response
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    async with AzureAssistantsClient(credential=AzureCliCredential()).create_agent(
+    async with AzureOpenAIAssistantsClient(credential=AzureCliCredential()).create_agent(
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    ) as agent:
@@ -41,7 +41,7 @@ async def streaming_example() -> None:

    # Since no assistant ID is provided, the assistant will be automatically created
    # and deleted after getting a response
-    async with AzureAssistantsClient(credential=AzureCliCredential()).create_agent(
+    async with AzureOpenAIAssistantsClient(credential=AzureCliCredential()).create_agent(
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    ) as agent:
@@ -3,7 +3,7 @@
 import asyncio

 from agent_framework import AgentRunResponseUpdate, ChatAgent, ChatResponseUpdate, HostedCodeInterpreterTool
-from agent_framework.azure import AzureAssistantsClient
+from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from openai.types.beta.threads.runs import (
    CodeInterpreterToolCallDelta,
@@ -40,7 +40,7 @@ async def main() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    async with ChatAgent(
-        chat_client=AzureAssistantsClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIAssistantsClient(credential=AzureCliCredential()),
        instructions="You are a helpful assistant that can write and execute Python code to solve problems.",
        tools=HostedCodeInterpreterTool(),
    ) as agent:
@@ -6,7 +6,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import ChatAgent
-from agent_framework.azure import AzureAssistantsClient
+from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential, get_bearer_token_provider
 from openai import AsyncAzureOpenAI
 from pydantic import Field
@@ -38,7 +38,7 @@ async def main() -> None:

    try:
        async with ChatAgent(
-            chat_client=AzureAssistantsClient(async_client=client, assistant_id=created_assistant.id),
+            chat_client=AzureOpenAIAssistantsClient(async_client=client, assistant_id=created_assistant.id),
            instructions="You are a helpful weather agent.",
            tools=get_weather,
        ) as agent:
@@ -5,7 +5,7 @@ import os
 from random import randint
 from typing import Annotated

-from agent_framework.azure import AzureAssistantsClient
+from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -23,7 +23,7 @@ async def main() -> None:

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    async with AzureAssistantsClient(
+    async with AzureOpenAIAssistantsClient(
        endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
        deployment_name=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"],
        credential=AzureCliCredential(),
@@ -6,7 +6,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import ChatAgent
-from agent_framework.azure import AzureAssistantsClient
+from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -34,7 +34,7 @@ async def tools_on_agent_level() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    async with ChatAgent(
-        chat_client=AzureAssistantsClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIAssistantsClient(credential=AzureCliCredential()),
        instructions="You are a helpful assistant that can provide weather and time information.",
        tools=[get_weather, get_time],  # Tools defined at agent creation
    ) as agent:
@@ -65,7 +65,7 @@ async def tools_on_run_level() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    async with ChatAgent(
-        chat_client=AzureAssistantsClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIAssistantsClient(credential=AzureCliCredential()),
        instructions="You are a helpful assistant.",
        # No tools defined here
    ) as agent:
@@ -96,7 +96,7 @@ async def mixed_tools_example() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    async with ChatAgent(
-        chat_client=AzureAssistantsClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIAssistantsClient(credential=AzureCliCredential()),
        instructions="You are a comprehensive assistant that can help with various information requests.",
        tools=[get_weather],  # Base tool available for all queries
    ) as agent:
@@ -5,7 +5,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import AgentThread, ChatAgent
-from agent_framework.azure import AzureAssistantsClient
+from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -25,7 +25,7 @@ async def example_with_automatic_thread_creation() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    async with ChatAgent(
-        chat_client=AzureAssistantsClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIAssistantsClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    ) as agent:
@@ -51,7 +51,7 @@ async def example_with_thread_persistence() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    async with ChatAgent(
-        chat_client=AzureAssistantsClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIAssistantsClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    ) as agent:
@@ -89,7 +89,7 @@ async def example_with_existing_thread_id() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    async with ChatAgent(
-        chat_client=AzureAssistantsClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIAssistantsClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    ) as agent:
@@ -109,7 +109,7 @@ async def example_with_existing_thread_id() -> None:

        # Create a new agent instance but use the existing thread ID
        async with ChatAgent(
-            chat_client=AzureAssistantsClient(thread_id=existing_thread_id, credential=AzureCliCredential()),
+            chat_client=AzureOpenAIAssistantsClient(thread_id=existing_thread_id, credential=AzureCliCredential()),
            instructions="You are a helpful weather agent.",
            tools=get_weather,
        ) as agent:
@@ -4,7 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated

-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -24,7 +24,7 @@ async def non_streaming_example() -> None:
    # Create agent with Azure Chat Client
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    agent = AzureChatClient(credential=AzureCliCredential()).create_agent(
+    agent = AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -42,7 +42,7 @@ async def streaming_example() -> None:
    # Create agent with Azure Chat Client
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    agent = AzureChatClient(credential=AzureCliCredential()).create_agent(
+    agent = AzureOpenAIChatClient(credential=AzureCliCredential()).create_agent(
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -5,7 +5,7 @@ import os
 from random import randint
 from typing import Annotated

-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -23,7 +23,7 @@ async def main() -> None:

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    agent = AzureChatClient(
+    agent = AzureOpenAIChatClient(
        deployment_name=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"],
        endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
        credential=AzureCliCredential(),
@@ -6,7 +6,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import ChatAgent
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -34,7 +34,7 @@ async def tools_on_agent_level() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureChatClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
        instructions="You are a helpful assistant that can provide weather and time information.",
        tools=[get_weather, get_time],  # Tools defined at agent creation
    )
@@ -66,7 +66,7 @@ async def tools_on_run_level() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureChatClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
        instructions="You are a helpful assistant.",
        # No tools defined here
    )
@@ -98,7 +98,7 @@ async def mixed_tools_example() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureChatClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
        instructions="You are a comprehensive assistant that can help with various information requests.",
        tools=[get_weather],  # Base tool available for all queries
    )
@@ -5,7 +5,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import AgentThread, ChatAgent, ChatMessageList
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -25,7 +25,7 @@ async def example_with_automatic_thread_creation() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureChatClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -52,7 +52,7 @@ async def example_with_thread_persistence() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureChatClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -87,7 +87,7 @@ async def example_with_existing_thread_messages() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureChatClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -109,7 +109,7 @@ async def example_with_existing_thread_messages() -> None:

    # Create a new agent instance but use the existing thread with its message history
    new_agent = ChatAgent(
-        chat_client=AzureChatClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -4,7 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated

-from agent_framework.azure import AzureResponsesClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -23,7 +23,7 @@ async def non_streaming_example() -> None:

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    agent = AzureResponsesClient(credential=AzureCliCredential()).create_agent(
+    agent = AzureOpenAIResponsesClient(credential=AzureCliCredential()).create_agent(
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -40,7 +40,7 @@ async def streaming_example() -> None:

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    agent = AzureResponsesClient(credential=AzureCliCredential()).create_agent(
+    agent = AzureOpenAIResponsesClient(credential=AzureCliCredential()).create_agent(
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -3,7 +3,7 @@
 import asyncio

 from agent_framework import ChatAgent, ChatResponse, HostedCodeInterpreterTool
-from agent_framework.azure import AzureResponsesClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from openai.types.responses.response import Response as OpenAIResponse
 from openai.types.responses.response_code_interpreter_tool_call import ResponseCodeInterpreterToolCall
@@ -16,7 +16,7 @@ async def main() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureResponsesClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
        instructions="You are a helpful assistant that can write and execute Python code to solve problems.",
        tools=HostedCodeInterpreterTool(),
    )
@@ -5,7 +5,7 @@ import os
 from random import randint
 from typing import Annotated

-from agent_framework.azure import AzureResponsesClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -23,7 +23,7 @@ async def main() -> None:

    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    agent = AzureResponsesClient(
+    agent = AzureOpenAIResponsesClient(
        deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
        endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
        credential=AzureCliCredential(),
@@ -6,7 +6,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import ChatAgent
-from agent_framework.azure import AzureResponsesClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -34,7 +34,7 @@ async def tools_on_agent_level() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureResponsesClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
        instructions="You are a helpful assistant that can provide weather and time information.",
        tools=[get_weather, get_time],  # Tools defined at agent creation
    )
@@ -66,7 +66,7 @@ async def tools_on_run_level() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureResponsesClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
        instructions="You are a helpful assistant.",
        # No tools defined here
    )
@@ -98,7 +98,7 @@ async def mixed_tools_example() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureResponsesClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
        instructions="You are a comprehensive assistant that can help with various information requests.",
        tools=[get_weather],  # Base tool available for all queries
    )
@@ -5,7 +5,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import AgentThread, ChatAgent
-from agent_framework.azure import AzureResponsesClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -25,7 +25,7 @@ async def example_with_automatic_thread_creation() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureResponsesClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -54,7 +54,7 @@ async def example_with_thread_persistence_in_memory() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureResponsesClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -95,7 +95,7 @@ async def example_with_existing_thread_id() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
    agent = ChatAgent(
-        chat_client=AzureResponsesClient(credential=AzureCliCredential()),
+        chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
        instructions="You are a helpful weather agent.",
        tools=get_weather,
    )
@@ -117,7 +117,7 @@ async def example_with_existing_thread_id() -> None:
        print("\n--- Continuing with the same thread ID in a new agent instance ---")

        agent = ChatAgent(
-            chat_client=AzureResponsesClient(credential=AzureCliCredential()),
+            chat_client=AzureOpenAIResponsesClient(credential=AzureCliCredential()),
            instructions="You are a helpful weather agent.",
            tools=get_weather,
        )
@@ -1,35 +0,0 @@
-# Azure Responses Agent Examples
-
-This folder contains examples demonstrating different ways to create and use agents with the Azure Responses client from the `agent_framework.azure` package.
-
-## Examples
-
-| File | Description |
-|------|-------------|
-| [`azure_responses_client_basic.py`](azure_responses_client_basic.py) | The simplest way to create an agent using `ChatAgent` with `AzureResponsesClient`. Shows both streaming and non-streaming responses for structured response generation with Azure OpenAI models. |
-| [`azure_responses_client_with_explicit_settings.py`](azure_responses_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific responses client, configuring settings explicitly including endpoint and deployment name. |
-| [`azure_responses_client_with_function_tools.py`](azure_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 query-level tools (provided with specific queries). |
-| [`azure_responses_client_with_code_interpreter.py`](azure_responses_client_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Azure agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
-| [`azure_responses_client_with_thread.py`](azure_responses_client_with_thread.py) | Demonstrates thread management with Azure agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
-
-## Environment Variables
-
-Make sure to set the following environment variables before running the examples:
-
- `AZURE_OPENAI_ENDPOINT`: Your Azure OpenAI endpoint
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your Azure OpenAI deployment
-
-## Authentication
-
-All examples use `AzureCliCredential` for authentication. Run `az login` in your terminal before running the examples, or replace `AzureCliCredential` with your preferred authentication method.
-
-## Required role-based access control (RBAC) roles
-
-To access the Azure OpenAI API, your Azure account or service principal needs one of the following RBAC roles assigned to the Azure OpenAI resource:
-
- **Cognitive Services OpenAI User**: Provides read access to Azure OpenAI resources and the ability to call the inference APIs. This is the minimum role required for running these examples.
- **Cognitive Services OpenAI Contributor**: Provides full access to Azure OpenAI resources, including the ability to create, update, and delete deployments and models.
-
-For most scenarios, the **Cognitive Services OpenAI User** role is sufficient. You can assign this role through the Azure portal under the Azure OpenAI resource's "Access control (IAM)" section.
-
-For more detailed information about Azure OpenAI RBAC roles, see: [Role-based access control for Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/role-based-access-control)
@@ -46,7 +46,7 @@ Your Azure AD App Registration should have:

 ```python
 import asyncio
-from agent_framework.copilotstudio import CopilotStudioAgent
+from agent_framework.microsoft import CopilotStudioAgent

 # Uses environment variables for configuration
 async def main():
@@ -63,7 +63,7 @@ asyncio.run(main())
 ### Explicit Configuration

 ```python
-from agent_framework.copilotstudio import CopilotStudioAgent, acquire_token
+from agent_framework.microsoft import CopilotStudioAgent, acquire_token
 from microsoft_agents.copilotstudio.client import ConnectionSettings, CopilotClient, PowerPlatformCloud, AgentType

 # Acquire token manually
@@ -2,7 +2,7 @@

 import asyncio

-from agent_framework.copilotstudio import CopilotStudioAgent
+from agent_framework.microsoft import CopilotStudioAgent

 # Environment variables needed:
 # COPILOTSTUDIOAGENT__ENVIRONMENTID - Environment ID where your copilot is deployed
@@ -3,7 +3,7 @@
 import asyncio
 import os

-from agent_framework.copilotstudio import CopilotStudioAgent, acquire_token
+from agent_framework.microsoft import CopilotStudioAgent, acquire_token
 from microsoft_agents.copilotstudio.client import AgentType, ConnectionSettings, CopilotClient, PowerPlatformCloud

 # Environment variables needed:
@@ -1,22 +0,0 @@
-# Foundry Agent Examples
-
-This folder contains examples demonstrating different ways to create and use agents with the Foundry chat client from the `agent_framework.foundry` package.
-
-## Examples
-
-| File | Description |
-|------|-------------|
-| [`foundry_basic.py`](foundry_basic.py) | The simplest way to create an agent using `ChatAgent` with `FoundryChatClient`. It automatically handles all configuration using environment variables. |
-| [`foundry_with_explicit_settings.py`](foundry_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `FoundryChatClient` settings, including project endpoint, model deployment, credentials, and agent name. |
-| [`foundry_with_existing_agent.py`](foundry_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent ID to the Foundry chat client. This example also demonstrates proper cleanup of manually created agents. |
-| [`foundry_with_function_tools.py`](foundry_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
-| [`foundry_with_code_interpreter.py`](foundry_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with Foundry agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
-| [`foundry_with_local_mcp.py`](foundry_with_local_mcp.py) | Shows how to integrate Foundry agents with Model Context Protocol (MCP) servers for enhanced functionality and tool integration. Demonstrates both agent-level and run-level tool configuration. |
-| [`foundry_with_thread.py`](foundry_with_thread.py) | Demonstrates thread management with Foundry agents, including automatic thread creation for stateless conversations and explicit thread management for maintaining conversation context across multiple interactions. |
-
-## Environment Variables
-
-Make sure to set the following environment variables before running the examples:
-
- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `FOUNDRY_MODEL_DEPLOYMENT_NAME`: The name of your model deployment
@@ -1,11 +1,24 @@
-# OpenAI Responses Agent Examples
+# OpenAI Assistants Agent Examples

-This folder contains examples demonstrating different ways to create and use agents with the OpenAI Responses client from the `agent_framework.openai` package.
+This folder contains examples demonstrating different ways to create and use agents with the OpenAI Assistants client from the `agent_framework.openai` package.

 ## Examples

 | File | Description |
 |------|-------------|
+| [`openai_assistants_basic.py`](openai_assistants_basic.py) | The simplest way to create an agent using `ChatAgent` with `OpenAIAssistantsClient`. Shows both streaming and non-streaming responses with automatic assistant creation and cleanup. |
+| [`openai_assistants_with_existing_assistant.py`](openai_assistants_with_existing_assistant.py) | Shows how to work with a pre-existing assistant by providing the assistant ID to the OpenAI Assistants client. Demonstrates proper cleanup of manually created assistants. |
+| [`openai_assistants_with_explicit_settings.py`](openai_assistants_with_explicit_settings.py) | Shows how to initialize an agent with a specific assistants client, configuring settings explicitly including API key and model ID. |
+| [`openai_assistants_with_function_tools.py`](openai_assistants_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
+| [`openai_assistants_with_code_interpreter.py`](openai_assistants_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with OpenAI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
+| [`openai_assistants_with_file_search.py`](openai_assistants_with_file_search.py) | Demonstrates how to use file search capabilities with OpenAI agents, allowing the agent to search through uploaded files to answer questions. |
+| [`openai_assistants_with_thread.py`](openai_assistants_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_chat_client_basic.py`](openai_chat_client_basic.py) | The simplest way to create an agent using `ChatAgent` with `OpenAIChatClient`. Shows both streaming and non-streaming responses for chat-based interactions with OpenAI models. |
+| [`openai_chat_client_with_explicit_settings.py`](openai_chat_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific chat client, configuring settings explicitly including API key and model ID. |
+| [`openai_chat_client_with_function_tools.py`](openai_chat_client_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
+| [`openai_chat_client_with_local_mcp.py`](openai_chat_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_chat_client_with_thread.py`](openai_chat_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_chat_client_with_web_search.py`](openai_chat_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. |
 | [`openai_responses_client_basic.py`](openai_responses_client_basic.py) | The simplest way to create an agent using `ChatAgent` with `OpenAIResponsesClient`. Shows both streaming and non-streaming responses for structured response generation with OpenAI models. |
 | [`openai_responses_client_reasoning.py`](openai_responses_client_reasoning.py) | Demonstrates how to use reasoning capabilities with OpenAI agents, showing how the agent can provide detailed reasoning for its responses. |
 | [`openai_responses_client_with_explicit_settings.py`](openai_responses_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific responses client, configuring settings explicitly including API key and model ID. |
@@ -25,9 +38,14 @@ This folder contains examples demonstrating different ways to create and use age
 Make sure to set the following environment variables before running the examples:

 - `OPENAI_API_KEY`: Your OpenAI API key
+- `OPENAI_CHAT_MODEL_ID`: The OpenAI model to use (e.g., `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo`)
 - `OPENAI_RESPONSES_MODEL_ID`: The OpenAI model to use (e.g., `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo`)
 - For image processing examples, use a vision-capable model like `gpt-4o` or `gpt-4o-mini`

+Optionally, you can set:
+- `OPENAI_ORG_ID`: Your OpenAI organization ID (if applicable)
+- `OPENAI_API_BASE_URL`: Your OpenAI base URL (if using a different base URL)
+
 ## Optional Dependencies

 Some examples require additional dependencies:
@@ -36,7 +54,7 @@ Some examples require additional dependencies:
  ```bash
  # Using uv
  uv add pillow
-  
+
  # Or using pip
  pip install pillow
  ```
@@ -1,22 +0,0 @@
-# OpenAI Assistants Agent Examples
-
-This folder contains examples demonstrating different ways to create and use agents with the OpenAI Assistants client from the `agent_framework.openai` package.
-
-## Examples
-
-| File | Description |
-|------|-------------|
-| [`openai_assistants_basic.py`](openai_assistants_basic.py) | The simplest way to create an agent using `ChatAgent` with `OpenAIAssistantsClient`. Shows both streaming and non-streaming responses with automatic assistant creation and cleanup. |
-| [`openai_assistants_with_existing_assistant.py`](openai_assistants_with_existing_assistant.py) | Shows how to work with a pre-existing assistant by providing the assistant ID to the OpenAI Assistants client. Demonstrates proper cleanup of manually created assistants. |
-| [`openai_assistants_with_explicit_settings.py`](openai_assistants_with_explicit_settings.py) | Shows how to initialize an agent with a specific assistants client, configuring settings explicitly including API key and model ID. |
-| [`openai_assistants_with_function_tools.py`](openai_assistants_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
-| [`openai_assistants_with_code_interpreter.py`](openai_assistants_with_code_interpreter.py) | Shows how to use the HostedCodeInterpreterTool with OpenAI agents to write and execute Python code. Includes helper methods for accessing code interpreter data from response chunks. |
-| [`openai_assistants_with_file_search.py`](openai_assistants_with_file_search.py) | Demonstrates how to use file search capabilities with OpenAI agents, allowing the agent to search through uploaded files to answer questions. |
-| [`openai_assistants_with_thread.py`](openai_assistants_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. |
-
-## Environment Variables
-
-Make sure to set the following environment variables before running the examples:
-
- `OPENAI_API_KEY`: Your OpenAI API key
- `OPENAI_CHAT_MODEL_ID`: The OpenAI model to use (e.g., `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo`)
@@ -1,21 +0,0 @@
-# OpenAI Chat Agent Examples
-
-This folder contains examples demonstrating different ways to create and use agents with the OpenAI Chat client from the `agent_framework.openai` package.
-
-## Examples
-
-| File | Description |
-|------|-------------|
-| [`openai_chat_client_basic.py`](openai_chat_client_basic.py) | The simplest way to create an agent using `ChatAgent` with `OpenAIChatClient`. Shows both streaming and non-streaming responses for chat-based interactions with OpenAI models. |
-| [`openai_chat_client_with_explicit_settings.py`](openai_chat_client_with_explicit_settings.py) | Shows how to initialize an agent with a specific chat client, configuring settings explicitly including API key and model ID. |
-| [`openai_chat_client_with_function_tools.py`](openai_chat_client_with_function_tools.py) | Demonstrates how to use function tools with agents. Shows both agent-level tools (defined when creating the agent) and query-level tools (provided with specific queries). |
-| [`openai_chat_client_with_local_mcp.py`](openai_chat_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_chat_client_with_thread.py`](openai_chat_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_chat_client_with_web_search.py`](openai_chat_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. |
-
-## Environment Variables
-
-Make sure to set the following environment variables before running the examples:
-
- `OPENAI_API_KEY`: Your OpenAI API key
- `OPENAI_CHAT_MODEL_ID`: The OpenAI model to use (e.g., `gpt-4o`, `gpt-4o-mini`, `gpt-3.5-turbo`)
@@ -10,7 +10,7 @@ This folder contains simple examples demonstrating direct usage of various chat
 | [`azure_chat_client.py`](azure_chat_client.py) | Direct usage of Azure Chat Client for chat interactions with Azure OpenAI models. |
 | [`azure_responses_client.py`](azure_responses_client.py) | Direct usage of Azure Responses Client for structured response generation with Azure OpenAI models. |
 | [`chat_response_cancellation.py`](chat_response_cancellation.py) | Demonstrates how to cancel chat responses during streaming, showing proper cancellation handling and cleanup. |
-| [`foundry_chat_client.py`](foundry_chat_client.py) | Direct usage of Foundry Chat Client for chat interactions with Azure AI Foundry models. |
+| [`azure_ai_chat_client.py`](azure_ai_chat_client.py) | Direct usage of Azure AI Chat Client for chat interactions with Azure AI models. |
 | [`openai_assistants_client.py`](openai_assistants_client.py) | Direct usage of OpenAI Assistants Client for basic chat interactions with OpenAI assistants. |
 | [`openai_chat_client.py`](openai_chat_client.py) | Direct usage of OpenAI Chat Client for chat interactions with OpenAI models. |
 | [`openai_responses_client.py`](openai_responses_client.py) | Direct usage of OpenAI Responses Client for structured response generation with OpenAI models. |
@@ -24,9 +24,9 @@ Depending on which client you're using, set the appropriate environment variable
 - `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME`: The name of your Azure OpenAI chat deployment
 - `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME`: The name of your Azure OpenAI responses deployment

-**For Foundry client:**
- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `FOUNDRY_MODEL_DEPLOYMENT_NAME`: The name of your model deployment
+**For Azure AI client:**
+- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI project endpoint
+- `AZURE_AI_MODEL_DEPLOYMENT_NAME`: The name of your model deployment

 **For OpenAI clients:**
 - `OPENAI_API_KEY`: Your OpenAI API key
@@ -4,7 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated

-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -20,7 +20,7 @@ def get_weather(
 async def main() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    async with FoundryChatClient(async_credential=AzureCliCredential()) as client:
+    async with AzureAIAgentClient(async_credential=AzureCliCredential()) as client:
        message = "What's the weather in Amsterdam and in Paris?"
        stream = False
        print(f"User: {message}")
@@ -4,7 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated

-from agent_framework.azure import AzureAssistantsClient
+from agent_framework.azure import AzureOpenAIAssistantsClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -20,7 +20,7 @@ def get_weather(
 async def main() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    async with AzureAssistantsClient(credential=AzureCliCredential()) as client:
+    async with AzureOpenAIAssistantsClient(credential=AzureCliCredential()) as client:
        message = "What's the weather in Amsterdam and in Paris?"
        stream = False
        print(f"User: {message}")
@@ -4,7 +4,7 @@ import asyncio
 from random import randint
 from typing import Annotated

-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import Field

@@ -20,7 +20,7 @@ def get_weather(
 async def main() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    client = AzureChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIChatClient(credential=AzureCliCredential())
    message = "What's the weather in Amsterdam and in Paris?"
    stream = False
    print(f"User: {message}")
@@ -5,7 +5,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import ChatResponse
-from agent_framework.azure import AzureResponsesClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from azure.identity import AzureCliCredential
 from pydantic import BaseModel, Field

@@ -28,7 +28,7 @@ class OutputStruct(BaseModel):
 async def main() -> None:
    # For authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
    # authentication option.
-    client = AzureResponsesClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(credential=AzureCliCredential())
    message = "What's the weather in Amsterdam and in Paris?"
    stream = True
    print(f"User: {message}")
@@ -16,7 +16,7 @@ This folder contains examples demonstrating how to use the Mem0 context provider
 ### Required Resources

 1. [Mem0 API Key](https://app.mem0.ai/) - Sign up for a Mem0 account and get your API key
-2. Azure AI Foundry project endpoint (used in these examples)
+2. Azure AI project endpoint (used in these examples)
 3. Azure CLI authentication (run `az login`)

 ## Configuration
@@ -28,9 +28,9 @@ Set the following environment variables:
 **For Mem0:**
 - `MEM0_API_KEY`: Your Mem0 API key (alternatively, pass it as `api_key` parameter to `Mem0Provider`)

-**For Azure AI Foundry:**
- `FOUNDRY_PROJECT_ENDPOINT`: Your Azure AI Foundry project endpoint
- `FOUNDRY_MODEL_DEPLOYMENT_NAME`: The name of your model deployment
+**For Azure AI:**
+- `AZURE_AI_PROJECT_ENDPOINT`: Your Azure AI project endpoint
+- `AZURE_AI_MODEL_DEPLOYMENT_NAME`: The name of your model deployment

 ## Key Concepts

@@ -3,7 +3,7 @@
 import asyncio
 import uuid

-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from agent_framework.mem0 import Mem0Provider
 from azure.identity.aio import AzureCliCredential

@@ -32,7 +32,7 @@ async def main() -> None:
    # For Mem0 authentication, set Mem0 API key via "api_key" parameter or MEM0_API_KEY environment variable.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="FriendlyAssistant",
            instructions="You are a friendly assistant.",
            tools=retrieve_company_report,
@@ -3,7 +3,7 @@
 import asyncio
 import uuid

-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from agent_framework.mem0 import Mem0Provider
 from azure.identity.aio import AzureCliCredential

@@ -27,7 +27,7 @@ async def example_global_thread_scope() -> None:

    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="GlobalMemoryAssistant",
            instructions="You are an assistant that remembers user preferences across conversations.",
            tools=get_user_preferences,
@@ -65,7 +65,7 @@ async def example_per_operation_thread_scope() -> None:

    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="ScopedMemoryAssistant",
            instructions="You are an assistant with thread-scoped memory.",
            tools=get_user_preferences,
@@ -113,14 +113,14 @@ async def example_multiple_agents() -> None:

    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="PersonalAssistant",
            instructions="You are a personal assistant that helps with personal tasks.",
            context_providers=Mem0Provider(
                agent_id=agent_id_1,
            ),
        ) as personal_agent,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="WorkAssistant",
            instructions="You are a work assistant that helps with professional tasks.",
            context_providers=Mem0Provider(
@@ -12,7 +12,7 @@ from agent_framework import (
    AgentRunResponse,
    FunctionInvocationContext,
 )
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -166,7 +166,7 @@ async def main() -> None:
    # authentication option.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="WeatherAgent",
            instructions="You are a helpful weather assistant.",
            tools=get_weather,
@@ -15,7 +15,7 @@ from agent_framework import (
    FunctionMiddleware,
    Role,
 )
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -99,7 +99,7 @@ async def main() -> None:
    # authentication option.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="WeatherAgent",
            instructions="You are a helpful weather assistant.",
            tools=get_weather,
@@ -7,7 +7,7 @@ from agent_framework import (
    agent_middleware,
    function_middleware,
 )
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential

 """
@@ -70,7 +70,7 @@ async def main() -> None:
    # authentication option.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="TimeAgent",
            instructions="You are a helpful time assistant. Call get_current_time when asked about time.",
            tools=get_current_time,
@@ -5,7 +5,7 @@ from collections.abc import Awaitable, Callable
 from typing import Annotated

 from agent_framework import FunctionInvocationContext
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -58,7 +58,7 @@ async def main() -> None:
    # authentication option.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="DataAgent",
            instructions="You are a helpful data assistant. Use the data service tool to fetch information for users.",
            tools=unstable_data_service,
@@ -10,7 +10,7 @@ from agent_framework import (
    AgentRunContext,
    FunctionInvocationContext,
 )
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -83,7 +83,7 @@ async def main() -> None:
    # authentication option.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="WeatherAgent",
            instructions="You are a helpful weather assistant.",
            tools=get_weather,
@@ -12,7 +12,7 @@ from agent_framework import (
    ChatMessage,
    Role,
 )
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -110,7 +110,7 @@ async def pre_termination_middleware() -> None:
    print("\n--- Example 1: Pre-termination Middleware ---")
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="WeatherAgent",
            instructions="You are a helpful weather assistant.",
            tools=get_weather,
@@ -137,7 +137,7 @@ async def post_termination_middleware() -> None:
    print("\n--- Example 2: Post-termination Middleware ---")
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="WeatherAgent",
            instructions="You are a helpful weather assistant.",
            tools=get_weather,
@@ -6,7 +6,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import FunctionInvocationContext
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential
 from pydantic import Field

@@ -67,7 +67,7 @@ async def main() -> None:
    # authentication option.
    async with (
        AzureCliCredential() as credential,
-        FoundryChatClient(async_credential=credential).create_agent(
+        AzureAIAgentClient(async_credential=credential).create_agent(
            name="WeatherAgent",
            instructions="You are a helpful weather assistant. Use the weather tool to get current conditions.",
            tools=get_weather,
@@ -2,13 +2,15 @@

 import asyncio
 import base64
+
 import requests
 from agent_framework import ChatMessage, DataContent, Role, TextContent
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
+

 async def test_image():
    """Test image analysis with Azure."""
-    client = AzureChatClient()
+    client = AzureOpenAIChatClient()

    # Fetch image from httpbin
    image_url = "https://httpbin.org/image/jpeg"
@@ -18,10 +20,7 @@ async def test_image():

    message = ChatMessage(
        role=Role.USER,
-        contents=[
-            TextContent(text="What's in this image?"),
-            DataContent(uri=image_uri, media_type="image/jpeg")
-        ]
+        contents=[TextContent(text="What's in this image?"), DataContent(uri=image_uri, media_type="image/jpeg")],
    )

    response = await client.get_response(message)
@@ -30,7 +29,8 @@ async def test_image():

 async def main():
    print("=== Testing Azure Multimodal ===")
-    await test_image() 
+    await test_image()
+

 if __name__ == "__main__":
    asyncio.run(main())
@@ -1,5 +1,5 @@
 # Connector environment variables
-# Foundry
+# Azure AI
 # see ../../../env.example for details
 # OpenAI
 # see ../../../env.example for details
@@ -6,7 +6,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import HostedCodeInterpreterTool
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from agent_framework.observability import get_tracer, setup_observability
 from azure.ai.projects.aio import AIProjectClient
 from azure.identity.aio import AzureCliCredential
@@ -15,12 +15,12 @@ from opentelemetry.trace.span import format_trace_id
 from pydantic import Field

 """
-This sample, shows you can leverage the built-in telemetry in Foundry.
-It uses the Foundry client to setup the telemetry, this calls
-out to Foundry for a telemetry connection strings,
+This sample, shows you can leverage the built-in telemetry in Azure AI.
+It uses the Azure AI client to setup the telemetry, this calls
+out to Azure AI for a telemetry connection strings,
 and then call the setup_observability function in the agent framework.
 If you want to compare with the trace sent to a generic OTLP endpoint,
-switch the `use_foundry_telemetry` variable to False.
+switch the `use_azure_ai_telemetry` variable to False.
 """


@@ -47,10 +47,10 @@ async def main() -> None:

    The telemetry will include information about the AI service execution.

-    In foundry you will also see specific operations happening that are called by the Foundry implementation,
+    In azure_ai you will also see specific operations happening that are called by the Azure AI implementation,
    such as `create_agent`.
    """
-    use_foundry_obs = True
+    use_azure_ai_obs = True
    questions = [
        "What's the weather in Amsterdam and in Paris?",
        "Why is the sky blue?",
@@ -59,16 +59,16 @@ async def main() -> None:
    ]
    async with (
        AzureCliCredential() as credential,
-        AIProjectClient(endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"], credential=credential) as project,
-        FoundryChatClient(client=project, setup_tracing=False) as client,
+        AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project,
+        AzureAIAgentClient(client=project, setup_tracing=False) as client,
    ):
-        if use_foundry_obs:
-            await client.setup_foundry_observability(enable_live_metrics=True)
+        if use_azure_ai_obs:
+            await client.setup_observability(enable_live_metrics=True)
        else:
            setup_observability()

        with get_tracer().start_as_current_span(
-            name="Foundry Telemetry from Agent Framework", kind=SpanKind.CLIENT
+            name="Azure AI Telemetry from Agent Framework", kind=SpanKind.CLIENT
        ) as span:
            for question in questions:
                print(f"{BLUE}User: {question}{RESET}")
@@ -6,16 +6,16 @@ from random import randint
 from typing import Annotated

 from agent_framework import ChatAgent
+from agent_framework.azure import AzureAIAgentClient
 from agent_framework.observability import get_tracer
-from agent_framework_foundry import FoundryChatClient
 from azure.ai.projects.aio import AIProjectClient
 from azure.identity.aio import AzureCliCredential
 from opentelemetry.trace import SpanKind
 from pydantic import Field

 """
-This sample shows you can can setup telemetry with a agent from Foundry.
-We once again call the `setup_foundry_observability` method to set up telemetry in order to include the overall spans.
+This sample shows you can can setup telemetry with a agent from Azure AI.
+We once again call the `setup_observability` method to set up telemetry in order to include the overall spans.
 """


@@ -34,11 +34,11 @@ async def main():
    questions = ["What's the weather in Amsterdam?", "and in Paris, and which is better?", "Why is the sky blue?"]
    async with (
        AzureCliCredential() as credential,
-        AIProjectClient(endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"], credential=credential) as project,
-        # this calls `setup_foundry_observability` through the context manager
-        FoundryChatClient(client=project) as client,
+        AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project,
+        # this calls `setup_observability` through the context manager
+        AzureAIAgentClient(client=project) as client,
    ):
-        await client.setup_foundry_observability(enable_live_metrics=True)
+        await client.setup_observability(enable_live_metrics=True)
        with get_tracer().start_as_current_span("Single Agent Chat", kind=SpanKind.CLIENT):
            print("Running Single Agent Chat")
            print("Welcome to the chat, type 'exit' to quit.")
@@ -26,7 +26,7 @@ The Agent Framework Python SDK is designed to efficiently generate comprehensive
 ### Required resources

 2. OpenAI or [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal)
-2. [Foundry project](https://ai.azure.com/doc/azure/ai-foundry/what-is-azure-ai-foundry)
+2. [Azure AI project](https://ai.azure.com/doc/azure/ai-foundry/what-is-azure-ai-foundry)

 ### Optional resources

@@ -55,12 +55,12 @@ This folder contains different samples demonstrating how to use telemetry in var
 ### [01 - zero_code](./01-zero_code.py):
 A simple example showing how to enable telemetry in a zero-touch scenario. When the above environment variables are set, telemetry will be automatically enabled, however since you do not define any overarching tracer, you will only see the spans for the specific calls to the chat client and tools.

-### [02a](./02a-generic_chat_client.py) and [02b](./02b-foundry_chat_client.py) Chat Clients:
-These two samples show how to first setup the telemetry by manually importing the `setup_observability` function from the `agent_framework.observability` module and calling it. After this is done, the trace that get's created will live in the same context as the chat client calls, allowing you to see the end-to-end flow of your application. For Foundry, there is a method in the Foundry project client to get the azure monitor connection string for your project, the `.setup_foundry_observability()` method in the `FoundryChatClient` class will use this url to configure telemetry and you then do not have to import and call `setup_observability()` manually.
+### [02a](./02a-generic_chat_client.py) and [02b](./02b-azure_ai_chat_client.py) Chat Clients:
+These two samples show how to first setup the telemetry by manually importing the `setup_observability` function from the `agent_framework.observability` module and calling it. After this is done, the trace that get's created will live in the same context as the chat client calls, allowing you to see the end-to-end flow of your application. For Azure AI, there is a method in the Azure AI project client to get the azure monitor connection string for your project, the `.setup_observability()` method in the `AzureAIAgentClient` class will use this url to configure telemetry and you then do not have to import and call `setup_observability()` manually.
 If you or some other process already configure global tracer_providers or metrics_providers, the `setup_observability()` function will not override them, but instead use the existing tracer_provider, if possible. Metrics cannot be setup this way, so if you want to use metrics, you will have to call `setup_observability()` manually, before another process.

-### [03a](./03a-generic_agent.py) and [03b](./03b-foundry_agent.py) Agents:
-These two samples show how to setup telemetry when using the Agent Framework's agent abstraction layer. They are similar to the chat client samples, but also show how to create an agent and invoke it. The same rules apply for setting up telemetry, you can either call `setup_observability()` manually, or use the `setup_foundry_observability()` method in the `FoundryChatClient` class.
+### [03a](./03a-generic_agent.py) and [03b](./03b-azure_ai_agent.py) Agents:
+These two samples show how to setup telemetry when using the Agent Framework's agent abstraction layer. They are similar to the chat client samples, but also show how to create an agent and invoke it. The same rules apply for setting up telemetry, you can either call `setup_observability()` manually, or use the `setup_observability()` method in the `AzureAIAgentClient` class.

 ### [04 - workflow](./04-workflow.py) Workflow:
 This sample shows how to setup telemetry when using the Agent Framework's workflow execution engine. It demonstrates a simple workflow scenario with telemetry.
@@ -35,7 +35,7 @@ Once comfortable with these, explore the rest of the samples below.
 |---|---|---|
 | Azure Chat Agents (Streaming) | [agents/azure_chat_agents_streaming.py](./agents/azure_chat_agents_streaming.py) | Add Azure agents as edges and handle streaming events |
 | Custom Agent Executors | [agents/custom_agent_executors.py](./agents/custom_agent_executors.py) | Create executors to handle agent run methods |
-| Foundry Chat Agents (Streaming) | [agents/foundry_chat_agents_streaming.py](./agents/foundry_chat_agents_streaming.py) | Add Foundry agents as edges and handle streaming events |
+| Azure AI Chat Agents (Streaming) | [agents/azure_ai_chat_agents_streaming.py](./agents/azure_ai_chat_agents_streaming.py) | Add Azure AI agents as edges and handle streaming events |
 | Workflow as Agent (Reflection Pattern) | [agents/workflow_as_agent_reflection_pattern.py](./agents/workflow_as_agent_reflection_pattern.py) | Wrap a workflow so it can behave like an agent (reflection pattern) |
 | Workflow as Agent + HITL | [agents/workflow_as_agent_human_in_the_loop.py](./agents/workflow_as_agent_human_in_the_loop.py) | Extend workflow-as-agent with human-in-the-loop capability |

@@ -119,9 +119,9 @@ concurrent’s dispatcher and aggregator and can be ignored if you only care abo

 ### Environment Variables

- **AzureChatClient**: Set Azure OpenAI environment variables as documented [here](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/chat_client/README.md#environment-variables).  
-  These variables are required for samples that construct `AzureChatClient`
+- **AzureOpenAIChatClient**: Set Azure OpenAI environment variables as documented [here](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/chat_client/README.md#environment-variables).
+  These variables are required for samples that construct `AzureOpenAIChatClient`

- **OpenAI** (used in orchestration samples):  
-  - [OpenAIChatClient env vars](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/agents/openai_chat_client/README.md)  
+- **OpenAI** (used in orchestration samples):
+  - [OpenAIChatClient env vars](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/agents/openai_chat_client/README.md)
  - [OpenAIResponsesClient env vars](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/agents/openai_responses_client/README.md)
@@ -3,7 +3,7 @@
 import asyncio

 from agent_framework import AgentRunEvent, WorkflowBuilder
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 """
@@ -13,12 +13,12 @@ This sample uses two custom executors. A Writer agent creates or edits content,
 then hands the conversation to a Reviewer agent which evaluates and finalizes the result.

 Purpose:
-Show how to wrap chat agents created by AzureChatClient inside workflow executors. Demonstrate how agents
+Show how to wrap chat agents created by AzureOpenAIChatClient inside workflow executors. Demonstrate how agents
 automatically yield outputs when they complete, removing the need for explicit completion events.
 The workflow completes when it becomes idle.

 Prerequisites:
- Azure OpenAI configured for AzureChatClient with required environment variables.
+- Azure OpenAI configured for AzureOpenAIChatClient with required environment variables.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with WorkflowBuilder, executors, edges, events, and streaming or non streaming runs.
 """
@@ -27,7 +27,7 @@ Prerequisites:
 async def main():
    """Build and run a simple two node agent workflow: Writer then Reviewer."""
    # Create the Azure chat client. AzureCliCredential uses your current az login.
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
    writer_agent = chat_client.create_agent(
        instructions=(
            "You are an excellent content writer. You create new content and edit contents based on the feedback."
@@ -2,8 +2,6 @@

 import asyncio

-from typing_extensions import Never
-
 from agent_framework import (
    ChatAgent,
    ChatMessage,
@@ -17,8 +15,9 @@ from agent_framework import (
    handler,
 )
 from agent_framework._workflow._events import WorkflowOutputEvent
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
+from typing_extensions import Never

 """
 Step 3: Agents in a workflow with streaming
@@ -28,14 +27,14 @@ then passes the conversation to a Reviewer agent that finalizes the result.
 The workflow is invoked with run_stream so you can observe events as they occur.

 Purpose:
-Show how to wrap chat agents created by AzureChatClient inside workflow executors, wire them with WorkflowBuilder,
+Show how to wrap chat agents created by AzureOpenAIChatClient inside workflow executors, wire them with WorkflowBuilder,
 and consume streaming events from the workflow. Demonstrate the @handler pattern with typed inputs and typed
 WorkflowContext[T_Out, T_W_Out] outputs. Agents automatically yield outputs when they complete.
 The streaming loop also surfaces WorkflowEvent.origin so you can distinguish runner-generated lifecycle events
 from executor-generated data-plane events.

 Prerequisites:
- Azure OpenAI configured for AzureChatClient with required environment variables.
+- Azure OpenAI configured for AzureOpenAIChatClient with required environment variables.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with WorkflowBuilder, executors, edges, events, and streaming runs.
 """
@@ -51,8 +50,8 @@ class Writer(Executor):

    agent: ChatAgent

-    def __init__(self, chat_client: AzureChatClient, id: str = "writer"):
-        # Create a domain specific agent using your configured AzureChatClient.
+    def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "writer"):
+        # Create a domain specific agent using your configured AzureOpenAIChatClient.
        agent = chat_client.create_agent(
            instructions=(
                "You are an excellent content writer. You create new content and edit contents based on the feedback."
@@ -88,7 +87,7 @@ class Reviewer(Executor):

    agent: ChatAgent

-    def __init__(self, chat_client: AzureChatClient, id: str = "reviewer"):
+    def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "reviewer"):
        # Create a domain specific agent that evaluates and refines content.
        agent = chat_client.create_agent(
            instructions=(
@@ -111,7 +110,7 @@ class Reviewer(Executor):
 async def main():
    """Build the two node workflow and run it with streaming to observe events."""
    # Create the Azure chat client. AzureCliCredential uses your current az login.
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
    # Instantiate the two agent backed executors.
    writer = Writer(chat_client)
    reviewer = Reviewer(chat_client)
@@ -6,7 +6,7 @@ from contextlib import AsyncExitStack
 from typing import Any

 from agent_framework import AgentRunUpdateEvent, WorkflowBuilder, WorkflowOutputEvent
-from agent_framework.foundry import FoundryChatClient
+from agent_framework.azure import AzureAIAgentClient
 from azure.identity.aio import AzureCliCredential

 """
@@ -24,21 +24,21 @@ Demonstrate:
 - The workflow completes when idle and outputs are available in events.get_outputs().

 Prerequisites:
- Foundry Agent Service configured, along with the required environment variables.
+- Azure AI Agent Service configured, along with the required environment variables.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with WorkflowBuilder, edges, events, and streaming runs.
 """


-async def create_foundry_agent() -> tuple[Callable[..., Awaitable[Any]], Callable[[], Awaitable[None]]]:
-    """Helper method to create a Foundry agent factory and a close function.
+async def create_azure_ai_agent() -> tuple[Callable[..., Awaitable[Any]], Callable[[], Awaitable[None]]]:
+    """Helper method to create a Azure AI agent factory and a close function.

    This makes sure the async context managers are properly handled.
    """
    stack = AsyncExitStack()
    cred = await stack.enter_async_context(AzureCliCredential())

-    client = await stack.enter_async_context(FoundryChatClient(async_credential=cred))
+    client = await stack.enter_async_context(AzureAIAgentClient(async_credential=cred))

    async def agent(**kwargs: Any) -> Any:
        return await stack.enter_async_context(client.create_agent(**kwargs))
@@ -50,7 +50,7 @@ async def create_foundry_agent() -> tuple[Callable[..., Awaitable[Any]], Callabl


 async def main() -> None:
-    agent, close = await create_foundry_agent()
+    agent, close = await create_azure_ai_agent()
    try:
        writer = await agent(
            name="Writer",
@@ -3,7 +3,7 @@
 import asyncio

 from agent_framework import AgentRunUpdateEvent, WorkflowBuilder, WorkflowOutputEvent
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 """
@@ -21,7 +21,7 @@ Demonstrate:
 - The workflow completes when idle and outputs are available in events.get_outputs().

 Prerequisites:
- Azure OpenAI configured for AzureChatClient with required environment variables.
+- Azure OpenAI configured for AzureOpenAIChatClient with required environment variables.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with WorkflowBuilder, edges, events, and streaming runs.
 """
@@ -30,7 +30,7 @@ Prerequisites:
 async def main():
    """Build and run a simple two node agent workflow: Writer then Reviewer."""
    # Create the Azure chat client. AzureCliCredential uses your current az login.
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

    # Define two domain specific chat agents. The builder will wrap these as executors.
    writer_agent = chat_client.create_agent(
@@ -10,7 +10,7 @@ from agent_framework import (
    WorkflowContext,
    handler,
 )
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 """
@@ -20,12 +20,12 @@ This sample uses two custom executors. A Writer agent creates or edits content,
 then hands the conversation to a Reviewer agent which evaluates and finalizes the result.

 Purpose:
-Show how to wrap chat agents created by AzureChatClient inside workflow executors. Demonstrate the @handler pattern
+Show how to wrap chat agents created by AzureOpenAIChatClient inside workflow executors. Demonstrate the @handler pattern
 with typed inputs and typed WorkflowContext[T] outputs, connect executors with the fluent WorkflowBuilder, and finish
 by yielding outputs from the terminal node.

 Prerequisites:
- Azure OpenAI configured for AzureChatClient with required environment variables.
+- Azure OpenAI configured for AzureOpenAIChatClient with required environment variables.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with WorkflowBuilder, executors, edges, events, and streaming or non streaming runs.
 """
@@ -41,8 +41,8 @@ class Writer(Executor):

    agent: ChatAgent

-    def __init__(self, chat_client: AzureChatClient, id: str = "writer"):
-        # Create a domain specific agent using your configured AzureChatClient.
+    def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "writer"):
+        # Create a domain specific agent using your configured AzureOpenAIChatClient.
        agent = chat_client.create_agent(
            instructions=(
                "You are an excellent content writer. You create new content and edit contents based on the feedback."
@@ -83,7 +83,7 @@ class Reviewer(Executor):

    agent: ChatAgent

-    def __init__(self, chat_client: AzureChatClient, id: str = "reviewer"):
+    def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "reviewer"):
        # Create a domain specific agent that evaluates and refines content.
        agent = chat_client.create_agent(
            instructions=(
@@ -106,7 +106,7 @@ class Reviewer(Executor):
 async def main():
    """Build and run a simple two node agent workflow: Writer then Reviewer."""
    # Create the Azure chat client. AzureCliCredential uses your current az login.
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

    # Instantiate the two agent backed executors.
    writer = Writer(chat_client)
@@ -25,7 +25,7 @@ from agent_framework import (
    WorkflowStatusEvent,
    handler,
 )
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 # NOTE: the Azure client imports above are real dependencies. When running this
@@ -203,7 +203,7 @@ def create_workflow(*, checkpoint_storage: FileCheckpointStorage | None = None)
    # The Azure client is created once so our agent executor can issue calls to
    # the hosted model. The agent id is stable across runs which keeps
    # checkpoints deterministic.
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
    writer = AgentExecutor(
        chat_client.create_agent(
            instructions="Write concise, warm release notes that sound human and helpful.",
@@ -18,7 +18,7 @@ from agent_framework import (
    WorkflowContext,
    handler,
 )
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 if TYPE_CHECKING:
@@ -51,7 +51,7 @@ What you learn:
 - How workflows complete by yielding outputs when idle, not via explicit completion events.

 Prerequisites:
- Azure AI or Azure OpenAI available for AzureChatClient.
+- Azure AI or Azure OpenAI available for AzureOpenAIChatClient.
 - Authentication with azure-identity via AzureCliCredential. Run az login locally.
 - Filesystem access for writing JSON checkpoint files in a temp directory.
 """
@@ -161,7 +161,7 @@ def create_workflow(checkpoint_storage: FileCheckpointStorage) -> "Workflow":
    reverse_text_executor = ReverseTextExecutor(id="reverse-text")

    # Configure the agent stage that lowercases the text.
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
    lower_agent = AgentExecutor(
        chat_client.create_agent(
            instructions=("You transform text to lowercase. Reply with ONLY the transformed text.")
@@ -16,7 +16,7 @@ from agent_framework import (  # Core chat primitives used to build requests
    WorkflowContext,  # Per-run context and event bus
    executor,  # Decorator to declare a Python function as a workflow executor
 )
-from agent_framework.azure import AzureChatClient  # Thin client wrapper for Azure OpenAI chat models
+from agent_framework.azure import AzureOpenAIChatClient  # Thin client wrapper for Azure OpenAI chat models
 from azure.identity import AzureCliCredential  # Uses your az CLI login for credentials
 from pydantic import BaseModel  # Structured outputs for safer parsing

@@ -35,7 +35,7 @@ Purpose:
 Prerequisites:
 - You understand the basics of WorkflowBuilder, executors, and events in this framework.
 - You know the concept of edge conditions and how they gate routes using a predicate function.
- Azure OpenAI access is configured for AzureChatClient. You should be logged in with Azure CLI (AzureCliCredential)
+- Azure OpenAI access is configured for AzureOpenAIChatClient. You should be logged in with Azure CLI (AzureCliCredential)
 and have the Azure OpenAI environment variables set as documented in the getting started chat client README.
 - The sample email resource file exists at workflow/resources/email.txt.

@@ -132,7 +132,7 @@ async def to_email_assistant_request(
 async def main() -> None:
    # Create agents
    # AzureCliCredential uses your current az login. This avoids embedding secrets in code.
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

    # Agent 1. Classifies spam and returns a DetectionResult object.
    # response_format enforces that the LLM returns parsable JSON for the Pydantic model.
@@ -22,7 +22,7 @@ from agent_framework import (
    WorkflowOutputEvent,
    executor,
 )
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import BaseModel

@@ -184,7 +184,7 @@ async def database_access(analysis: AnalysisResult, ctx: WorkflowContext[Never,

 async def main() -> None:
    # Agents
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

    email_analysis_agent = AgentExecutor(
        chat_client.create_agent(
@@ -16,7 +16,7 @@ from agent_framework import (
    WorkflowOutputEvent,
    handler,
 )
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 """
@@ -28,7 +28,7 @@ What it does:
 - The workflow completes when the correct number is guessed.

 Prerequisites:
- Azure AI/ Azure OpenAI for `AzureChatClient` agent.
+- Azure AI/ Azure OpenAI for `AzureOpenAIChatClient` agent.
 - Authentication via `azure-identity` — uses `AzureCliCredential()` (run `az login`).
 """

@@ -122,7 +122,7 @@ async def main():
    guess_number_executor = GuessNumberExecutor((1, 100))

    # Agent judge setup
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
    judge_agent = AgentExecutor(
        chat_client.create_agent(
            instructions=(
@@ -20,7 +20,7 @@ from agent_framework import (  # Core chat primitives used to form LLM requests
    WorkflowContext,  # Per-run context and event bus
    executor,  # Decorator to turn a function into a workflow executor
 )
-from agent_framework.azure import AzureChatClient  # Thin client for Azure OpenAI chat models
+from agent_framework.azure import AzureOpenAIChatClient  # Thin client for Azure OpenAI chat models
 from azure.identity import AzureCliCredential  # Uses your az CLI login for credentials
 from pydantic import BaseModel  # Structured outputs with validation

@@ -42,7 +42,7 @@ on that type.
 Prerequisites:
 - Familiarity with WorkflowBuilder, executors, edges, and events.
 - Understanding of switch-case edge groups and how Case and Default are evaluated in order.
- Working Azure OpenAI configuration for AzureChatClient, with Azure CLI login and required environment variables.
+- Working Azure OpenAI configuration for AzureOpenAIChatClient, with Azure CLI login and required environment variables.
 - Access to workflow/resources/ambiguous_email.txt, or accept the inline fallback string.
 """

@@ -155,7 +155,7 @@ async def handle_uncertain(detection: DetectionResult, ctx: WorkflowContext[Neve

 async def main():
    """Main function to run the workflow."""
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

    # Agents. response_format enforces that the LLM returns JSON that Pydantic can validate.
    spam_detection_agent = AgentExecutor(
@@ -21,7 +21,7 @@ from agent_framework import (
    WorkflowStatusEvent,  # Event emitted on run state changes
    handler,  # Decorator to expose an Executor method as a step
 )
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import BaseModel

@@ -42,7 +42,7 @@ Demonstrate:
 - Driving the loop in application code with run_stream and send_responses_streaming.

 Prerequisites:
- Azure OpenAI configured for AzureChatClient with required environment variables.
+- Azure OpenAI configured for AzureOpenAIChatClient with required environment variables.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Basic familiarity with WorkflowBuilder, executors, edges, events, and streaming runs.
 """
@@ -158,7 +158,7 @@ class TurnManager(Executor):
 async def main() -> None:
    # Create the chat agent and wrap it in an AgentExecutor.
    # response_format enforces that the model produces JSON compatible with GuessOutput.
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
    agent = chat_client.create_agent(
        instructions=(
            "You guess a number between 1 and 10. "
@@ -4,7 +4,7 @@ import asyncio
 from typing import Any

 from agent_framework import ChatMessage, ConcurrentBuilder
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 """
@@ -21,14 +21,14 @@ Demonstrates:
 - Workflow completion when idle with no pending work

 Prerequisites:
- Azure OpenAI access configured for AzureChatClient (use az login + env vars)
+- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
 - Familiarity with Workflow events (AgentRunEvent, WorkflowOutputEvent)
 """


 async def main() -> None:
-    # 1) Create three domain agents using AzureChatClient
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    # 1) Create three domain agents using AzureOpenAIChatClient
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

    researcher = chat_client.create_agent(
        instructions=(
@@ -13,7 +13,7 @@ from agent_framework import (
    WorkflowContext,
    handler,
 )
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 """
@@ -25,21 +25,21 @@ and emit AgentExecutorResponse outputs, which allows reuse of the high-level
 ConcurrentBuilder API and the default aggregator.

 Demonstrates:
- Executors that create their ChatAgent in __init__ (via AzureChatClient)
+- Executors that create their ChatAgent in __init__ (via AzureOpenAIChatClient)
 - A @handler that converts AgentExecutorRequest -> AgentExecutorResponse
 - ConcurrentBuilder().participants([...]) to build fan-out/fan-in
 - Default aggregator returning list[ChatMessage] (one user + one assistant per agent)
 - Workflow completion when all participants become idle

 Prerequisites:
- Azure OpenAI configured for AzureChatClient (az login + required env vars)
+- Azure OpenAI configured for AzureOpenAIChatClient (az login + required env vars)
 """


 class ResearcherExec(Executor):
    agent: ChatAgent

-    def __init__(self, chat_client: AzureChatClient, id: str = "researcher"):
+    def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "researcher"):
        agent = chat_client.create_agent(
            instructions=(
                "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
@@ -59,7 +59,7 @@ class ResearcherExec(Executor):
 class MarketerExec(Executor):
    agent: ChatAgent

-    def __init__(self, chat_client: AzureChatClient, id: str = "marketer"):
+    def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "marketer"):
        agent = chat_client.create_agent(
            instructions=(
                "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
@@ -79,7 +79,7 @@ class MarketerExec(Executor):
 class LegalExec(Executor):
    agent: ChatAgent

-    def __init__(self, chat_client: AzureChatClient, id: str = "legal"):
+    def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "legal"):
        agent = chat_client.create_agent(
            instructions=(
                "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
@@ -97,7 +97,7 @@ class LegalExec(Executor):


 async def main() -> None:
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

    researcher = ResearcherExec(chat_client)
    marketer = MarketerExec(chat_client)
@@ -4,7 +4,7 @@ import asyncio
 from typing import Any

 from agent_framework import ChatMessage, ConcurrentBuilder, Role
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 """
@@ -12,7 +12,7 @@ Sample: Concurrent Orchestration with Custom Aggregator

 Build a concurrent workflow with ConcurrentBuilder that fans out one prompt to
 multiple domain agents and fans in their responses. Override the default
-aggregator with a custom async callback that uses AzureChatClient.get_response()
+aggregator with a custom async callback that uses AzureOpenAIChatClient.get_response()
 to synthesize a concise, consolidated summary from the experts' outputs.
 The workflow completes when all participants become idle.

@@ -23,12 +23,12 @@ Demonstrates:
 - Workflow output yielded with the synthesized summary string

 Prerequisites:
- Azure OpenAI configured for AzureChatClient (az login + required env vars)
+- Azure OpenAI configured for AzureOpenAIChatClient (az login + required env vars)
 """


 async def main() -> None:
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

    researcher = chat_client.create_agent(
        instructions=(
@@ -4,7 +4,7 @@ import asyncio
 from typing import cast

 from agent_framework import ChatMessage, Role, SequentialBuilder, WorkflowOutputEvent
-from agent_framework.azure import AzureChatClient
+from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential

 """
@@ -23,13 +23,13 @@ Note on internal adapters:
  You can safely ignore them when focusing on agent progress.

 Prerequisites:
- Azure OpenAI access configured for AzureChatClient (use az login + env vars)
+- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
 """


 async def main() -> None:
    # 1) Create agents
-    chat_client = AzureChatClient(credential=AzureCliCredential())
+    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())

    writer = chat_client.create_agent(
        instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
--- a/Show More
+++ b/Show More