Python: Update workflow orchestration samples to use AzureOpenAIResponsesClient (#4285)

* Update workflow orchestration samples to use AzureOpenAIResponsesClient

* Fix broken link

docs/decisions/0001-agent-run-response.md

@@ -498,7 +498,7 @@ We need to decide what AIContent types, each agent response type will be mapped
 | Google ADK | **Approach 1** Both [input and output schemas can be specified for LLM Agents](https://google.github.io/adk-docs/agents/llm-agents/#structuring-data-input_schema-output_schema-output_key) at construction time. This option is specific to this agent type and other agent types do not necessarily support |
 | AWS (Strands) | **Approach 2** Supports a special invocation method called [structured_output](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.structured_output) |
 | LangGraph | **Approach 1** Supports [configuring an agent](https://langchain-ai.github.io/langgraph/agents/agents/?h=structured#6-configure-structured-output) at agent construction time, and a [structured response](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) can be retrieved as a special property on the agent response |
-| Agno | **Approach 1** Supports [configuring an agent](https://docs.agno.com/examples/getting-started/structured-output) at agent construction time |
+| Agno | **Approach 1** Supports [configuring an agent](https://docs.agno.com/input-output/structured-output/agent) at agent construction time |
 | A2A | **Informal Approach 2** Doesn't formally support schema negotiation, but [hints can be provided via metadata](https://a2a-protocol.org/latest/specification/#97-structured-data-exchange-requesting-and-providing-json) at invocation time |
 | Protocol Activity | Supports returning [Complex types](https://github.com/microsoft/Agents/blob/main/specs/activity/protocol-activity.md#complex-types) but no support for requesting a type |
 

python/samples/03-workflows/README.md

@@ -160,6 +160,14 @@ Sequential orchestration uses a few small adapter nodes for plumbing:
   These may appear in event streams (executor_invoked/executor_completed). They're analogous to
   concurrent’s dispatcher and aggregator and can be ignored if you only care about agent activity.
 
+### AzureOpenAIResponsesClient vs AzureAIAgent
+
+Workflow and orchestration samples use `AzureOpenAIResponsesClient` rather than the CRUD-style `AzureAIAgent` client. The key difference:
+
+- **`AzureOpenAIResponsesClient`** — A lightweight client that uses the underlying Agent Service V2 (Responses API) for non-CRUD-style agents. Orchestrations use this client because agents are created locally and do not require server-side lifecycle management (create/update/delete). This is the recommended client for orchestration patterns (Sequential, Concurrent, Handoff, GroupChat, Magentic).
+
+- **`AzureAIAgent`** — A CRUD-style client for server-managed agents. Use this when you need persistent, server-side agent definitions with features like file search, code interpreter sessions, or thread management provided by the Azure AI Agent Service.
+
 ### Environment Variables
 
 Workflow samples that use `AzureOpenAIResponsesClient` expect:

python/samples/03-workflows/declarative/agent_to_function_tool/main.py

@@ -18,10 +18,11 @@ Run with:
 """
 
 import asyncio
+import os
 from pathlib import Path
 from typing import Any
 
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.declarative import WorkflowFactory
 from azure.identity import AzureCliCredential
 from pydantic import BaseModel, Field
@@ -196,8 +197,12 @@ def format_order_confirmation(order_data: dict[str, Any], order_calculation: dic
 
 async def main():
     """Run the agent to function tool workflow."""
-    # Create Azure OpenAI client
-    chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # Create Azure OpenAI Responses client
+    chat_client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
 
     # Create the order analysis agent with structured output
     order_analysis_agent = chat_client.as_agent(

python/samples/03-workflows/declarative/function_tools/README.md

@@ -6,7 +6,7 @@ This sample demonstrates an agent with function tools responding to user queries
 
 The workflow showcases:
 - **Function Tools**: Agent equipped with tools to query menu data
-- **Real Azure OpenAI Agent**: Uses `AzureOpenAIChatClient` to create an agent with tools
+- **Real Azure OpenAI Agent**: Uses `AzureOpenAIResponsesClient` to create an agent with tools
 - **Agent Registration**: Shows how to register agents with the `WorkflowFactory`
 
 ## Tools
@@ -72,7 +72,11 @@ Session Complete
 
 ```python
 # Create the agent with tools
-client = AzureOpenAIChatClient(credential=AzureCliCredential())
+client = AzureOpenAIResponsesClient(
+    project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+    deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+    credential=AzureCliCredential(),
+)
 menu_agent = client.as_agent(
     name="MenuAgent",
     instructions="You are a helpful restaurant menu assistant...",

python/samples/03-workflows/orchestrations/README.md

@@ -92,6 +92,10 @@ from agent_framework.orchestrations import (
 
 These may appear in event streams (executor_invoked/executor_completed). They're analogous to concurrent's dispatcher and aggregator and can be ignored if you only care about agent activity.
 
+## Why AzureOpenAIResponsesClient?
+
+Orchestration samples use `AzureOpenAIResponsesClient` rather than the CRUD-style `AzureAIAgent` client. Orchestrations create agents locally and do not require server-side lifecycle management (create/update/delete). `AzureOpenAIResponsesClient` is a lightweight client that uses the underlying Agent Service V2 (Responses API) for non-CRUD-style agents, which is ideal for orchestration patterns like Sequential, Concurrent, Handoff, GroupChat, and Magentic.
+
 ## Environment Variables
 
 Orchestration samples that use `AzureOpenAIResponsesClient` expect:

python/samples/03-workflows/orchestrations/concurrent_agents.py

@@ -1,10 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 from typing import Any
 
 from agent_framework import Message
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import ConcurrentBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -26,14 +27,20 @@ Demonstrates:
 - Workflow completion when idle with no pending work
 
 Prerequisites:
-- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Familiarity with Workflow events (WorkflowEvent)
 """
 
 
 async def main() -> None:
-    # 1) Create three domain agents using AzureOpenAIChatClient
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # 1) Create three domain agents using AzureOpenAIResponsesClient
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
 
     researcher = client.as_agent(
         instructions=(

python/samples/03-workflows/orchestrations/concurrent_custom_agent_executors.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 from typing import Any
 
 from agent_framework import (
@@ -12,7 +13,7 @@ from agent_framework import (
     WorkflowContext,
     handler,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import ConcurrentBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -29,21 +30,23 @@ and emit AgentExecutorResponse outputs, which allows reuse of the high-level
 ConcurrentBuilder API and the default aggregator.
 
 Demonstrates:
-- Executors that create their Agent in __init__ (via AzureOpenAIChatClient)
+- Executors that create their Agent in __init__ (via AzureOpenAIResponsesClient)
 - A @handler that converts AgentExecutorRequest -> AgentExecutorResponse
 - ConcurrentBuilder(participants=[...]) to build fan-out/fan-in
 - Default aggregator returning list[Message] (one user + one assistant per agent)
 - Workflow completion when all participants become idle
 
 Prerequisites:
-- Azure OpenAI configured for AzureOpenAIChatClient (az login + required env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 
 class ResearcherExec(Executor):
     agent: Agent
 
-    def __init__(self, client: AzureOpenAIChatClient, id: str = "researcher"):
+    def __init__(self, client: AzureOpenAIResponsesClient, id: str = "researcher"):
         self.agent = client.as_agent(
             instructions=(
                 "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
@@ -63,7 +66,7 @@ class ResearcherExec(Executor):
 class MarketerExec(Executor):
     agent: Agent
 
-    def __init__(self, client: AzureOpenAIChatClient, id: str = "marketer"):
+    def __init__(self, client: AzureOpenAIResponsesClient, id: str = "marketer"):
         self.agent = client.as_agent(
             instructions=(
                 "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
@@ -83,7 +86,7 @@ class MarketerExec(Executor):
 class LegalExec(Executor):
     agent: Agent
 
-    def __init__(self, client: AzureOpenAIChatClient, id: str = "legal"):
+    def __init__(self, client: AzureOpenAIResponsesClient, id: str = "legal"):
         self.agent = client.as_agent(
             instructions=(
                 "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
@@ -101,7 +104,11 @@ class LegalExec(Executor):
 
 
 async def main() -> None:
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
 
     researcher = ResearcherExec(client)
     marketer = MarketerExec(client)

python/samples/03-workflows/orchestrations/concurrent_custom_aggregator.py

@@ -1,10 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 from typing import Any
 
 from agent_framework import Message
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import ConcurrentBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -17,7 +18,7 @@ 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 AzureOpenAIChatClient.get_response()
+aggregator with a custom async callback that uses AzureOpenAIResponsesClient.get_response()
 to synthesize a concise, consolidated summary from the experts' outputs.
 The workflow completes when all participants become idle.
 
@@ -28,12 +29,18 @@ Demonstrates:
 - Workflow output yielded with the synthesized summary string
 
 Prerequisites:
-- Azure OpenAI configured for AzureOpenAIChatClient (az login + required env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 
 async def main() -> None:
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
 
     researcher = client.as_agent(
         instructions=(

python/samples/03-workflows/orchestrations/group_chat_agent_manager.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 from typing import cast
 
 from agent_framework import (
@@ -8,7 +9,7 @@ from agent_framework import (
     AgentResponseUpdate,
     Message,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import GroupChatBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -25,7 +26,9 @@ What it does:
 - Coordinates a researcher and writer agent to solve tasks collaboratively
 
 Prerequisites:
-- OpenAI environment variables configured for OpenAIChatClient
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 ORCHESTRATOR_AGENT_INSTRUCTIONS = """
@@ -39,8 +42,12 @@ Guidelines:
 
 
 async def main() -> None:
-    # Create a chat client using Azure OpenAI and Azure CLI credentials for all agents
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # Create a Responses client using Azure OpenAI and Azure CLI credentials for all agents
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
 
     # Orchestrator agent that manages the conversation
     # Note: This agent (and the underlying chat client) must support structured outputs.

python/samples/03-workflows/orchestrations/group_chat_philosophical_debate.py

@@ -2,6 +2,7 @@
 
 import asyncio
 import logging
+import os
 from typing import cast
 
 from agent_framework import (
@@ -9,7 +10,7 @@ from agent_framework import (
     AgentResponseUpdate,
     Message,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import GroupChatBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -38,15 +39,21 @@ Participants represent:
 - Doctor from Scandinavia (public health, equity, societal support)
 
 Prerequisites:
-- OpenAI environment variables configured for OpenAIChatClient
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 # Load environment variables from .env file
 load_dotenv()
 
 
-def _get_chat_client() -> AzureOpenAIChatClient:
-    return AzureOpenAIChatClient(credential=AzureCliCredential())
+def _get_chat_client() -> AzureOpenAIResponsesClient:
+    return AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
 
 
 async def main() -> None:

python/samples/03-workflows/orchestrations/group_chat_simple_selector.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 from typing import cast
 
 from agent_framework import (
@@ -8,7 +9,7 @@ from agent_framework import (
     AgentResponseUpdate,
     Message,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import GroupChatBuilder, GroupChatState
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -24,7 +25,9 @@ What it does:
 - Uses a pure Python function to control speaker selection based on conversation state
 
 Prerequisites:
-- OpenAI environment variables configured for OpenAIChatClient
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 
@@ -36,8 +39,12 @@ def round_robin_selector(state: GroupChatState) -> str:
 
 
 async def main() -> None:
-    # Create a chat client using Azure OpenAI and Azure CLI credentials for all agents
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # Create a Responses client using Azure OpenAI and Azure CLI credentials for all agents
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
 
     # Participant agents
     expert = Agent(

python/samples/03-workflows/orchestrations/handoff_autonomous.py

@@ -2,6 +2,7 @@
 
 import asyncio
 import logging
+import os
 from typing import cast
 
 from agent_framework import (
@@ -10,7 +11,7 @@ from agent_framework import (
     Message,
     resolve_agent_id,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import HandoffBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -28,8 +29,9 @@ Routing Pattern:
     User -> Coordinator -> Specialist (iterates N times) -> Handoff -> Final Output
 
 Prerequisites:
-    - `az login` (Azure CLI authentication)
-    - Environment variables for AzureOpenAIChatClient (AZURE_OPENAI_ENDPOINT, etc.)
+    - AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+    - Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+    - Authentication via azure-identity. Use AzureCliCredential and run `az login` before executing the sample.
 
 Key Concepts:
     - Autonomous interaction mode: agents iterate until they handoff
@@ -41,7 +43,7 @@ load_dotenv()
 
 
 def create_agents(
-    client: AzureOpenAIChatClient,
+    client: AzureOpenAIResponsesClient,
 ) -> tuple[Agent, Agent, Agent]:
     """Create coordinator and specialists for autonomous iteration."""
     coordinator = client.as_agent(
@@ -77,7 +79,11 @@ def create_agents(
 
 async def main() -> None:
     """Run an autonomous handoff workflow with specialist iteration enabled."""
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
     coordinator, research_agent, summary_agent = create_agents(client)
 
     # Build the workflow with autonomous mode

python/samples/03-workflows/orchestrations/handoff_simple.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 from typing import Annotated, cast
 
 from agent_framework import (
@@ -11,7 +12,7 @@ from agent_framework import (
     WorkflowRunState,
     tool,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import HandoffAgentUserRequest, HandoffBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -25,8 +26,9 @@ A handoff workflow defines a pattern that assembles agents in a mesh topology, a
 them to transfer control to each other based on the conversation context.
 
 Prerequisites:
-    - `az login` (Azure CLI authentication)
-    - Environment variables configured for AzureOpenAIChatClient (AZURE_OPENAI_ENDPOINT, etc.)
+    - AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+    - Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+    - Authentication via azure-identity. Use AzureCliCredential and run `az login` before executing the sample.
 
 Key Concepts:
     - Auto-registered handoff tools: HandoffBuilder automatically creates handoff tools
@@ -58,11 +60,11 @@ def process_return(order_number: Annotated[str, "Order number to process return
     return f"Return initiated successfully for order {order_number}. You will receive return instructions via email."
 
 
-def create_agents(client: AzureOpenAIChatClient) -> tuple[Agent, Agent, Agent, Agent]:
+def create_agents(client: AzureOpenAIResponsesClient) -> tuple[Agent, Agent, Agent, Agent]:
     """Create and configure the triage and specialist agents.
 
     Args:
-        client: The AzureOpenAIChatClient to use for creating agents.
+        client: The AzureOpenAIResponsesClient to use for creating agents.
 
     Returns:
         Tuple of (triage_agent, refund_agent, order_agent, return_agent)
@@ -192,8 +194,12 @@ async def main() -> None:
     the demo reproducible and testable. In a production application, you would
     replace the scripted_responses with actual user input collection.
     """
-    # Initialize the Azure OpenAI chat client
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    # Initialize the Azure OpenAI Responses client
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
 
     # Create all agents: triage + specialists
     triage, refund, order, support = create_agents(client)

python/samples/03-workflows/orchestrations/magentic.py

@@ -3,6 +3,7 @@
 import asyncio
 import json
 import logging
+import os
 from typing import cast
 
 from agent_framework import (
@@ -11,8 +12,9 @@ from agent_framework import (
     Message,
     WorkflowEvent,
 )
-from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import GroupChatRequestSentEvent, MagenticBuilder, MagenticProgressLedger
+from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
 
 logging.basicConfig(level=logging.WARNING)
@@ -40,7 +42,9 @@ energy efficiency and CO2 emissions of several ML models, streams intermediate
 events, and prints the final answer. The workflow completes when idle.
 
 Prerequisites:
-- OpenAI credentials configured for `OpenAIChatClient` and `OpenAIResponsesClient`.
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 # Load environment variables from .env file
@@ -48,25 +52,29 @@ load_dotenv()
 
 
 async def main() -> None:
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
+
     researcher_agent = Agent(
         name="ResearcherAgent",
         description="Specialist in research and information gathering",
         instructions=(
             "You are a Researcher. You find information without additional computation or quantitative analysis."
         ),
-        # This agent requires the gpt-4o-search-preview model to perform web searches.
-        client=OpenAIChatClient(model_id="gpt-4o-search-preview"),
+        client=client,
     )
 
     # Create code interpreter tool using instance method
-    coder_client = OpenAIResponsesClient()
-    code_interpreter_tool = coder_client.get_code_interpreter_tool()
+    code_interpreter_tool = client.get_code_interpreter_tool()
 
     coder_agent = Agent(
         name="CoderAgent",
         description="A helpful assistant that writes and executes code to process and analyze data.",
         instructions="You solve questions using code. Please provide detailed analysis and computation process.",
-        client=coder_client,
+        client=client,
         tools=code_interpreter_tool,
     )
 
@@ -75,7 +83,7 @@ async def main() -> None:
         name="MagenticManager",
         description="Orchestrator that coordinates the research and coding workflow",
         instructions="You coordinate a team to complete complex tasks efficiently.",
-        client=OpenAIChatClient(),
+        client=client,
     )
 
     print("\nBuilding Magentic Workflow...")

python/samples/03-workflows/orchestrations/magentic_checkpoint.py

@@ -2,6 +2,7 @@
 
 import asyncio
 import json
+import os
 from datetime import datetime
 from pathlib import Path
 from typing import cast
@@ -14,9 +15,9 @@ from agent_framework import (
     WorkflowEvent,
     WorkflowRunState,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import MagenticBuilder, MagenticPlanReviewRequest
-from azure.identity._credentials import AzureCliCredential
+from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
 
 # Load environment variables from .env file
@@ -38,7 +39,9 @@ Concepts highlighted here:
    `responses` mapping so we can inject the stored human reply during restoration.
 
 Prerequisites:
-- OpenAI environment variables configured for `OpenAIChatClient`.
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 TASK = (
@@ -61,14 +64,22 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
         name="ResearcherAgent",
         description="Collects background facts and references for the project.",
         instructions=("You are the research lead. Gather crisp bullet points the team should know."),
-        client=AzureOpenAIChatClient(credential=AzureCliCredential()),
+        client=AzureOpenAIResponsesClient(
+            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            credential=AzureCliCredential(),
+        ),
     )
 
     writer = Agent(
         name="WriterAgent",
         description="Synthesizes the final brief for stakeholders.",
         instructions=("You convert the research notes into a structured brief with milestones and risks."),
-        client=AzureOpenAIChatClient(credential=AzureCliCredential()),
+        client=AzureOpenAIResponsesClient(
+            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            credential=AzureCliCredential(),
+        ),
     )
 
     # Create a manager agent for orchestration
@@ -76,7 +87,11 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
         name="MagenticManager",
         description="Orchestrator that coordinates the research and writing workflow",
         instructions="You coordinate a team to complete complex tasks efficiently.",
-        client=AzureOpenAIChatClient(credential=AzureCliCredential()),
+        client=AzureOpenAIResponsesClient(
+            project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+            deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            credential=AzureCliCredential(),
+        ),
     )
 
     # The builder wires in the Magentic orchestrator, sets the plan review path, and

python/samples/03-workflows/orchestrations/magentic_human_plan_review.py

@@ -2,6 +2,7 @@
 
 import asyncio
 import json
+import os
 from collections.abc import AsyncIterable
 from typing import cast
 
@@ -11,8 +12,9 @@ from agent_framework import (
     Message,
     WorkflowEvent,
 )
-from agent_framework.openai import OpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import MagenticBuilder, MagenticPlanReviewRequest, MagenticPlanReviewResponse
+from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
 
 # Load environment variables from .env file
@@ -35,7 +37,9 @@ Plan review options:
 - revise(feedback): Provide textual feedback to modify the plan
 
 Prerequisites:
-- OpenAI credentials configured for `OpenAIChatClient`.
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 # Keep track of the last response to format output nicely in streaming mode
@@ -96,25 +100,31 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
 
 
 async def main() -> None:
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
+
     researcher_agent = Agent(
         name="ResearcherAgent",
         description="Specialist in research and information gathering",
         instructions="You are a Researcher. You find information and gather facts.",
-        client=OpenAIChatClient(model_id="gpt-4o"),
+        client=client,
     )
 
     analyst_agent = Agent(
         name="AnalystAgent",
         description="Data analyst who processes and summarizes research findings",
         instructions="You are an Analyst. You analyze findings and create summaries.",
-        client=OpenAIChatClient(model_id="gpt-4o"),
+        client=client,
     )
 
     manager_agent = Agent(
         name="MagenticManager",
         description="Orchestrator that coordinates the workflow",
         instructions="You coordinate a team to complete tasks efficiently.",
-        client=OpenAIChatClient(model_id="gpt-4o"),
+        client=client,
     )
 
     print("\nBuilding Magentic Workflow with Human Plan Review...")

python/samples/03-workflows/orchestrations/sequential_agents.py

@@ -1,10 +1,11 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 from typing import cast
 
 from agent_framework import Message
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import SequentialBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -28,13 +29,19 @@ Note on internal adapters:
   You can safely ignore them when focusing on agent progress.
 
 Prerequisites:
-- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 
 async def main() -> None:
     # 1) Create agents
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
 
     writer = client.as_agent(
         instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),

python/samples/03-workflows/orchestrations/sequential_custom_executors.py

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import os
 from typing import Any
 
 from agent_framework import (
@@ -10,7 +11,7 @@ from agent_framework import (
     WorkflowContext,
     handler,
 )
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.azure import AzureOpenAIResponsesClient
 from agent_framework.orchestrations import SequentialBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -32,7 +33,9 @@ Custom executor contract:
 - Emit the updated conversation via ctx.send_message([...])
 
 Prerequisites:
-- Azure OpenAI access configured for AzureOpenAIChatClient (use az login + env vars)
+- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
 
@@ -62,7 +65,11 @@ class Summarizer(Executor):
 
 async def main() -> None:
     # 1) Create a content agent
-    client = AzureOpenAIChatClient(credential=AzureCliCredential())
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
     content = client.as_agent(
         instructions="Produce a concise paragraph answering the user's request.",
         name="content",