Python: Fix samples (#4980)

* First samples 1st batch

* Fix sample paths

* Fix workflow samples

* Fix workflow dependency

* Correct env vars

* Increase idle timeout

* Fix workflows HIL sample

* Fix more workflow samples

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

@@ -28,7 +28,7 @@ Demonstrates:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 - Familiarity with Workflow events (WorkflowEvent)
 """
@@ -38,7 +38,7 @@ async def main() -> None:
     # 1) Create three domain agents using FoundryChatClient
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -38,7 +38,7 @@ Demonstrates:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -109,7 +109,7 @@ class LegalExec(Executor):
 async def main() -> None:
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -30,7 +30,7 @@ Demonstrates:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -38,7 +38,7 @@ Prerequisites:
 async def main() -> None:
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -27,7 +27,7 @@ What it does:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -45,7 +45,7 @@ async def main() -> None:
     # Create a Responses client using Azure OpenAI and Azure CLI credentials for all agents
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -40,7 +40,7 @@ Participants represent:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -51,7 +51,7 @@ load_dotenv()
 def _get_chat_client() -> FoundryChatClient:
     return FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -26,7 +26,7 @@ What it does:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -42,7 +42,7 @@ async def main() -> None:
     # Create a Responses client using Azure OpenAI and Azure CLI credentials for all agents
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -84,7 +84,7 @@ async def main() -> None:
     """Run an autonomous handoff workflow with specialist iteration enabled."""
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
     coordinator, research_agent, summary_agent = create_agents(client)

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

@@ -27,7 +27,7 @@ them to transfer control to each other based on the conversation context.
 
 Prerequisites:
     - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-    - Azure OpenAI configured for FoundryChatClient with required environment variables.
+    - FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
     - Authentication via azure-identity. Use AzureCliCredential and run `az login` before executing the sample.
 
 Key Concepts:
@@ -201,7 +201,7 @@ async def main() -> None:
     # Initialize the Azure OpenAI Responses client
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -13,8 +13,8 @@ HandoffBuilder workflows can be properly retrieved.
 
 Prerequisites:
     - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+    - FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
     - `az login` (Azure CLI authentication)
-    - AZURE_AI_MODEL_DEPLOYMENT_NAME
 """
 
 import asyncio
@@ -93,7 +93,7 @@ async def main() -> None:
 
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -46,8 +46,8 @@ Pattern:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Azure CLI authentication (az login).
-- Environment variables configured for FoundryChatClient.
 """
 
 CHECKPOINT_DIR = Path(__file__).parent / "tmp" / "handoff_checkpoints"
@@ -102,7 +102,7 @@ def create_workflow(checkpoint_storage: FileCheckpointStorage) -> Workflow:
 
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
     triage, refund, order = create_agents(client)

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

@@ -43,7 +43,7 @@ events, and prints the final answer. The workflow completes when idle.
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -54,7 +54,7 @@ load_dotenv()
 async def main() -> None:
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -40,7 +40,7 @@ Concepts highlighted here:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -66,7 +66,7 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
         instructions=("You are the research lead. Gather crisp bullet points the team should know."),
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )
@@ -77,7 +77,7 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
         instructions=("You convert the research notes into a structured brief with milestones and risks."),
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )
@@ -89,7 +89,7 @@ def build_workflow(checkpoint_storage: FileCheckpointStorage):
         instructions="You coordinate a team to complete complex tasks efficiently.",
         client=FoundryChatClient(
             project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-            model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+            model=os.environ["FOUNDRY_MODEL"],
             credential=AzureCliCredential(),
         ),
     )

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

@@ -38,7 +38,7 @@ Plan review options:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -102,7 +102,7 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
 async def main() -> None:
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -30,7 +30,7 @@ Note on internal adapters:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -39,7 +39,7 @@ async def main() -> None:
     # 1) Create agents
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 

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

@@ -3,8 +3,8 @@
 import asyncio
 import os
 
-from agent_framework import AgentResponseUpdate
-from agent_framework.azure import AzureOpenAIResponsesClient
+from agent_framework import Agent, AgentResponseUpdate
+from agent_framework.foundry import FoundryChatClient
 from agent_framework.orchestrations import SequentialBuilder
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -25,8 +25,8 @@ Compare with `sequential_agents.py`, which uses the default behavior where the f
 conversation context is passed to each agent.
 
 Prerequisites:
-- AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for AzureOpenAIResponsesClient with required environment variables.
+- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
+- FOUNDRY_MODEL must be the deployment name of a model in your Foundry project.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -36,23 +36,26 @@ load_dotenv()
 
 async def main() -> None:
     # 1) Create agents
-    client = AzureOpenAIResponsesClient(
-        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
-        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
 
-    writer = client.as_agent(
+    writer = Agent(
+        client=client,
         instructions="You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt.",
         name="writer",
     )
 
-    translator = client.as_agent(
+    translator = Agent(
+        client=client,
         instructions="You are a translator. Translate the given text into French. Output only the translation.",
         name="translator",
     )
 
-    reviewer = client.as_agent(
+    reviewer = Agent(
+        client=client,
         instructions="You are a reviewer. Evaluate the quality of the marketing tagline.",
         name="reviewer",
     )

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

@@ -35,7 +35,7 @@ Custom executor contract:
 
 Prerequisites:
 - FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-- Azure OpenAI configured for FoundryChatClient with required environment variables.
+- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
 - Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
 """
 
@@ -68,7 +68,7 @@ async def main() -> None:
     # 1) Create a content agent
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+        model=os.environ["FOUNDRY_MODEL"],
         credential=AzureCliCredential(),
     )
     content = Agent(