Python: fix: use workflow factory to avoid RuntimeError under parallel requests

python/samples/05-end-to-end/hosted_agents/writer_reviewer_agents_in_workflow/main.py

@@ -0,0 +1,54 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import os
+
+from agent_framework import WorkflowBuilder
+from agent_framework.azure import AzureOpenAIResponsesClient
+from azure.ai.agentserver.agentframework import from_agent_framework
+from azure.identity import DefaultAzureCredential  # pyright: ignore[reportUnknownVariableType]
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Configure these for your Foundry project
+PROJECT_ENDPOINT = os.getenv(
+    "PROJECT_ENDPOINT"
+)  # e.g., "https://<project>.services.ai.azure.com/api/projects/<project-name>"
+MODEL_DEPLOYMENT_NAME = os.getenv(
+    "MODEL_DEPLOYMENT_NAME", "gpt-4.1-mini"
+)  # Your model deployment name e.g., "gpt-4.1-mini"
+
+
+def main():
+    """
+    The writer and reviewer multi-agent workflow.
+
+    Environment variables required:
+    - PROJECT_ENDPOINT: Your Microsoft Foundry project endpoint
+    - MODEL_DEPLOYMENT_NAME: Your Microsoft Foundry model deployment name
+    """
+    client = AzureOpenAIResponsesClient(
+        project_endpoint=PROJECT_ENDPOINT,
+        deployment_name=MODEL_DEPLOYMENT_NAME,
+        credential=DefaultAzureCredential(),
+    )
+    writer = client.as_agent(
+        name="Writer",
+        instructions="You are an excellent content writer. You create new content and edit contents based on the feedback.",
+    )
+    reviewer = client.as_agent(
+        name="Reviewer",
+        instructions="You are an excellent content reviewer. Provide actionable feedback to the writer about the provided content in the most concise manner possible.",
+    )
+
+    # Build the workflow and convert to agent
+    workflow = WorkflowBuilder(start_executor=writer).add_edge(writer, reviewer).build()
+    workflow_agent = workflow.as_agent()
+
+    # Run the agent as a hosted agent
+    from_agent_framework(workflow_agent).run()
+
+
+if __name__ == "__main__":
+    main()