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Python: (samples): adopt AzureOpenAIResponsesClient, reorganize orchestration examples, and fix workflow/orchestration bugs (#3873)

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* adopt AzureOpenAIResponsesClient, reorganize orchestration examples, and fix workflow/orchestration bugs

* Updates

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This commit is contained in:
Evan Mattson
2026-02-12 19:46:58 +09:00
committed by GitHub
Unverified
parent 8457533c69
commit 1b10b051fd
73 changed files with 1612 additions and 686 deletions
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python/samples/getting_started/workflows/_start-here/step2_agents_in_a_workflow.py
+10 -4
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@@ -1,10 +1,11 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from typing import cast
from agent_framework import AgentResponse, WorkflowBuilder
from agent_framework.azure import AzureOpenAIChatClient
from agent_framework.azure import AzureOpenAIResponsesClient
from azure.identity import AzureCliCredential
"""
@@ -14,11 +15,12 @@ This sample creates two agents: a Writer agent creates or edits content, and a R
evaluates and provides feedback.
Purpose:
Show how to create agents from AzureOpenAIChatClient and use them directly in a workflow. Demonstrate
Show how to create agents from AzureOpenAIResponsesClient and use them directly in a workflow. Demonstrate
how agents can be used in a workflow.
Prerequisites:
- Azure OpenAI configured for AzureOpenAIChatClient with required environment variables.
- 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.
- Basic familiarity with WorkflowBuilder, edges, events, and streaming or non-streaming runs.
"""
@@ -27,7 +29,11 @@ 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.
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 = client.as_agent(
instructions=(
"You are an excellent content writer. You create new content and edit contents based on the feedback."
python/samples/getting_started/workflows/_start-here/step3_streaming.py
+10 -4
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@@ -1,9 +1,10 @@
# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework import AgentResponseUpdate, Message, WorkflowBuilder
from agent_framework.azure import AzureOpenAIChatClient
from agent_framework.azure import AzureOpenAIResponsesClient
from azure.identity import AzureCliCredential
"""
@@ -13,11 +14,12 @@ This sample creates two agents: a Writer agent creates or edits content, and a R
evaluates and provides feedback.
Purpose:
Show how to create agents from AzureOpenAIChatClient and use them directly in a workflow. Demonstrate
Show how to create agents from AzureOpenAIResponsesClient and use them directly in a workflow. Demonstrate
how agents can be used in a workflow.
Prerequisites:
- Azure OpenAI configured for AzureOpenAIChatClient with required environment variables.
- 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.
- Basic familiarity with WorkflowBuilder, executors, edges, events, and streaming runs.
"""
@@ -26,7 +28,11 @@ Prerequisites:
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.
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 = client.as_agent(
instructions=(
"You are an excellent content writer. You create new content and edit contents based on the feedback."