mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Evan Mattson
8e2cc4bedc
* Update workflow orchestration samples to use AzureOpenAIResponsesClient * Fix broken link
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
|
|
|
|
import asyncio
|
|
import os
|
|
from typing import cast
|
|
|
|
from agent_framework import Message
|
|
from agent_framework.azure import AzureOpenAIResponsesClient
|
|
from agent_framework.orchestrations import SequentialBuilder
|
|
from azure.identity import AzureCliCredential
|
|
from dotenv import load_dotenv
|
|
|
|
# Load environment variables from .env file
|
|
load_dotenv()
|
|
|
|
"""
|
|
Sample: Sequential workflow (agent-focused API) with shared conversation context
|
|
|
|
Build a high-level sequential workflow using SequentialBuilder and two domain agents.
|
|
The shared conversation (list[Message]) flows through each participant. Each agent
|
|
appends its assistant message to the context. The workflow outputs the final conversation
|
|
list when complete.
|
|
|
|
Note on internal adapters:
|
|
- Sequential orchestration includes small adapter nodes for input normalization
|
|
("input-conversation"), agent-response conversion ("to-conversation:<participant>"),
|
|
and completion ("complete"). These may appear as ExecutorInvoke/Completed events in
|
|
the stream—similar to how concurrent orchestration includes a dispatcher/aggregator.
|
|
You can safely ignore them when focusing on agent progress.
|
|
|
|
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.
|
|
- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
|
|
"""
|
|
|
|
|
|
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"],
|
|
credential=AzureCliCredential(),
|
|
)
|
|
|
|
writer = client.as_agent(
|
|
instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
|
|
name="writer",
|
|
)
|
|
|
|
reviewer = client.as_agent(
|
|
instructions=("You are a thoughtful reviewer. Give brief feedback on the previous assistant message."),
|
|
name="reviewer",
|
|
)
|
|
|
|
# 2) Build sequential workflow: writer -> reviewer
|
|
workflow = SequentialBuilder(participants=[writer, reviewer]).build()
|
|
|
|
# 3) Run and collect outputs
|
|
outputs: list[list[Message]] = []
|
|
async for event in workflow.run("Write a tagline for a budget-friendly eBike.", stream=True):
|
|
if event.type == "output":
|
|
outputs.append(cast(list[Message], event.data))
|
|
|
|
if outputs:
|
|
print("===== Final Conversation =====")
|
|
for i, msg in enumerate(outputs[-1], start=1):
|
|
name = msg.author_name or ("assistant" if msg.role == "assistant" else "user")
|
|
print(f"{'-' * 60}\n{i:02d} [{name}]\n{msg.text}")
|
|
|
|
"""
|
|
Sample Output:
|
|
|
|
===== Final Conversation =====
|
|
------------------------------------------------------------
|
|
01 [user]
|
|
Write a tagline for a budget-friendly eBike.
|
|
------------------------------------------------------------
|
|
02 [writer]
|
|
Ride farther, spend less—your affordable eBike adventure starts here.
|
|
------------------------------------------------------------
|
|
03 [reviewer]
|
|
This tagline clearly communicates affordability and the benefit of extended travel, making it
|
|
appealing to budget-conscious consumers. It has a friendly and motivating tone, though it could
|
|
be slightly shorter for more punch. Overall, a strong and effective suggestion!
|
|
"""
|
|
|
|
|
|
if __name__ == "__main__":
|
|
asyncio.run(main())
|