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# pylint: disable=line-too-long,useless-suppression
# ------------------------------------
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
# ------------------------------------
"""
DESCRIPTION:
This sample demonstrates how to run basic Prompt Agent operations
using the asynchronous AIProjectClient, while defining a desired
JSON schema for the response ("structured output").
The Responses and Conversations calls in this sample are made using
the OpenAI client from the `openai` package. See https://platform.openai.com/docs/api-reference
for more information.
This sample is inspired by the OpenAI example here:
https://platform.openai.com/docs/guides/structured-outputs/supported-schemas
USAGE:
python sample_agent_structured_output_async.py
Before running the sample:
pip install "azure-ai-projects>=2.0.0b1" openai azure-identity aiohttp python-dotenv pydantic
Set these environment variables with your own values:
1) AZURE_AI_PROJECT_ENDPOINT - The Azure AI Project endpoint, as found in the Overview
page of your Microsoft Foundry portal.
2) AZURE_AI_MODEL_DEPLOYMENT_NAME - The deployment name of the AI model, as found under the "Name" column in
the "Models + endpoints" tab in your Microsoft Foundry project.
"""
import asyncio
import os
from dotenv import load_dotenv
from azure.identity.aio import DefaultAzureCredential
from azure.ai.projects.aio import AIProjectClient
from azure.ai.projects.models import (
PromptAgentDefinition,
PromptAgentDefinitionText,
ResponseTextFormatConfigurationJsonSchema,
)
from pydantic import BaseModel, Field
load_dotenv()
endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
class CalendarEvent(BaseModel):
model_config = {"extra": "forbid"}
name: str
date: str = Field(description="Date in YYYY-MM-DD format")
participants: list[str]
async def main() -> None:
async with (
DefaultAzureCredential() as credential,
AIProjectClient(endpoint=endpoint, credential=credential) as project_client,
project_client.get_openai_client() as openai_client,
):
agent = await project_client.agents.create_version(
agent_name="MyAgent",
definition=PromptAgentDefinition(
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
text=PromptAgentDefinitionText(
format=ResponseTextFormatConfigurationJsonSchema(
name="CalendarEvent", schema=CalendarEvent.model_json_schema()
)
),
instructions="""
You are a helpful assistant that extracts calendar event information from the input user messages,
and returns it in the desired structured output format.
""",
),
)
print(f"Agent created (id: {agent.id}, name: {agent.name}, version: {agent.version})")
conversation = await openai_client.conversations.create(
items=[
{
"type": "message",
"role": "user",
"content": "Alice and Bob are going to a science fair this Friday, November 7, 2025.",
}
],
)
print(f"Created conversation with initial user message (id: {conversation.id})")
response = await openai_client.responses.create(
conversation=conversation.id,
extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
input="",
)
print(f"Response output: {response.output_text}")
await openai_client.conversations.delete(conversation_id=conversation.id)
print("Conversation deleted")
await project_client.agents.delete_version(agent_name=agent.name, agent_version=agent.version)
print("Agent deleted")
if __name__ == "__main__":
asyncio.run(main())
|
