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Python: Added support for application endpoints in Azure AI client (#2460)

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* Added support for application endpoints in Azure AI client

* Fixed tests

* Update python/samples/getting_started/agents/azure_ai/azure_ai_with_application_endpoint.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Addressed comments

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Dmytro Struk
2025-11-25 11:32:55 -08:00
committed by GitHub
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parent 0c1d12feca
commit 5d8be5836c
4 changed files with 128 additions and 3 deletions
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python/samples/getting_started/agents/azure_ai/README.md
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@@ -16,6 +16,7 @@ This folder contains examples demonstrating different ways to create and use age
| [`azure_ai_with_code_interpreter.py`](azure_ai_with_code_interpreter.py) | Shows how to use the `HostedCodeInterpreterTool` with Azure AI agents to write and execute Python code for mathematical problem solving and data analysis. |
| [`azure_ai_with_existing_agent.py`](azure_ai_with_existing_agent.py) | Shows how to work with a pre-existing agent by providing the agent name and version to the Azure AI client. Demonstrates agent reuse patterns for production scenarios. |
| [`azure_ai_with_existing_conversation.py`](azure_ai_with_existing_conversation.py) | Demonstrates how to use an existing conversation created on the service side with Azure AI agents. Shows two approaches: specifying conversation ID at the client level and using AgentThread with an existing conversation ID. |
| [`azure_ai_with_application_endpoint.py`](azure_ai_with_application_endpoint.py) | Demonstrates calling the Azure AI application-scoped endpoint. |
| [`azure_ai_with_explicit_settings.py`](azure_ai_with_explicit_settings.py) | Shows how to create an agent with explicitly configured `AzureAIClient` settings, including project endpoint, model deployment, and credentials rather than relying on environment variable defaults. |
| [`azure_ai_with_file_search.py`](azure_ai_with_file_search.py) | Shows how to use the `HostedFileSearchTool` with Azure AI agents to upload files, create vector stores, and enable agents to search through uploaded documents to answer user questions. |
| [`azure_ai_with_hosted_mcp.py`](azure_ai_with_hosted_mcp.py) | Shows how to integrate hosted Model Context Protocol (MCP) tools with Azure AI Agent. |
python/samples/getting_started/agents/azure_ai/azure_ai_with_application_endpoint.py
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# Copyright (c) Microsoft. All rights reserved.
import asyncio
import os
from agent_framework import ChatAgent
from agent_framework.azure import AzureAIClient
from azure.ai.projects.aio import AIProjectClient
from azure.identity.aio import AzureCliCredential
"""
Azure AI Agent with Application Endpoint Example
This sample demonstrates working with pre-existing Azure AI Agents by providing
application endpoint instead of project endpoint.
"""
async def main() -> None:
# Create the client
async with (
AzureCliCredential() as credential,
# Endpoint here should be application endpoint with format:
# /api/projects/<project-name>/applications/<application-name>/protocols
AIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=credential) as project_client,
ChatAgent(
chat_client=AzureAIClient(
project_client=project_client,
),
) as agent,
):
query = "How are you?"
print(f"User: {query}")
result = await agent.run(query)
print(f"Agent: {result}\n")
if __name__ == "__main__":
asyncio.run(main())