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Python: [BREAKING] Remove deprecated Python OpenAI/Azure AI surfaces (#4990)
* [BREAKING] Remove deprecated Python OpenAI/Azure AI surfaces Also clean up follow-on docs, environment guidance, package metadata, and lab test stability. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix deleted semantic-kernel sample links Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address PR review feedback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * improve foundry language * Fix A2A Foundry sample regression Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Eduard van Valkenburg
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@@ -14,9 +14,9 @@ This gallery helps Semantic Kernel (SK) developers move to the Microsoft Agent F
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### Azure AI agent parity
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### OpenAI Assistants API parity
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- [01_basic_openai_assistant.py](openai_assistant/01_basic_openai_assistant.py) — Baseline assistant comparison.
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- [02_openai_assistant_with_code_interpreter.py](openai_assistant/02_openai_assistant_with_code_interpreter.py) — Code interpreter tool usage.
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- [03_openai_assistant_function_tool.py](openai_assistant/03_openai_assistant_function_tool.py) — Custom function tooling.
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OpenAI Assistants parity samples were removed alongside the deprecated Python assistants surface and are no longer
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part of this migration gallery.
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### OpenAI Responses API parity
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- [01_basic_responses_agent.py](openai_responses/01_basic_responses_agent.py) — Basic responses agent migration.
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@@ -44,7 +44,7 @@ Each script is fully async and the `main()` routine runs both implementations ba
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- Python 3.10 or later.
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- Access to the necessary model endpoints (Azure OpenAI, OpenAI, Azure AI, Copilot Studio, etc.).
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- Installed SDKs: `semantic-kernel` and the Microsoft Agent Framework (`pip install semantic-kernel agent-framework`), or the repo’s editable packages if you are developing locally.
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- Service credentials exposed through environment variables (for example `OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_KEY`, or Copilot Studio auth settings).
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- Service credentials exposed through environment variables (for example `OPENAI_API_KEY`, `AZURE_OPENAI_ENDPOINT`, `AZURE_OPENAI_API_KEY`, or Copilot Studio auth settings).
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## Running Single-Agent Samples
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From the repository root:
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-64
@@ -1,64 +0,0 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "semantic-kernel",
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# ]
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# ///
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# Run with any PEP 723 compatible runner, e.g.:
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# uv run samples/semantic-kernel-migration/openai_assistant/01_basic_openai_assistant.py
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# Copyright (c) Microsoft. All rights reserved.
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"""Create an OpenAI Assistant using SK and Agent Framework."""
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import asyncio
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import os
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from agent_framework import Agent
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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ASSISTANT_MODEL = os.environ.get("OPENAI_ASSISTANT_MODEL", "gpt-4o-mini")
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async def run_semantic_kernel() -> None:
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from semantic_kernel.agents import AssistantAgentThread, OpenAIAssistantAgent
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client = OpenAIAssistantAgent.create_client()
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# Provision the assistant on the OpenAI Assistants service.
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definition = await client.beta.assistants.create(
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model=ASSISTANT_MODEL,
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name="Helper",
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instructions="Answer questions in one concise paragraph.",
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)
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agent = OpenAIAssistantAgent(client=client, definition=definition)
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thread: AssistantAgentThread | None = None
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response = await agent.get_response("What is the capital of Denmark?", thread=thread)
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thread = response.thread
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print("[SK]", response.message.content)
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if thread is not None:
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print("[SK][thread-id]", thread.id)
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async def run_agent_framework() -> None:
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from agent_framework.openai import OpenAIAssistantsClient
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assistants_client = OpenAIAssistantsClient()
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# AF wraps the assistant lifecycle with an async context manager.
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async with Agent(
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client=assistants_client,
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) as assistant_agent:
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session = assistant_agent.create_session()
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reply = await assistant_agent.run("What is the capital of Denmark?", session=session)
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print("[AF]", reply.text)
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follow_up = await assistant_agent.run(
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"How many residents live there?",
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session=session,
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)
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print("[AF][follow-up]", follow_up.text)
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async def main() -> None:
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await run_semantic_kernel()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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-74
@@ -1,74 +0,0 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "semantic-kernel",
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# ]
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# ///
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# Run with any PEP 723 compatible runner, e.g.:
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# uv run samples/semantic-kernel-migration/openai_assistant/02_openai_assistant_with_code_interpreter.py
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# Copyright (c) Microsoft. All rights reserved.
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"""Enable the code interpreter tool for OpenAI Assistants in SK and AF."""
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import asyncio
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from agent_framework import Agent
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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async def run_semantic_kernel() -> None:
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from semantic_kernel.agents import OpenAIAssistantAgent
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from semantic_kernel.connectors.ai.open_ai import OpenAISettings
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client = OpenAIAssistantAgent.create_client()
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code_interpreter_tool, code_interpreter_tool_resources = OpenAIAssistantAgent.configure_code_interpreter_tool()
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# Enable the hosted code interpreter tool on the assistant definition.
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definition = await client.beta.assistants.create(
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model=OpenAISettings().chat_model_id,
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name="CodeRunner",
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instructions="Run the provided request as code and return the result.",
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tools=code_interpreter_tool,
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tool_resources=code_interpreter_tool_resources,
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)
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agent = OpenAIAssistantAgent(client=client, definition=definition)
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response = await agent.get_response(
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"Use Python to calculate the mean of [41, 42, 45] and explain the steps.",
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)
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print(f"[SK]: {response}")
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async def run_agent_framework() -> None:
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from agent_framework.openai import OpenAIAssistantsClient
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assistants_client = OpenAIAssistantsClient()
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# Create code interpreter tool using static method
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code_interpreter_tool = OpenAIAssistantsClient.get_code_interpreter_tool()
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# AF exposes the same tool configuration via create_agent.
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async with Agent(client=assistants_client,
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name="CodeRunner",
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instructions="Use the code interpreter when calculations are required.",
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model="gpt-4.1",
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tools=[code_interpreter_tool],
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) as assistant_agent:
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response = await assistant_agent.run(
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"Use Python to calculate the mean of [41, 42, 45] and explain the steps.",
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tool_choice="auto",
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)
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print(f"[AF]: {response.text}")
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async def main() -> None:
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await run_semantic_kernel()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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-103
@@ -1,103 +0,0 @@
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "semantic-kernel",
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# ]
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# ///
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# Run with any PEP 723 compatible runner, e.g.:
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# uv run samples/semantic-kernel-migration/openai_assistant/03_openai_assistant_function_tool.py
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# Copyright (c) Microsoft. All rights reserved.
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"""Implement a function tool for OpenAI Assistants in SK and AF."""
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import asyncio
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import os
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from typing import Any
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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ASSISTANT_MODEL = os.environ.get("OPENAI_ASSISTANT_MODEL", "gpt-4o-mini")
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async def fake_weather_lookup(city: str, day: str) -> dict[str, Any]:
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"""Pretend to call a weather service."""
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return {
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"city": city,
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"day": day,
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"forecast": "Sunny with scattered clouds",
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"high_c": 22,
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"low_c": 14,
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}
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async def run_semantic_kernel() -> None:
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from semantic_kernel.agents import AssistantAgentThread, OpenAIAssistantAgent
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from semantic_kernel.functions import kernel_function
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class WeatherPlugin:
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@kernel_function(name="get_forecast", description="Look up the forecast for a city and day.")
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async def fake_weather_lookup(self, city: str, day: str) -> dict[str, Any]:
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"""Pretend to call a weather service."""
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return {
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"city": city,
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"day": day,
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"forecast": "Sunny with scattered clouds",
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"high_c": 22,
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"low_c": 14,
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}
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client = OpenAIAssistantAgent.create_client()
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# Tool schema is registered on the assistant definition.
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definition = await client.beta.assistants.create(
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model=ASSISTANT_MODEL,
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name="WeatherHelper",
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instructions="Call get_forecast to fetch weather details.",
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)
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agent = OpenAIAssistantAgent(client=client, definition=definition, plugins=[WeatherPlugin()])
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thread: AssistantAgentThread | None = None
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response = await agent.get_response(
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"What will the weather be like in Seattle tomorrow?",
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thread=thread,
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)
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thread = response.thread
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print("[SK][initial]", response.message.content)
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async def run_agent_framework() -> None:
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from agent_framework import Agent, tool
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from agent_framework.openai import OpenAIAssistantsClient
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@tool(
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name="get_forecast",
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description="Look up the forecast for a city and day.",
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)
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async def get_forecast(city: str, day: str) -> dict[str, Any]:
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return await fake_weather_lookup(city, day)
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assistants_client = OpenAIAssistantsClient()
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# AF converts the decorated function into an assistant-compatible tool.
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async with Agent(client=assistants_client,
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name="WeatherHelper",
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instructions="Call get_forecast to fetch weather details.",
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model=ASSISTANT_MODEL,
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tools=[get_forecast],
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) as assistant_agent:
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reply = await assistant_agent.run(
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"What will the weather be like in Seattle tomorrow?",
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tool_choice="auto",
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)
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print("[AF]", reply.text)
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async def main() -> None:
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await run_semantic_kernel()
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await run_agent_framework()
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if __name__ == "__main__":
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asyncio.run(main())
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+3
-3
@@ -36,11 +36,11 @@ async def run_semantic_kernel() -> None:
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async def run_agent_framework() -> None:
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from agent_framework import Agent
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from agent_framework.openai import OpenAIResponsesClient
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from agent_framework.openai import OpenAIChatClient
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# AF Agent can swap in an OpenAIResponsesClient directly.
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# AF Agent can swap in an OpenAIChatClient directly.
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chat_agent = Agent(
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client=OpenAIResponsesClient(),
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client=OpenAIChatClient(),
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instructions="Answer in one concise sentence.",
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name="Expert",
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)
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+2
-2
@@ -43,14 +43,14 @@ async def run_semantic_kernel() -> None:
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async def run_agent_framework() -> None:
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from agent_framework import Agent, tool
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from agent_framework.openai import OpenAIResponsesClient
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from agent_framework.openai import OpenAIChatClient
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@tool(name="add", description="Add two numbers")
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async def add(a: float, b: float) -> float:
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return a + b
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chat_agent = Agent(
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client=OpenAIResponsesClient(),
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client=OpenAIChatClient(),
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instructions="Use the add tool when math is required.",
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name="MathExpert",
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# AF registers the async function as a tool at construction.
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+2
-2
@@ -46,10 +46,10 @@ async def run_semantic_kernel() -> None:
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async def run_agent_framework() -> None:
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from agent_framework import Agent
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from agent_framework.openai import OpenAIResponsesClient
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from agent_framework.openai import OpenAIChatClient
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chat_agent = Agent(
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client=OpenAIResponsesClient(),
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client=OpenAIChatClient(),
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instructions="Return launch briefs as structured JSON.",
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name="ProductMarketer",
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)
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@@ -16,7 +16,7 @@ from collections.abc import Sequence
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from typing import cast
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from agent_framework import Agent, Message
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.openai import OpenAIChatCompletionClient
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from agent_framework.orchestrations import ConcurrentBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -89,7 +89,7 @@ def _print_semantic_kernel_outputs(outputs: Sequence[ChatMessageContent]) -> Non
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async def run_agent_framework_example(prompt: str) -> Sequence[list[Message]]:
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client = AzureOpenAIChatClient(credential=AzureCliCredential())
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client = OpenAIChatCompletionClient(credential=AzureCliCredential())
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physics = Agent(client=client,
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instructions=("You are an expert in physics. Answer questions from a physics perspective."),
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@@ -15,7 +15,7 @@ import asyncio
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from collections.abc import Sequence
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from agent_framework import Agent
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from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
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from agent_framework.openai import OpenAIChatClient
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from agent_framework.orchestrations import MagenticBuilder
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from dotenv import load_dotenv
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from semantic_kernel.agents import (
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@@ -141,8 +141,8 @@ async def run_agent_framework_example(prompt: str) -> str | None:
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)
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# Create code interpreter tool using static method
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coder_client = OpenAIResponsesClient()
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code_interpreter_tool = OpenAIResponsesClient.get_code_interpreter_tool()
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coder_client = OpenAIChatClient()
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code_interpreter_tool = OpenAIChatClient.get_code_interpreter_tool()
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coder = Agent(
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name="CoderAgent",
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@@ -16,7 +16,7 @@ from collections.abc import Sequence
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from typing import cast
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from agent_framework import Agent, Message
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from agent_framework.azure import AzureOpenAIChatClient
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from agent_framework.openai import OpenAIChatCompletionClient
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from agent_framework.orchestrations import SequentialBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -76,7 +76,7 @@ async def sk_agent_response_callback(
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async def run_agent_framework_example(prompt: str) -> list[Message]:
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client = AzureOpenAIChatClient(credential=AzureCliCredential())
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client = OpenAIChatCompletionClient(credential=AzureCliCredential())
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writer = Agent(client=client,
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instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
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