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Python: [BREAKING] Remove deprecated Python OpenAI/Azure AI surfaces (#4990)

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* [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>
This commit is contained in:
Eduard van Valkenburg
2026-03-31 20:36:21 +00:00
committed by GitHub
co-authored by Copilot
parent a5eacbbe65
commit 3a49b1d6dd
144 changed files with 669 additions and 18739 deletions
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python/samples/05-end-to-end/ag_ui_workflow_handoff/README.md
+2 -2
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@@ -27,8 +27,8 @@ The backend uses Azure OpenAI responses and supports intent-driven, non-linear h
- Node.js 18+
- npm 9+
- Azure AI project + model deployment configured in environment variables:
- `AZURE_AI_PROJECT_ENDPOINT`
- `AZURE_AI_MODEL_DEPLOYMENT_NAME`
- `FOUNDRY_PROJECT_ENDPOINT`
- `FOUNDRY_MODEL`
## 1) Run Backend
python/samples/05-end-to-end/ag_ui_workflow_handoff/backend/server.py
+1 -1
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@@ -85,7 +85,7 @@ def create_agents() -> tuple[Agent, Agent, Agent]:
client = FoundryChatClient(
project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
model=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
model=os.environ["FOUNDRY_MODEL"],
credential=AzureCliCredential(),
)
python/samples/05-end-to-end/chatkit-integration/README.md
+1 -1
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@@ -177,7 +177,7 @@ pip install agent-framework-chatkit fastapi uvicorn azure-identity
```bash
export AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
export AZURE_OPENAI_API_VERSION="2024-06-01"
export AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="gpt-4o"
export AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o"
```
3. **Authenticate with Azure:**
python/samples/05-end-to-end/evaluation/red_teaming/.env.example
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@@ -5,4 +5,4 @@ AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o
# Azure AI Project Configuration (for red teaming)
# Create these resources at: https://portal.azure.com
AZURE_AI_PROJECT_ENDPOINT=your-ai-project-name
FOUNDRY_PROJECT_ENDPOINT=your-ai-project-name
python/samples/05-end-to-end/evaluation/red_teaming/README.md
+4 -4
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@@ -11,7 +11,7 @@ For more details on the Red Team setup see [the Azure AI Foundry docs](https://l
A focused sample demonstrating Azure AI's RedTeam functionality to assess the safety and resilience of Agent Framework agents against adversarial attacks.
**What it demonstrates:**
1. Creating a financial advisor agent inline using `AzureOpenAIChatClient`
1. Creating a financial advisor agent inline using `FoundryChatClient`
2. Setting up an async callback to interface the agent with RedTeam evaluator
3. Running comprehensive evaluations with 11 different attack strategies:
- Basic: EASY and MODERATE difficulty levels
@@ -47,7 +47,7 @@ AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4o
# AZURE_OPENAI_API_KEY is optional if using Azure CLI authentication
# Azure AI Project (for red teaming)
AZURE_AI_PROJECT_ENDPOINT=https://your-project.api.azureml.ms
FOUNDRY_PROJECT_ENDPOINT=https://your-project.api.azureml.ms
```
See `.env.example` for a template.
@@ -113,7 +113,7 @@ async def main() -> None:
credential = AzureCliCredential()
# 2. Create agent inline
agent = AzureOpenAIChatClient(credential=credential).as_agent(
agent = FoundryChatClient(credential=credential).as_agent(
model="gpt-4o",
instructions="You are a helpful financial advisor..."
)
@@ -125,7 +125,7 @@ async def main() -> None:
# 4. Run red team scan with multiple strategies
red_team = RedTeam(
azure_ai_project=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
azure_ai_project=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
credential=credential
)
results = await red_team.scan(
python/samples/05-end-to-end/hosted_agents/README.md
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@@ -10,7 +10,7 @@ These samples demonstrate how to build and host AI agents in Python using the [A
| [`agent_with_text_search_rag`](./agent_with_text_search_rag/) | Retrieval-augmented generation using a custom `BaseContextProvider` with Contoso Outdoors sample data |
| [`agents_in_workflow`](./agents_in_workflow/) | Concurrent workflow that combines researcher, marketer, and legal specialist agents |
| [`agent_with_local_tools`](./agent_with_local_tools/) | Local Python tool execution for Seattle hotel search |
| [`writer_reviewer_agents_in_workflow`](./writer_reviewer_agents_in_workflow/) | Writer/Reviewer workflow using `AzureOpenAIResponsesClient` |
| [`writer_reviewer_agents_in_workflow`](./writer_reviewer_agents_in_workflow/) | Writer/Reviewer workflow using `FoundryChatClient` |
## Common Prerequisites
@@ -76,14 +76,14 @@ Example `.env` for Azure OpenAI samples:
```dotenv
AZURE_OPENAI_ENDPOINT=https://<your-openai-resource>.openai.azure.com/
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=gpt-4.1
AZURE_OPENAI_DEPLOYMENT_NAME=gpt-4.1
```
Example `.env` for Foundry project samples:
```dotenv
PROJECT_ENDPOINT=https://<your-resource>.services.ai.azure.com/api/projects/<your-project>
MODEL_DEPLOYMENT_NAME=gpt-4.1
FOUNDRY_PROJECT_ENDPOINT=https://<your-resource>.services.ai.azure.com/api/projects/<your-project>
FOUNDRY_MODEL=gpt-4.1
```
## Interacting with the Agent
python/samples/05-end-to-end/hosted_agents/agent_with_hosted_mcp/agent.yaml
+1 -1
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@@ -22,7 +22,7 @@ template:
environment_variables:
- name: AZURE_OPENAI_ENDPOINT
value: ${AZURE_OPENAI_ENDPOINT}
- name: AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
- name: AZURE_OPENAI_DEPLOYMENT_NAME
value: "{{chat}}"
resources:
- kind: model
python/samples/05-end-to-end/hosted_agents/agent_with_text_search_rag/agent.yaml
+1 -1
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@@ -25,7 +25,7 @@ template:
environment_variables:
- name: AZURE_OPENAI_ENDPOINT
value: ${AZURE_OPENAI_ENDPOINT}
- name: AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
- name: AZURE_OPENAI_DEPLOYMENT_NAME
value: "{{chat}}"
resources:
- kind: model
python/samples/05-end-to-end/hosted_agents/agents_in_workflow/agent.yaml
+1 -1
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@@ -20,7 +20,7 @@ template:
environment_variables:
- name: AZURE_OPENAI_ENDPOINT
value: ${AZURE_OPENAI_ENDPOINT}
- name: AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
- name: AZURE_OPENAI_DEPLOYMENT_NAME
value: "{{chat}}"
resources:
- kind: model
python/samples/05-end-to-end/workflow_evaluation/.env.example
+3 -3
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@@ -1,3 +1,3 @@
AZURE_AI_PROJECT_ENDPOINT="<your-project-endpoint>"
AZURE_AI_MODEL_DEPLOYMENT_NAME_WORKFLOW="<your-model-deployment>"
AZURE_AI_MODEL_DEPLOYMENT_NAME_EVAL="<your-model-deployment>"
FOUNDRY_PROJECT_ENDPOINT="<your-project-endpoint>"
FOUNDRY_MODEL_WORKFLOW="<your-model-deployment>"
FOUNDRY_MODEL_EVAL="<your-model-deployment>"
python/samples/05-end-to-end/workflow_evaluation/run_evaluation.py
+3 -3
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@@ -99,7 +99,7 @@ def fetch_agent_responses(openai_client: OpenAI, workflow_data: dict[str, Any],
def create_evaluation(openai_client: OpenAI, deployment_name: str | None = "gpt-5.2") -> EvalCreateResponse:
"""Create evaluation with multiple evaluators."""
deployment_name = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME", deployment_name)
deployment_name = os.environ.get("FOUNDRY_MODEL", deployment_name)
data_source_config = {"type": "azure_ai_source", "scenario": "responses"}
testing_criteria = [
@@ -199,8 +199,8 @@ async def main():
openai_client = create_openai_client()
# Model configuration
workflow_agent_model = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME_WORKFLOW", "gpt-4.1-nano")
eval_model = os.environ.get("AZURE_AI_MODEL_DEPLOYMENT_NAME_EVAL", "gpt-5.2")
workflow_agent_model = os.environ.get("FOUNDRY_MODEL_WORKFLOW", "gpt-4.1-nano")
eval_model = os.environ.get("FOUNDRY_MODEL_EVAL", "gpt-5.2")
# Focus on these agents, uncomment other ones you want to have evals run on
agents_to_evaluate = [