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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

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* improve foundry language

* Fix A2A Foundry sample regression

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

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Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Eduard van Valkenburg
2026-03-31 22:36:21 +02:00
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python/samples/03-workflows/orchestrations/README.md
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@@ -92,15 +92,17 @@ from agent_framework.orchestrations import (
These may appear in event streams (executor_invoked/executor_completed). They're analogous to concurrent's dispatcher and aggregator and can be ignored if you only care about agent activity.
## Why AzureOpenAIResponsesClient?
## Why FoundryChatClient?
Orchestration samples use `AzureOpenAIResponsesClient` rather than the CRUD-style `AzureAIAgent` client. Orchestrations create agents locally and do not require server-side lifecycle management (create/update/delete). `AzureOpenAIResponsesClient` is a lightweight client that uses the underlying Agent Service V2 (Responses API) for non-CRUD-style agents, which is ideal for orchestration patterns like Sequential, Concurrent, Handoff, GroupChat, and Magentic.
Orchestration samples use `FoundryChatClient` because they create agents locally and do not require
server-side lifecycle management. `FoundryChatClient` is a lightweight, project-backed client that fits
patterns like Sequential, Concurrent, Handoff, GroupChat, and Magentic.
## Environment Variables
Orchestration samples that use `AzureOpenAIResponsesClient` expect:
Orchestration samples that use `FoundryChatClient` expect:
- `AZURE_AI_PROJECT_ENDPOINT` (Azure AI Foundry Agent Service (V2) project endpoint)
- `AZURE_AI_MODEL_DEPLOYMENT_NAME` (model deployment name)
- `FOUNDRY_PROJECT_ENDPOINT` (Azure AI Foundry Agent Service (V2) project endpoint)
- `FOUNDRY_MODEL` (model deployment name)
These values are passed directly into the client constructor via `os.getenv()` in sample code.