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>

python/samples/03-workflows/README.md

@@ -160,17 +160,17 @@ Sequential orchestration uses a few small adapter nodes for plumbing:
   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.
 
-### AzureOpenAIResponsesClient vs AzureAIAgent
+### Why FoundryChatClient?
 
-Workflow and orchestration samples use `AzureOpenAIResponsesClient` rather than the CRUD-style `AzureAIAgent` client. The key difference:
+Workflow and orchestration samples use `FoundryChatClient` because they create agents locally and do not need
+server-managed agent resources. This lightweight, project-backed chat client is a good fit for orchestration
+patterns such as Sequential, Concurrent, Handoff, GroupChat, and Magentic.
 
-- **`AzureOpenAIResponsesClient`** — A lightweight client that uses the underlying Agent Service V2 (Responses API) for non-CRUD-style agents. Orchestrations use this client because agents are created locally and do not require server-side lifecycle management (create/update/delete). This is the recommended client for orchestration patterns (Sequential, Concurrent, Handoff, GroupChat, Magentic).
-
-- **`AzureAIAgent`** — A CRUD-style client for server-managed agents. Use this when you need persistent, server-side agent definitions with features like file search, code interpreter sessions, or thread management provided by the Azure AI Agent Service.
+If you need persistent server-side agent resources, use the hosted-agent flows rather than these workflow samples.
 
 ### Environment Variables
 
-Workflow samples that use `AzureOpenAIResponsesClient` expect:
+Workflow samples that use `FoundryChatClient` expect:
 
 - `FOUNDRY_PROJECT_ENDPOINT` (Azure AI Foundry Agent Service (V2) project endpoint)
 - `FOUNDRY_MODEL` (model deployment name)

python/samples/03-workflows/declarative/function_tools/README.md

@@ -6,7 +6,7 @@ This sample demonstrates an agent with function tools responding to user queries
 
 The workflow showcases:
 - **Function Tools**: Agent equipped with tools to query menu data
-- **Real Azure OpenAI Agent**: Uses `AzureOpenAIResponsesClient` to create an agent with tools
+- **Real Foundry-backed agent**: Uses `FoundryChatClient` to create an agent with tools
 - **Agent Registration**: Shows how to register agents with the `WorkflowFactory`
 
 ## Tools
@@ -37,7 +37,7 @@ Drinks:
 
 ## Prerequisites
 
-- Azure OpenAI configured with required environment variables
+- Microsoft Foundry configured with required environment variables
 - Authentication via azure-identity (run `az login` before executing)
 
 ## Usage
@@ -65,16 +65,16 @@ Session Complete
 
 ## How It Works
 
-1. Create an Azure OpenAI chat client
+1. Create a Foundry chat client
 2. Create an agent with instructions and function tools
 3. Register the agent with the workflow factory
 4. Load the workflow YAML and run it with `run()` and `stream=True`
 
 ```python
 # Create the agent with tools
-client = AzureOpenAIResponsesClient(
-    project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
-    deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
+client = FoundryChatClient(
+    project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+    model=os.environ["FOUNDRY_MODEL"],
     credential=AzureCliCredential(),
 )
 menu_agent = client.as_agent(

python/samples/03-workflows/orchestrations/README.md

@@ -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.