Python: [BREAKING] Python: Provider-leading client design & OpenAI package extraction (#4818)

* Python: Provider-leading client design & OpenAI package extraction

Major refactoring of the Python Agent Framework client architecture:

- Extract OpenAI clients into new `agent-framework-openai` package
- Core package no longer depends on openai, azure-identity, azure-ai-projects
- Rename clients for discoverability: OpenAIResponsesClient → OpenAIChatClient,
  OpenAIChatClient → OpenAIChatCompletionClient
- Unify `model_id`/`deployment_name`/`model_deployment_name` → `model` param
- New FoundryChatClient for Azure AI Foundry Responses API
- New FoundryAgent/FoundryAgentClient for connecting to pre-configured Foundry agents
- Remove OpenAIBase/OpenAIConfigMixin from non-deprecated client MRO
- Deprecate AzureOpenAI* clients, AzureAIClient, OpenAIAssistantsClient
- Reorganize samples: azure_openai+azure_ai+azure_ai_agent → azure/
- ADR-0020: Provider-Leading Client Design

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

* fix: missing Agent imports in samples, .model_id → .model in foundry_local sample

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

* fix: CI failures — mypy errors, coverage targets, sample imports

- azure-ai mypy: add type ignores for TypedDict total=, model arg, forward ref
- Coverage: replace core.azure/openai targets with openai package target
- project_provider: add type annotation for opts dict

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

* fix: populate openai .pyi stub, fix broken README links, coverage targets

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

* fixes

* updated observabilitty

* reset azure init.pyi

* fix errors

* updated adr number

* fix foundry local

* fixed not renamed docstrings and comments, and added deprecated markers to old classes

* fix tests and pyprojects

* fix test vars

* updated function tests

* update durable

* updated test setup for functions

* Fix Foundry auth in workflow samples

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

* Stabilize Python integration workflows

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

* Update hosting samples for Foundry

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

* Trigger full CI rerun

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

* Trigger CI rerun again

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

* trigger rerun

* trigger rerun

* fix for litellm

* undo durabletask changes

* Move Foundry APIs into foundry namespace

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

* Fix Foundry pyproject formatting

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

* Split provider samples by Foundry surface

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

* Restore hosting sample requirements

Also fix the Foundry Local sample link after the provider sample move.

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

* updated tests

* udpated foundry integration tests

* removed dist from azurefunctions tests

* Use separate Foundry clients for concurrent agents

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

* fix client setup in azfunc and durable

* disabled two tests

* updated setup for some function and durable tests

* improved azure openai setup with new clients

* ignore deprecated

* fixes

* skip 11

* remove openai assistants int tests

---------

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

python/samples/02-agents/context_providers/mem0/mem0_basic.py

@@ -3,8 +3,8 @@
 import asyncio
 import uuid
 
-from agent_framework import tool
-from agent_framework.azure import AzureAIAgentClient
+from agent_framework import Agent, tool
+from agent_framework.foundry import FoundryChatClient
 from agent_framework.mem0 import Mem0ContextProvider
 from azure.identity.aio import AzureCliCredential
 from dotenv import load_dotenv
@@ -30,19 +30,17 @@ def retrieve_company_report(company_code: str, detailed: bool) -> str:
 
 async def main() -> None:
     """Example of memory usage with Mem0 context provider."""
-
     print("=== Mem0 Context Provider Example ===")
-
     # Each record in Mem0 should be associated with agent_id or user_id or application_id or thread_id.
     # In this example, we associate Mem0 records with user_id.
     user_id = str(uuid.uuid4())
-
     # For Azure authentication, run `az login` command in terminal or replace AzureCliCredential with preferred
     # authentication option.
     # For Mem0 authentication, set Mem0 API key via "api_key" parameter or MEM0_API_KEY environment variable.
     async with (
         AzureCliCredential() as credential,
-        AzureAIAgentClient(credential=credential).as_agent(
+        Agent(
+            client=FoundryChatClient(credential=credential),
             name="FriendlyAssistant",
             instructions="You are a friendly assistant.",
             tools=retrieve_company_report,
@@ -56,33 +54,23 @@ async def main() -> None:
         print(f"User: {query}")
         result = await agent.run(query)
         print(f"Agent: {result}\n")
-
         # Now tell the agent the company code and the report format that you want to use
         # and it should be able to invoke the tool and return the report.
         query = "I always work with CNTS and I always want a detailed report format. Please remember and retrieve it."
-        print(f"User: {query}")
-        result = await agent.run(query)
-        print(f"Agent: {result}\n")
-
         # Mem0 processes and indexes memories asynchronously.
         # Wait for memories to be indexed before querying in a new thread.
         # In production, consider implementing retry logic or using Mem0's
         # eventual consistency handling instead of a fixed delay.
         print("Waiting for memories to be processed...")
         await asyncio.sleep(12)  # Empirically determined delay for Mem0 indexing
-
         print("\nRequest within a new session:")
         # Create a new session for the agent.
         # The new session has no context of the previous conversation.
         session = agent.create_session()
-
         # Since we have the mem0 component in the session, the agent should be able to
         # retrieve the company report without asking for clarification, as it will
         # be able to remember the user preferences from Mem0 component.
-        query = "Please retrieve my company report"
-        print(f"User: {query}")
         result = await agent.run(query, session=session)
-        print(f"Agent: {result}\n")
 
 
 if __name__ == "__main__":