Python: [BREAKING] Simplify API: ChatAgent -> Agent, ChatMessage -> Message (#3747)

* [BREAKING] Rename ChatAgent -> Agent, ChatMessage -> Message, ChatClientProtocol -> SupportsChatGetResponse

Simplify the public API by removing redundant 'Chat' prefix from core types:
- ChatAgent -> Agent
- RawChatAgent -> RawAgent
- ChatMessage -> Message
- ChatClientProtocol -> SupportsChatGetResponse

Also renamed internal WorkflowMessage (was Message in _runner_context) to avoid collision.

No backward compatibility aliases - this is a clean breaking change.

* [BREAKING] Rename Agent chat_client parameter to client

* Fix rebase issues: WorkflowMessage references and broken markdown links

* Fix formatting and lint issues from code quality checks

* Fix import ordering in workflow sample files

* fixed rebase

* Fix test failures: use WorkflowMessage and A2AMessage after ChatMessage→Message rename

- Replace Message(data=..., source_id=...) with WorkflowMessage(...) in workflow tests
- Fix isinstance check in A2A agent to use A2AMessage instead of Message
- Fix import in test_workflow_observability.py (Message→WorkflowMessage)

* Fix lint, fmt, and sample errors after ChatMessage→Message rename

- Auto-fix 70+ ruff lint issues across samples (ChatMessage→Message refs)
- Fix HostedVectorStoreContent→Content.from_hosted_vector_store in file search sample
- Fix _normalize_messages→normalize_messages in custom agent sample
- Fix context.terminate→raise MiddlewareTermination in middleware samples
- Fix with_update_hook→with_transform_hook in override middleware sample
- Add TOptions_co import back to custom_chat_client sample
- Add noqa for FastAPI File() default in chatkit sample
- Fix B023 loop variable capture in weather agent sample

* fix: update Agent constructor calls from chat_client to client in declaration-only tool tests

* fix: add register_cleanup to devui lazy-loading proxy and type stub

* fixed tests and updated new pieces

* fix agui typevar

* fix merge errors

* fix merge conflicts

* fiux merge

* Remove unused links

---------

Co-authored-by: Evan Mattson <evan.mattson@microsoft.com>

python/samples/getting_started/orchestrations/magentic.py

@@ -6,10 +6,10 @@ import logging
 from typing import cast
 
 from agent_framework import (
+    Agent,
     AgentResponseUpdate,
-    ChatAgent,
-    ChatMessage,
     HostedCodeInterpreterTool,
+    Message,
     WorkflowEvent,
 )
 from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
@@ -24,9 +24,9 @@ Sample: Magentic Orchestration (multi-agent)
 What it does:
 - Orchestrates multiple agents using `MagenticBuilder` with streaming callbacks.
 
-- ResearcherAgent (ChatAgent backed by an OpenAI chat client) for
+- ResearcherAgent (Agent backed by an OpenAI chat client) for
     finding information.
-- CoderAgent (ChatAgent backed by OpenAI Assistants with the hosted
+- CoderAgent (Agent backed by OpenAI Assistants with the hosted
     code interpreter tool) for analysis and computation.
 
 The workflow is configured with:
@@ -44,30 +44,30 @@ Prerequisites:
 
 
 async def main() -> None:
-    researcher_agent = ChatAgent(
+    researcher_agent = Agent(
         name="ResearcherAgent",
         description="Specialist in research and information gathering",
         instructions=(
             "You are a Researcher. You find information without additional computation or quantitative analysis."
         ),
         # This agent requires the gpt-4o-search-preview model to perform web searches.
-        chat_client=OpenAIChatClient(model_id="gpt-4o-search-preview"),
+        client=OpenAIChatClient(model_id="gpt-4o-search-preview"),
     )
 
-    coder_agent = ChatAgent(
+    coder_agent = Agent(
         name="CoderAgent",
         description="A helpful assistant that writes and executes code to process and analyze data.",
         instructions="You solve questions using code. Please provide detailed analysis and computation process.",
-        chat_client=OpenAIResponsesClient(),
+        client=OpenAIResponsesClient(),
         tools=HostedCodeInterpreterTool(),
     )
 
     # Create a manager agent for orchestration
-    manager_agent = ChatAgent(
+    manager_agent = Agent(
         name="MagenticManager",
         description="Orchestrator that coordinates the research and coding workflow",
         instructions="You coordinate a team to complete complex tasks efficiently.",
-        chat_client=OpenAIChatClient(),
+        client=OpenAIChatClient(),
     )
 
     print("\nBuilding Magentic Workflow...")
@@ -110,7 +110,7 @@ async def main() -> None:
 
         elif event.type == "magentic_orchestrator":
             print(f"\n[Magentic Orchestrator Event] Type: {event.data.event_type.name}")
-            if isinstance(event.data.content, ChatMessage):
+            if isinstance(event.data.content, Message):
                 print(f"Please review the plan:\n{event.data.content.text}")
             elif isinstance(event.data.content, MagenticProgressLedger):
                 print(f"Please review progress ledger:\n{json.dumps(event.data.content.to_dict(), indent=2)}")
@@ -130,7 +130,7 @@ async def main() -> None:
 
     if output_event:
         # The output of the magentic workflow is a collection of chat messages from all participants
-        outputs = cast(list[ChatMessage], output_event.data)
+        outputs = cast(list[Message], output_event.data)
         print("\n" + "=" * 80)
         print("\nFinal Conversation Transcript:\n")
         for message in outputs: