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/workflows/tool-approval/concurrent_builder_tool_approval.py

@@ -5,8 +5,8 @@ from collections.abc import AsyncIterable
 from typing import Annotated
 
 from agent_framework import (
-    ChatMessage,
     Content,
+    Message,
     WorkflowEvent,
     tool,
 )
@@ -91,10 +91,10 @@ def _print_output(event: WorkflowEvent) -> None:
     if not event.data:
         raise ValueError("WorkflowEvent has no data")
 
-    if not isinstance(event.data, list) and not all(isinstance(msg, ChatMessage) for msg in event.data):
-        raise ValueError("WorkflowEvent data is not a list of ChatMessage")
+    if not isinstance(event.data, list) and not all(isinstance(msg, Message) for msg in event.data):
+        raise ValueError("WorkflowEvent data is not a list of Message")
 
-    messages: list[ChatMessage] = event.data  # type: ignore
+    messages: list[Message] = event.data  # type: ignore
 
     print("\n" + "-" * 60)
     print("Workflow completed. Aggregated results from both agents:")
@@ -126,9 +126,9 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
 
 async def main() -> None:
     # 3. Create two agents focused on different stocks but with the same tool sets
-    chat_client = OpenAIChatClient()
+    client = OpenAIChatClient()
 
-    microsoft_agent = chat_client.as_agent(
+    microsoft_agent = client.as_agent(
         name="MicrosoftAgent",
         instructions=(
             "You are a personal trading assistant focused on Microsoft (MSFT). "
@@ -137,7 +137,7 @@ async def main() -> None:
         tools=[get_stock_price, get_market_sentiment, get_portfolio_balance, execute_trade],
     )
 
-    google_agent = chat_client.as_agent(
+    google_agent = client.as_agent(
         name="GoogleAgent",
         instructions=(
             "You are a personal trading assistant focused on Google (GOOGL). "

python/samples/getting_started/workflows/tool-approval/group_chat_builder_tool_approval.py

@@ -5,8 +5,8 @@ from collections.abc import AsyncIterable
 from typing import Annotated, cast
 
 from agent_framework import (
-    ChatMessage,
     Content,
+    Message,
     WorkflowEvent,
     tool,
 )
@@ -105,7 +105,7 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
             # The output of the workflow comes from the orchestrator and it's a list of messages
             print("\n" + "=" * 60)
             print("Workflow summary:")
-            outputs = cast(list[ChatMessage], event.data)
+            outputs = cast(list[Message], event.data)
             for msg in outputs:
                 speaker = msg.author_name or msg.role
                 print(f"[{speaker}]: {msg.text}")
@@ -126,9 +126,9 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
 
 async def main() -> None:
     # 3. Create specialized agents
-    chat_client = OpenAIChatClient()
+    client = OpenAIChatClient()
 
-    qa_engineer = chat_client.as_agent(
+    qa_engineer = client.as_agent(
         name="QAEngineer",
         instructions=(
             "You are a QA engineer responsible for running tests before deployment. "
@@ -137,7 +137,7 @@ async def main() -> None:
         tools=[run_tests],
     )
 
-    devops_engineer = chat_client.as_agent(
+    devops_engineer = client.as_agent(
         name="DevOpsEngineer",
         instructions=(
             "You are a DevOps engineer responsible for deployments. First check staging "

python/samples/getting_started/workflows/tool-approval/sequential_builder_tool_approval.py

@@ -5,8 +5,8 @@ from collections.abc import AsyncIterable
 from typing import Annotated, cast
 
 from agent_framework import (
-    ChatMessage,
     Content,
+    Message,
     WorkflowEvent,
     tool,
 )
@@ -78,7 +78,7 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
             # The output of the workflow comes from the orchestrator and it's a list of messages
             print("\n" + "=" * 60)
             print("Workflow summary:")
-            outputs = cast(list[ChatMessage], event.data)
+            outputs = cast(list[Message], event.data)
             for msg in outputs:
                 speaker = msg.author_name or msg.role
                 print(f"[{speaker}]: {msg.text}")
@@ -99,8 +99,8 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
 
 async def main() -> None:
     # 2. Create the agent with tools (approval mode is set per-tool via decorator)
-    chat_client = OpenAIChatClient()
-    database_agent = chat_client.as_agent(
+    client = OpenAIChatClient()
+    database_agent = client.as_agent(
         name="DatabaseAgent",
         instructions=(
             "You are a database assistant. You can view the database schema and execute "