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/demos/chatkit-integration/README.md

@@ -38,7 +38,7 @@ graph TB
         subgraph Integration["Agent Framework Integration"]
             Converter[ThreadItemConverter]
             Streamer[stream_agent_response]
-            Agent[ChatAgent]
+            Agent[Agent]
         end
 
         Widgets[Widget Rendering<br/>render_weather_widget<br/>render_city_selector_widget]
@@ -61,7 +61,7 @@ graph TB
     AttStore -.->|save files| Files
     AttStore -.->|save metadata| SQLite
 
-    Converter -->|ChatMessage array| Agent
+    Converter -->|Message array| Agent
     Agent -->|AgentResponseUpdate| Streamer
     Streamer -->|ThreadStreamEvent| ChatKit
 
@@ -88,7 +88,7 @@ The sample implements a ChatKit server using the `ChatKitServer` base class from
 - **`WeatherChatKitServer`**: Custom ChatKit server implementation that:
 
   - Extends `ChatKitServer[dict[str, Any]]`
-  - Uses Agent Framework's `ChatAgent` with Azure OpenAI
+  - Uses Agent Framework's `Agent` with Azure OpenAI
   - Converts ChatKit messages to Agent Framework format using `ThreadItemConverter`
   - Streams responses back to ChatKit using `stream_agent_response`
   - Creates and streams interactive widgets after agent responses

python/samples/demos/chatkit-integration/app.py

@@ -28,7 +28,7 @@ from typing import Annotated, Any
 import uvicorn
 
 # Agent Framework imports
-from agent_framework import AgentResponseUpdate, ChatAgent, ChatMessage, tool
+from agent_framework import Agent, AgentResponseUpdate, FunctionResultContent, Message, Role, tool
 from agent_framework.azure import AzureOpenAIChatClient
 
 # Agent Framework ChatKit integration
@@ -217,8 +217,8 @@ class WeatherChatKitServer(ChatKitServer[dict[str, Any]]):
         # Create Agent Framework agent with Azure OpenAI
         # For authentication, run `az login` command in terminal
         try:
-            self.weather_agent = ChatAgent(
-                chat_client=AzureOpenAIChatClient(credential=AzureCliCredential()),
+            self.weather_agent = Agent(
+                client=AzureOpenAIChatClient(credential=AzureCliCredential()),
                 instructions=(
                     "You are a helpful weather assistant with image analysis capabilities. "
                     "You can provide weather information for any location, tell the current time, "
@@ -290,8 +290,8 @@ class WeatherChatKitServer(ChatKitServer[dict[str, Any]]):
             conversation_context = "\n".join(user_messages[:3])
 
             title_prompt = [
-                ChatMessage(
-                    role="user",
+                Message(
+                    role=Role.USER,
                     text=(
                         f"Generate a very short, concise title (max 40 characters) for a conversation "
                         f"that starts with:\n\n{conversation_context}\n\n"
@@ -301,7 +301,7 @@ class WeatherChatKitServer(ChatKitServer[dict[str, Any]]):
             ]
 
             # Use the chat client directly for a quick, lightweight call
-            response = await self.weather_agent.chat_client.get_response(
+            response = await self.weather_agent.client.get_response(
                 messages=title_prompt,
                 options={
                     "temperature": 0.3,
@@ -342,6 +342,7 @@ class WeatherChatKitServer(ChatKitServer[dict[str, Any]]):
         runs the agent, converts the response back to ChatKit events using stream_agent_response,
         and creates interactive weather widgets when weather data is queried.
         """
+        from agent_framework import FunctionResultContent
 
         if input_user_message is None:
             logger.debug("Received None user message, skipping")
@@ -384,7 +385,7 @@ class WeatherChatKitServer(ChatKitServer[dict[str, Any]]):
                     # Check for function results in the update
                     if update.contents:
                         for content in update.contents:
-                            if content.type == "function_result":
+                            if isinstance(content, FunctionResultContent):
                                 result = content.result
 
                                 # Check if it's a WeatherResponse (string subclass with weather_data attribute)
@@ -467,7 +468,7 @@ class WeatherChatKitServer(ChatKitServer[dict[str, Any]]):
             weather_data: WeatherData | None = None
 
             # Create an agent message asking about the weather
-            agent_messages = [ChatMessage(role="user", text=f"What's the weather in {city_label}?")]
+            agent_messages = [Message(role=Role.USER, text=f"What's the weather in {city_label}?")]
 
             logger.debug(f"Processing weather query: {agent_messages[0].text}")
 
@@ -481,7 +482,7 @@ class WeatherChatKitServer(ChatKitServer[dict[str, Any]]):
                     # Check for function results in the update
                     if update.contents:
                         for content in update.contents:
-                            if content.type == "function_result":
+                            if isinstance(content, FunctionResultContent):
                                 result = content.result
 
                                 # Check if it's a WeatherResponse (string subclass with weather_data attribute)
@@ -572,7 +573,7 @@ async def chatkit_endpoint(request: Request):
 
 
 @app.post("/upload/{attachment_id}")
-async def upload_file(attachment_id: str, file: Annotated[UploadFile, File()]):
+async def upload_file(attachment_id: str, file: UploadFile = File(...)):  # noqa: B008
     """Handle file upload for two-phase upload.
 
     The client POSTs the file bytes here after creating the attachment
@@ -594,7 +595,7 @@ async def upload_file(attachment_id: str, file: Annotated[UploadFile, File()]):
         attachment = await data_store.load_attachment(attachment_id, {"user_id": DEFAULT_USER_ID})
 
         # Clear the upload_url since upload is complete
-        attachment.upload_url = None  # type: ignore[union-attr]
+        attachment.upload_url = None
 
         # Save the updated attachment back to the store
         await data_store.save_attachment(attachment, {"user_id": DEFAULT_USER_ID})

python/samples/demos/hosted_agents/agent_with_text_search_rag/main.py

@@ -6,7 +6,7 @@ from collections.abc import MutableSequence
 from dataclasses import dataclass
 from typing import Any
 
-from agent_framework import ChatMessage, Context, ContextProvider
+from agent_framework import Context, ContextProvider, Message
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.ai.agentserver.agentframework import from_agent_framework  # pyright: ignore[reportUnknownVariableType]
 from azure.identity import DefaultAzureCredential
@@ -27,16 +27,16 @@ class TextSearchResult:
 class TextSearchContextProvider(ContextProvider):
     """A simple context provider that simulates text search results based on keywords in the user's message."""
 
-    def _get_most_recent_message(self, messages: ChatMessage | MutableSequence[ChatMessage]) -> ChatMessage:
+    def _get_most_recent_message(self, messages: Message | MutableSequence[Message]) -> Message:
         """Helper method to extract the most recent message from the input."""
-        if isinstance(messages, ChatMessage):
+        if isinstance(messages, Message):
             return messages
         if messages:
             return messages[-1]
         raise ValueError("No messages provided")
 
     @override
-    async def invoking(self, messages: ChatMessage | MutableSequence[ChatMessage], **kwargs: Any) -> Context:
+    async def invoking(self, messages: Message | MutableSequence[Message], **kwargs: Any) -> Context:
         message = self._get_most_recent_message(messages)
         query = message.text.lower()
 
@@ -84,7 +84,7 @@ class TextSearchContextProvider(ContextProvider):
 
         return Context(
             messages=[
-                ChatMessage(
+                Message(
                     role="user", text="\n\n".join(json.dumps(result.__dict__, indent=2) for result in results)
                 )
             ]

python/samples/demos/m365-agent/m365_agent_demo/app.py

@@ -18,7 +18,7 @@ from dataclasses import dataclass
 from random import randint
 from typing import Annotated
 
-from agent_framework import ChatAgent, tool
+from agent_framework import Agent, tool
 from agent_framework.openai import OpenAIChatClient
 from aiohttp import web
 from aiohttp.web_middlewares import middleware
@@ -95,7 +95,7 @@ def get_weather(
     return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
 
 
-def build_agent() -> ChatAgent:
+def build_agent() -> Agent:
     """Create and return the chat agent instance with weather tool registered."""
     return OpenAIChatClient().as_agent(
         name="WeatherAgent", instructions="You are a helpful weather agent.", tools=get_weather

python/samples/demos/workflow_evaluation/create_workflow.py

@@ -48,8 +48,8 @@ from _tools import (
 from agent_framework import (
     AgentExecutorResponse,
     AgentResponseUpdate,
-    ChatMessage,
     Executor,
+    Message,
     WorkflowBuilder,
     WorkflowContext,
     executor,
@@ -65,17 +65,17 @@ load_dotenv()
 
 
 @executor(id="start_executor")
-async def start_executor(input: str, ctx: WorkflowContext[list[ChatMessage]]) -> None:
+async def start_executor(input: str, ctx: WorkflowContext[list[Message]]) -> None:
     """Initiates the workflow by sending the user query to all specialized agents."""
-    await ctx.send_message([ChatMessage("user", [input])])
+    await ctx.send_message([Message("user", [input])])
 
 
 class ResearchLead(Executor):
     """Aggregates and summarizes travel planning findings from all specialized agents."""
 
-    def __init__(self, chat_client: AzureAIClient, id: str = "travel-planning-coordinator"):
+    def __init__(self, client: AzureAIClient, id: str = "travel-planning-coordinator"):
         # store=True to preserve conversation history for evaluation
-        self.agent = chat_client.as_agent(
+        self.agent = client.as_agent(
             id="travel-planning-coordinator",
             instructions=(
                 "You are the final coordinator. You will receive responses from multiple agents: "
@@ -102,11 +102,11 @@ class ResearchLead(Executor):
 
         # Generate comprehensive travel plan summary
         messages = [
-            ChatMessage(
+            Message(
                 role="system",
                 text="You are a travel planning coordinator. Summarize findings from multiple specialized travel agents and provide a clear, comprehensive travel plan based on the user's query.",
             ),
-            ChatMessage(
+            Message(
                 role="user",
                 text=f"Original query: {user_query}\n\nFindings from specialized travel agents:\n{summary_text}\n\nPlease provide a comprehensive travel plan based on these findings.",
             ),
@@ -142,17 +142,17 @@ class ResearchLead(Executor):
         return agent_findings
 
 
-async def run_workflow_with_response_tracking(query: str, chat_client: AzureAIClient | None = None) -> dict:
+async def run_workflow_with_response_tracking(query: str, client: AzureAIClient | None = None) -> dict:
     """Run multi-agent workflow and track conversation IDs, response IDs, and interaction sequence.
 
     Args:
         query: The user query to process through the multi-agent workflow
-        chat_client: Optional AzureAIClient instance
+        client: Optional AzureAIClient instance
 
     Returns:
         Dictionary containing interaction sequence, conversation/response IDs, and conversation analysis
     """
-    if chat_client is None:
+    if client is None:
         try:
             async with DefaultAzureCredential() as credential:
                 # Create AIProjectClient with the correct API version for V2 prompt agents
@@ -171,10 +171,10 @@ async def run_workflow_with_response_tracking(query: str, chat_client: AzureAICl
             print(f"Error during workflow execution: {e}")
             raise
     else:
-        return await _run_workflow_with_client(query, chat_client)
+        return await _run_workflow_with_client(query, client)
 
 
-async def _run_workflow_with_client(query: str, chat_client: AzureAIClient) -> dict:
+async def _run_workflow_with_client(query: str, client: AzureAIClient) -> dict:
     """Execute workflow with given client and track all interactions."""
 
     # Initialize tracking variables - use lists to track multiple responses per agent
@@ -184,7 +184,7 @@ async def _run_workflow_with_client(query: str, chat_client: AzureAIClient) -> d
 
     # Create workflow components and keep agent references
     # Pass project_client and credential to create separate client instances per agent
-    workflow, agent_map = await _create_workflow(chat_client.project_client, chat_client.credential)
+    workflow, agent_map = await _create_workflow(client.project_client, client.credential)
 
     # Process workflow events
     events = workflow.run(query, stream=True)
@@ -210,7 +210,7 @@ async def _create_workflow(project_client, credential):
     final_coordinator_client = AzureAIClient(
         project_client=project_client, credential=credential, agent_name="final-coordinator"
     )
-    final_coordinator = ResearchLead(chat_client=final_coordinator_client, id="final-coordinator")
+    final_coordinator = ResearchLead(client=final_coordinator_client, id="final-coordinator")
 
     # Agent 1: Travel Request Handler (initial coordinator)
     # Create separate client with unique agent_name