@@ -69,7 +69,7 @@ def equal(arg1: str, arg2: str) -> bool:
 
 ```python
 # Core
-from agent_framework import ChatAgent, ChatMessage, tool
+from agent_framework import ChatAgent, Message, tool
 
 # Components
 from agent_framework.observability import enable_instrumentation
@@ -84,10 +84,10 @@ from agent_framework.azure import AzureOpenAIChatClient
 Define `__all__` in each module. Avoid `from module import *` in `__init__.py` files:
 
 ```python
-__all__ = ["ChatAgent", "ChatMessage", "ChatResponse"]
+__all__ = ["ChatAgent", "Message", "ChatResponse"]
 
 from ._agents import ChatAgent
-from ._types import ChatMessage, ChatResponse
+from ._types import Message, ChatResponse
 ```
 
 ## Performance Guidelines
 
@@ -10,6 +10,13 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 
 - **agent-framework-core**: Add long-running agents and background responses support with `ContinuationToken` TypedDict, `background` option in `OpenAIResponsesOptions`, and continuation token propagation through response types ([#2478](https://github.com/microsoft/agent-framework/issues/2478))
+### Changed
+
+- **agent-framework-core**: [BREAKING] Renamed core types for simpler API:
+  - `ChatAgent` → `Agent`
+  - `RawChatAgent` → `RawAgent`
+  - `ChatMessage` → `Message`
+  - `ChatClientProtocol` → `SupportsChatGetResponse`
 
 ## [1.0.0b260130] - 2026-01-30
 
@@ -272,7 +279,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Changed
 
 - **agent-framework-core**: [BREAKING] Support Magentic agent tool call approvals and plan stalling HITL behavior (#2569)
-- **agent-framework-core**: [BREAKING] Standardize orchestration outputs as list of `ChatMessage`; allow agent as group chat manager (#2291)
+- **agent-framework-core**: [BREAKING] Standardize orchestration outputs as list of `Message`; allow agent as group chat manager (#2291)
 - **agent-framework-core**: [BREAKING] Respond with `AgentRunResponse` including serialized structured output (#2285)
 - **observability**: Use `executor_id` and `edge_group_id` as span names for clearer traces (#2538)
 - **agent-framework-devui**: Add multimodal input support for workflows and refactor chat input (#2593)
@@ -318,7 +325,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - **agent-framework-core**: Fix tool execution bleed-over in aiohttp/Bot Framework scenarios ([#2314](https://github.com/microsoft/agent-framework/pull/2314))
 - **agent-framework-core**: `@ai_function` now correctly handles `self` parameter ([#2266](https://github.com/microsoft/agent-framework/pull/2266))
 - **agent-framework-core**: Resolve string annotations in `FunctionExecutor` ([#2308](https://github.com/microsoft/agent-framework/pull/2308))
-- **agent-framework-core**: Langfuse observability captures ChatAgent system instructions ([#2316](https://github.com/microsoft/agent-framework/pull/2316))
+- **agent-framework-core**: Langfuse observability captures Agent system instructions ([#2316](https://github.com/microsoft/agent-framework/pull/2316))
 - **agent-framework-core**: Incomplete URL substring sanitization fix ([#2274](https://github.com/microsoft/agent-framework/pull/2274))
 - **observability**: Handle datetime serialization in tool results ([#2248](https://github.com/microsoft/agent-framework/pull/2248))
 
 
@@ -118,10 +118,10 @@ Prefer attributes over inheritance when parameters are mostly the same:
 
 ```python
 # ✅ Preferred - using attributes
-from agent_framework import ChatMessage
+from agent_framework import Message
 
-user_msg = ChatMessage("user", ["Hello, world!"])
-asst_msg = ChatMessage("assistant", ["Hello, world!"])
+user_msg = Message("user", ["Hello, world!"])
+asst_msg = Message("assistant", ["Hello, world!"])
 
 # ❌ Not preferred - unnecessary inheritance
 from agent_framework import UserMessage, AssistantMessage
@@ -157,7 +157,7 @@ The package follows a flat import structure:
 
 - **Core**: Import directly from `agent_framework`
   ```python
-  from agent_framework import ChatAgent, tool
+  from agent_framework import Agent, tool
   ```
 
 - **Components**: Import from `agent_framework.<component>`
@@ -381,12 +381,12 @@ def create_client(
 Use Google-style docstrings for all public APIs:
 
 ```python
-def create_agent(name: str, chat_client: ChatClientProtocol) -> Agent:
+def create_agent(name: str, client: SupportsChatGetResponse) -> Agent:
     """Create a new agent with the specified configuration.
 
     Args:
         name: The name of the agent.
-        chat_client: The chat client to use for communication.
+        client: The chat client to use for communication.
 
     Returns:
         True if the strings are the same, False otherwise.
@@ -409,10 +409,10 @@ Define `__all__` in each module to explicitly declare the public API. Avoid usin
 
 ```python
 # ✅ Preferred - explicit __all__ and imports
-__all__ = ["ChatAgent", "ChatMessage", "ChatResponse"]
+__all__ = ["Agent", "Message", "ChatResponse"]
 
-from ._agents import ChatAgent
-from ._types import ChatMessage, ChatResponse
+from ._agents import Agent
+from ._types import Message, ChatResponse
 
 # ❌ Avoid - star imports
 from ._agents import *
 
@@ -116,7 +116,7 @@ You will then configure the ChatClient class with the keyword argument `env_file
 ```python
 from agent_framework.openai import OpenAIChatClient
 
-chat_client = OpenAIChatClient(env_file_path="openai.env")
+client = OpenAIChatClient(env_file_path="openai.env")
 ```
 
 ## Tests
 
@@ -62,7 +62,7 @@ You can also override environment variables by explicitly passing configuration
 ```python
 from agent_framework.azure import AzureOpenAIChatClient
 
-chat_client = AzureOpenAIChatClient(
+client = AzureOpenAIChatClient(
     api_key='',
     endpoint='',
     deployment_name='',
@@ -78,12 +78,12 @@ Create agents and invoke them directly:
 
 ```python
 import asyncio
-from agent_framework import ChatAgent
+from agent_framework import Agent
 from agent_framework.openai import OpenAIChatClient
 
 async def main():
-    agent = ChatAgent(
-        chat_client=OpenAIChatClient(),
+    agent = Agent(
+        client=OpenAIChatClient(),
         instructions="""
         1) A robot may not injure a human being...
         2) A robot must obey orders given it by human beings...
@@ -106,15 +106,15 @@ You can use the chat client classes directly for advanced workflows:
 
 ```python
 import asyncio
-from agent_framework import ChatMessage
+from agent_framework import Message
 from agent_framework.openai import OpenAIChatClient
 
 async def main():
     client = OpenAIChatClient()
 
     messages = [
-        ChatMessage("system", ["You are a helpful assistant."]),
-        ChatMessage("user", ["Write a haiku about Agent Framework."])
+        Message("system", ["You are a helpful assistant."]),
+        Message("user", ["Write a haiku about Agent Framework."])
     ]
 
     response = await client.get_response(messages)
@@ -140,7 +140,7 @@ import asyncio
 from typing import Annotated
 from random import randint
 from pydantic import Field
-from agent_framework import ChatAgent
+from agent_framework import Agent
 from agent_framework.openai import OpenAIChatClient
 
 
@@ -162,8 +162,8 @@ def get_menu_specials() -> str:
 
 
 async def main():
-    agent = ChatAgent(
-        chat_client=OpenAIChatClient(),
+    agent = Agent(
+        client=OpenAIChatClient(),
         instructions="You are a helpful assistant that can provide weather and restaurant information.",
         tools=[get_weather, get_menu_specials]
     )
@@ -189,20 +189,20 @@ Coordinate multiple agents to collaborate on complex tasks using orchestration p
 
 ```python
 import asyncio
-from agent_framework import ChatAgent
+from agent_framework import Agent
 from agent_framework.openai import OpenAIChatClient
 
 
 async def main():
     # Create specialized agents
-    writer = ChatAgent(
-        chat_client=OpenAIChatClient(),
+    writer = Agent(
+        client=OpenAIChatClient(),
         name="Writer",
         instructions="You are a creative content writer. Generate and refine slogans based on feedback."
     )
 
-    reviewer = ChatAgent(
-        chat_client=OpenAIChatClient(),
+    reviewer = Agent(
+        client=OpenAIChatClient(),
         name="Reviewer",
         instructions="You are a critical reviewer. Provide detailed feedback on proposed slogans."
     )
 
@@ -18,7 +18,6 @@
     FilePart,
     FileWithBytes,
     FileWithUri,
-    Message,
     Task,
     TaskIdParams,
     TaskQueryParams,
@@ -34,9 +33,9 @@
     AgentResponseUpdate,
     AgentThread,
     BaseAgent,
-    ChatMessage,
     Content,
     ContinuationToken,
+    Message,
     ResponseStream,
     normalize_messages,
     prepend_agent_framework_to_user_agent,
@@ -83,7 +82,7 @@ class A2AAgent(AgentTelemetryLayer, BaseAgent):
     """Agent2Agent (A2A) protocol implementation.
 
     Wraps an A2A Client to connect the Agent Framework with external A2A-compliant agents
-    via HTTP/JSON-RPC. Converts framework ChatMessages to A2A Messages on send, and converts
+    via HTTP/JSON-RPC. Converts framework Messages to A2A Messages on send, and converts
     A2A responses (Messages/Tasks) back to framework types. Inherits BaseAgent capabilities
     while managing the underlying A2A protocol communication.
 
@@ -209,7 +208,7 @@ async def __aexit__(
     @overload
     def run(
         self,
-        messages: str | ChatMessage | Sequence[str | ChatMessage] | None = None,
+        messages: str | Message | Sequence[str | Message] | None = None,
         *,
         stream: Literal[False] = ...,
         thread: AgentThread | None = None,
@@ -221,7 +220,7 @@ def run(
     @overload
     def run(
         self,
-        messages: str | ChatMessage | Sequence[str | ChatMessage] | None = None,
+        messages: str | Message | Sequence[str | Message] | None = None,
         *,
         stream: Literal[True],
         thread: AgentThread | None = None,
@@ -232,7 +231,7 @@ def run(
 
     def run(
         self,
-        messages: str | ChatMessage | Sequence[str | ChatMessage] | None = None,
+        messages: str | Message | Sequence[str | Message] | None = None,
         *,
         stream: bool = False,
         thread: AgentThread | None = None,
@@ -268,7 +267,7 @@ def run(
 
         response = ResponseStream(
             self._map_a2a_stream(a2a_stream, background=background),
-            finalizer=lambda updates: AgentResponse.from_updates(list(updates)),
+            finalizer=AgentResponse.from_updates,
         )
         if stream:
             return response
@@ -291,7 +290,8 @@ async def _map_a2a_stream(
                 When True, they are yielded with a continuation token.
         """
         async for item in a2a_stream:
-            if isinstance(item, Message):
+            if isinstance(item, A2AMessage):
+                # Process A2A Message
                 contents = self._parse_contents_from_a2a(item.parts)
                 yield AgentResponseUpdate(
                     contents=contents,
@@ -377,10 +377,10 @@ async def poll_task(self, continuation_token: A2AContinuationToken) -> AgentResp
             return AgentResponse.from_updates(updates)
         return AgentResponse(messages=[], response_id=task.id, raw_representation=task)
 
-    def _prepare_message_for_a2a(self, message: ChatMessage) -> A2AMessage:
-        """Prepare a ChatMessage for the A2A protocol.
+    def _prepare_message_for_a2a(self, message: Message) -> A2AMessage:
+        """Prepare a Message for the A2A protocol.
 
-        Transforms Agent Framework ChatMessage objects into A2A protocol Messages by:
+        Transforms Agent Framework Message objects into A2A protocol Messages by:
         - Converting all message contents to appropriate A2A Part types
         - Mapping text content to TextPart objects
         - Converting file references (URI/data/hosted_file) to FilePart objects
@@ -389,7 +389,7 @@ def _prepare_message_for_a2a(self, message: ChatMessage) -> A2AMessage:
         """
         parts: list[A2APart] = []
         if not message.contents:
-            raise ValueError("ChatMessage.contents is empty; cannot convert to A2AMessage.")
+            raise ValueError("Message.contents is empty; cannot convert to A2AMessage.")
 
         # Process ALL contents
         for content in message.contents:
@@ -511,9 +511,9 @@ def _parse_contents_from_a2a(self, parts: Sequence[A2APart]) -> list[Content]:
                     raise ValueError(f"Unknown Part kind: {inner_part.kind}")
         return contents
 
-    def _parse_messages_from_task(self, task: Task) -> list[ChatMessage]:
-        """Parse A2A Task artifacts into ChatMessages with ASSISTANT role."""
-        messages: list[ChatMessage] = []
+    def _parse_messages_from_task(self, task: Task) -> list[Message]:
+        """Parse A2A Task artifacts into Messages with ASSISTANT role."""
+        messages: list[Message] = []
 
         if task.artifacts is not None:
             for artifact in task.artifacts:
@@ -523,7 +523,7 @@ def _parse_messages_from_task(self, task: Task) -> list[ChatMessage]:
             history_item = task.history[-1]
             contents = self._parse_contents_from_a2a(history_item.parts)
             messages.append(
-                ChatMessage(
+                Message(
                     role="assistant" if history_item.role == A2ARole.agent else "user",
                     contents=contents,
                     raw_representation=history_item,
@@ -532,10 +532,10 @@ def _parse_messages_from_task(self, task: Task) -> list[ChatMessage]:
 
         return messages
 
-    def _parse_message_from_artifact(self, artifact: Artifact) -> ChatMessage:
-        """Parse A2A Artifact into ChatMessage using part contents."""
+    def _parse_message_from_artifact(self, artifact: Artifact) -> Message:
+        """Parse A2A Artifact into Message using part contents."""
         contents = self._parse_contents_from_a2a(artifact.parts)
-        return ChatMessage(
+        return Message(
             role="assistant",
             contents=contents,
             raw_representation=artifact,