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

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* [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>
This commit is contained in:
Eduard van Valkenburg
2026-02-11 00:04:32 +01:00
committed by GitHub
Unverified
parent a4c9e43afb
commit 0521f5bed8
418 changed files with 5385 additions and 5389 deletions
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python/samples/semantic-kernel-migration/README.md
+1 -1
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@@ -7,7 +7,7 @@ This gallery helps Semantic Kernel (SK) developers move to the Microsoft Agent F
## What’s Included
### Chat completion parity
- [01_basic_chat_completion.py](chat_completion/01_basic_chat_completion.py) — Minimal SK `ChatCompletionAgent` and AF `ChatAgent` conversation.
- [01_basic_chat_completion.py](chat_completion/01_basic_chat_completion.py) — Minimal SK `ChatCompletionAgent` and AF `Agent` conversation.
- [02_chat_completion_with_tool.py](chat_completion/02_chat_completion_with_tool.py) — Adds a simple tool/function call in both SDKs.
- [03_chat_completion_thread_and_stream.py](chat_completion/03_chat_completion_thread_and_stream.py) — Demonstrates thread reuse and streaming prompts.
python/samples/semantic-kernel-migration/chat_completion/01_basic_chat_completion.py
+3 -3
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@@ -8,7 +8,7 @@
# uv run samples/semantic-kernel-migration/chat_completion/01_basic_chat_completion.py
# Copyright (c) Microsoft. All rights reserved.
"""Basic SK ChatCompletionAgent vs Agent Framework ChatAgent.
"""Basic SK ChatCompletionAgent vs Agent Framework Agent.
Both samples expect OpenAI-compatible environment variables (OPENAI_API_KEY or
Azure OpenAI configuration). Update the prompts or client wiring to match your
@@ -34,10 +34,10 @@ async def run_semantic_kernel() -> None:
async def run_agent_framework() -> None:
"""Call Agent Framework's ChatAgent created from OpenAIChatClient."""
"""Call Agent Framework's Agent created from OpenAIChatClient."""
from agent_framework.openai import OpenAIChatClient
# AF constructs a lightweight ChatAgent backed by OpenAIChatClient.
# AF constructs a lightweight Agent backed by OpenAIChatClient.
chat_agent = OpenAIChatClient().as_agent(
name="Support",
instructions="Answer in one sentence.",
python/samples/semantic-kernel-migration/openai_responses/01_basic_responses_agent.py
+4 -4
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@@ -35,12 +35,12 @@ async def run_semantic_kernel() -> None:
async def run_agent_framework() -> None:
from agent_framework import ChatAgent
from agent_framework import Agent
from agent_framework.openai import OpenAIResponsesClient
# AF ChatAgent can swap in an OpenAIResponsesClient directly.
chat_agent = ChatAgent(
chat_client=OpenAIResponsesClient(),
# AF Agent can swap in an OpenAIResponsesClient directly.
chat_agent = Agent(
client=OpenAIResponsesClient(),
instructions="Answer in one concise sentence.",
name="Expert",
)
python/samples/semantic-kernel-migration/openai_responses/02_responses_agent_with_tool.py
+3 -3
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@@ -42,7 +42,7 @@ async def run_semantic_kernel() -> None:
async def run_agent_framework() -> None:
from agent_framework import ChatAgent
from agent_framework import Agent
from agent_framework._tools import tool
from agent_framework.openai import OpenAIResponsesClient
@@ -50,8 +50,8 @@ async def run_agent_framework() -> None:
async def add(a: float, b: float) -> float:
return a + b
chat_agent = ChatAgent(
chat_client=OpenAIResponsesClient(),
chat_agent = Agent(
client=OpenAIResponsesClient(),
instructions="Use the add tool when math is required.",
name="MathExpert",
# AF registers the async function as a tool at construction.
python/samples/semantic-kernel-migration/openai_responses/03_responses_agent_structured_output.py
+3 -3
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@@ -47,11 +47,11 @@ async def run_semantic_kernel() -> None:
async def run_agent_framework() -> None:
from agent_framework import ChatAgent
from agent_framework import Agent
from agent_framework.openai import OpenAIResponsesClient
chat_agent = ChatAgent(
chat_client=OpenAIResponsesClient(),
chat_agent = Agent(
client=OpenAIResponsesClient(),
instructions="Return launch briefs as structured JSON.",
name="ProductMarketer",
)
python/samples/semantic-kernel-migration/orchestrations/concurrent_basic.py
+10 -9
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@@ -15,10 +15,11 @@ import asyncio
from collections.abc import Sequence
from typing import cast
from agent_framework import ChatMessage
from agent_framework import Message
from agent_framework.azure import AzureOpenAIChatClient
from agent_framework.orchestrations import ConcurrentBuilder
from azure.identity import AzureCliCredential
from semantic_kernel.agents import Agent, ChatCompletionAgent, ConcurrentOrchestration
from semantic_kernel.agents import ChatCompletionAgent, ConcurrentOrchestration
from semantic_kernel.agents.runtime import InProcessRuntime
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
from semantic_kernel.contents import ChatMessageContent
@@ -83,30 +84,30 @@ def _print_semantic_kernel_outputs(outputs: Sequence[ChatMessageContent]) -> Non
######################################################################
async def run_agent_framework_example(prompt: str) -> Sequence[list[ChatMessage]]:
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
async def run_agent_framework_example(prompt: str) -> Sequence[list[Message]]:
client = AzureOpenAIChatClient(credential=AzureCliCredential())
physics = chat_client.as_agent(
physics = client.as_agent(
instructions=("You are an expert in physics. Answer questions from a physics perspective."),
name="physics",
)
chemistry = chat_client.as_agent(
chemistry = client.as_agent(
instructions=("You are an expert in chemistry. Answer questions from a chemistry perspective."),
name="chemistry",
)
workflow = ConcurrentBuilder(participants=[physics, chemistry]).build()
outputs: list[list[ChatMessage]] = []
outputs: list[list[Message]] = []
async for event in workflow.run(prompt, stream=True):
if event.type == "output":
outputs.append(cast(list[ChatMessage], event.data))
outputs.append(cast(list[Message], event.data))
return outputs
def _print_agent_framework_outputs(conversations: Sequence[Sequence[ChatMessage]]) -> None:
def _print_agent_framework_outputs(conversations: Sequence[Sequence[Message]]) -> None:
if not conversations:
print("No Agent Framework output.")
return
python/samples/semantic-kernel-migration/orchestrations/group_chat.py
+6 -6
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@@ -16,7 +16,7 @@ import sys
from collections.abc import Sequence
from typing import Any, cast
from agent_framework import ChatAgent, ChatMessage
from agent_framework import Agent, Message
from agent_framework.azure import AzureOpenAIChatClient, AzureOpenAIResponsesClient
from agent_framework.orchestrations import GroupChatBuilder
from azure.identity import AzureCliCredential
@@ -224,21 +224,21 @@ async def run_semantic_kernel_example(task: str) -> str:
async def run_agent_framework_example(task: str) -> str:
credential = AzureCliCredential()
researcher = ChatAgent(
researcher = Agent(
name="Researcher",
description="Collects background information and potential resources.",
instructions=(
"Gather concise facts or considerations that help plan a community hackathon. "
"Keep your responses factual and scannable."
),
chat_client=AzureOpenAIChatClient(credential=credential),
client=AzureOpenAIChatClient(credential=credential),
)
planner = ChatAgent(
planner = Agent(
name="Planner",
description="Turns the collected notes into a concrete action plan.",
instructions=("Propose a structured action plan that accounts for logistics, roles, and timeline."),
chat_client=AzureOpenAIResponsesClient(credential=credential),
client=AzureOpenAIResponsesClient(credential=credential),
)
workflow = GroupChatBuilder(
@@ -253,7 +253,7 @@ async def run_agent_framework_example(task: str) -> str:
if isinstance(data, list) and len(data) > 0:
# Get the final message from the conversation
final_message = data[-1]
final_response = final_message.text or "" if isinstance(final_message, ChatMessage) else str(data)
final_response = final_message.text or "" if isinstance(final_message, Message) else str(data)
else:
final_response = str(data)
return final_response
python/samples/semantic-kernel-migration/orchestrations/handoff.py
+3 -3
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@@ -16,7 +16,7 @@ from collections.abc import AsyncIterable, Iterator, Sequence
from typing import cast
from agent_framework import (
ChatMessage,
Message,
WorkflowEvent,
)
from agent_framework.orchestrations import HandoffBuilder, HandoffUserInputRequest
@@ -228,10 +228,10 @@ def _collect_handoff_requests(events: list[WorkflowEvent]) -> list[WorkflowEvent
return requests
def _extract_final_conversation(events: list[WorkflowEvent]) -> list[ChatMessage]:
def _extract_final_conversation(events: list[WorkflowEvent]) -> list[Message]:
for event in events:
if event.type == "output":
data = cast(list[ChatMessage], event.data)
data = cast(list[Message], event.data)
return data
return []
python/samples/semantic-kernel-migration/orchestrations/magentic.py
+7 -7
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@@ -15,7 +15,7 @@ import asyncio
from collections.abc import Sequence
from typing import cast
from agent_framework import ChatAgent, HostedCodeInterpreterTool
from agent_framework import Agent, HostedCodeInterpreterTool
from agent_framework.openai import OpenAIChatClient, OpenAIResponsesClient
from agent_framework.orchestrations import MagenticBuilder
from semantic_kernel.agents import (
@@ -129,29 +129,29 @@ def _print_semantic_kernel_outputs(outputs: Sequence[ChatMessageContent]) -> Non
async def run_agent_framework_example(prompt: str) -> str | None:
researcher = ChatAgent(
researcher = Agent(
name="ResearcherAgent",
description="Specialist in research and information gathering",
instructions=(
"You are a Researcher. You find information without additional computation or quantitative analysis."
),
chat_client=OpenAIChatClient(ai_model_id="gpt-4o-search-preview"),
client=OpenAIChatClient(ai_model_id="gpt-4o-search-preview"),
)
coder = ChatAgent(
coder = 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(),
)
workflow = MagenticBuilder(participants=[researcher, coder], manager_agent=manager_agent).build()
python/samples/semantic-kernel-migration/orchestrations/sequential.py
+8 -8
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@@ -15,7 +15,7 @@ import asyncio
from collections.abc import Sequence
from typing import cast
from agent_framework import ChatMessage
from agent_framework import Message
from agent_framework.azure import AzureOpenAIChatClient
from agent_framework.orchestrations import SequentialBuilder
from azure.identity import AzureCliCredential
@@ -70,25 +70,25 @@ async def sk_agent_response_callback(
######################################################################
async def run_agent_framework_example(prompt: str) -> list[ChatMessage]:
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
async def run_agent_framework_example(prompt: str) -> list[Message]:
client = AzureOpenAIChatClient(credential=AzureCliCredential())
writer = chat_client.as_agent(
writer = client.as_agent(
instructions=("You are a concise copywriter. Provide a single, punchy marketing sentence based on the prompt."),
name="writer",
)
reviewer = chat_client.as_agent(
reviewer = client.as_agent(
instructions=("You are a thoughtful reviewer. Give brief feedback on the previous assistant message."),
name="reviewer",
)
workflow = SequentialBuilder(participants=[writer, reviewer]).build()
conversation_outputs: list[list[ChatMessage]] = []
conversation_outputs: list[list[Message]] = []
async for event in workflow.run(prompt, stream=True):
if event.type == "output":
conversation_outputs.append(cast(list[ChatMessage], event.data))
conversation_outputs.append(cast(list[Message], event.data))
return conversation_outputs[-1] if conversation_outputs else []
@@ -112,7 +112,7 @@ async def run_semantic_kernel_example(prompt: str) -> str:
await runtime.stop_when_idle()
def _format_conversation(conversation: list[ChatMessage]) -> None:
def _format_conversation(conversation: list[Message]) -> None:
if not conversation:
print("No Agent Framework output.")
return