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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
Download Patch File Download Diff File Expand all files Collapse all files
python/samples/getting_started/workflows/human-in-the-loop/agents_with_HITL.py
+6 -6
View File
@@ -9,8 +9,8 @@ from agent_framework import (
AgentExecutorResponse,
AgentResponse,
AgentResponseUpdate,
ChatMessage,
Executor,
Message,
WorkflowBuilder,
WorkflowContext,
WorkflowEvent,
@@ -47,7 +47,7 @@ class DraftFeedbackRequest:
"""Payload sent for human review."""
prompt: str = ""
conversation: list[ChatMessage] = field(default_factory=lambda: [])
conversation: list[Message] = field(default_factory=lambda: [])
class Coordinator(Executor):
@@ -71,7 +71,7 @@ class Coordinator(Executor):
# Writer agent response; request human feedback.
# Preserve the full conversation so that the final editor has context.
conversation: list[ChatMessage]
conversation: list[Message]
if draft.full_conversation is not None:
conversation = list(draft.full_conversation)
else:
@@ -100,7 +100,7 @@ class Coordinator(Executor):
# Human approved the draft as-is; forward it unchanged.
await ctx.send_message(
AgentExecutorRequest(
messages=original_request.conversation + [ChatMessage("user", text="The draft is approved as-is.")],
messages=original_request.conversation + [Message("user", text="The draft is approved as-is.")],
should_respond=True,
),
target_id=self.final_editor_name,
@@ -108,14 +108,14 @@ class Coordinator(Executor):
return
# Human provided feedback; prompt the writer to revise.
conversation: list[ChatMessage] = list(original_request.conversation)
conversation: list[Message] = list(original_request.conversation)
instruction = (
"A human reviewer shared the following guidance:\n"
f"{note or 'No specific guidance provided.'}\n\n"
"Rewrite the draft from the previous assistant message into a polished final version. "
"Keep the response under 120 words and reflect any requested tone adjustments."
)
conversation.append(ChatMessage("user", text=instruction))
conversation.append(Message("user", text=instruction))
await ctx.send_message(
AgentExecutorRequest(messages=conversation, should_respond=True), target_id=self.writer_name
)
python/samples/getting_started/workflows/human-in-the-loop/concurrent_request_info.py
+3 -3
View File
@@ -27,7 +27,7 @@ from typing import Any
from agent_framework import (
AgentExecutorResponse,
ChatMessage,
Message,
WorkflowEvent,
)
from agent_framework.azure import AzureOpenAIChatClient
@@ -76,7 +76,7 @@ async def aggregate_with_synthesis(results: list[AgentExecutorResponse]) -> Any:
# Build prompt with human guidance if provided
guidance_text = f"\n\nHuman guidance: {human_guidance}" if human_guidance else ""
system_msg = ChatMessage(
system_msg = Message(
"system",
text=(
"You are a synthesis expert. Consolidate the following analyst perspectives "
@@ -84,7 +84,7 @@ async def aggregate_with_synthesis(results: list[AgentExecutorResponse]) -> Any:
"prioritize aspects as directed."
),
)
user_msg = ChatMessage("user", text="\n\n".join(expert_sections) + guidance_text)
user_msg = Message("user", text="\n\n".join(expert_sections) + guidance_text)
response = await _chat_client.get_response([system_msg, user_msg])
return response.messages[-1].text if response.messages else ""
python/samples/getting_started/workflows/human-in-the-loop/group_chat_request_info.py
+7 -7
View File
@@ -28,7 +28,7 @@ from typing import cast
from agent_framework import (
AgentExecutorResponse,
ChatMessage,
Message,
WorkflowEvent,
)
from agent_framework.azure import AzureOpenAIChatClient
@@ -51,7 +51,7 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
print("=" * 60)
print("Final discussion summary:")
# To make the type checker happy, we cast event.data to the expected type
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}")
@@ -91,10 +91,10 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
async def main() -> None:
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
client = AzureOpenAIChatClient(credential=AzureCliCredential())
# Create agents for a group discussion
optimist = chat_client.as_agent(
optimist = client.as_agent(
name="optimist",
instructions=(
"You are an optimistic team member. You see opportunities and potential "
@@ -103,7 +103,7 @@ async def main() -> None:
),
)
pragmatist = chat_client.as_agent(
pragmatist = client.as_agent(
name="pragmatist",
instructions=(
"You are a pragmatic team member. You focus on practical implementation "
@@ -112,7 +112,7 @@ async def main() -> None:
),
)
creative = chat_client.as_agent(
creative = client.as_agent(
name="creative",
instructions=(
"You are a creative team member. You propose innovative solutions and "
@@ -122,7 +122,7 @@ async def main() -> None:
)
# Orchestrator coordinates the discussion
orchestrator = chat_client.as_agent(
orchestrator = client.as_agent(
name="orchestrator",
instructions=(
"You are a discussion manager coordinating a team conversation between participants. "
python/samples/getting_started/workflows/human-in-the-loop/guessing_game_with_human_input.py
+3 -3
View File
@@ -8,8 +8,8 @@ from agent_framework import (
AgentExecutorRequest,
AgentExecutorResponse,
AgentResponseUpdate,
ChatMessage,
Executor,
Message,
WorkflowBuilder,
WorkflowContext,
WorkflowEvent,
@@ -84,7 +84,7 @@ class TurnManager(Executor):
- Input is a simple starter token (ignored here).
- Output is an AgentExecutorRequest that triggers the agent to produce a guess.
"""
user = ChatMessage("user", text="Start by making your first guess.")
user = Message("user", text="Start by making your first guess.")
await ctx.send_message(AgentExecutorRequest(messages=[user], should_respond=True))
@handler
@@ -136,7 +136,7 @@ class TurnManager(Executor):
f"Feedback: {reply}. Your last guess was {last_guess}. "
f"Use this feedback to adjust and make your next guess (1-10)."
)
user_msg = ChatMessage("user", text=feedback_text)
user_msg = Message("user", text=feedback_text)
await ctx.send_message(AgentExecutorRequest(messages=[user_msg], should_respond=True))
python/samples/getting_started/workflows/human-in-the-loop/sequential_request_info.py
+6 -6
View File
@@ -27,7 +27,7 @@ from typing import cast
from agent_framework import (
AgentExecutorResponse,
ChatMessage,
Message,
WorkflowEvent,
)
from agent_framework.azure import AzureOpenAIChatClient
@@ -49,7 +49,7 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
print("WORKFLOW COMPLETE")
print("=" * 60)
print("Final output:")
outputs = cast(list[ChatMessage], event.data)
outputs = cast(list[Message], event.data)
for message in outputs:
print(f"[{message.author_name or message.role}]: {message.text}")
@@ -88,15 +88,15 @@ async def process_event_stream(stream: AsyncIterable[WorkflowEvent]) -> dict[str
async def main() -> None:
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
client = AzureOpenAIChatClient(credential=AzureCliCredential())
# Create agents for a sequential document review workflow
drafter = chat_client.as_agent(
drafter = client.as_agent(
name="drafter",
instructions=("You are a document drafter. When given a topic, create a brief draft (2-3 sentences)."),
)
editor = chat_client.as_agent(
editor = client.as_agent(
name="editor",
instructions=(
"You are an editor. Review the draft and make improvements. "
@@ -104,7 +104,7 @@ async def main() -> None:
),
)
finalizer = chat_client.as_agent(
finalizer = client.as_agent(
name="finalizer",
instructions=(
"You are a finalizer. Take the edited content and create a polished final version. "