Merge branch 'main' into copilot/port-magentic-orchestration-sample

When A2AAgent receives a TaskStatusUpdateEvent during streaming,
ConvertToAgentResponseUpdate now sets AgentResponseUpdate.MessageId
from Status.Message.MessageId when the message is present.

This fixes the missing message correlation metadata reported in
microsoft/agent-framework#4987.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

dotnet/agent-framework-dotnet.slnx

@@ -281,6 +281,7 @@
   </Folder>
   <Folder Name="/Samples/03-workflows/Orchestration/">
     <Project Path="samples/03-workflows/Orchestration/Handoff/Handoff.csproj" />
+    <Project Path="samples/03-workflows/Orchestration/Magentic/Magentic.csproj" />
   </Folder>
   <Folder Name="/Samples/03-workflows/Observability/">
     <Project Path="samples/03-workflows/Observability/ApplicationInsights/ApplicationInsights.csproj" />

dotnet/samples/03-workflows/Orchestration/Magentic/Magentic.csproj

@@ -0,0 +1,23 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);MAAIW001;OPENAI001</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.Projects" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Workflows\Microsoft.Agents.AI.Workflows.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Foundry\Microsoft.Agents.AI.Foundry.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI\Microsoft.Agents.AI.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/03-workflows/Orchestration/Magentic/Program.cs

@@ -0,0 +1,193 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample ports the Python Magentic orchestration sample to .NET.
+// A Magentic workflow coordinates a researcher and a coder, streams orchestration
+// events as the plan evolves, and prints the final conversation transcript.
+
+using Azure.AI.Projects;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Workflows;
+using Microsoft.Agents.AI.Workflows.Specialized.Magentic;
+using Microsoft.Extensions.AI;
+
+namespace WorkflowMagenticOrchestrationSample;
+
+/// <summary>
+/// Demonstrates Magentic orchestration with a researcher, a coder, and an LLM manager.
+/// </summary>
+/// <remarks>
+/// Pre-requisites:
+/// - An Azure AI Foundry project endpoint and model deployment must be configured.
+/// - Run <c>az login</c> before executing the sample.
+/// </remarks>
+public static class Program
+{
+    private const string TaskPrompt =
+        "I am preparing a report on the energy efficiency of different machine learning model architectures. " +
+        "Compare the estimated training and inference energy consumption of ResNet-50, BERT-base, and GPT-2 " +
+        "on standard datasets (e.g., ImageNet for ResNet, GLUE for BERT, WebText for GPT-2). " +
+        "Then, estimate the CO2 emissions associated with each, assuming training on an Azure Standard_NC6s_v3 " +
+        "VM for 24 hours. Provide tables for clarity, and recommend the most energy-efficient model " +
+        "per task type (image classification, text classification, and text generation).";
+
+    private static async Task Main()
+    {
+        string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT")
+            ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
+        string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+        // WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+        // In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+        // latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+        AIProjectClient projectClient = new(new Uri(endpoint), new DefaultAzureCredential());
+
+        AIAgent researcherAgent = projectClient.AsAIAgent(
+            deploymentName,
+            name: "ResearcherAgent",
+            description: "Specialist in research and information gathering.",
+            instructions: "You are a researcher. Find relevant information without doing additional computation or quantitative analysis.");
+
+        AIAgent coderAgent = projectClient.AsAIAgent(
+            deploymentName,
+            name: "CoderAgent",
+            description: "A helpful assistant that writes and executes code to analyze data.",
+            instructions: "You solve quantitative questions by writing and running code. Show the analysis and the computation process clearly.",
+            tools: [new HostedCodeInterpreterTool()]);
+
+        AIAgent managerAgent = projectClient.AsAIAgent(
+            deploymentName,
+            name: "MagenticManager",
+            description: "Orchestrator that coordinates the research and coding workflow.",
+            instructions: "You coordinate the team to complete complex tasks efficiently.");
+
+        Workflow workflow = new MagenticWorkflowBuilder(managerAgent)
+            .AddParticipants([researcherAgent, coderAgent])
+            .WithName("Magentic Orchestration Workflow")
+            .WithDescription("Coordinates a researcher and coder to solve a complex analytical task.")
+            .RequirePlanSignoff(false)
+            .WithMaxRounds(10)
+            .WithMaxStalls(3)
+            .WithMaxResets(2)
+            .Build();
+
+        Console.WriteLine("Building Magentic workflow...");
+        Console.WriteLine();
+        Console.WriteLine($"Task: {TaskPrompt}");
+        Console.WriteLine();
+        Console.WriteLine("Starting workflow execution...");
+
+        await using StreamingRun run = await InProcessExecution.RunStreamingAsync(
+            workflow,
+            new List<ChatMessage> { new(ChatRole.User, TaskPrompt) });
+
+        await run.TrySendMessageAsync(new TurnToken(emitEvents: true));
+
+        string? lastResponseId = null;
+        WorkflowOutputEvent? finalOutput = null;
+
+        await foreach (WorkflowEvent workflowEvent in run.WatchStreamAsync())
+        {
+            switch (workflowEvent)
+            {
+                case AgentResponseUpdateEvent updateEvent:
+                    WriteStreamingUpdate(updateEvent, ref lastResponseId);
+                    break;
+
+                case MagenticPlanCreatedEvent planCreated:
+                    WriteMagenticMessage("Initial Plan", planCreated.FullTaskLedger.Text);
+                    PauseIfInteractive();
+                    break;
+
+                case MagenticReplannedEvent replanned:
+                    WriteMagenticMessage("Replanned", replanned.FullTaskLedger.Text);
+                    PauseIfInteractive();
+                    break;
+
+                case MagenticProgressLedgerUpdatedEvent progressUpdated:
+                    WriteMagenticMessage("Progress Ledger", FormatProgressLedger(progressUpdated.ProgressLedger));
+                    PauseIfInteractive();
+                    break;
+
+                case WorkflowOutputEvent outputEvent when outputEvent.Is<List<ChatMessage>>():
+                    finalOutput = outputEvent;
+                    break;
+
+                case WorkflowErrorEvent workflowError:
+                    Console.ForegroundColor = ConsoleColor.Red;
+                    Console.Error.WriteLine(workflowError.Exception?.ToString() ?? "Unknown workflow error occurred.");
+                    Console.ResetColor();
+                    break;
+
+                case ExecutorFailedEvent executorFailed:
+                    Console.ForegroundColor = ConsoleColor.Red;
+                    Console.Error.WriteLine($"Executor '{executorFailed.ExecutorId}' failed with {(executorFailed.Data is null ? "unknown error" : $"exception {executorFailed.Data}")}.");
+                    Console.ResetColor();
+                    break;
+            }
+        }
+
+        if (finalOutput?.As<List<ChatMessage>>() is { } transcript)
+        {
+            Console.WriteLine();
+            Console.WriteLine(new string('=', 80));
+            Console.WriteLine();
+            Console.WriteLine("Final Conversation Transcript:");
+            Console.WriteLine();
+
+            foreach (ChatMessage message in transcript)
+            {
+                Console.WriteLine($"{message.AuthorName ?? message.Role.ToString()}: {message.Text}");
+                Console.WriteLine();
+            }
+        }
+    }
+
+    private static void WriteStreamingUpdate(AgentResponseUpdateEvent updateEvent, ref string? lastResponseId)
+    {
+        string responseId = updateEvent.Update.ResponseId ?? updateEvent.Update.MessageId ?? updateEvent.ExecutorId;
+        if (!string.Equals(responseId, lastResponseId, StringComparison.Ordinal))
+        {
+            if (lastResponseId is not null)
+            {
+                Console.WriteLine();
+                Console.WriteLine();
+            }
+
+            Console.Write($"- {updateEvent.ExecutorId}: ");
+            lastResponseId = responseId;
+        }
+
+        if (!string.IsNullOrEmpty(updateEvent.Update.Text))
+        {
+            Console.Write(updateEvent.Update.Text);
+        }
+    }
+
+    private static void WriteMagenticMessage(string title, string? content)
+    {
+        Console.WriteLine();
+        Console.WriteLine($"[Magentic {title}]");
+        Console.WriteLine(content);
+    }
+
+    private static string FormatProgressLedger(MagenticProgressLedger ledger) =>
+        string.Join(Environment.NewLine,
+            $"Request satisfied: {ledger.IsRequestSatisfied}",
+            $"In loop: {ledger.IsInLoop}",
+            $"Making progress: {ledger.IsProgressBeingMade}",
+            $"Next speaker: {ledger.NextSpeaker}",
+            $"Instruction: {ledger.InstructionOrQuestion}");
+
+    private static void PauseIfInteractive()
+    {
+        if (Console.IsInputRedirected || Console.IsOutputRedirected)
+        {
+            return;
+        }
+
+        Console.Write("Press Enter to continue...");
+        Console.ReadLine();
+        Console.WriteLine();
+    }
+}

dotnet/samples/03-workflows/Orchestration/Magentic/README.md

@@ -0,0 +1,40 @@
+# Magentic Orchestration Sample
+
+This sample showcases the Magentic Orchestration Pattern in .NET, setting up a team with three roles:
+
+- **ResearcherAgent** gathers factual background information.
+- **CoderAgent** uses `HostedCodeInterpreterTool` for quantitative analysis.
+- **MagenticManager** plans the work, tracks progress, and decides who should act next.
+
+## What This Sample Demonstrates
+
+- Building a Magentic workflow with `MagenticWorkflowBuilder`
+- Combining standard responses-based agents with a code interpreter-enabled participant
+- Streaming orchestration events such as the initial plan, replans, and progress-ledger updates
+- Printing the final multi-agent conversation transcript
+
+## Prerequisites
+
+- `AZURE_AI_PROJECT_ENDPOINT` set to your Azure AI Foundry project endpoint
+- `AZURE_AI_MODEL_DEPLOYMENT_NAME` set to your model deployment name (defaults to `gpt-5.4-mini`)
+- `az login` completed before running the sample
+
+## Running the Sample
+
+```bash
+dotnet run
+```
+
+## Expected Output
+
+The sample prints:
+
+1. The original task prompt
+2. Streamed updates from the participating agents
+3. Magentic plan and progress-ledger events as the workflow coordinates the team
+4. The final conversation transcript returned by the workflow
+
+## Related Samples
+
+- [Handoff Orchestration](../Handoff) - another multi-agent orchestration pattern in .NET workflows
+- [Python Magentic workflow sample](../../../../../python/samples/03-workflows/orchestrations/magentic.py) - the source scenario that this sample ports

dotnet/samples/03-workflows/README.md

@@ -62,3 +62,4 @@ Once completed, please proceed to the other samples listed below.
 | Sample | Concepts |
 |--------|----------|
 | [Handoff Orchestration](./Orchestration/Handoff) | Introduces the Handoff Orchestration pattern |
+| [Magentic Orchestration](./Orchestration/Magentic) | Coordinates multiple agents with a Magentic manager, streamed plan events, and a final transcript |

dotnet/src/Microsoft.Agents.AI.A2A/A2AAgent.cs

@@ -474,6 +474,7 @@ public sealed class A2AAgent : AIAgent
             ResponseId = statusUpdateEvent.TaskId,
             RawRepresentation = statusUpdateEvent,
             Role = ChatRole.Assistant,
+            MessageId = statusUpdateEvent.Status.Message?.MessageId,
             FinishReason = MapTaskStateToFinishReason(statusUpdateEvent.Status.State),
             AdditionalProperties = statusUpdateEvent.Metadata?.ToAdditionalProperties() ?? [],
             Contents = statusUpdateEvent.Status.GetUserInputRequests(),

dotnet/tests/Microsoft.Agents.AI.A2A.UnitTests/A2AAgentTests.cs

@@ -1124,6 +1124,7 @@ public sealed class A2AAgentTests : IDisposable
         Assert.Equal(TaskId, update0.ResponseId);
         Assert.Equal(this._agent.Id, update0.AgentId);
         Assert.Null(update0.FinishReason);
+        Assert.Null(update0.MessageId);
         Assert.IsType<TaskStatusUpdateEvent>(update0.RawRepresentation);
 
         // Assert - session should be updated with context and task IDs
@@ -1132,6 +1133,50 @@ public sealed class A2AAgentTests : IDisposable
         Assert.Equal(TaskId, a2aSession.TaskId);
     }
 
+    [Fact]
+    public async Task RunStreamingAsync_WithTaskStatusUpdateEventAndMessageId_YieldsMessageIdAsync()
+    {
+        // Arrange
+        const string TaskId = "task-status-msg-123";
+        const string ContextId = "ctx-status-msg-456";
+        const string ExpectedMessageId = "msg-status-789";
+
+        this._handler.StreamingResponseToReturn = new StreamResponse
+        {
+            StatusUpdate = new TaskStatusUpdateEvent
+            {
+                TaskId = TaskId,
+                ContextId = ContextId,
+                Status = new()
+                {
+                    State = TaskState.Working,
+                    Message = new Message
+                    {
+                        MessageId = ExpectedMessageId,
+                        Parts = [Part.FromText("Processing your request...")]
+                    }
+                }
+            }
+        };
+
+        var session = await this._agent.CreateSessionAsync();
+
+        // Act
+        var updates = new List<AgentResponseUpdate>();
+        await foreach (var update in this._agent.RunStreamingAsync("Check task status", session))
+        {
+            updates.Add(update);
+        }
+
+        // Assert
+        Assert.Single(updates);
+
+        var update0 = updates[0];
+        Assert.Equal(ExpectedMessageId, update0.MessageId);
+        Assert.Equal(TaskId, update0.ResponseId);
+        Assert.IsType<TaskStatusUpdateEvent>(update0.RawRepresentation);
+    }
+
     [Fact]
     public async Task RunStreamingAsync_WithInputRequiredStatusUpdate_YieldsStatusContentsAsync()
     {
@@ -1150,6 +1195,7 @@ public sealed class A2AAgentTests : IDisposable
                     State = TaskState.InputRequired,
                     Message = new Message
                     {
+                        MessageId = "input-msg-789",
                         Parts = [Part.FromText("Where would you like to fly?")]
                     }
                 }
@@ -1170,6 +1216,7 @@ public sealed class A2AAgentTests : IDisposable
 
         var update0 = updates[0];
         Assert.Equal(TaskId, update0.ResponseId);
+        Assert.Equal("input-msg-789", update0.MessageId);
         Assert.Null(update0.FinishReason);
 
         var textContent = Assert.Single(update0.Contents.OfType<TextContent>());

python/packages/core/agent_framework/_feature_stage.py

@@ -2,10 +2,14 @@
 
 from __future__ import annotations
 
+import abc
 import asyncio.coroutines
+import contextlib
 import functools
 import inspect
+import os
 import sys
+import typing
 import warnings
 from collections.abc import Callable
 from enum import Enum
@@ -75,6 +79,51 @@ class ExperimentalWarning(FeatureStageWarning):
     """Warning emitted when an experimental API is used."""
 
 
+# Sentinel attribute used to detect (and reuse) a formatter we've already
+# installed. This lets the install be idempotent across re-imports / reloads
+# and keeps a stable reference to the previous formatter for testing or
+# external restoration via ``warnings.formatwarning = original``.
+_FEATURE_STAGE_FORMATTER_MARKER = "__feature_stage_formatter__"
+
+
+def _install_feature_stage_formatter() -> None:
+    """Install a single-line formatter for FeatureStageWarning categories.
+
+    The stdlib default formatter emits two lines (header + source snippet)
+    which is noisy for our warnings — the offending class/function name is
+    already in the message, so a one-line ``file:lineno: Category: message``
+    is enough. Other warning categories are delegated to the previous
+    formatter so we never change behaviour for unrelated warnings.
+
+    The install is idempotent: if a formatter installed by this module is
+    already in place, we leave it alone so re-imports (and any third-party
+    formatter wrapped on top of ours) don't get wrapped multiple times.
+    """
+    current = warnings.formatwarning
+    if getattr(current, _FEATURE_STAGE_FORMATTER_MARKER, False):
+        return
+
+    def _formatwarning(
+        message: Warning | str,
+        category: type[Warning],
+        filename: str,
+        lineno: int,
+        line: str | None = None,
+    ) -> str:
+        if issubclass(category, FeatureStageWarning):
+            return f"{filename}:{lineno}: {category.__name__}: {message}\n"
+        return current(message, category, filename, lineno, line)
+
+    setattr(_formatwarning, _FEATURE_STAGE_FORMATTER_MARKER, True)
+    # Keep a reference to the wrapped formatter so callers (tests, embedders)
+    # can restore the previous behaviour if they need to.
+    _formatwarning.__wrapped__ = current  # type: ignore[attr-defined]
+    warnings.formatwarning = _formatwarning
+
+
+_install_feature_stage_formatter()
+
+
 def _normalize_feature_id(feature_id: str | Enum) -> str:
     return str(feature_id.value if isinstance(feature_id, Enum) else feature_id)
 
@@ -109,23 +158,91 @@ def _set_feature_stage_metadata(obj: Any, *, stage: FeatureStageName, feature_id
     setattr(obj, _FEATURE_ID_ATTR, feature_id)
 
 
+_INTERNAL_FRAME_FILE = os.path.normcase(__file__)
+# Module names whose frames we never want to surface as the caller. ``abc`` is
+# the big one (its ``__new__`` shows up as ``<frozen abc>:106`` for ABC-driven
+# subclass creation on modern CPython, so we cannot rely on filename matching).
+# ``functools``/``typing``/``contextlib`` are added because they often wrap our
+# decorators or appear in the metaclass call path.
+_INTERNAL_FRAME_MODULES: frozenset[str] = frozenset({
+    abc.__name__,
+    functools.__name__,
+    typing.__name__,
+    contextlib.__name__,
+})
+
+
+def _is_internal_frame(frame: Any) -> bool:
+    if os.path.normcase(frame.f_code.co_filename) == _INTERNAL_FRAME_FILE:
+        return True
+    module_name = frame.f_globals.get("__name__", "")
+    if module_name in _INTERNAL_FRAME_MODULES:
+        return True
+    # Submodules of the skipped stdlib packages (``typing.ext``, ``functools``
+    # wrappers under ``concurrent.futures._base``, etc.) are also wrappers we
+    # don't want to surface.
+    return any(module_name.startswith(prefix + ".") for prefix in _INTERNAL_FRAME_MODULES)
+
+
+def _resolve_user_frame() -> tuple[str, int, str] | None:
+    """Resolve the user frame that triggered an experimental warning.
+
+    Walk the stack and return ``(filename, lineno, module_name)`` for the first
+    frame outside this module and the wrapping/metaclass machinery.
+
+    Returns ``None`` if no such frame is found; callers fall back to plain
+    ``warnings.warn`` with a fixed stacklevel.
+    """
+    # Frame objects participate in reference cycles (``frame -> f_locals ->
+    # frame``) and can delay GC if held implicitly. Capture the user frame's
+    # data into plain values inside the try, and explicitly delete the frame
+    # references in finally so we never leak frames across this call. This
+    # follows CPython's own guidance for code that uses ``inspect.currentframe``.
+    frame = inspect.currentframe()
+    candidate: Any = None
+    try:
+        if frame is None:
+            return None
+        # Skip _resolve_user_frame itself + the warn helper that called it.
+        candidate = frame.f_back.f_back if frame.f_back and frame.f_back.f_back else None
+        while candidate is not None:
+            if not _is_internal_frame(candidate):
+                return (
+                    candidate.f_code.co_filename,
+                    candidate.f_lineno,
+                    candidate.f_globals.get("__name__", "<unknown>"),
+                )
+            candidate = candidate.f_back
+        return None
+    finally:
+        del frame, candidate
+
+
 def _warn_on_feature_use(
     *,
     stage: FeatureStageName,
     feature_id: str,
     object_name: str,
     category: type[Warning],
-    stacklevel: int,
 ) -> None:
     warning_key = (category, feature_id)
     if warning_key in _WARNED_FEATURES:
         return
 
-    warnings.warn(
-        _build_stage_warning_message(stage=stage, feature_id=feature_id, object_name=object_name),
-        category=category,
-        stacklevel=stacklevel,
-    )
+    message = _build_stage_warning_message(stage=stage, feature_id=feature_id, object_name=object_name)
+    user_frame = _resolve_user_frame()
+    if user_frame is None:
+        # Last-resort fallback: emit at the immediate caller of this helper.
+        warnings.warn(message, category=category, stacklevel=2)
+    else:
+        filename, lineno, module = user_frame
+        warnings.warn_explicit(
+            message,
+            category=category,
+            filename=filename,
+            lineno=lineno,
+            module=module,
+        )
     _WARNED_FEATURES.add(warning_key)
 
 
@@ -150,7 +267,6 @@ def _add_runtime_warning(
                     feature_id=feature_id,
                     object_name=object_name,
                     category=category,
-                    stacklevel=3,
                 )
             if original_new is not object.__new__:
                 return original_new(cls, *args, **kwargs)
@@ -171,7 +287,6 @@ def _add_runtime_warning(
                     feature_id=feature_id,
                     object_name=object_name,
                     category=category,
-                    stacklevel=3,
                 )
                 return original_init_subclass_func(*args, **kwargs)
 
@@ -185,7 +300,6 @@ def _add_runtime_warning(
                     feature_id=feature_id,
                     object_name=object_name,
                     category=category,
-                    stacklevel=3,
                 )
                 return original_init_subclass(*args, **kwargs)
 
@@ -200,7 +314,6 @@ def _add_runtime_warning(
             feature_id=feature_id,
             object_name=object_name,
             category=category,
-            stacklevel=3,
         )
         return obj(*args, **kwargs)
 

python/packages/core/tests/core/test_feature_stage.py

@@ -142,6 +142,39 @@ def test_experimental_class_warns_on_instantiation_and_not_on_definition() -> No
     assert ExperimentalClass.__feature_id__ == AlternateExperimentalFeature.EXPERIMENTAL_FEATURE.value
 
 
+def test_experimental_abc_subclass_warning_points_at_user_file() -> None:
+    """Subclassing an experimental ABC must report the warning at the user's
+    ``class Sub(...):`` line, not at internal abc.py / <frozen abc> frames.
+
+    Regression: previously the fixed ``stacklevel=3`` landed inside abc.py for
+    ABC-driven class creation, surfacing ``<frozen abc>:106`` to users.
+    """
+    from abc import ABC, abstractmethod
+
+    @experimental(feature_id=AlternateExperimentalFeature.EXPERIMENTAL_FEATURE)  # type: ignore[arg-type]
+    class ExperimentalABC(ABC):
+        @abstractmethod
+        def do(self) -> int: ...
+
+    with warnings.catch_warnings(record=True) as caught:
+        warnings.simplefilter("always")
+        subclass_line = inspect.currentframe().f_lineno + 1
+
+        class Concrete(ExperimentalABC):
+            def do(self) -> int:
+                return 1
+
+    assert len(caught) == 1
+    assert caught[0].filename == __file__
+    # __init_subclass__ fires at the end of the class body, so the lineno
+    # points somewhere inside the Concrete class definition rather than at
+    # the ``class Concrete`` header itself. The key behaviour we want to
+    # guarantee is that it is in the *user* file at all (not abc.py).
+    assert subclass_line <= caught[0].lineno <= subclass_line + 5
+    assert issubclass(caught[0].category, ExperimentalWarning)
+    assert Concrete().do() == 1
+
+
 def test_experimental_runtime_checkable_protocol_keeps_protocol_runtime_checks() -> None:
     with warnings.catch_warnings(record=True) as caught:
         warnings.simplefilter("always")

python/packages/foundry/tests/foundry/test_foundry_chat_client.py

@@ -900,7 +900,7 @@ async def test_integration_web_search() -> None:
         "messages": [
             Message(
                 role="user",
-                contents=["Who are the main characters of Kpop Demon Hunters? Do a web search to find the answer."],
+                contents=["Where is Microsoft's headquarters? Do a web search to find the answer."],
             )
         ],
         "options": {"tool_choice": "auto", "tools": [web_search_tool]},
@@ -908,9 +908,7 @@ async def test_integration_web_search() -> None:
     response = await client.get_response(stream=True, **content).get_final_response()
 
     assert isinstance(response, ChatResponse)
-    assert "Rumi" in response.text
-    assert "Mira" in response.text
-    assert "Zoey" in response.text
+    assert "redmond" in response.text.lower()
 
 
 @pytest.mark.flaky

python/packages/foundry_hosting/agent_framework_foundry_hosting/_responses.py

@@ -9,8 +9,9 @@ import logging
 import os
 import tempfile
 import threading
-from collections.abc import AsyncIterable, AsyncIterator, Generator, Mapping, Sequence
+from collections.abc import AsyncIterable, AsyncIterator, Generator, Sequence
 from contextlib import suppress
+from dataclasses import asdict, is_dataclass
 from pathlib import Path
 from contextlib import AbstractAsyncContextManager, AsyncExitStack, suppress
 from typing import Protocol, cast
@@ -1505,11 +1506,20 @@ def _convert_message_content(content: MessageContent) -> Content:
 # region Output Item Conversion
 
 
-def _arguments_to_str(arguments: str | Mapping[str, Any] | None) -> str:
+def _argument_json_default(value: Any) -> Any:
+    if is_dataclass(value) and not isinstance(value, type):
+        return asdict(value)
+    to_dict = getattr(value, "to_dict", None)
+    if callable(to_dict):
+        return to_dict()
+    raise TypeError(f"Object of type {type(value).__name__} is not JSON serializable")
+
+
+def _arguments_to_str(arguments: Any | None) -> str:
     """Convert arguments to a JSON string.
 
     Args:
-        arguments: The arguments to convert, can be a string, mapping, or None.
+        arguments: The arguments to convert, can be a string, JSON-like object, or None.
 
     Returns:
         The arguments as a JSON string.
@@ -1518,7 +1528,7 @@ def _arguments_to_str(arguments: str | Mapping[str, Any] | None) -> str:
         return ""
     if isinstance(arguments, str):
         return arguments
-    return json.dumps(arguments)
+    return json.dumps(arguments, default=_argument_json_default)
 
 
 async def _to_outputs(

python/packages/foundry_hosting/tests/test_responses.py

@@ -12,6 +12,7 @@ from __future__ import annotations
 
 import json
 from collections.abc import AsyncIterator, Callable
+from dataclasses import dataclass
 from unittest.mock import AsyncMock, MagicMock
 
 import httpx
@@ -405,6 +406,36 @@ class TestStreaming:
         assert len(args_done) == 1
         assert args_done[0]["data"]["arguments"] == '{"q": "hello"}'
 
+    async def test_function_call_streaming_serializes_dataclass_arguments(self) -> None:
+        @dataclass
+        class HandoffLikeRequest:
+            agent_response: AgentResponse
+
+        request = HandoffLikeRequest(
+            agent_response=AgentResponse(
+                messages=[Message(role="assistant", contents=[Content.from_text("Need more details")])]
+            )
+        )
+        agent = _make_agent(
+            stream_updates=[
+                AgentResponseUpdate(
+                    contents=[Content.from_function_call("call_1", "handoff_to_refund", arguments=request)],
+                    role="assistant",
+                ),
+            ]
+        )
+        server = _make_server(agent)
+        resp = await _post(server, stream=True)
+
+        assert resp.status_code == 200
+        events = _parse_sse_events(resp.text)
+        args_done = [e for e in events if e["event"] == "response.function_call_arguments.done"]
+        assert len(args_done) == 1
+
+        payload = json.loads(args_done[0]["data"]["arguments"])
+        assert payload["agent_response"]["type"] == "agent_response"
+        assert payload["agent_response"]["messages"][0]["contents"][0]["text"] == "Need more details"
+
     async def test_alternating_text_and_function_call(self) -> None:
         agent = _make_agent(
             stream_updates=[

python/samples/03-workflows/orchestrations/sequential_agents.py

@@ -4,7 +4,7 @@ import asyncio
 import os
 from typing import cast
 
-from agent_framework import Agent, Message
+from agent_framework import Agent, AgentResponse, Message
 from agent_framework.foundry import FoundryChatClient
 from agent_framework.orchestrations import SequentialBuilder
 from azure.identity import AzureCliCredential
@@ -17,9 +17,9 @@ load_dotenv()
 Sample: Sequential workflow (agent-focused API) with shared conversation context
 
 Build a high-level sequential workflow using SequentialBuilder and two domain agents.
-The shared conversation (list[Message]) flows through each participant. Each agent
-appends its assistant message to the context. The workflow outputs the final conversation
-list when complete.
+The shared conversation flows through each participant. Each agent appends its
+assistant message to the context. The sample prints the original user message plus
+the visible outputs from both agents.
 
 Note on internal adapters:
 - Sequential orchestration includes small adapter nodes for input normalization
@@ -56,17 +56,19 @@ async def main() -> None:
     )
 
     # 2) Build sequential workflow: writer -> reviewer
-    workflow = SequentialBuilder(participants=[writer, reviewer]).build()
+    workflow = SequentialBuilder(participants=[writer, reviewer], output_from="all").build()
 
     # 3) Run and collect outputs
-    outputs: list[list[Message]] = []
-    async for event in workflow.run("Write a tagline for a budget-friendly eBike.", stream=True):
-        if event.type == "output":
-            outputs.append(cast(list[Message], event.data))
+    prompt = "Write a tagline for a budget-friendly eBike."
+    result = await workflow.run(prompt)
+    conversation = [Message(role="user", contents=[prompt])]
+    for output in result.get_outputs():
+        response = cast(AgentResponse, output)
+        conversation.extend(response.messages)
 
-    if outputs:
+    if conversation:
         print("===== Final Conversation =====")
-        for i, msg in enumerate(outputs[-1], start=1):
+        for i, msg in enumerate(conversation, start=1):
             name = msg.author_name or ("assistant" if msg.role == "assistant" else "user")
             print(f"{'-' * 60}\n{i:02d} [{name}]\n{msg.text}")