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

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}")