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Python: [BREAKING] Standardize orchestration terminal outputs as AgentResponse (#5301)
* Fix orchestration outputs so as_agent() returns the final answer only. Align other orchestration outputs * Fix orchestration output issues from review comments 1. Sample cleanup: Remove commented-out FoundryChatClient block and update prerequisites to reference OPENAI_CHAT_MODEL_ID instead of FOUNDRY_* vars. 2. Sequential approval output: Change _EndWithConversation.end_with_agent_executor_response from a no-op sink to yield response.agent_response. When the last participant is AgentApprovalExecutor (via with_request_info), _EndWithConversation is the output executor so the yield produces the terminal answer. When the last participant is a regular AgentExecutor, _EndWithConversation is not in output_executors so the yield is silently filtered out. 3. Forward data events through WorkflowExecutor: _process_workflow_result now also forwards 'data' events from sub-workflows so that emit_intermediate_data=True on AgentExecutor works correctly when wrapped in AgentApprovalExecutor. 4. Concurrent docstring: Update _AggregateAgentConversations docstring to say 'deterministic participant order' instead of 'completion order'. 5. Add test_concurrent_intermediate_outputs_emits_data_events verifying that ConcurrentBuilder(intermediate_outputs=True) emits per-participant data events alongside the single aggregated output event. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Add tests for sequential workflow with_request_info and intermediate_outputs (#5301) Address PR review comments 2, 3, and 5: - Add test_sequential_request_info_last_participant_emits_output: Verifies that when the last participant is wrapped via with_request_info() (AgentApprovalExecutor), the workflow still emits a terminal output after approval, exercising the _EndWithConversation.end_with_agent_executor_response fallback path. - Add test_sequential_request_info_with_intermediate_outputs_emits_data_events: Verifies that emit_intermediate_data=True works correctly through AgentApprovalExecutor wrapping—WorkflowExecutor._process_result already forwards data events from sub-workflows, so intermediate agent responses surface as data events in the parent workflow. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix pyright type errors from AgentResponse output refactor (#5301) Update cast() calls in _group_chat.py and _magentic.py to use WorkflowContext[Never, AgentResponse] instead of the old WorkflowContext[Never, list[Message]], matching the updated method signatures in _base_group_chat_orchestrator.py. Fix _sequential.py _EndWithConversation.end_with_agent_executor_response to declare WorkflowContext[Any, AgentResponse] so yield_output accepts AgentResponse[None]. Fix _workflow_executor.py data event forwarding to handle nullable executor_id. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix pyright reportUnknownVariableType in _agent.py (#5301) Extract event.data into a typed local variable before the isinstance check to avoid pyright narrowing it to AgentResponse[Unknown]. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix pyright reportMissingImports for orjson in file history samples (#5301) Add pyright: ignore[reportMissingImports] to orjson imports that are already guarded by try/except ImportError, matching the existing pattern used elsewhere in the samples. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback for #5301: review comment fixes * Address review feedback for #5301: review comment fixes * Revert sequential_workflow_as_agent sample to FoundryChatClient Reverts the mistaken switch from FoundryChatClient to OpenAIChatClient in the sequential workflow as agent sample. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address ultrareview feedback: emit_data_events rename + WorkflowAgent reasoning conversion Layered on top of the prior review-feedback work in this branch. Renames: - AgentExecutor.emit_intermediate_data -> emit_data_events (mechanical rename; orchestration semantics live at the orchestration layer, not the general-purpose executor). Forwarded through MagenticAgentExecutor, AgentApprovalExecutor, and all orchestration call sites. - HandoffAgentExecutor._check_terminate_and_yield -> _should_terminate (pure predicate; no longer yields anything). HandoffBuilder docstring rewritten to describe the new per-agent AgentResponse output contract. WorkflowAgent reasoning-content conversion: - Add _rewrite_text_to_reasoning(contents) and _msg_as_reasoning(msg) helpers; the as_agent() path now reframes text content from data events as text_reasoning Content blocks before merging into the AgentResponse. - Consumers iterate msg.contents and branch on content.type — same path they already use for Claude thinking and OpenAI reasoning. No new field on Message/AgentResponse/WorkflowEvent. - Streaming branch constructs fresh AgentResponseUpdate instances instead of mutating shared payloads (regression test added). - Helper _msg_maybe_reasoning consolidates the conditional rewrite at three call sites in the non-streaming conversion. Tests: - TestWorkflowAgentReasoningHelpers + TestWorkflowAgentDataEventReasoningConversion add 9 new tests covering helpers, non-streaming, streaming, mixed content, already-reasoning passthrough, and mutation-safety regression. - Updated test_sequential_as_agent_with_intermediate_outputs_includes_chain to assert text_reasoning content for intermediate agents. * Fix pyright: widen event.data to Any to avoid partial-unknown narrowing The streaming conversion path narrowed event.data via isinstance against generic AgentResponse, producing AgentResponse[Unknown] and tripping reportUnknownVariableType/reportUnknownMemberType. Binding data: Any before the check keeps runtime behavior identical while restoring a fully known type for downstream access. * Clean up design * Scope to agent output semantics only * yield AgentResponseUpdate streaming, AgentResponse non-streaming * Fix mypy/pyright: widen cast types at GroupChat callsites Eight callsites in _group_chat.py still cast to WorkflowContext[Never, AgentResponse] but the base orchestrator methods now accept the wider WorkflowContext[Never, AgentResponse | AgentResponseUpdate] (mode-aware yields). W_OutT is invariant, so the narrower cast is not assignable. Magentic was widened in the same commit; this catches the GroupChat callsites that were missed. * Python: skip flaky Foundry / Foundry Hosting integration tests (#5553) These two integration tests have been failing in the merge queue across multiple unrelated PRs (5301, 5531). Both are marked `@pytest.mark.flaky` with 3 retries, but all attempts fail back-to-back. Skipping both with a reason pointing to #5553 so they can be fixed properly without continuing to block unrelated merges. - packages/foundry_hosting/tests/test_responses_int.py::TestOptions::test_temperature_and_max_tokens - packages/foundry/tests/foundry/test_foundry_embedding_client.py::TestFoundryEmbeddingIntegration::test_text_embedding_live Also includes a one-line uv.lock specifier-ordering normalization auto-applied by the poe-check pre-commit hook. --------- Co-authored-by: Copilot <copilot@github.com> Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Evan Mattson
co-authored by
Copilot
Copilot
Copilot
Copilot
parent
40e90c96c3
commit
866a325b48
@@ -238,18 +238,16 @@ async def test_group_chat_builder_basic_flow() -> None:
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orchestrator_name="manager",
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).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run("coordinate task", stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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assert len(outputs) == 1
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assert len(outputs[0]) >= 1
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# Check that both agents contributed
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authors = {msg.author_name for msg in outputs[0] if msg.author_name in ["alpha", "beta"]}
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assert len(authors) == 2
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# Exactly one terminal `output` event = the orchestrator's completion AgentResponseUpdate
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# (mode-aware: streaming yields a single update chunk for the synthesized message).
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assert len(updates) == 1
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# The completion message is authored by the orchestrator.
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assert updates[0].author_name == "manager"
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async def test_group_chat_as_agent_accepts_conversation() -> None:
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@@ -283,18 +281,16 @@ async def test_agent_manager_handles_concatenated_json_output() -> None:
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orchestrator_agent=manager,
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).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run("coordinate task", stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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assert outputs
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conversation = outputs[-1]
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assert any(msg.author_name == "agent" and msg.text == "worker response" for msg in conversation)
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assert conversation[-1].author_name == manager.name
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assert conversation[-1].text == "concatenated manager final"
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assert updates
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final_update = updates[-1]
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# Terminal update is the orchestrator's completion message.
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assert final_update.author_name == manager.name
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assert final_update.text == "concatenated manager final"
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# Comprehensive tests for group chat functionality
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@@ -400,20 +396,14 @@ class TestGroupChatWorkflow:
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selection_func=selector,
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).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run("test task", stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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# Should have terminated due to max_rounds, expect at least one output
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assert len(outputs) >= 1
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# The final message in the conversation should be about round limit
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conversation = outputs[-1]
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assert len(conversation) >= 1
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final_output = conversation[-1]
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assert "maximum number of rounds" in final_output.text.lower()
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# Exactly one terminal output event = orchestrator's max-rounds completion update.
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assert len(updates) == 1
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assert "maximum number of rounds" in (updates[0].text or "").lower()
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async def test_termination_condition_halts_conversation(self) -> None:
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"""Test that a custom termination condition stops the workflow."""
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@@ -433,20 +423,89 @@ class TestGroupChatWorkflow:
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selection_func=selector,
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).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run("test task", stream=True):
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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assert updates, "Expected termination to yield output"
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# Terminal update is the orchestrator's completion message only.
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assert "termination condition" in (updates[-1].text or "").lower()
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async def test_termination_yields_update_in_streaming(self) -> None:
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"""In streaming mode, the orchestrator's terminal completion surfaces as `AgentResponseUpdate`.
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Mirrors AgentExecutor's mode-aware behavior: streaming workflows produce per-chunk
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`AgentResponseUpdate` events; the synthesized termination message is logically a
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single chunk, so it should be a single `AgentResponseUpdate`.
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"""
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def selector(state: GroupChatState) -> str:
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return "agent"
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def termination_condition(conversation: list[Message]) -> bool:
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replies = [msg for msg in conversation if msg.role == "assistant" and msg.author_name == "agent"]
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return len(replies) >= 2
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workflow = GroupChatBuilder(
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participants=[StubAgent("agent", "response")],
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termination_condition=termination_condition,
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selection_func=selector,
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).build()
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terminal: AgentResponseUpdate | None = None
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async for event in workflow.run("test task", stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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terminal = event.data # last output event wins
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assert outputs, "Expected termination to yield output"
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conversation = outputs[-1]
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agent_replies = [msg for msg in conversation if msg.author_name == "agent" and msg.role == "assistant"]
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assert len(agent_replies) == 2
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final_output = conversation[-1]
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# The orchestrator uses its ID as author_name by default
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assert "termination condition" in final_output.text.lower()
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assert isinstance(terminal, AgentResponseUpdate), (
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f"Expected AgentResponseUpdate in streaming mode, got {type(terminal).__name__}"
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)
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assert "termination condition" in (terminal.text or "").lower()
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async def test_termination_yields_response_in_non_streaming(self) -> None:
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"""In non-streaming mode, the orchestrator's terminal completion surfaces as `AgentResponse`."""
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def selector(state: GroupChatState) -> str:
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return "agent"
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def termination_condition(conversation: list[Message]) -> bool:
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replies = [msg for msg in conversation if msg.role == "assistant" and msg.author_name == "agent"]
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return len(replies) >= 2
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workflow = GroupChatBuilder(
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participants=[StubAgent("agent", "response")],
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termination_condition=termination_condition,
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selection_func=selector,
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).build()
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events = await workflow.run("test task")
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outputs = [ev for ev in events if ev.type == "output"]
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assert len(outputs) == 1
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assert isinstance(outputs[0].data, AgentResponse)
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assert "termination condition" in outputs[0].data.messages[-1].text.lower()
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async def test_max_rounds_yields_update_in_streaming(self) -> None:
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"""Max-rounds completion in streaming mode surfaces as `AgentResponseUpdate`."""
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def selector(state: GroupChatState) -> str:
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return "agent"
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workflow = GroupChatBuilder(
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participants=[StubAgent("agent", "response")],
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max_rounds=2,
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selection_func=selector,
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).build()
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terminal: AgentResponseUpdate | None = None
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async for event in workflow.run("test task", stream=True):
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if event.type == "output":
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terminal = event.data
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assert isinstance(terminal, AgentResponseUpdate), (
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f"Expected AgentResponseUpdate in streaming mode, got {type(terminal).__name__}"
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)
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assert "maximum number of rounds" in (terminal.text or "").lower()
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async def test_termination_condition_agent_manager_finalizes(self) -> None:
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"""Test that termination condition with agent orchestrator produces default termination message."""
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@@ -459,17 +518,15 @@ class TestGroupChatWorkflow:
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orchestrator_agent=manager,
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).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run("test task", stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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assert outputs, "Expected termination to yield output"
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conversation = outputs[-1]
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assert conversation[-1].text == BaseGroupChatOrchestrator.TERMINATION_CONDITION_MET_MESSAGE
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assert conversation[-1].author_name == manager.name
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assert updates, "Expected termination to yield output"
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final_update = updates[-1]
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assert final_update.text == BaseGroupChatOrchestrator.TERMINATION_CONDITION_MET_MESSAGE
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assert final_update.author_name == manager.name
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async def test_unknown_participant_error(self) -> None:
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"""Test that unknown participant selection raises error."""
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@@ -505,14 +562,12 @@ class TestCheckpointing:
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selection_func=selector,
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).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run("test task", stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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assert len(outputs) == 1 # Should complete normally
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assert len(updates) == 1 # Should complete normally
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class TestConversationHandling:
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@@ -546,14 +601,12 @@ class TestConversationHandling:
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workflow = GroupChatBuilder(participants=[agent], max_rounds=1, selection_func=selector).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run("test string", stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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assert len(outputs) == 1
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assert len(updates) == 1
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async def test_handle_chat_message_input(self) -> None:
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"""Test handling Message input directly."""
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@@ -569,14 +622,12 @@ class TestConversationHandling:
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workflow = GroupChatBuilder(participants=[agent], max_rounds=1, selection_func=selector).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run(task_message, stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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assert len(outputs) == 1
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assert len(updates) == 1
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async def test_handle_conversation_list_input(self) -> None:
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"""Test handling conversation list preserves context."""
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@@ -595,14 +646,12 @@ class TestConversationHandling:
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workflow = GroupChatBuilder(participants=[agent], max_rounds=1, selection_func=selector).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run(conversation, stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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assert len(outputs) == 1
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assert len(updates) == 1
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class TestRoundLimitEnforcement:
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@@ -625,20 +674,14 @@ class TestRoundLimitEnforcement:
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selection_func=selector,
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).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run("test", stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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# Should have at least one output (the round limit message)
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assert len(outputs) >= 1
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# The last message in the conversation should be about round limit
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conversation = outputs[-1]
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assert len(conversation) >= 1
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final_output = conversation[-1]
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assert "maximum number of rounds" in final_output.text.lower()
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# Exactly one terminal output event = orchestrator's max-rounds completion update.
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assert len(updates) == 1
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assert "maximum number of rounds" in (updates[0].text or "").lower()
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async def test_round_limit_in_ingest_participant_message(self) -> None:
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"""Test round limit enforcement after participant response."""
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@@ -658,20 +701,14 @@ class TestRoundLimitEnforcement:
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selection_func=selector,
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).build()
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outputs: list[list[Message]] = []
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updates: list[AgentResponseUpdate] = []
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async for event in workflow.run("test", stream=True):
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if event.type == "output":
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data = event.data
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if isinstance(data, list):
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outputs.append(cast(list[Message], data))
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if event.type == "output" and isinstance(event.data, AgentResponseUpdate):
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updates.append(event.data)
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# Should have at least one output (the round limit message)
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assert len(outputs) >= 1
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# The last message in the conversation should be about round limit
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conversation = outputs[-1]
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assert len(conversation) >= 1
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final_output = conversation[-1]
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assert "maximum number of rounds" in final_output.text.lower()
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# Exactly one terminal output event = orchestrator's max-rounds completion update.
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assert len(updates) == 1
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assert "maximum number of rounds" in (updates[0].text or "").lower()
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async def test_group_chat_checkpoint_runtime_only() -> None:
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@@ -684,17 +721,17 @@ async def test_group_chat_checkpoint_runtime_only() -> None:
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wf = GroupChatBuilder(participants=[agent_a, agent_b], max_rounds=2, selection_func=selector).build()
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baseline_output: list[Message] | None = None
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baseline_update: AgentResponseUpdate | None = None
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async for ev in wf.run("runtime checkpoint test", checkpoint_storage=storage, stream=True):
|
||||
if ev.type == "output":
|
||||
baseline_output = cast(list[Message], ev.data) if isinstance(ev.data, list) else None # type: ignore
|
||||
if ev.type == "output" and isinstance(ev.data, AgentResponseUpdate):
|
||||
baseline_update = ev.data
|
||||
if ev.type == "status" and ev.state in (
|
||||
WorkflowRunState.IDLE,
|
||||
WorkflowRunState.IDLE_WITH_PENDING_REQUESTS,
|
||||
):
|
||||
break
|
||||
|
||||
assert baseline_output is not None
|
||||
assert baseline_update is not None
|
||||
|
||||
checkpoints = await storage.list_checkpoints(workflow_name=wf.name)
|
||||
assert len(checkpoints) > 0, "Runtime-only checkpointing should have created checkpoints"
|
||||
@@ -720,17 +757,17 @@ async def test_group_chat_checkpoint_runtime_overrides_buildtime() -> None:
|
||||
checkpoint_storage=buildtime_storage,
|
||||
selection_func=selector,
|
||||
).build()
|
||||
baseline_output: list[Message] | None = None
|
||||
baseline_update: AgentResponseUpdate | None = None
|
||||
async for ev in wf.run("override test", checkpoint_storage=runtime_storage, stream=True):
|
||||
if ev.type == "output":
|
||||
baseline_output = cast(list[Message], ev.data) if isinstance(ev.data, list) else None # type: ignore
|
||||
if ev.type == "output" and isinstance(ev.data, AgentResponseUpdate):
|
||||
baseline_update = ev.data
|
||||
if ev.type == "status" and ev.state in (
|
||||
WorkflowRunState.IDLE,
|
||||
WorkflowRunState.IDLE_WITH_PENDING_REQUESTS,
|
||||
):
|
||||
break
|
||||
|
||||
assert baseline_output is not None
|
||||
assert baseline_update is not None
|
||||
|
||||
buildtime_checkpoints = await buildtime_storage.list_checkpoints(workflow_name=wf.name)
|
||||
runtime_checkpoints = await runtime_storage.list_checkpoints(workflow_name=wf.name)
|
||||
@@ -974,14 +1011,11 @@ async def test_group_chat_with_orchestrator_factory_returning_chat_agent():
|
||||
outputs.append(event)
|
||||
|
||||
assert len(outputs) == 1
|
||||
# The DynamicManagerAgent terminates after second call with final_message
|
||||
final_messages = outputs[0].data
|
||||
assert isinstance(final_messages, list)
|
||||
assert any(
|
||||
msg.text == "dynamic manager final"
|
||||
for msg in cast(list[Message], final_messages)
|
||||
if msg.author_name == "dynamic_manager"
|
||||
)
|
||||
# Streaming mode: terminal yield is AgentResponseUpdate. The DynamicManagerAgent
|
||||
# terminates after second call with final_message.
|
||||
final_update = outputs[0].data
|
||||
assert isinstance(final_update, AgentResponseUpdate)
|
||||
assert final_update.text == "dynamic manager final"
|
||||
|
||||
|
||||
def test_group_chat_with_orchestrator_factory_returning_base_orchestrator():
|
||||
|
||||
Reference in New Issue
Block a user