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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>
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@@ -232,16 +232,18 @@ async def test_groupchat_kwargs_flow_to_agents() -> None:
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async def test_kwargs_stored_in_state() -> None:
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"""Test that function_invocation_kwargs are stored in State with the correct key."""
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from agent_framework import Executor, WorkflowContext, handler
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from typing_extensions import Never
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from agent_framework import AgentResponse, Executor, WorkflowContext, handler
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stored_kwargs: dict[str, Any] | None = None
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class _StateInspector(Executor):
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@handler
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async def inspect(self, msgs: list[Message], ctx: WorkflowContext[list[Message]]) -> None:
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async def inspect(self, msgs: list[Message], ctx: WorkflowContext[Never, AgentResponse]) -> None:
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nonlocal stored_kwargs
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stored_kwargs = ctx.get_state(WORKFLOW_RUN_KWARGS_KEY)
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await ctx.send_message(msgs)
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await ctx.yield_output(AgentResponse(messages=msgs))
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inspector = _StateInspector(id="inspector")
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workflow = SequentialBuilder(participants=[inspector]).build()
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@@ -256,16 +258,18 @@ async def test_kwargs_stored_in_state() -> None:
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async def test_empty_kwargs_stored_as_empty_dict() -> None:
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"""Test that empty kwargs are stored as empty dict in State."""
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from agent_framework import Executor, WorkflowContext, handler
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from typing_extensions import Never
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from agent_framework import AgentResponse, Executor, WorkflowContext, handler
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stored_kwargs: Any = "NOT_CHECKED"
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class _StateChecker(Executor):
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@handler
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async def check(self, msgs: list[Message], ctx: WorkflowContext[list[Message]]) -> None:
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async def check(self, msgs: list[Message], ctx: WorkflowContext[Never, AgentResponse]) -> None:
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nonlocal stored_kwargs
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stored_kwargs = ctx.get_state(WORKFLOW_RUN_KWARGS_KEY)
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await ctx.send_message(msgs)
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await ctx.yield_output(AgentResponse(messages=msgs))
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checker = _StateChecker(id="checker")
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workflow = SequentialBuilder(participants=[checker]).build()
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@@ -695,7 +699,9 @@ async def test_subworkflow_kwargs_accessible_via_state() -> None:
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Verifies that WORKFLOW_RUN_KWARGS_KEY is populated in the subworkflow's State
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with kwargs from the parent workflow.
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"""
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from agent_framework import Executor, WorkflowContext, handler
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from typing_extensions import Never
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from agent_framework import AgentResponse, Executor, WorkflowContext, handler
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from agent_framework._workflows._workflow_executor import WorkflowExecutor
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captured_kwargs_from_state: list[dict[str, Any]] = []
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@@ -704,10 +710,10 @@ async def test_subworkflow_kwargs_accessible_via_state() -> None:
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"""Executor that reads kwargs from State for verification."""
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@handler
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async def read_kwargs(self, msgs: list[Message], ctx: WorkflowContext[list[Message]]) -> None:
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async def read_kwargs(self, msgs: list[Message], ctx: WorkflowContext[Never, AgentResponse]) -> None:
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kwargs_from_state = ctx.get_state(WORKFLOW_RUN_KWARGS_KEY)
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captured_kwargs_from_state.append(kwargs_from_state or {})
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await ctx.send_message(msgs)
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await ctx.yield_output(AgentResponse(messages=msgs))
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# Build inner workflow with State reader
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state_reader = _StateReader(id="state_reader")
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