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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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Evan Mattson
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@@ -26,8 +26,8 @@ Note on internal adapters:
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You can safely ignore them when focusing on agent progress.
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Prerequisites:
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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- FOUNDRY_MODEL must be set to your Azure OpenAI model deployment name.
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- FOUNDRY_PROJECT_ENDPOINT must be set to the Azure Foundry project endpoint.
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- FOUNDRY_MODEL must be set to the model name for the Foundry chat client.
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"""
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@@ -68,28 +68,18 @@ async def main() -> None:
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"""
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Sample Output:
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===== Final Conversation =====
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===== Conversation =====
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------------------------------------------------------------
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01 [user]
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Write a tagline for a budget-friendly eBike.
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------------------------------------------------------------
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02 [writer]
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Ride farther, spend less—your affordable eBike adventure starts here.
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------------------------------------------------------------
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03 [reviewer]
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This tagline clearly communicates affordability and the benefit of extended travel, making it
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appealing to budget-conscious consumers. It has a friendly and motivating tone, though it could
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be slightly shorter for more punch. Overall, a strong and effective suggestion!
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===== as_agent() Conversation =====
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------------------------------------------------------------
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01 [writer]
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Go electric, save big—your affordable ride awaits!
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------------------------------------------------------------
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02 [reviewer]
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01 [reviewer]
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Catchy and straightforward! The tagline clearly emphasizes both the electric aspect and the affordability of the
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eBike. It's inviting and actionable. For even more impact, consider making it slightly shorter:
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"Go electric, save big." Overall, this is an effective and appealing suggestion for a budget-friendly eBike.
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Note:
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`workflow.as_agent()` returns ONLY the final agent's response (the "answer") — the prior agents' work
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is not included in the response. To observe intermediate agents while running as an agent, build with
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`SequentialBuilder(participants=[...], intermediate_outputs=True)`; the intermediate replies are then
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surfaced as `data` events and merged into the AgentResponse.
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"""
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@@ -2,11 +2,11 @@
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import asyncio
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import os
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from typing import Any
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from agent_framework import (
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Agent,
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AgentExecutorResponse,
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AgentResponse,
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Executor,
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Message,
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WorkflowContext,
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@@ -16,6 +16,7 @@ from agent_framework.foundry import FoundryChatClient
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from agent_framework.orchestrations import SequentialBuilder
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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from typing_extensions import Never
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# Load environment variables from .env file
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load_dotenv()
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@@ -25,13 +26,14 @@ Sample: Sequential workflow mixing agents and a custom summarizer executor
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This demonstrates how SequentialBuilder chains participants with a shared
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conversation context (list[Message]). An agent produces content; a custom
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executor appends a compact summary to the conversation. The workflow completes
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after all participants have executed in sequence, and the final output contains
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the complete conversation.
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executor synthesizes a compact summary and yields it as the workflow's terminal
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output.
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Custom executor contract:
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- Provide at least one @handler accepting AgentExecutorResponse and a WorkflowContext[list[Message]]
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- Emit the updated conversation via ctx.send_message([...])
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- Intermediate custom executors: handle the message type from the prior participant
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and forward `list[Message]` via `ctx.send_message(...)` for the next participant.
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- Terminator custom executors: handle the message type from the prior participant and
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yield the workflow's final answer as an `AgentResponse` via `ctx.yield_output(...)`.
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Prerequisites:
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- FOUNDRY_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
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@@ -41,27 +43,29 @@ Prerequisites:
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class Summarizer(Executor):
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"""Simple summarizer: consumes full conversation and appends an assistant summary."""
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"""Terminator custom executor: synthesizes a one-line summary as the workflow's final answer."""
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@handler
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async def summarize(self, agent_response: AgentExecutorResponse, ctx: WorkflowContext[list[Message]]) -> None:
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"""Append a summary message to a copy of the full conversation.
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async def summarize(
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self,
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agent_response: AgentExecutorResponse,
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ctx: WorkflowContext[Never, AgentResponse],
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) -> None:
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"""Yield a terminal AgentResponse containing the summary.
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Note: A custom executor must be able to handle the message type from the prior participant, and produce
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the message type expected by the next participant. In this case, the prior participant is an agent thus
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the input is AgentExecutorResponse (an agent will be wrapped in an AgentExecutor, which produces
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`AgentExecutorResponse`). If the next participant is also an agent or this is the final participant,
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the output must be `list[Message]`.
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The prior participant is an agent, which is wrapped in an `AgentExecutor` that
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produces `AgentExecutorResponse`. As the last participant in the sequential workflow,
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this executor calls `ctx.yield_output(AgentResponse(...))` so its output becomes the
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workflow's terminal output (rather than being forwarded to a downstream participant).
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"""
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if not agent_response.full_conversation:
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await ctx.send_message([Message("assistant", ["No conversation to summarize."])])
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await ctx.yield_output(AgentResponse(messages=[Message("assistant", ["No conversation to summarize."])]))
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return
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users = sum(1 for m in agent_response.full_conversation if m.role == "user")
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assistants = sum(1 for m in agent_response.full_conversation if m.role == "assistant")
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summary = Message("assistant", [f"Summary -> users:{users} assistants:{assistants}"])
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final_conversation = list(agent_response.full_conversation) + [summary]
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await ctx.send_message(final_conversation)
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await ctx.yield_output(AgentResponse(messages=[summary]))
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async def main() -> None:
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@@ -81,33 +85,20 @@ async def main() -> None:
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summarizer = Summarizer(id="summarizer")
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workflow = SequentialBuilder(participants=[content, summarizer]).build()
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# 3) Run workflow and extract final conversation
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# 3) Run workflow and extract the final summary
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events = await workflow.run("Explain the benefits of budget eBikes for commuters.")
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outputs = events.get_outputs()
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if outputs:
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print("===== Final Conversation =====")
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messages: list[Message] | Any = outputs[0]
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for i, msg in enumerate(messages, start=1):
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name = msg.author_name or ("assistant" if msg.role == "assistant" else "user")
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print(f"{'-' * 60}\n{i:02d} [{name}]\n{msg.text}")
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print("===== Final Summary =====")
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final: AgentResponse = outputs[0]
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for msg in final.messages:
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print(msg.text)
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"""
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Sample Output:
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------------------------------------------------------------
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01 [user]
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Explain the benefits of budget eBikes for commuters.
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------------------------------------------------------------
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02 [content]
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Budget eBikes offer commuters an affordable, eco-friendly alternative to cars and public transport.
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Their electric assistance reduces physical strain and allows riders to cover longer distances quickly,
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minimizing travel time and fatigue. Budget models are low-cost to maintain and operate, making them accessible
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for a wider range of people. Additionally, eBikes help reduce traffic congestion and carbon emissions,
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supporting greener urban environments. Overall, budget eBikes provide cost-effective, efficient, and
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sustainable transportation for daily commuting needs.
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------------------------------------------------------------
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03 [assistant]
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===== Final Summary =====
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Summary -> users:1 assistants:1
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"""
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