mirror of
https://github.com/microsoft/agent-framework.git
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866a325b48
* 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>
477 lines
19 KiB
Python
477 lines
19 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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from collections.abc import AsyncIterable, Awaitable
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from typing import Any, Literal, overload
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import pytest
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from agent_framework import (
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AgentExecutorResponse,
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AgentResponse,
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AgentResponseUpdate,
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AgentRunInputs,
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AgentSession,
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BaseAgent,
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Content,
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Executor,
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Message,
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ResponseStream,
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TypeCompatibilityError,
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WorkflowContext,
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WorkflowRunState,
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handler,
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)
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from agent_framework._workflows._checkpoint import InMemoryCheckpointStorage
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from agent_framework.orchestrations import SequentialBuilder
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from typing_extensions import Never
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class _EchoAgent(BaseAgent):
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"""Simple agent that appends a single assistant message with its name."""
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@overload
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def run(
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self,
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messages: AgentRunInputs | None = ...,
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*,
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stream: Literal[False] = ...,
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session: AgentSession | None = ...,
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**kwargs: Any,
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) -> Awaitable[AgentResponse[Any]]: ...
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@overload
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def run(
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self,
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messages: AgentRunInputs | None = ...,
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*,
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stream: Literal[True],
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session: AgentSession | None = ...,
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**kwargs: Any,
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) -> ResponseStream[AgentResponseUpdate, AgentResponse[Any]]: ...
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def run(
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self,
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messages: AgentRunInputs | None = None,
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*,
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stream: bool = False,
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session: AgentSession | None = None,
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**kwargs: Any,
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) -> Awaitable[AgentResponse[Any]] | ResponseStream[AgentResponseUpdate, AgentResponse[Any]]:
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if stream:
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async def _stream() -> AsyncIterable[AgentResponseUpdate]:
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yield AgentResponseUpdate(contents=[Content.from_text(text=f"{self.name} reply")])
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return ResponseStream(_stream(), finalizer=AgentResponse.from_updates)
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async def _run() -> AgentResponse:
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return AgentResponse(messages=[Message("assistant", [f"{self.name} reply"])])
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return _run()
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class _SummarizerTerminator(Executor):
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"""Custom-executor terminator that yields a synthesized summary as the workflow's final answer."""
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@handler
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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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conversation = agent_response.full_conversation or []
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user_texts = [m.text for m in conversation if m.role == "user"]
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agents = [m.author_name or m.role for m in conversation if m.role == "assistant"]
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summary = Message("assistant", [f"Summary of users:{len(user_texts)} agents:{len(agents)}"])
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await ctx.yield_output(AgentResponse(messages=[summary]))
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class _InvalidExecutor(Executor):
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"""Invalid executor that does not have a handler that accepts a list of chat messages"""
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@handler
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async def summarize(self, conversation: list[str], ctx: WorkflowContext[list[Message]]) -> None:
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pass
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def test_sequential_builder_rejects_empty_participants() -> None:
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with pytest.raises(ValueError):
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SequentialBuilder(participants=[])
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def test_sequential_builder_validation_rejects_invalid_executor() -> None:
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"""Test that adding an invalid executor to the builder raises an error."""
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with pytest.raises(TypeCompatibilityError):
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SequentialBuilder(participants=[_EchoAgent(id="agent1", name="A1"), _InvalidExecutor(id="invalid")]).build()
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async def test_sequential_streaming_yields_only_last_agent_updates() -> None:
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"""Streaming mode surfaces only the last agent's AgentResponseUpdate chunks as outputs.
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Intermediate agents do NOT emit `output` events; only the last agent (the workflow's
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output_executor) emits chunks of the final answer.
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"""
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a1 = _EchoAgent(id="agent1", name="A1")
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a2 = _EchoAgent(id="agent2", name="A2")
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wf = SequentialBuilder(participants=[a1, a2]).build()
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completed = False
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update_events: list[AgentResponseUpdate] = []
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async for ev in wf.run("hello sequential", stream=True):
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if ev.type == "status" and ev.state == WorkflowRunState.IDLE:
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completed = True
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elif ev.type == "output":
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update_events.append(ev.data) # type: ignore[arg-type]
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if completed:
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break
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assert completed
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# Only the last agent's streaming chunks surface as `output` events.
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assert update_events, "Expected at least one streaming update from the last agent"
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for upd in update_events:
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assert isinstance(upd, AgentResponseUpdate)
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combined_text = "".join(u.text for u in update_events if hasattr(u, "text"))
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assert "A2 reply" in combined_text
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assert "A1 reply" not in combined_text
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async def test_sequential_non_streaming_yields_only_last_agent_response() -> None:
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"""Non-streaming mode emits a single `output` event with the last agent's AgentResponse."""
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a1 = _EchoAgent(id="agent1", name="A1")
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a2 = _EchoAgent(id="agent2", name="A2")
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wf = SequentialBuilder(participants=[a1, a2]).build()
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output_events = [ev for ev in await wf.run("hello sequential") if ev.type == "output"]
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assert len(output_events) == 1
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response = output_events[0].data
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assert isinstance(response, AgentResponse)
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assert all(m.role == "assistant" for m in response.messages)
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combined = " ".join(m.text for m in response.messages)
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assert "A2 reply" in combined
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assert "A1 reply" not in combined
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async def test_sequential_as_agent_returns_only_last_agent_response() -> None:
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"""`workflow.as_agent().run(prompt)` returns ONLY the last agent's messages — not the user
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input or earlier agents' replies. This is the core fix for the orchestration-as-agent
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output contract."""
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a1 = _EchoAgent(id="agent1", name="A1")
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a2 = _EchoAgent(id="agent2", name="A2")
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agent = SequentialBuilder(participants=[a1, a2]).build().as_agent()
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response = await agent.run("hello as_agent")
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assert isinstance(response, AgentResponse)
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# Only the last agent's reply — no user prompt, no agent1 messages.
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combined = " ".join(m.text for m in response.messages)
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assert "A2 reply" in combined
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assert "A1 reply" not in combined
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assert "hello as_agent" not in combined
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async def test_sequential_with_custom_executor_summary() -> None:
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"""A custom-executor terminator yields its own AgentResponse — that becomes the workflow output.
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Custom executors used as the terminator must call `ctx.yield_output(AgentResponse(...))`
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directly (rather than `ctx.send_message(list[Message])` like an intermediate executor would),
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because the terminator IS the workflow's output executor.
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"""
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a1 = _EchoAgent(id="agent1", name="A1")
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summarizer = _SummarizerTerminator(id="summarizer")
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wf = SequentialBuilder(participants=[a1, summarizer]).build()
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output_events = [ev for ev in await wf.run("topic X") if ev.type == "output"]
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assert len(output_events) == 1
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response = output_events[0].data
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assert isinstance(response, AgentResponse)
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assert len(response.messages) == 1
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assert response.messages[0].role == "assistant"
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assert response.messages[0].text.startswith("Summary of users:")
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async def test_sequential_checkpoint_resume_round_trip() -> None:
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storage = InMemoryCheckpointStorage()
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initial_agents = (_EchoAgent(id="agent1", name="A1"), _EchoAgent(id="agent2", name="A2"))
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wf = SequentialBuilder(participants=list(initial_agents), checkpoint_storage=storage).build()
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baseline_updates: list[AgentResponseUpdate] = []
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async for ev in wf.run("checkpoint sequential", stream=True):
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if ev.type == "output":
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baseline_updates.append(ev.data) # type: ignore[arg-type]
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if ev.type == "status" and ev.state == WorkflowRunState.IDLE:
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break
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assert baseline_updates
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checkpoints = await storage.list_checkpoints(workflow_name=wf.name)
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assert checkpoints
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checkpoints.sort(key=lambda cp: cp.timestamp)
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resume_checkpoint = checkpoints[0]
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resumed_agents = (_EchoAgent(id="agent1", name="A1"), _EchoAgent(id="agent2", name="A2"))
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wf_resume = SequentialBuilder(participants=list(resumed_agents), checkpoint_storage=storage).build()
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resumed_updates: list[AgentResponseUpdate] = []
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async for ev in wf_resume.run(checkpoint_id=resume_checkpoint.checkpoint_id, stream=True):
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if ev.type == "output":
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resumed_updates.append(ev.data) # type: ignore[arg-type]
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if ev.type == "status" and ev.state in (
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WorkflowRunState.IDLE,
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WorkflowRunState.IDLE_WITH_PENDING_REQUESTS,
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):
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break
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assert resumed_updates
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baseline_text = "".join(u.text for u in baseline_updates if hasattr(u, "text"))
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resumed_text = "".join(u.text for u in resumed_updates if hasattr(u, "text"))
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assert baseline_text == resumed_text
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async def test_sequential_checkpoint_runtime_only() -> None:
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"""Test checkpointing configured ONLY at runtime, not at build time."""
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storage = InMemoryCheckpointStorage()
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agents = (_EchoAgent(id="agent1", name="A1"), _EchoAgent(id="agent2", name="A2"))
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wf = SequentialBuilder(participants=list(agents)).build()
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baseline_updates: list[AgentResponseUpdate] = []
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async for ev in wf.run("runtime checkpoint test", checkpoint_storage=storage, stream=True):
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if ev.type == "output":
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baseline_updates.append(ev.data) # type: ignore[arg-type]
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if ev.type == "status" and ev.state == WorkflowRunState.IDLE:
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break
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assert baseline_updates
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checkpoints = await storage.list_checkpoints(workflow_name=wf.name)
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assert checkpoints
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checkpoints.sort(key=lambda cp: cp.timestamp)
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resume_checkpoint = checkpoints[0]
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resumed_agents = (_EchoAgent(id="agent1", name="A1"), _EchoAgent(id="agent2", name="A2"))
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wf_resume = SequentialBuilder(participants=list(resumed_agents)).build()
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resumed_updates: list[AgentResponseUpdate] = []
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async for ev in wf_resume.run(
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checkpoint_id=resume_checkpoint.checkpoint_id, checkpoint_storage=storage, stream=True
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):
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if ev.type == "output":
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resumed_updates.append(ev.data) # type: ignore[arg-type]
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if ev.type == "status" and ev.state in (
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WorkflowRunState.IDLE,
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WorkflowRunState.IDLE_WITH_PENDING_REQUESTS,
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):
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break
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assert resumed_updates
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baseline_text = "".join(u.text for u in baseline_updates if hasattr(u, "text"))
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resumed_text = "".join(u.text for u in resumed_updates if hasattr(u, "text"))
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assert baseline_text == resumed_text
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async def test_sequential_checkpoint_runtime_overrides_buildtime() -> None:
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"""Test that runtime checkpoint storage overrides build-time configuration."""
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import tempfile
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with tempfile.TemporaryDirectory() as temp_dir1, tempfile.TemporaryDirectory() as temp_dir2:
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from agent_framework._workflows._checkpoint import FileCheckpointStorage
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buildtime_storage = FileCheckpointStorage(temp_dir1)
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runtime_storage = FileCheckpointStorage(temp_dir2)
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agents = (_EchoAgent(id="agent1", name="A1"), _EchoAgent(id="agent2", name="A2"))
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wf = SequentialBuilder(participants=list(agents), checkpoint_storage=buildtime_storage).build()
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baseline_output: list[Message] | None = None
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async for ev in wf.run("override test", checkpoint_storage=runtime_storage, stream=True):
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if ev.type == "output":
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baseline_output = ev.data # type: ignore[assignment]
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if ev.type == "status" and ev.state == WorkflowRunState.IDLE:
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break
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assert baseline_output is not None
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buildtime_checkpoints = await buildtime_storage.list_checkpoints(workflow_name=wf.name)
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runtime_checkpoints = await runtime_storage.list_checkpoints(workflow_name=wf.name)
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assert len(runtime_checkpoints) > 0, "Runtime storage should have checkpoints"
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assert len(buildtime_checkpoints) == 0, "Build-time storage should have no checkpoints when overridden"
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async def test_sequential_builder_reusable_after_build_with_participants() -> None:
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"""Test that the builder can be reused to build multiple identical workflows with participants()."""
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a1 = _EchoAgent(id="agent1", name="A1")
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a2 = _EchoAgent(id="agent2", name="A2")
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builder = SequentialBuilder(participants=[a1, a2])
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# Build first workflow
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builder.build()
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assert builder._participants[0] is a1 # type: ignore
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assert builder._participants[1] is a2 # type: ignore
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# ---------------------------------------------------------------------------
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# chain_only_agent_responses tests
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# ---------------------------------------------------------------------------
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class _CapturingAgent(BaseAgent):
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"""Agent that records the messages it received and returns a configurable reply."""
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def __init__(self, *, reply_text: str = "reply", **kwargs: Any):
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super().__init__(**kwargs)
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self.reply_text = reply_text
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self.last_messages: list[Message] = []
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@overload
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def run(
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self,
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messages: AgentRunInputs | None = ...,
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*,
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stream: Literal[False] = ...,
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session: AgentSession | None = ...,
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**kwargs: Any,
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) -> Awaitable[AgentResponse[Any]]: ...
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@overload
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def run(
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self,
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messages: AgentRunInputs | None = ...,
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*,
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stream: Literal[True],
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session: AgentSession | None = ...,
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**kwargs: Any,
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) -> ResponseStream[AgentResponseUpdate, AgentResponse[Any]]: ...
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def run(
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self,
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messages: AgentRunInputs | None = None,
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*,
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stream: bool = False,
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session: AgentSession | None = None,
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**kwargs: Any,
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) -> Awaitable[AgentResponse[Any]] | ResponseStream[AgentResponseUpdate, AgentResponse[Any]]:
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captured: list[Message] = []
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if messages:
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for m in messages: # type: ignore[union-attr]
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if isinstance(m, Message):
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captured.append(m)
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elif isinstance(m, str):
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captured.append(Message("user", [m]))
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self.last_messages = captured
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if stream:
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async def _stream() -> AsyncIterable[AgentResponseUpdate]:
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yield AgentResponseUpdate(contents=[Content.from_text(text=self.reply_text)])
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return ResponseStream(_stream(), finalizer=AgentResponse.from_updates)
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async def _run() -> AgentResponse:
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return AgentResponse(messages=[Message("assistant", [self.reply_text])])
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return _run()
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|
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async def test_chain_only_agent_responses_false_passes_full_conversation() -> None:
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"""Default (chain_only_agent_responses=False) passes full conversation to the second agent."""
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a1 = _CapturingAgent(id="agent1", name="A1", reply_text="A1 reply")
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a2 = _CapturingAgent(id="agent2", name="A2", reply_text="A2 reply")
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|
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wf = SequentialBuilder(participants=[a1, a2], chain_only_agent_responses=False).build()
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|
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async for ev in wf.run("hello", stream=True):
|
|
if ev.type == "status" and ev.state == WorkflowRunState.IDLE:
|
|
break
|
|
|
|
# Second agent should see full conversation: [user("hello"), assistant("A1 reply")]
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seen = a2.last_messages
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assert len(seen) == 2
|
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assert seen[0].role == "user" and "hello" in (seen[0].text or "")
|
|
assert seen[1].role == "assistant" and "A1 reply" in (seen[1].text or "")
|
|
|
|
|
|
async def test_chain_only_agent_responses_true_passes_only_agent_messages() -> None:
|
|
"""chain_only_agent_responses=True passes only the previous agent's response messages."""
|
|
a1 = _CapturingAgent(id="agent1", name="A1", reply_text="A1 reply")
|
|
a2 = _CapturingAgent(id="agent2", name="A2", reply_text="A2 reply")
|
|
|
|
wf = SequentialBuilder(participants=[a1, a2], chain_only_agent_responses=True).build()
|
|
|
|
async for ev in wf.run("hello", stream=True):
|
|
if ev.type == "status" and ev.state == WorkflowRunState.IDLE:
|
|
break
|
|
|
|
# Second agent should see only the assistant message: [assistant("A1 reply")]
|
|
seen = a2.last_messages
|
|
assert len(seen) == 1
|
|
assert seen[0].role == "assistant" and "A1 reply" in (seen[0].text or "")
|
|
|
|
|
|
async def test_chain_only_agent_responses_three_agents() -> None:
|
|
"""chain_only_agent_responses=True with three agents: each sees only the prior agent's reply."""
|
|
a1 = _CapturingAgent(id="agent1", name="A1", reply_text="A1 reply")
|
|
a2 = _CapturingAgent(id="agent2", name="A2", reply_text="A2 reply")
|
|
a3 = _CapturingAgent(id="agent3", name="A3", reply_text="A3 reply")
|
|
|
|
wf = SequentialBuilder(participants=[a1, a2, a3], chain_only_agent_responses=True).build()
|
|
|
|
async for ev in wf.run("hello", stream=True):
|
|
if ev.type == "status" and ev.state == WorkflowRunState.IDLE:
|
|
break
|
|
|
|
# a2 should see only A1's reply
|
|
assert len(a2.last_messages) == 1
|
|
assert a2.last_messages[0].role == "assistant" and "A1 reply" in (a2.last_messages[0].text or "")
|
|
|
|
# a3 should see only A2's reply
|
|
assert len(a3.last_messages) == 1
|
|
assert a3.last_messages[0].role == "assistant" and "A2 reply" in (a3.last_messages[0].text or "")
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# with_request_info tests
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
async def test_sequential_request_info_last_participant_emits_output() -> None:
|
|
"""When the last participant is wrapped via with_request_info(), the workflow
|
|
still emits a terminal output event after approval.
|
|
|
|
This exercises the _EndWithConversation.end_with_agent_executor_response path
|
|
that converts the AgentApprovalExecutor's forwarded AgentExecutorResponse into
|
|
the workflow's final AgentResponse output.
|
|
"""
|
|
from agent_framework_orchestrations._orchestration_request_info import AgentRequestInfoResponse
|
|
|
|
a1 = _EchoAgent(id="agent1", name="A1")
|
|
a2 = _EchoAgent(id="agent2", name="A2")
|
|
|
|
wf = SequentialBuilder(participants=[a1, a2]).with_request_info().build()
|
|
|
|
# First run: collect request_info events for both agents
|
|
request_events: list[Any] = []
|
|
async for ev in wf.run("hello with approval", stream=True):
|
|
if ev.type == "request_info" and isinstance(ev.data, AgentExecutorResponse):
|
|
request_events.append(ev)
|
|
|
|
# Approve each agent in sequence until the workflow completes
|
|
while request_events:
|
|
responses = {req.request_id: AgentRequestInfoResponse.approve() for req in request_events}
|
|
request_events = []
|
|
output_events: list[Any] = []
|
|
async for ev in wf.run(stream=True, responses=responses):
|
|
if ev.type == "request_info" and isinstance(ev.data, AgentExecutorResponse):
|
|
request_events.append(ev)
|
|
elif ev.type == "output":
|
|
output_events.append(ev)
|
|
|
|
# The workflow must produce a terminal output with the last agent's response.
|
|
assert len(output_events) == 1
|
|
response = output_events[0].data
|
|
assert isinstance(response, AgentResponse)
|
|
assert any("A2 reply" in m.text for m in response.messages)
|