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https://github.com/microsoft/agent-framework.git
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5c0b037e2c
* Add support for the Sequential Builder. Add samples. Add tests * AgentExecutor: always compute full convo during response * Upgrade azure-ai-agents ToolOutput to FunctionToolOutput * Explicit notes around allows types for custom agent executors
161 lines
6.0 KiB
Python
161 lines
6.0 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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from collections.abc import AsyncIterable
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from typing import Any
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from agent_framework import (
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AgentRunResponse,
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AgentRunResponseUpdate,
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AgentThread,
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BaseAgent,
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ChatMessage,
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Role,
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TextContent,
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)
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from pydantic import PrivateAttr
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from agent_framework_workflow import (
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AgentExecutor,
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SequentialBuilder,
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WorkflowBuilder,
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WorkflowCompletedEvent,
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WorkflowContext,
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handler,
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)
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from agent_framework_workflow._executor import AgentExecutorResponse, Executor
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class _SimpleAgent(BaseAgent):
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"""Agent that returns a single assistant message (non-streaming path)."""
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def __init__(self, *, reply_text: str, **kwargs: Any) -> None:
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super().__init__(**kwargs)
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self._reply_text = reply_text
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async def run( # type: ignore[override]
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self,
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messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
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*,
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thread: AgentThread | None = None,
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**kwargs: Any,
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) -> AgentRunResponse:
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return AgentRunResponse(messages=[ChatMessage(role=Role.ASSISTANT, text=self._reply_text)])
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async def run_stream( # type: ignore[override]
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self,
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messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
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*,
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thread: AgentThread | None = None,
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**kwargs: Any,
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) -> AsyncIterable[AgentRunResponseUpdate]:
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# This agent does not support streaming; yield a single complete response
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yield AgentRunResponseUpdate(contents=[TextContent(text=self._reply_text)])
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class _CaptureFullConversation(Executor):
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"""Captures AgentExecutorResponse.full_conversation and completes the workflow."""
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@handler
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async def capture(self, response: AgentExecutorResponse, ctx: WorkflowContext[None]) -> None:
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full = response.full_conversation
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# The AgentExecutor contract guarantees full_conversation is populated.
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assert full is not None
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await ctx.add_event(
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WorkflowCompletedEvent(
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data={
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"length": len(full),
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"roles": [m.role for m in full],
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"texts": [m.text for m in full],
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}
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)
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)
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async def test_agent_executor_populates_full_conversation_non_streaming() -> None:
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# Arrange: non-streaming AgentExecutor for deterministic response composition
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agent = _SimpleAgent(id="agent1", name="A", reply_text="agent-reply")
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agent_exec = AgentExecutor(agent, streaming=False, id="agent1-exec")
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capturer = _CaptureFullConversation(id="capture")
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wf = WorkflowBuilder().set_start_executor(agent_exec).add_edge(agent_exec, capturer).build()
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# Act: run with a simple user prompt
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completed: WorkflowCompletedEvent | None = None
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async for ev in wf.run_stream("hello world"):
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if isinstance(ev, WorkflowCompletedEvent):
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completed = ev
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break
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# Assert: full_conversation contains [user("hello world"), assistant("agent-reply")]
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assert completed is not None
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payload = completed.data # type: ignore[assignment]
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assert isinstance(payload, dict)
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assert payload["length"] == 2
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assert payload["roles"][0] == Role.USER and "hello world" in (payload["texts"][0] or "")
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assert payload["roles"][1] == Role.ASSISTANT and "agent-reply" in (payload["texts"][1] or "")
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class _CaptureAgent(BaseAgent):
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"""Streaming-capable agent that records the messages it received."""
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_last_messages: list[ChatMessage] = PrivateAttr(default_factory=list) # type: ignore
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def __init__(self, *, reply_text: str, **kwargs: Any) -> None:
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super().__init__(**kwargs)
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self._reply_text = reply_text
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async def run( # type: ignore[override]
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self,
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messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
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*,
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thread: AgentThread | None = None,
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**kwargs: Any,
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) -> AgentRunResponse:
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# Normalize and record messages for verification when running non-streaming
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norm: list[ChatMessage] = []
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if messages:
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for m in messages: # type: ignore[iteration-over-optional]
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if isinstance(m, ChatMessage):
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norm.append(m)
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elif isinstance(m, str):
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norm.append(ChatMessage(role=Role.USER, text=m))
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self._last_messages = norm
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return AgentRunResponse(messages=[ChatMessage(role=Role.ASSISTANT, text=self._reply_text)])
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async def run_stream( # type: ignore[override]
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self,
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messages: str | ChatMessage | list[str] | list[ChatMessage] | None = None,
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*,
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thread: AgentThread | None = None,
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**kwargs: Any,
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) -> AsyncIterable[AgentRunResponseUpdate]:
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# Normalize and record messages for verification when running streaming
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norm: list[ChatMessage] = []
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if messages:
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for m in messages: # type: ignore[iteration-over-optional]
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if isinstance(m, ChatMessage):
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norm.append(m)
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elif isinstance(m, str):
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norm.append(ChatMessage(role=Role.USER, text=m))
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self._last_messages = norm
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yield AgentRunResponseUpdate(contents=[TextContent(text=self._reply_text)])
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async def test_sequential_adapter_uses_full_conversation() -> None:
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# Arrange: two streaming agents; the second records what it receives
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a1 = _CaptureAgent(id="agent1", name="A1", reply_text="A1 reply")
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a2 = _CaptureAgent(id="agent2", name="A2", reply_text="A2 reply")
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wf = SequentialBuilder().participants([a1, a2]).build()
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# Act
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async for ev in wf.run_stream("hello seq"):
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if isinstance(ev, WorkflowCompletedEvent):
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break
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# Assert: second agent should have seen the user prompt and A1's assistant reply
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seen = a2._last_messages # pyright: ignore[reportPrivateUsage]
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assert len(seen) == 2
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assert seen[0].role == Role.USER and "hello seq" in (seen[0].text or "")
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assert seen[1].role == Role.ASSISTANT and "A1 reply" in (seen[1].text or "")
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