Files
agent-framework/python/packages/workflow/tests/test_full_conversation.py
T
Evan Mattson 5c0b037e2c Python: Add Sequential orchestration builder support. Samples. Tests. (#703)
* 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
2025-09-16 01:52:34 +00:00

161 lines
6.0 KiB
Python

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