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Add simple workflow sample that mixes agents and executors (#1946)

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* Add workflow mix agent executor samples

* Apply suggestion from @Copilot

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

---------

Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Tao Chen
2025-11-05 14:40:25 -08:00
committed by GitHub
Unverified
parent afd8b7ecb4
commit 9b471cc479
2 changed files with 107 additions and 5 deletions
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python/samples/getting_started/workflows/human-in-the-loop/guessing_game_with_human_input.py
+4 -5
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@@ -4,7 +4,6 @@ import asyncio
from dataclasses import dataclass
from agent_framework import (
AgentExecutor, # Executor that runs the agent
AgentExecutorRequest, # Message bundle sent to an AgentExecutor
AgentExecutorResponse, # Result returned by an AgentExecutor
ChatMessage, # Chat message structure
@@ -148,6 +147,7 @@ async def main() -> None:
# response_format enforces that the model produces JSON compatible with GuessOutput.
chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
agent = chat_client.create_agent(
name="GuessingAgent",
instructions=(
"You guess a number between 1 and 10. "
"If the user says 'higher' or 'lower', adjust your next guess. "
@@ -158,16 +158,15 @@ async def main() -> None:
response_format=GuessOutput,
)
# Build a simple loop: TurnManager <-> AgentExecutor.
# TurnManager coordinates and gathers human replies while AgentExecutor runs the model.
turn_manager = TurnManager(id="turn_manager")
agent_exec = AgentExecutor(agent=agent, id="agent")
# Build a simple loop: TurnManager <-> AgentExecutor.
workflow = (
WorkflowBuilder()
.set_start_executor(turn_manager)
.add_edge(turn_manager, agent_exec) # Ask agent to make/adjust a guess
.add_edge(agent_exec, turn_manager) # Agent's response comes back to coordinator
.add_edge(turn_manager, agent) # Ask agent to make/adjust a guess
.add_edge(agent, turn_manager) # Agent's response comes back to coordinator
).build()
# Human in the loop run: alternate between invoking the workflow and supplying collected responses.