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[BREAKING] Python: Remove workflow register factory methods. Update tests and samples (#3781)

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* Remove workflow register factory methods. Update tests and samples

* Address Copilot feedback
This commit is contained in:
Evan Mattson
2026-02-11 07:16:17 +09:00
committed by GitHub
Unverified
parent f407f726a7
commit a4c9e43afb
46 changed files with 650 additions and 3660 deletions
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python/samples/getting_started/workflows/visualization/concurrent_with_visualization.py
+39 -42
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@@ -4,9 +4,9 @@ import asyncio
from dataclasses import dataclass
from agent_framework import (
AgentExecutor,
AgentExecutorRequest,
AgentExecutorResponse,
ChatAgent,
ChatMessage,
Executor,
WorkflowBuilder,
@@ -85,52 +85,49 @@ class AggregateInsights(Executor):
await ctx.yield_output(consolidated)
def create_researcher_agent() -> ChatAgent:
"""Creates a research domain expert agent."""
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
instructions=(
"You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
" opportunities, and risks."
),
name="researcher",
)
def create_marketer_agent() -> ChatAgent:
"""Creates a marketing domain expert agent."""
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
instructions=(
"You're a creative marketing strategist. Craft compelling value propositions and target messaging"
" aligned to the prompt."
),
name="marketer",
)
def create_legal_agent() -> ChatAgent:
"""Creates a legal domain expert agent."""
return AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
instructions=(
"You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
" based on the prompt."
),
name="legal",
)
async def main() -> None:
"""Build and run the concurrent workflow with visualization."""
# Create agent instances
researcher = AgentExecutor(
AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
instructions=(
"You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
" opportunities, and risks."
),
name="researcher",
)
)
marketer = AgentExecutor(
AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
instructions=(
"You're a creative marketing strategist. Craft compelling value propositions and target messaging"
" aligned to the prompt."
),
name="marketer",
)
)
legal = AgentExecutor(
AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
instructions=(
"You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
" based on the prompt."
),
name="legal",
)
)
# Create executor instances
dispatcher = DispatchToExperts(id="dispatcher")
aggregator = AggregateInsights(id="aggregator")
# Build a simple fan-out/fan-in workflow
workflow = (
WorkflowBuilder(start_executor="dispatcher")
.register_agent(create_researcher_agent, name="researcher")
.register_agent(create_marketer_agent, name="marketer")
.register_agent(create_legal_agent, name="legal")
.register_executor(lambda: DispatchToExperts(id="dispatcher"), name="dispatcher")
.register_executor(lambda: AggregateInsights(id="aggregator"), name="aggregator")
.add_fan_out_edges("dispatcher", ["researcher", "marketer", "legal"])
.add_fan_in_edges(["researcher", "marketer", "legal"], "aggregator")
WorkflowBuilder(start_executor=dispatcher)
.add_fan_out_edges(dispatcher, [researcher, marketer, legal])
.add_fan_in_edges([researcher, marketer, legal], aggregator)
.build()
)