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Python: [BREAKING] Python: Rename workflow to workflows (#1007)
* Rename workflow to workflows * Update occurence of workflow to new name
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Evan Mattson
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import os
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from dataclasses import dataclass
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from typing import Any
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from uuid import uuid4
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from agent_framework import (
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AgentExecutorRequest,
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AgentExecutorResponse,
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ChatMessage,
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Role,
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WorkflowBuilder,
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WorkflowContext,
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executor,
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)
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from agent_framework.azure import AzureOpenAIChatClient
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from azure.identity import AzureCliCredential
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from pydantic import BaseModel
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from typing_extensions import Never
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"""
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Sample: Shared state with agents and conditional routing.
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Store an email once by id, classify it with a detector agent, then either draft a reply with an assistant
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agent or finish with a spam notice. Stream events as the workflow runs.
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Purpose:
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Show how to:
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- Use shared state to decouple large payloads from messages and pass around lightweight references.
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- Enforce structured agent outputs with Pydantic models via response_format for robust parsing.
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- Route using conditional edges based on a typed intermediate DetectionResult.
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- Compose agent backed executors with function style executors and yield the final output when the workflow completes.
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Prerequisites:
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- Azure OpenAI configured for AzureOpenAIChatClient with required environment variables.
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- Authentication via azure-identity. Use AzureCliCredential and run az login before executing the sample.
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- Familiarity with WorkflowBuilder, executors, conditional edges, and streaming runs.
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"""
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EMAIL_STATE_PREFIX = "email:"
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CURRENT_EMAIL_ID_KEY = "current_email_id"
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class DetectionResultAgent(BaseModel):
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"""Structured output returned by the spam detection agent."""
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is_spam: bool
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reason: str
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class EmailResponse(BaseModel):
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"""Structured output returned by the email assistant agent."""
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response: str
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@dataclass
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class DetectionResult:
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"""Internal detection result enriched with the shared state email_id for later lookups."""
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is_spam: bool
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reason: str
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email_id: str
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@dataclass
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class Email:
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"""In memory record stored in shared state to avoid re-sending large bodies on edges."""
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email_id: str
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email_content: str
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def get_condition(expected_result: bool):
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"""Create a condition predicate for DetectionResult.is_spam.
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Contract:
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- If the message is not a DetectionResult, allow it to pass to avoid accidental dead ends.
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- Otherwise, return True only when is_spam matches expected_result.
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"""
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def condition(message: Any) -> bool:
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if not isinstance(message, DetectionResult):
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return True
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return message.is_spam == expected_result
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return condition
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@executor(id="store_email")
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async def store_email(email_text: str, ctx: WorkflowContext[AgentExecutorRequest]) -> None:
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"""Persist the raw email content in shared state and trigger spam detection.
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Responsibilities:
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- Generate a unique email_id (UUID) for downstream retrieval.
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- Store the Email object under a namespaced key and set the current id pointer.
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- Emit an AgentExecutorRequest asking the detector to respond.
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"""
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new_email = Email(email_id=str(uuid4()), email_content=email_text)
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await ctx.set_shared_state(f"{EMAIL_STATE_PREFIX}{new_email.email_id}", new_email)
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await ctx.set_shared_state(CURRENT_EMAIL_ID_KEY, new_email.email_id)
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await ctx.send_message(
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AgentExecutorRequest(messages=[ChatMessage(Role.USER, text=new_email.email_content)], should_respond=True)
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)
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@executor(id="to_detection_result")
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async def to_detection_result(response: AgentExecutorResponse, ctx: WorkflowContext[DetectionResult]) -> None:
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"""Parse spam detection JSON into a structured model and enrich with email_id.
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Steps:
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1) Validate the agent's JSON output into DetectionResultAgent.
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2) Retrieve the current email_id from shared state.
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3) Send a typed DetectionResult for conditional routing.
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"""
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parsed = DetectionResultAgent.model_validate_json(response.agent_run_response.text)
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email_id: str = await ctx.get_shared_state(CURRENT_EMAIL_ID_KEY)
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await ctx.send_message(DetectionResult(is_spam=parsed.is_spam, reason=parsed.reason, email_id=email_id))
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@executor(id="submit_to_email_assistant")
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async def submit_to_email_assistant(detection: DetectionResult, ctx: WorkflowContext[AgentExecutorRequest]) -> None:
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"""Forward non spam email content to the drafting agent.
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Guard:
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- This path should only receive non spam. Raise if misrouted.
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"""
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if detection.is_spam:
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raise RuntimeError("This executor should only handle non-spam messages.")
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# Load the original content by id from shared state and forward it to the assistant.
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email: Email = await ctx.get_shared_state(f"{EMAIL_STATE_PREFIX}{detection.email_id}")
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await ctx.send_message(
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AgentExecutorRequest(messages=[ChatMessage(Role.USER, text=email.email_content)], should_respond=True)
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)
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@executor(id="finalize_and_send")
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async def finalize_and_send(response: AgentExecutorResponse, ctx: WorkflowContext[Never, str]) -> None:
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"""Validate the drafted reply and yield the final output."""
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parsed = EmailResponse.model_validate_json(response.agent_run_response.text)
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await ctx.yield_output(f"Email sent: {parsed.response}")
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@executor(id="handle_spam")
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async def handle_spam(detection: DetectionResult, ctx: WorkflowContext[Never, str]) -> None:
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"""Yield output describing why the email was marked as spam."""
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if detection.is_spam:
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await ctx.yield_output(f"Email marked as spam: {detection.reason}")
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else:
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raise RuntimeError("This executor should only handle spam messages.")
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async def main() -> None:
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# Create chat client and agents. response_format enforces structured JSON from each agent.
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chat_client = AzureOpenAIChatClient(credential=AzureCliCredential())
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spam_detection_agent = chat_client.create_agent(
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instructions=(
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"You are a spam detection assistant that identifies spam emails. "
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"Always return JSON with fields is_spam (bool) and reason (string)."
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),
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response_format=DetectionResultAgent,
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name="spam_detection_agent",
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)
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email_assistant_agent = chat_client.create_agent(
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instructions=(
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"You are an email assistant that helps users draft responses to emails with professionalism. "
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"Return JSON with a single field 'response' containing the drafted reply."
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),
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response_format=EmailResponse,
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name="email_assistant_agent",
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)
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# Build the workflow graph with conditional edges.
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# Flow:
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# store_email -> spam_detection_agent -> to_detection_result -> branch:
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# False -> submit_to_email_assistant -> email_assistant_agent -> finalize_and_send
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# True -> handle_spam
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workflow = (
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WorkflowBuilder()
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.set_start_executor(store_email)
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.add_edge(store_email, spam_detection_agent)
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.add_edge(spam_detection_agent, to_detection_result)
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.add_edge(to_detection_result, submit_to_email_assistant, condition=get_condition(False))
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.add_edge(to_detection_result, handle_spam, condition=get_condition(True))
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.add_edge(submit_to_email_assistant, email_assistant_agent)
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.add_edge(email_assistant_agent, finalize_and_send)
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.build()
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)
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# Read an email from resources/spam.txt if available; otherwise use a default sample.
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resources_path = os.path.join(
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os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
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"resources",
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"spam.txt",
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)
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if os.path.exists(resources_path):
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with open(resources_path, encoding="utf-8") as f: # noqa: ASYNC230
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email = f.read()
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else:
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print("Unable to find resource file, using default text.")
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email = "You are a WINNER! Click here for a free lottery offer!!!"
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# Run and print the final result. Streaming surfaces intermediate execution events as well.
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events = await workflow.run(email)
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outputs = events.get_outputs()
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if outputs:
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print(f"Final result: {outputs[0]}")
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"""
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Sample Output:
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Final result: Email marked as spam: This email exhibits several common spam and scam characteristics:
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unrealistic claims of large cash winnings, urgent time pressure, requests for sensitive personal and financial
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information, and a demand for a processing fee. The sender impersonates a generic lottery commission, and the
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message contains a suspicious link. All these are typical of phishing and lottery scam emails.
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"""
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if __name__ == "__main__":
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asyncio.run(main())
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