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Python: [BREAKING] Python: Make executor ID required, improvements around handling rehydrating checkpoints (#832)
* Make executor ID required, improvements around handling rehydrating checkpoints. * Duplicate executor validation added * fix remaining issues --------- Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
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Evan Mattson
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@@ -1,108 +1,34 @@
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# Copyright (c) Microsoft. All rights reserved.
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# import asyncio
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# from agent_framework.foundry import FoundryChatClient
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# from agent_framework import AgentRunUpdateEvent, WorkflowBuilder, WorkflowCompletedEvent
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# from azure.identity.aio import AzureCliCredential
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# """
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# Sample: Agents in a workflow with streaming
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# A Writer agent generates content, then a Reviewer agent critiques it.
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# The workflow uses streaming so you can observe incremental AgentRunUpdateEvent chunks as each agent produces tokens.
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# Purpose:
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# Show how to wire chat agents directly into a WorkflowBuilder pipeline where agents are auto wrapped as executors.
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# Demonstrate:
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# - Automatic streaming of agent deltas via AgentRunUpdateEvent.
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# - A simple console aggregator that groups updates by executor id and prints them as they arrive.
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# - A final WorkflowCompletedEvent that contains the reviewer outcome after both agents finish.
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# Prerequisites:
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# - Foundry Agent Service configured, along with the 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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# - Basic familiarity with WorkflowBuilder, edges, events, and streaming runs.
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# """
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# async def main():
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# """Build and run a simple two node agent workflow: Writer then Reviewer."""
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# # Create the Foundry chat client.
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# async with (
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# AzureCliCredential() as credential,
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# FoundryChatClient(async_credential=credential).create_agent(
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# name="Writer",
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# instructions=(
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# "You are an excellent content writer.You create new content and edit contents based on the feedback."
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# ),
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# ) as writer_agent,
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# FoundryChatClient(async_credential=credential).create_agent(
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# name="Reviewer",
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# instructions=(
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# "You are an excellent content reviewer."
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# "Provide actionable feedback to the writer about the provided content."
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# "Provide the feedback in the most concise manner possible."
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# ),
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# ) as reviewer_agent,
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# ):
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# # Build the workflow using the fluent builder.
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# # Set the start node and connect an edge from writer to reviewer.
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# workflow = WorkflowBuilder().set_start_executor(writer_agent).add_edge(writer_agent, reviewer_agent).build()
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# # Stream events from the workflow. We aggregate partial token updates per executor for readable output.
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# completed_event: WorkflowCompletedEvent | None = None
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# last_executor_id = None
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# async for event in workflow.run_stream(
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# "Create a slogan for a new electric SUV that is affordable and fun to drive."
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# ):
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# if isinstance(event, AgentRunUpdateEvent):
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# # AgentRunUpdateEvent contains incremental text deltas from the underlying agent.
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# # Print a prefix when the executor changes, then append updates on the same line.
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# eid = event.executor_id
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# if eid != last_executor_id:
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# if last_executor_id is not None:
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# print()
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# print(f"{eid}:", end=" ", flush=True)
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# last_executor_id = eid
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# print(event.data, end="", flush=True)
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# elif isinstance(event, WorkflowCompletedEvent):
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# # Terminal event with the final reviewer output.
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# completed_event = event
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# # Print the final consolidated reviewer result.
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# if completed_event:
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# print("\n===== Final Output =====")
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# print(completed_event.data)
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# """
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# Sample Output:
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# writer_agent: Charge Up Your Journey. Fun, Affordable, Electric.
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# reviewer_agent: Clear message, but consider highlighting SUV specific benefits
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# (space, versatility) for stronger impact. Try more vivid language to evoke
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# excitement. Example: "Big on Space. Big on Fun. Electric for Everyone."
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# ===== Final Output =====
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# Clear message, but consider highlighting SUV specific benefits (space, versatility)
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# for stronger impact. Try more vivid language to evoke excitement. Example:
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# "Big on Space. Big on Fun. Electric for Everyone."
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# """
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# if __name__ == "__main__":
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# asyncio.run(main())
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import asyncio
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from collections.abc import Awaitable, Callable
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from contextlib import AsyncExitStack
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from typing import Any
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from collections.abc import Awaitable, Callable
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from agent_framework.foundry import FoundryChatClient
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from agent_framework import AgentRunUpdateEvent, WorkflowBuilder, WorkflowCompletedEvent
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from agent_framework.foundry import FoundryChatClient
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from azure.identity.aio import AzureCliCredential
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"""
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Sample: Agents in a workflow with streaming
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A Writer agent generates content, then a Reviewer agent critiques it.
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The workflow uses streaming so you can observe incremental AgentRunUpdateEvent chunks as each agent produces tokens.
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Purpose:
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Show how to wire chat agents directly into a WorkflowBuilder pipeline where agents are auto wrapped as executors.
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Demonstrate:
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- Automatic streaming of agent deltas via AgentRunUpdateEvent.
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- A simple console aggregator that groups updates by executor id and prints them as they arrive.
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- A final WorkflowCompletedEvent that contains the reviewer outcome after both agents finish.
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Prerequisites:
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- Foundry Agent Service configured, along with the 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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- Basic familiarity with WorkflowBuilder, edges, events, and streaming runs.
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"""
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async def create_foundry_agent() -> tuple[Callable[..., Awaitable[Any]], Callable[[], Awaitable[None]]]:
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"""Helper method to create a Foundry agent factory and a close function.
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+17
-12
@@ -1,27 +1,31 @@
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# Copyright (c) Microsoft. All rights reserved.
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import asyncio
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import sys
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from dataclasses import dataclass
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from pathlib import Path
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from agent_framework import (
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# Ensure local getting_started package can be imported when running as a script.
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_SAMPLES_ROOT = Path(__file__).resolve().parents[3]
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if str(_SAMPLES_ROOT) not in sys.path:
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sys.path.insert(0, str(_SAMPLES_ROOT))
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from agent_framework import ( # noqa: E402
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ChatMessage,
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Executor,
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FunctionCallContent,
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FunctionResultContent,
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Role,
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)
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from agent_framework.openai import OpenAIChatClient
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from agent_framework import (
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Executor,
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RequestInfoExecutor,
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RequestInfoMessage,
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RequestResponse,
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Role,
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WorkflowAgent,
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WorkflowBuilder,
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WorkflowContext,
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handler,
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)
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from samples.getting_started.workflow.agents.workflow_as_agent_reflection_pattern import (
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from agent_framework.openai import OpenAIChatClient # noqa: E402
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from getting_started.workflow.agents.workflow_as_agent_reflection_pattern import ( # noqa: E402
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ReviewRequest,
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ReviewResponse,
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Worker,
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@@ -56,8 +60,9 @@ class HumanReviewRequest(RequestInfoMessage):
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class ReviewerWithHumanInTheLoop(Executor):
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"""Executor that always escalates reviews to a human manager."""
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def __init__(self, worker_id: str, request_info_id: str) -> None:
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super().__init__()
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def __init__(self, worker_id: str, request_info_id: str, reviewer_id: str | None = None) -> None:
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unique_id = reviewer_id or f"{worker_id}-reviewer"
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super().__init__(id=unique_id)
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self._worker_id = worker_id
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self._request_info_id = request_info_id
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@@ -96,8 +101,8 @@ async def main() -> None:
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# Create executors for the workflow.
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print("Creating chat client and executors...")
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mini_chat_client = OpenAIChatClient(ai_model_id="gpt-4.1-nano")
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worker = Worker(chat_client=mini_chat_client)
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request_info_executor = RequestInfoExecutor()
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worker = Worker(id="sub-worker", chat_client=mini_chat_client)
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request_info_executor = RequestInfoExecutor(id="request_info")
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reviewer = ReviewerWithHumanInTheLoop(worker_id=worker.id, request_info_id=request_info_executor.id)
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print("Building workflow with Worker ↔ Reviewer cycle...")
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+18
-8
@@ -4,9 +4,19 @@ import asyncio
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from dataclasses import dataclass
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from uuid import uuid4
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from agent_framework import AgentRunResponseUpdate, ChatClientProtocol, ChatMessage, Contents, Role
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from agent_framework import (
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AgentRunResponseUpdate,
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AgentRunUpdateEvent,
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ChatClientProtocol,
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ChatMessage,
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Contents,
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Executor,
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Role,
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WorkflowBuilder,
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WorkflowContext,
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handler,
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)
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from agent_framework.openai import OpenAIChatClient
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from agent_framework import AgentRunUpdateEvent, Executor, WorkflowBuilder, WorkflowContext, handler
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from pydantic import BaseModel
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"""
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@@ -54,8 +64,8 @@ class ReviewResponse:
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class Reviewer(Executor):
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"""Executor that reviews agent responses and provides structured feedback."""
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def __init__(self, chat_client: ChatClientProtocol) -> None:
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super().__init__()
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def __init__(self, id: str, chat_client: ChatClientProtocol) -> None:
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super().__init__(id=id)
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self._chat_client = chat_client
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@handler
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@@ -106,8 +116,8 @@ class Reviewer(Executor):
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class Worker(Executor):
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"""Executor that generates responses and incorporates feedback when necessary."""
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def __init__(self, chat_client: ChatClientProtocol) -> None:
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super().__init__()
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def __init__(self, id: str, chat_client: ChatClientProtocol) -> None:
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super().__init__(id=id)
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self._chat_client = chat_client
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self._pending_requests: dict[str, tuple[ReviewRequest, list[ChatMessage]]] = {}
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@@ -189,8 +199,8 @@ async def main() -> None:
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print("Creating chat client and executors...")
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mini_chat_client = OpenAIChatClient(ai_model_id="gpt-4.1-nano")
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chat_client = OpenAIChatClient(ai_model_id="gpt-4.1")
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reviewer = Reviewer(chat_client=chat_client)
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worker = Worker(chat_client=mini_chat_client)
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reviewer = Reviewer(id="reviewer", chat_client=chat_client)
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worker = Worker(id="worker", chat_client=mini_chat_client)
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print("Building workflow with Worker ↔ Reviewer cycle...")
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agent = (
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