Python: [BREAKING] Python: Rename workflow to workflows (#1007)

* Rename workflow to workflows

* Update occurence of workflow to new name

python/samples/getting_started/workflows/README.md

@@ -1,8 +1,8 @@
-# Workflow Getting Started Samples
+# Workflows Getting Started Samples
 
 ## Installation
 
-Workflow support ships with the core `agent-framework` package, so no extra installation step is required.
+Microsoft Agent Framework Workflows support ships with the core `agent-framework` or `agent-framework-core` package, so no extra installation step is required.
 
 To install with visualization support:
 

python/samples/getting_started/workflows/_start-here/step3_streaming.py

@@ -14,7 +14,7 @@ from agent_framework import (
     WorkflowStatusEvent,
     handler,
 )
-from agent_framework._workflow._events import WorkflowOutputEvent
+from agent_framework._workflows._events import WorkflowOutputEvent
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from typing_extensions import Never
@@ -52,13 +52,13 @@ class Writer(Executor):
 
     def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "writer"):
         # Create a domain specific agent using your configured AzureOpenAIChatClient.
-        agent = chat_client.create_agent(
+        self.agent = chat_client.create_agent(
             instructions=(
                 "You are an excellent content writer. You create new content and edit contents based on the feedback."
             ),
         )
         # Associate this agent with the executor node. The base Executor stores it on self.agent.
-        super().__init__(agent=agent, id=id)
+        super().__init__(id=id)
 
     @handler
     async def handle(self, message: ChatMessage, ctx: WorkflowContext[list[ChatMessage]]) -> None:
@@ -89,12 +89,12 @@ class Reviewer(Executor):
 
     def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "reviewer"):
         # Create a domain specific agent that evaluates and refines content.
-        agent = chat_client.create_agent(
+        self.agent = chat_client.create_agent(
             instructions=(
                 "You are an excellent content reviewer. You review the content and provide feedback to the writer."
             ),
         )
-        super().__init__(agent=agent, id=id)
+        super().__init__(id=id)
 
     @handler
     async def handle(self, messages: list[ChatMessage], ctx: WorkflowContext[Never, str]) -> None:

python/samples/getting_started/workflows/agents/custom_agent_executors.py

@@ -43,13 +43,13 @@ class Writer(Executor):
 
     def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "writer"):
         # Create a domain specific agent using your configured AzureOpenAIChatClient.
-        agent = chat_client.create_agent(
+        self.agent = chat_client.create_agent(
             instructions=(
                 "You are an excellent content writer. You create new content and edit contents based on the feedback."
             ),
         )
         # Associate the agent with this executor node. The base Executor stores it on self.agent.
-        super().__init__(agent=agent, id=id)
+        super().__init__(id=id)
 
     @handler
     async def handle(self, message: ChatMessage, ctx: WorkflowContext[list[ChatMessage], str]) -> None:
@@ -85,12 +85,12 @@ class Reviewer(Executor):
 
     def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "reviewer"):
         # Create a domain specific agent that evaluates and refines content.
-        agent = chat_client.create_agent(
+        self.agent = chat_client.create_agent(
             instructions=(
                 "You are an excellent content reviewer. You review the content and provide feedback to the writer."
             ),
         )
-        super().__init__(agent=agent, id=id)
+        super().__init__(id=id)
 
     @handler
     async def handle(self, messages: list[ChatMessage], ctx: WorkflowContext[list[ChatMessage], str]) -> None:

python/samples/getting_started/workflows/agents/workflow_as_agent_human_in_the_loop.py

@@ -27,7 +27,7 @@ from agent_framework import (  # noqa: E402
     handler,
 )
 from agent_framework.openai import OpenAIChatClient  # noqa: E402
-from getting_started.workflow.agents.workflow_as_agent_reflection_pattern import (  # noqa: E402
+from getting_started.workflows.agents.workflow_as_agent_reflection_pattern import (  # noqa: E402
     ReviewRequest,
     ReviewResponse,
     Worker,

python/samples/getting_started/workflows/checkpoint/checkpoint_with_human_in_the_loop.py

@@ -35,7 +35,7 @@ from azure.identity import AzureCliCredential
 
 if TYPE_CHECKING:
     from agent_framework import Workflow
-    from agent_framework._workflow._checkpoint import WorkflowCheckpoint
+    from agent_framework._workflows._checkpoint import WorkflowCheckpoint
 
 """
 Sample: Checkpoint + human-in-the-loop quickstart.

python/samples/getting_started/workflows/checkpoint/checkpoint_with_resume.py

@@ -23,7 +23,7 @@ from azure.identity import AzureCliCredential
 
 if TYPE_CHECKING:
     from agent_framework import Workflow
-    from agent_framework._workflow._checkpoint import WorkflowCheckpoint
+    from agent_framework._workflows._checkpoint import WorkflowCheckpoint
 
 """
 Sample: Checkpointing and Resuming a Workflow (with an Agent stage)
@@ -212,7 +212,7 @@ def _render_checkpoint_summary(checkpoints: list["WorkflowCheckpoint"]) -> None:
 async def main():
     # Clear existing checkpoints in this sample directory for a clean run.
     checkpoint_dir = Path(TEMP_DIR)
-    for file in checkpoint_dir.glob("*.json"):
+    for file in checkpoint_dir.glob("*.json"):  # noqa: ASYNC240
         file.unlink()
 
     # Backing store for checkpoints written by with_checkpointing.

python/samples/getting_started/workflows/control-flow/edge_condition.py

@@ -4,8 +4,6 @@ import asyncio
 import os
 from typing import Any
 
-from typing_extensions import Never
-
 from agent_framework import (  # Core chat primitives used to build requests
     AgentExecutor,  # Wraps an LLM agent that can be invoked inside a workflow
     AgentExecutorRequest,  # Input message bundle for an AgentExecutor
@@ -19,6 +17,7 @@ from agent_framework import (  # Core chat primitives used to build requests
 from agent_framework.azure import AzureOpenAIChatClient  # Thin client wrapper for Azure OpenAI chat models
 from azure.identity import AzureCliCredential  # Uses your az CLI login for credentials
 from pydantic import BaseModel  # Structured outputs for safer parsing
+from typing_extensions import Never
 
 """
 Sample: Conditional routing with structured outputs

python/samples/getting_started/workflows/control-flow/multi_selection_edge_group.py

@@ -8,8 +8,6 @@ from dataclasses import dataclass
 from typing import Literal
 from uuid import uuid4
 
-from typing_extensions import Never
-
 from agent_framework import (
     AgentExecutor,
     AgentExecutorRequest,
@@ -25,6 +23,7 @@ from agent_framework import (
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import BaseModel
+from typing_extensions import Never
 
 """
 Sample: Multi-Selection Edge Group for email triage and response.

python/samples/getting_started/workflows/control-flow/sequential_executors.py

@@ -3,8 +3,6 @@
 import asyncio
 from typing import cast
 
-from typing_extensions import Never
-
 from agent_framework import (
     Executor,
     WorkflowBuilder,
@@ -12,6 +10,7 @@ from agent_framework import (
     WorkflowOutputEvent,
     handler,
 )
+from typing_extensions import Never
 
 """
 Sample: Sequential workflow with streaming.

python/samples/getting_started/workflows/control-flow/sequential_streaming.py

@@ -2,9 +2,8 @@
 
 import asyncio
 
-from typing_extensions import Never
-
 from agent_framework import WorkflowBuilder, WorkflowContext, WorkflowOutputEvent, executor
+from typing_extensions import Never
 
 """
 Sample: Foundational sequential workflow with streaming using function-style executors.

python/samples/getting_started/workflows/control-flow/switch_case_edge_group.py

@@ -6,8 +6,6 @@ from dataclasses import dataclass
 from typing import Any, Literal
 from uuid import uuid4
 
-from typing_extensions import Never
-
 from agent_framework import (  # Core chat primitives used to form LLM requests
     AgentExecutor,  # Wraps an agent so it can run inside a workflow
     AgentExecutorRequest,  # Message bundle sent to an AgentExecutor
@@ -23,6 +21,7 @@ from agent_framework import (  # Core chat primitives used to form LLM requests
 from agent_framework.azure import AzureOpenAIChatClient  # Thin client for Azure OpenAI chat models
 from azure.identity import AzureCliCredential  # Uses your az CLI login for credentials
 from pydantic import BaseModel  # Structured outputs with validation
+from typing_extensions import Never
 
 """
 Sample: Switch-Case Edge Group with an explicit Uncertain branch.

python/samples/getting_started/workflows/orchestration/concurrent_custom_agent_executors.py

@@ -40,14 +40,14 @@ class ResearcherExec(Executor):
     agent: ChatAgent
 
     def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "researcher"):
-        agent = chat_client.create_agent(
+        self.agent = chat_client.create_agent(
             instructions=(
                 "You're an expert market and product researcher. Given a prompt, provide concise, factual insights,"
                 " opportunities, and risks."
             ),
             name=id,
         )
-        super().__init__(agent=agent, id=id)
+        super().__init__(id=id)
 
     @handler
     async def run(self, request: AgentExecutorRequest, ctx: WorkflowContext[AgentExecutorResponse]) -> None:
@@ -60,14 +60,14 @@ class MarketerExec(Executor):
     agent: ChatAgent
 
     def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "marketer"):
-        agent = chat_client.create_agent(
+        self.agent = chat_client.create_agent(
             instructions=(
                 "You're a creative marketing strategist. Craft compelling value propositions and target messaging"
                 " aligned to the prompt."
             ),
             name=id,
         )
-        super().__init__(agent=agent, id=id)
+        super().__init__(id=id)
 
     @handler
     async def run(self, request: AgentExecutorRequest, ctx: WorkflowContext[AgentExecutorResponse]) -> None:
@@ -80,14 +80,14 @@ class LegalExec(Executor):
     agent: ChatAgent
 
     def __init__(self, chat_client: AzureOpenAIChatClient, id: str = "legal"):
-        agent = chat_client.create_agent(
+        self.agent = chat_client.create_agent(
             instructions=(
                 "You're a cautious legal/compliance reviewer. Highlight constraints, disclaimers, and policy concerns"
                 " based on the prompt."
             ),
             name=id,
         )
-        super().__init__(agent=agent, id=id)
+        super().__init__(id=id)
 
     @handler
     async def run(self, request: AgentExecutorRequest, ctx: WorkflowContext[AgentExecutorResponse]) -> None:

python/samples/getting_started/workflows/orchestration/sequential_custom_executors.py

@@ -3,8 +3,6 @@
 import asyncio
 from typing import Any
 
-from typing_extensions import Never
-
 from agent_framework import (
     ChatMessage,
     Executor,
@@ -15,6 +13,7 @@ from agent_framework import (
 )
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
+from typing_extensions import Never
 
 """
 Sample: Sequential workflow mixing agents and a custom summarizer executor

python/samples/getting_started/workflows/parallelism/fan_out_fan_in_edges.py

@@ -3,8 +3,6 @@
 import asyncio
 from dataclasses import dataclass
 
-from typing_extensions import Never
-
 from agent_framework import (  # Core chat primitives to build LLM requests
     AgentExecutor,  # Wraps an LLM agent for use inside a workflow
     AgentExecutorRequest,  # The message bundle sent to an AgentExecutor
@@ -20,6 +18,7 @@ from agent_framework import (  # Core chat primitives to build LLM requests
 )
 from agent_framework.azure import AzureOpenAIChatClient  # Client wrapper for Azure OpenAI chat models
 from azure.identity import AzureCliCredential  # Uses your az CLI login for credentials
+from typing_extensions import Never
 
 """
 Sample: Concurrent fan out and fan in with three domain agents

python/samples/getting_started/workflows/parallelism/map_reduce_and_visualization.py

@@ -7,8 +7,6 @@ from collections import defaultdict
 from dataclasses import dataclass
 
 import aiofiles
-from typing_extensions import Never
-
 from agent_framework import (
     Executor,  # Base class for custom workflow steps
     WorkflowBuilder,  # Fluent builder for executors and edges
@@ -17,6 +15,7 @@ from agent_framework import (
     WorkflowViz,  # Utility to visualize a workflow graph
     handler,  # Decorator to expose an Executor method as a step
 )
+from typing_extensions import Never
 
 """
 Sample: Map reduce word count with fan out and fan in over file backed intermediate results

python/samples/getting_started/workflows/state-management/shared_states_with_agents.py

@@ -6,8 +6,6 @@ from dataclasses import dataclass
 from typing import Any
 from uuid import uuid4
 
-from typing_extensions import Never
-
 from agent_framework import (
     AgentExecutorRequest,
     AgentExecutorResponse,
@@ -20,6 +18,7 @@ from agent_framework import (
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
 from pydantic import BaseModel
+from typing_extensions import Never
 
 """
 Sample: Shared state with agents and conditional routing.

python/samples/getting_started/workflows/visualization/concurrent_with_visualization.py

@@ -3,8 +3,6 @@
 import asyncio
 from dataclasses import dataclass
 
-from typing_extensions import Never
-
 from agent_framework import (
     AgentExecutor,
     AgentExecutorRequest,
@@ -21,6 +19,7 @@ from agent_framework import (
 )
 from agent_framework.azure import AzureOpenAIChatClient
 from azure.identity import AzureCliCredential
+from typing_extensions import Never
 
 """
 Sample: Concurrent (Fan-out/Fan-in) with Agents + Visualization