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

* Remove workflow register factory methods. Update tests and samples

* Address Copilot feedback

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

@@ -17,6 +17,7 @@ else:
 # `agent_framework.builtin` chat client or mock the writer executor. We keep the
 # concrete import here so readers can see an end-to-end configuration.
 from agent_framework import (
+    AgentExecutor,
     AgentExecutorRequest,
     AgentExecutorResponse,
     ChatMessage,
@@ -178,23 +179,21 @@ def create_workflow(checkpoint_storage: FileCheckpointStorage) -> Workflow:
     # Wire the workflow DAG. Edges mirror the numbered steps described in the
     # module docstring. Because `WorkflowBuilder` is declarative, reading these
     # edges is often the quickest way to understand execution order.
+    writer_agent = AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
+        instructions="Write concise, warm release notes that sound human and helpful.",
+        name="writer",
+    )
+    writer = AgentExecutor(writer_agent)
+    review_gateway = ReviewGateway(id="review_gateway", writer_id="writer")
+    prepare_brief = BriefPreparer(id="prepare_brief", agent_id="writer")
+
     workflow_builder = (
         WorkflowBuilder(
-            max_iterations=6, start_executor="prepare_brief", checkpoint_storage=checkpoint_storage
+            max_iterations=6, start_executor=prepare_brief, checkpoint_storage=checkpoint_storage
         )
-        .register_agent(
-            lambda: AzureOpenAIChatClient(credential=AzureCliCredential()).as_agent(
-                instructions="Write concise, warm release notes that sound human and helpful.",
-                # The agent name is stable across runs which keeps checkpoints deterministic.
-                name="writer",
-            ),
-            name="writer",
-        )
-        .register_executor(lambda: ReviewGateway(id="review_gateway", writer_id="writer"), name="review_gateway")
-        .register_executor(lambda: BriefPreparer(id="prepare_brief", agent_id="writer"), name="prepare_brief")
-        .add_edge("prepare_brief", "writer")
-        .add_edge("writer", "review_gateway")
-        .add_edge("review_gateway", "writer")  # revisions loop
+        .add_edge(prepare_brief, writer)
+        .add_edge(writer, review_gateway)
+        .add_edge(review_gateway, writer)  # revisions loop
     )
 
     return workflow_builder.build()

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

@@ -105,12 +105,12 @@ class WorkerExecutor(Executor):
 async def main():
     # Build workflow with checkpointing enabled
     checkpoint_storage = InMemoryCheckpointStorage()
+    start = StartExecutor(id="start")
+    worker = WorkerExecutor(id="worker")
     workflow_builder = (
-        WorkflowBuilder(start_executor="start", checkpoint_storage=checkpoint_storage)
-        .register_executor(lambda: StartExecutor(id="start"), name="start")
-        .register_executor(lambda: WorkerExecutor(id="worker"), name="worker")
-        .add_edge("start", "worker")
-        .add_edge("worker", "worker")  # Self-loop for iterative processing
+        WorkflowBuilder(start_executor=start, checkpoint_storage=checkpoint_storage)
+        .add_edge(start, worker)
+        .add_edge(worker, worker)  # Self-loop for iterative processing
     )
 
     # Run workflow with automatic checkpoint recovery

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

@@ -297,14 +297,14 @@ class LaunchCoordinator(Executor):
 
 def build_sub_workflow() -> WorkflowExecutor:
     """Assemble the sub-workflow used by the parent workflow executor."""
+    writer = DraftWriter()
+    router = DraftReviewRouter()
+    finaliser = DraftFinaliser()
     sub_workflow = (
-        WorkflowBuilder(start_executor="writer")
-        .register_executor(DraftWriter, name="writer")
-        .register_executor(DraftReviewRouter, name="router")
-        .register_executor(DraftFinaliser, name="finaliser")
-        .add_edge("writer", "router")
-        .add_edge("router", "finaliser")
-        .add_edge("finaliser", "writer")  # permits revision loops
+        WorkflowBuilder(start_executor=writer)
+        .add_edge(writer, router)
+        .add_edge(router, finaliser)
+        .add_edge(finaliser, writer)  # permits revision loops
         .build()
     )
 
@@ -313,12 +313,12 @@ def build_sub_workflow() -> WorkflowExecutor:
 
 def build_parent_workflow(storage: FileCheckpointStorage) -> Workflow:
     """Assemble the parent workflow that embeds the sub-workflow."""
+    coordinator = LaunchCoordinator()
+    sub_executor = build_sub_workflow()
     return (
-        WorkflowBuilder(start_executor="coordinator", checkpoint_storage=storage)
-        .register_executor(LaunchCoordinator, name="coordinator")
-        .register_executor(build_sub_workflow, name="sub_executor")
-        .add_edge("coordinator", "sub_executor")
-        .add_edge("sub_executor", "coordinator")
+        WorkflowBuilder(start_executor=coordinator, checkpoint_storage=storage)
+        .add_edge(coordinator, sub_executor)
+        .add_edge(sub_executor, coordinator)
         .build()
     )
 

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

@@ -27,7 +27,6 @@ import asyncio
 
 from agent_framework import (
     AgentThread,
-    ChatAgent,
     ChatMessageStore,
     InMemoryCheckpointStorage,
 )
@@ -43,20 +42,17 @@ async def basic_checkpointing() -> None:
 
     chat_client = OpenAIChatClient()
 
-    def create_assistant() -> ChatAgent:
-        return chat_client.as_agent(
-            name="assistant",
-            instructions="You are a helpful assistant. Keep responses brief.",
-        )
+    assistant = chat_client.as_agent(
+        name="assistant",
+        instructions="You are a helpful assistant. Keep responses brief.",
+    )
 
-    def create_reviewer() -> ChatAgent:
-        return chat_client.as_agent(
-            name="reviewer",
-            instructions="You are a reviewer. Provide a one-sentence summary of the assistant's response.",
-        )
+    reviewer = chat_client.as_agent(
+        name="reviewer",
+        instructions="You are a reviewer. Provide a one-sentence summary of the assistant's response.",
+    )
 
-    # Build sequential workflow with participant factories
-    workflow = SequentialBuilder(participant_factories=[create_assistant, create_reviewer]).build()
+    workflow = SequentialBuilder(participants=[assistant, reviewer]).build()
     agent = workflow.as_agent(name="CheckpointedAgent")
 
     # Create checkpoint storage
@@ -87,13 +83,12 @@ async def checkpointing_with_thread() -> None:
 
     chat_client = OpenAIChatClient()
 
-    def create_assistant() -> ChatAgent:
-        return chat_client.as_agent(
-            name="memory_assistant",
-            instructions="You are a helpful assistant with good memory. Reference previous conversation when relevant.",
-        )
+    assistant = chat_client.as_agent(
+        name="memory_assistant",
+        instructions="You are a helpful assistant with good memory. Reference previous conversation when relevant.",
+    )
 
-    workflow = SequentialBuilder(participant_factories=[create_assistant]).build()
+    workflow = SequentialBuilder(participants=[assistant]).build()
     agent = workflow.as_agent(name="MemoryAgent")
 
     # Create both thread (for conversation) and checkpoint storage (for workflow state)
@@ -131,13 +126,12 @@ async def streaming_with_checkpoints() -> None:
 
     chat_client = OpenAIChatClient()
 
-    def create_assistant() -> ChatAgent:
-        return chat_client.as_agent(
-            name="streaming_assistant",
-            instructions="You are a helpful assistant.",
-        )
+    assistant = chat_client.as_agent(
+        name="streaming_assistant",
+        instructions="You are a helpful assistant.",
+    )
 
-    workflow = SequentialBuilder(participant_factories=[create_assistant]).build()
+    workflow = SequentialBuilder(participants=[assistant]).build()
     agent = workflow.as_agent(name="StreamingCheckpointAgent")
 
     checkpoint_storage = InMemoryCheckpointStorage()