[BREAKING] Python: Move single-config fluent methods to constructor parameters (#3693)

* Move single-config fluent methods to constructor parameters

* Updates

* Adjust magentic and group chat

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

@@ -179,7 +179,9 @@ def create_workflow(checkpoint_storage: FileCheckpointStorage) -> Workflow:
     # module docstring. Because `WorkflowBuilder` is declarative, reading these
     # edges is often the quickest way to understand execution order.
     workflow_builder = (
-        WorkflowBuilder(max_iterations=6)
+        WorkflowBuilder(
+            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.",
@@ -190,11 +192,9 @@ def create_workflow(checkpoint_storage: FileCheckpointStorage) -> Workflow:
         )
         .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")
-        .set_start_executor("prepare_brief")
         .add_edge("prepare_brief", "writer")
         .add_edge("writer", "review_gateway")
         .add_edge("review_gateway", "writer")  # revisions loop
-        .with_checkpointing(checkpoint_storage=checkpoint_storage)
     )
 
     return workflow_builder.build()

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

@@ -104,16 +104,14 @@ class WorkerExecutor(Executor):
 
 async def main():
     # Build workflow with checkpointing enabled
+    checkpoint_storage = InMemoryCheckpointStorage()
     workflow_builder = (
-        WorkflowBuilder()
+        WorkflowBuilder(start_executor="start", checkpoint_storage=checkpoint_storage)
         .register_executor(lambda: StartExecutor(id="start"), name="start")
         .register_executor(lambda: WorkerExecutor(id="worker"), name="worker")
-        .set_start_executor("start")
         .add_edge("start", "worker")
         .add_edge("worker", "worker")  # Self-loop for iterative processing
     )
-    checkpoint_storage = InMemoryCheckpointStorage()
-    workflow_builder = workflow_builder.with_checkpointing(checkpoint_storage=checkpoint_storage)
 
     # Run workflow with automatic checkpoint recovery
     latest_checkpoint: WorkflowCheckpoint | None = None

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

@@ -97,17 +97,16 @@ def create_workflow(checkpoint_storage: FileCheckpointStorage) -> tuple[Workflow
     client = AzureOpenAIChatClient(credential=AzureCliCredential())
     triage, refund, order = create_agents(client)
 
+    # checkpoint_storage: Enable checkpointing for resume
+    # termination_condition: Terminate after 5 user messages for this demo
     workflow = (
         HandoffBuilder(
             name="checkpoint_handoff_demo",
             participants=[triage, refund, order],
+            checkpoint_storage=checkpoint_storage,
+            termination_condition=lambda conv: sum(1 for msg in conv if msg.role == "user") >= 5,
         )
         .with_start_agent(triage)
-        .with_checkpointing(checkpoint_storage)
-        .with_termination_condition(
-            # Terminate after 5 user messages for this demo
-            lambda conv: sum(1 for msg in conv if msg.role == "user") >= 5
-        )
         .build()
     )
 

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

@@ -298,11 +298,10 @@ class LaunchCoordinator(Executor):
 def build_sub_workflow() -> WorkflowExecutor:
     """Assemble the sub-workflow used by the parent workflow executor."""
     sub_workflow = (
-        WorkflowBuilder()
+        WorkflowBuilder(start_executor="writer")
         .register_executor(DraftWriter, name="writer")
         .register_executor(DraftReviewRouter, name="router")
         .register_executor(DraftFinaliser, name="finaliser")
-        .set_start_executor("writer")
         .add_edge("writer", "router")
         .add_edge("router", "finaliser")
         .add_edge("finaliser", "writer")  # permits revision loops
@@ -315,13 +314,11 @@ def build_sub_workflow() -> WorkflowExecutor:
 def build_parent_workflow(storage: FileCheckpointStorage) -> Workflow:
     """Assemble the parent workflow that embeds the sub-workflow."""
     return (
-        WorkflowBuilder()
+        WorkflowBuilder(start_executor="coordinator", checkpoint_storage=storage)
         .register_executor(LaunchCoordinator, name="coordinator")
         .register_executor(build_sub_workflow, name="sub_executor")
-        .set_start_executor("coordinator")
         .add_edge("coordinator", "sub_executor")
         .add_edge("sub_executor", "coordinator")
-        .with_checkpointing(storage)
         .build()
     )
 

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

@@ -56,7 +56,7 @@ async def basic_checkpointing() -> None:
         )
 
     # Build sequential workflow with participant factories
-    workflow = SequentialBuilder().register_participants([create_assistant, create_reviewer]).build()
+    workflow = SequentialBuilder(participant_factories=[create_assistant, create_reviewer]).build()
     agent = workflow.as_agent(name="CheckpointedAgent")
 
     # Create checkpoint storage
@@ -93,7 +93,7 @@ async def checkpointing_with_thread() -> None:
             instructions="You are a helpful assistant with good memory. Reference previous conversation when relevant.",
         )
 
-    workflow = SequentialBuilder().register_participants([create_assistant]).build()
+    workflow = SequentialBuilder(participant_factories=[create_assistant]).build()
     agent = workflow.as_agent(name="MemoryAgent")
 
     # Create both thread (for conversation) and checkpoint storage (for workflow state)
@@ -137,7 +137,7 @@ async def streaming_with_checkpoints() -> None:
             instructions="You are a helpful assistant.",
         )
 
-    workflow = SequentialBuilder().register_participants([create_assistant]).build()
+    workflow = SequentialBuilder(participant_factories=[create_assistant]).build()
     agent = workflow.as_agent(name="StreamingCheckpointAgent")
 
     checkpoint_storage = InMemoryCheckpointStorage()