Python: Rebase durable task feature branch with main (#2806)

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

@@ -122,11 +122,17 @@ def _print_handoff_request(request: HandoffUserInputRequest, request_id: str) ->
     print(f"Awaiting agent: {request.awaiting_agent_id}")
     print(f"Prompt: {request.prompt}")
 
-    print("\nConversation so far:")
-    for msg in request.conversation[-3:]:
-        author = msg.author_name or msg.role.value
-        snippet = msg.text[:120] + "..." if len(msg.text) > 120 else msg.text
-        print(f"  {author}: {snippet}")
+    # Note: After checkpoint restore, conversation may be empty because it's not serialized
+    # to prevent duplication (the conversation is preserved in the coordinator's state).
+    # See issue #2667.
+    if request.conversation:
+        print("\nConversation so far:")
+        for msg in request.conversation[-3:]:
+            author = msg.author_name or msg.role.value
+            snippet = msg.text[:120] + "..." if len(msg.text) > 120 else msg.text
+            print(f"  {author}: {snippet}")
+    else:
+        print("\n(Conversation restored from checkpoint - context preserved in workflow state)")
 
     print(f"{'=' * 60}\n")
 
@@ -273,11 +279,7 @@ async def resume_with_responses(
 
         elif isinstance(event, WorkflowOutputEvent):
             print("\n[Workflow Output Event - Conversation Update]")
-            if (
-                event.data
-                and isinstance(event.data, list)
-                and all(isinstance(msg, ChatMessage) for msg in event.data)
-            ):
+            if event.data and isinstance(event.data, list) and all(isinstance(msg, ChatMessage) for msg in event.data):
                 # Now safe to cast event.data to list[ChatMessage]
                 conversation = cast(list[ChatMessage], event.data)
                 for msg in conversation[-3:]:  # Show last 3 messages

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

@@ -0,0 +1,163 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""
+Sample: Workflow as Agent with Checkpointing
+
+Purpose:
+This sample demonstrates how to use checkpointing with a workflow wrapped as an agent.
+It shows how to enable checkpoint storage when calling agent.run() or agent.run_stream(),
+allowing workflow execution state to be persisted and potentially resumed.
+
+What you learn:
+- How to pass checkpoint_storage to WorkflowAgent.run() and run_stream()
+- How checkpoints are created during workflow-as-agent execution
+- How to combine thread conversation history with workflow checkpointing
+- How to resume a workflow-as-agent from a checkpoint
+
+Key concepts:
+- Thread (AgentThread): Maintains conversation history across agent invocations
+- Checkpoint: Persists workflow execution state for pause/resume capability
+- These are complementary: threads track conversation, checkpoints track workflow state
+
+Prerequisites:
+- OpenAI environment variables configured for OpenAIChatClient
+"""
+
+import asyncio
+
+from agent_framework import (
+    AgentThread,
+    ChatAgent,
+    ChatMessageStore,
+    InMemoryCheckpointStorage,
+    SequentialBuilder,
+)
+from agent_framework.openai import OpenAIChatClient
+
+
+async def basic_checkpointing() -> None:
+    """Demonstrate basic checkpoint storage with workflow-as-agent."""
+    print("=" * 60)
+    print("Basic Checkpointing with Workflow as Agent")
+    print("=" * 60)
+
+    chat_client = OpenAIChatClient()
+
+    def create_assistant() -> ChatAgent:
+        return chat_client.create_agent(
+            name="assistant",
+            instructions="You are a helpful assistant. Keep responses brief.",
+        )
+
+    def create_reviewer() -> ChatAgent:
+        return chat_client.create_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().register_participants([create_assistant, create_reviewer]).build()
+    agent = workflow.as_agent(name="CheckpointedAgent")
+
+    # Create checkpoint storage
+    checkpoint_storage = InMemoryCheckpointStorage()
+
+    # Run with checkpointing enabled
+    query = "What are the benefits of renewable energy?"
+    print(f"\nUser: {query}")
+
+    response = await agent.run(query, checkpoint_storage=checkpoint_storage)
+
+    for msg in response.messages:
+        speaker = msg.author_name or msg.role.value
+        print(f"[{speaker}]: {msg.text}")
+
+    # Show checkpoints that were created
+    checkpoints = await checkpoint_storage.list_checkpoints(workflow.id)
+    print(f"\nCheckpoints created: {len(checkpoints)}")
+    for i, cp in enumerate(checkpoints[:5], 1):
+        print(f"  {i}. {cp.checkpoint_id}")
+
+
+async def checkpointing_with_thread() -> None:
+    """Demonstrate combining thread history with checkpointing."""
+    print("\n" + "=" * 60)
+    print("Checkpointing with Thread Conversation History")
+    print("=" * 60)
+
+    chat_client = OpenAIChatClient()
+
+    def create_assistant() -> ChatAgent:
+        return chat_client.create_agent(
+            name="memory_assistant",
+            instructions="You are a helpful assistant with good memory. Reference previous conversation when relevant.",
+        )
+
+    workflow = SequentialBuilder().register_participants([create_assistant]).build()
+    agent = workflow.as_agent(name="MemoryAgent")
+
+    # Create both thread (for conversation) and checkpoint storage (for workflow state)
+    thread = AgentThread(message_store=ChatMessageStore())
+    checkpoint_storage = InMemoryCheckpointStorage()
+
+    # First turn
+    query1 = "My favorite color is blue. Remember that."
+    print(f"\n[Turn 1] User: {query1}")
+    response1 = await agent.run(query1, thread=thread, checkpoint_storage=checkpoint_storage)
+    if response1.messages:
+        print(f"[assistant]: {response1.messages[0].text}")
+
+    # Second turn - agent should remember from thread history
+    query2 = "What's my favorite color?"
+    print(f"\n[Turn 2] User: {query2}")
+    response2 = await agent.run(query2, thread=thread, checkpoint_storage=checkpoint_storage)
+    if response2.messages:
+        print(f"[assistant]: {response2.messages[0].text}")
+
+    # Show accumulated state
+    checkpoints = await checkpoint_storage.list_checkpoints(workflow.id)
+    print(f"\nTotal checkpoints across both turns: {len(checkpoints)}")
+
+    if thread.message_store:
+        history = await thread.message_store.list_messages()
+        print(f"Messages in thread history: {len(history)}")
+
+
+async def streaming_with_checkpoints() -> None:
+    """Demonstrate streaming with checkpoint storage."""
+    print("\n" + "=" * 60)
+    print("Streaming with Checkpointing")
+    print("=" * 60)
+
+    chat_client = OpenAIChatClient()
+
+    def create_assistant() -> ChatAgent:
+        return chat_client.create_agent(
+            name="streaming_assistant",
+            instructions="You are a helpful assistant.",
+        )
+
+    workflow = SequentialBuilder().register_participants([create_assistant]).build()
+    agent = workflow.as_agent(name="StreamingCheckpointAgent")
+
+    checkpoint_storage = InMemoryCheckpointStorage()
+
+    query = "List three interesting facts about the ocean."
+    print(f"\nUser: {query}")
+    print("[assistant]: ", end="", flush=True)
+
+    # Stream with checkpointing
+    async for update in agent.run_stream(query, checkpoint_storage=checkpoint_storage):
+        if update.text:
+            print(update.text, end="", flush=True)
+
+    print()  # Newline after streaming
+
+    checkpoints = await checkpoint_storage.list_checkpoints(workflow.id)
+    print(f"\nCheckpoints created during stream: {len(checkpoints)}")
+
+
+if __name__ == "__main__":
+    asyncio.run(basic_checkpointing())
+    asyncio.run(checkpointing_with_thread())
+    asyncio.run(streaming_with_checkpoints())