Python: Fix streamed workflow agent continuation context by finalizing AgentExecutor streams (#3882)

* Fix streamed workflow agent continuation context by finalizing AgentExecutor streams

* Fix stream handling

* Fixes

* Fix DevUI and tests

python/packages/core/agent_framework/_agents.py

@@ -911,19 +911,21 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]):  # type: ignore[misc]
             if ctx is None:
                 return  # No context available (shouldn't happen in normal flow)
 
+            # Update thread with conversation_id derived from streaming raw updates.
+            # Using response_id here can break function-call continuation for APIs
+            # where response IDs are not valid conversation handles.
+            conversation_id = self._extract_conversation_id_from_streaming_response(response)
             # Ensure author names are set for all messages
             for message in response.messages:
                 if message.author_name is None:
                     message.author_name = ctx["agent_name"]
 
-            # Propagate conversation_id back to session from streaming updates
+            # Propagate conversation_id back to session from streaming updates.
+            # For Responses-style APIs this can rotate every turn (response_id-based continuation),
+            # so refresh when a newer value is returned.
             sess = ctx["session"]
-            if sess and not sess.service_session_id and response.raw_representation:
-                raw_items = response.raw_representation if isinstance(response.raw_representation, list) else []
-                for item in raw_items:
-                    if hasattr(item, "conversation_id") and item.conversation_id:
-                        sess.service_session_id = item.conversation_id
-                        break
+            if sess and conversation_id and sess.service_session_id != conversation_id:
+                sess.service_session_id = conversation_id
 
             # Run after_run providers (reverse order)
             session_context = ctx["session_context"]
@@ -974,6 +976,27 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]):  # type: ignore[misc]
         output_format_type = response_format if isinstance(response_format, type) else None
         return AgentResponse.from_updates(updates, output_format_type=output_format_type)
 
+    @staticmethod
+    def _extract_conversation_id_from_streaming_response(response: AgentResponse[Any]) -> str | None:
+        """Extract conversation_id from streaming raw updates, if present."""
+        raw = response.raw_representation
+        if raw is None:
+            return None
+
+        raw_items: list[Any] = raw if isinstance(raw, list) else [raw]
+        for item in reversed(raw_items):
+            if isinstance(item, Mapping):
+                value = item.get("conversation_id")
+                if isinstance(value, str) and value:
+                    return value
+                continue
+
+            value = getattr(item, "conversation_id", None)
+            if isinstance(value, str) and value:
+                return value
+
+        return None
+
     async def _prepare_run_context(
         self,
         *,
@@ -1100,8 +1123,10 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]):  # type: ignore[misc]
             if message.author_name is None:
                 message.author_name = agent_name
 
-        # Propagate conversation_id back to session (e.g. thread ID from Assistants API)
-        if session and response.conversation_id and not session.service_session_id:
+        # Propagate conversation_id back to session (e.g. thread ID from Assistants API).
+        # For Responses-style APIs this can rotate every turn (response_id-based continuation),
+        # so refresh when a newer value is returned.
+        if session and response.conversation_id and session.service_session_id != response.conversation_id:
             session.service_session_id = response.conversation_id
 
         # Set the response on the context for after_run providers

python/packages/core/agent_framework/_mcp.py

@@ -872,7 +872,16 @@ class MCPTool:
             k: v
             for k, v in kwargs.items()
             if k
-            not in {"chat_options", "tools", "tool_choice", "session", "thread", "conversation_id", "options", "response_format"}
+            not in {
+                "chat_options",
+                "tools",
+                "tool_choice",
+                "session",
+                "thread",
+                "conversation_id",
+                "options",
+                "response_format",
+            }
         }
 
         parser = self.parse_tool_results or _parse_tool_result_from_mcp

python/packages/core/agent_framework/_workflows/_agent_executor.py

@@ -2,7 +2,7 @@
 
 import logging
 import sys
-from collections.abc import Mapping
+from collections.abc import Awaitable, Callable, Mapping
 from dataclasses import dataclass
 from typing import Any, cast
 
@@ -358,22 +358,31 @@ class AgentExecutor(Executor):
         run_kwargs, options = self._prepare_agent_run_args(ctx.get_state(WORKFLOW_RUN_KWARGS_KEY) or {})
 
         updates: list[AgentResponseUpdate] = []
-        user_input_requests: list[Content] = []
-        async for update in self._agent.run(
+        streamed_user_input_requests: list[Content] = []
+        stream = self._agent.run(
             self._cache,
             stream=True,
             session=self._session,
             options=options,
             **run_kwargs,
-        ):
+        )
+        async for update in stream:
             updates.append(update)
             await ctx.yield_output(update)
-
             if update.user_input_requests:
-                user_input_requests.extend(update.user_input_requests)
+                streamed_user_input_requests.extend(update.user_input_requests)
 
-        # Build the final AgentResponse from the collected updates
-        if is_chat_agent(self._agent):
+        # Prefer stream finalization when available so result hooks run
+        # (e.g., thread conversation updates). Fall back to reconstructing from updates
+        # for legacy/custom agents that return a plain async iterable.
+        # TODO(evmattso): Integrate workflow agent run handling around ResponseStream so
+        # AgentExecutor does not need this conditional stream-finalization branch.
+        maybe_get_final_response = getattr(stream, "get_final_response", None)
+        get_final_response = maybe_get_final_response if callable(maybe_get_final_response) else None
+        response: AgentResponse[Any]
+        if get_final_response is not None:
+            response = await cast(Callable[[], Awaitable[AgentResponse[Any]]], get_final_response)()
+        elif is_chat_agent(self._agent):
             response_format = self._agent.default_options.get("response_format")
             response = AgentResponse.from_updates(
                 updates,
@@ -383,6 +392,16 @@ class AgentExecutor(Executor):
             response = AgentResponse.from_updates(updates)
 
         # Handle any user input requests after the streaming completes
+        user_input_requests: list[Content] = []
+        seen_request_ids: set[str] = set()
+        for user_input_request in [*streamed_user_input_requests, *response.user_input_requests]:
+            request_id = getattr(user_input_request, "id", None)
+            if isinstance(request_id, str) and request_id:
+                if request_id in seen_request_ids:
+                    continue
+                seen_request_ids.add(request_id)
+            user_input_requests.append(user_input_request)
+
         if user_input_requests:
             for user_input_request in user_input_requests:
                 self._pending_agent_requests[user_input_request.id] = user_input_request  # type: ignore[index]

python/packages/core/tests/core/test_agents.py

@@ -17,6 +17,7 @@ from agent_framework import (
     BaseContextProvider,
     ChatOptions,
     ChatResponse,
+    ChatResponseUpdate,
     Content,
     FunctionTool,
     Message,
@@ -154,6 +155,111 @@ async def test_chat_client_agent_run_with_session(chat_client_base: SupportsChat
     assert session.service_session_id == "123"
 
 
+async def test_chat_client_agent_updates_existing_session_id_non_streaming(
+    chat_client_base: SupportsChatGetResponse,
+) -> None:
+    chat_client_base.run_responses = [
+        ChatResponse(
+            messages=[Message(role="assistant", contents=[Content.from_text("test response")])],
+            conversation_id="resp_new_123",
+        )
+    ]
+
+    agent = Agent(client=chat_client_base)
+    session = agent.get_session(service_session_id="resp_old_123")
+
+    await agent.run("Hello", session=session)
+    assert session.service_session_id == "resp_new_123"
+
+
+async def test_chat_client_agent_update_session_id_streaming_uses_conversation_id(
+    chat_client_base: SupportsChatGetResponse,
+) -> None:
+    chat_client_base.streaming_responses = [
+        [
+            ChatResponseUpdate(
+                contents=[Content.from_text("stream part 1")],
+                role="assistant",
+                response_id="resp_stream_123",
+                conversation_id="conv_stream_456",
+            ),
+            ChatResponseUpdate(
+                contents=[Content.from_text(" stream part 2")],
+                role="assistant",
+                response_id="resp_stream_123",
+                conversation_id="conv_stream_456",
+                finish_reason="stop",
+            ),
+        ]
+    ]
+
+    agent = Agent(client=chat_client_base)
+    session = agent.create_session()
+
+    stream = agent.run("Hello", session=session, stream=True)
+    async for _ in stream:
+        pass
+    result = await stream.get_final_response()
+    assert result.text == "stream part 1 stream part 2"
+    assert session.service_session_id == "conv_stream_456"
+
+
+async def test_chat_client_agent_updates_existing_session_id_streaming(
+    chat_client_base: SupportsChatGetResponse,
+) -> None:
+    chat_client_base.streaming_responses = [
+        [
+            ChatResponseUpdate(
+                contents=[Content.from_text("stream part 1")],
+                role="assistant",
+                response_id="resp_stream_123",
+                conversation_id="resp_new_456",
+            ),
+            ChatResponseUpdate(
+                contents=[Content.from_text(" stream part 2")],
+                role="assistant",
+                response_id="resp_stream_123",
+                conversation_id="resp_new_456",
+                finish_reason="stop",
+            ),
+        ]
+    ]
+
+    agent = Agent(client=chat_client_base)
+    session = agent.get_session(service_session_id="resp_old_456")
+
+    stream = agent.run("Hello", session=session, stream=True)
+    async for _ in stream:
+        pass
+    await stream.get_final_response()
+    assert session.service_session_id == "resp_new_456"
+
+
+async def test_chat_client_agent_update_session_id_streaming_does_not_use_response_id(
+    chat_client_base: SupportsChatGetResponse,
+) -> None:
+    chat_client_base.streaming_responses = [
+        [
+            ChatResponseUpdate(
+                contents=[Content.from_text("stream response without conversation id")],
+                role="assistant",
+                response_id="resp_only_123",
+                finish_reason="stop",
+            ),
+        ]
+    ]
+
+    agent = Agent(client=chat_client_base)
+    session = agent.create_session()
+
+    stream = agent.run("Hello", session=session, stream=True)
+    async for _ in stream:
+        pass
+    result = await stream.get_final_response()
+    assert result.text == "stream response without conversation id"
+    assert session.service_session_id is None
+
+
 async def test_chat_client_agent_update_session_messages(client: SupportsChatGetResponse) -> None:
     agent = Agent(client=client)
     session = agent.create_session()

python/packages/core/tests/workflow/__init__.py

@@ -0,0 +1 @@
+# Copyright (c) Microsoft. All rights reserved.

python/packages/core/tests/workflow/test_agent_executor.py

@@ -50,6 +50,57 @@ class _CountingAgent(BaseAgent):
         return _run()
 
 
+class _StreamingHookAgent(BaseAgent):
+    """Agent that exposes whether its streaming result hook was executed."""
+
+    def __init__(self, **kwargs: Any):
+        super().__init__(**kwargs)
+        self.result_hook_called = False
+
+    def run(
+        self,
+        messages: str | Message | list[str] | list[Message] | None = None,
+        *,
+        stream: bool = False,
+        **kwargs: Any,
+    ) -> Awaitable[AgentResponse] | ResponseStream[AgentResponseUpdate, AgentResponse]:
+        if stream:
+
+            async def _stream() -> AsyncIterable[AgentResponseUpdate]:
+                yield AgentResponseUpdate(
+                    contents=[Content.from_text(text="hook test")],
+                    role="assistant",
+                )
+
+            async def _mark_result_hook_called(response: AgentResponse) -> AgentResponse:
+                self.result_hook_called = True
+                return response
+
+            return ResponseStream(_stream(), finalizer=AgentResponse.from_updates).with_result_hook(
+                _mark_result_hook_called
+            )
+
+        async def _run() -> AgentResponse:
+            return AgentResponse(messages=[Message("assistant", ["hook test"])])
+
+        return _run()
+
+
+async def test_agent_executor_streaming_finalizes_stream_and_runs_result_hooks() -> None:
+    """AgentExecutor should call get_final_response() so stream result hooks execute."""
+    agent = _StreamingHookAgent(id="hook_agent", name="HookAgent")
+    executor = AgentExecutor(agent, id="hook_exec")
+    workflow = SequentialBuilder(participants=[executor]).build()
+
+    output_events: list[Any] = []
+    async for event in workflow.run("run hook test", stream=True):
+        if event.type == "output":
+            output_events.append(event)
+
+    assert output_events
+    assert agent.result_hook_called
+
+
 async def test_agent_executor_checkpoint_stores_and_restores_state() -> None:
     """Test that workflow checkpoint stores AgentExecutor's cache and session states and restores them correctly."""
     storage = InMemoryCheckpointStorage()

python/packages/core/tests/workflow/test_request_info_event_rehydrate.py

@@ -12,7 +12,7 @@ from agent_framework import (
     WorkflowRunState,
 )
 from agent_framework._workflows._checkpoint_encoding import (
-    _PICKLE_MARKER,
+    _PICKLE_MARKER,  # type: ignore
     encode_checkpoint_value,
 )
 from agent_framework._workflows._events import WorkflowEvent

python/packages/devui/agent_framework_devui/_conversations.py

@@ -14,7 +14,7 @@ from abc import ABC, abstractmethod
 from typing import Any, Literal, cast
 
 from agent_framework import AgentSession, Message
-from agent_framework._workflows._checkpoint import InMemoryCheckpointStorage
+from agent_framework._workflows._checkpoint import InMemoryCheckpointStorage, WorkflowCheckpoint
 from openai.types.conversations import Conversation, ConversationDeletedResource
 from openai.types.conversations.conversation_item import ConversationItem
 from openai.types.conversations.message import Message as OpenAIMessage
@@ -480,7 +480,7 @@ class InMemoryConversationStore(ConversationStore):
         checkpoint_storage = conv_data.get("checkpoint_storage")
         if checkpoint_storage:
             # Get all checkpoints for this conversation
-            checkpoints = await checkpoint_storage.list_checkpoints()
+            checkpoints = self._list_all_checkpoints(checkpoint_storage)
             for checkpoint in checkpoints:
                 # Create a conversation item for each checkpoint with summary metadata
                 # Full checkpoint state is NOT included here (too large for list view)
@@ -495,7 +495,9 @@ class InMemoryConversationStore(ConversationStore):
                     "id": f"checkpoint_{checkpoint.checkpoint_id}",
                     "type": "checkpoint",
                     "checkpoint_id": checkpoint.checkpoint_id,
-                    "workflow_id": checkpoint.workflow_id,
+                    # Keep workflow_id for backward compatibility with existing UI payloads.
+                    "workflow_id": checkpoint.workflow_name,
+                    "workflow_name": checkpoint.workflow_name,
                     "timestamp": checkpoint.timestamp,
                     "status": "completed",
                     "metadata": {
@@ -506,6 +508,7 @@ class InMemoryConversationStore(ConversationStore):
                         "message_count": sum(len(msgs) for msgs in checkpoint.messages.values()),
                         "size_bytes": checkpoint_size,
                         "version": checkpoint.version,
+                        "graph_signature_hash": checkpoint.graph_signature_hash,
                     },
                 }
                 items.append(cast(ConversationItem, checkpoint_item))
@@ -551,8 +554,9 @@ class InMemoryConversationStore(ConversationStore):
                 return None
 
             # Load full checkpoint from storage
-            checkpoint = await checkpoint_storage.load_checkpoint(checkpoint_id)
-            if not checkpoint:
+            try:
+                checkpoint = await checkpoint_storage.load(checkpoint_id)
+            except Exception:
                 return None
 
             # Calculate size of checkpoint
@@ -566,7 +570,9 @@ class InMemoryConversationStore(ConversationStore):
                 "id": item_id,
                 "type": "checkpoint",
                 "checkpoint_id": checkpoint.checkpoint_id,
-                "workflow_id": checkpoint.workflow_id,
+                # Keep workflow_id for backward compatibility with existing UI payloads.
+                "workflow_id": checkpoint.workflow_name,
+                "workflow_name": checkpoint.workflow_name,
                 "timestamp": checkpoint.timestamp,
                 "status": "completed",
                 "metadata": {
@@ -577,6 +583,7 @@ class InMemoryConversationStore(ConversationStore):
                     "message_count": sum(len(msgs) for msgs in checkpoint.messages.values()),
                     "size_bytes": checkpoint_size,
                     "version": checkpoint.version,
+                    "graph_signature_hash": checkpoint.graph_signature_hash,
                     # 🔥 FULL checkpoint state (lazy loaded)
                     "full_checkpoint": checkpoint.to_dict(),
                 },
@@ -631,8 +638,8 @@ class InMemoryConversationStore(ConversationStore):
                 if conv_meta.get("type") == "workflow_session":
                     checkpoint_storage = conv_data.get("checkpoint_storage")
                     if checkpoint_storage:
-                        checkpoints = await checkpoint_storage.list_checkpoints()
-                        latest = checkpoints[0] if checkpoints else None
+                        checkpoints = self._list_all_checkpoints(checkpoint_storage)
+                        latest = max(checkpoints, key=lambda cp: cp.timestamp) if checkpoints else None
                         conv_meta["checkpoint_summary"] = {
                             "count": len(checkpoints),
                             "latest_iteration": latest.iteration_count if latest else 0,
@@ -654,6 +661,19 @@ class InMemoryConversationStore(ConversationStore):
 
         return results
 
+    @staticmethod
+    def _list_all_checkpoints(checkpoint_storage: Any) -> list[WorkflowCheckpoint]:
+        """Return all checkpoints from a conversation-scoped storage instance.
+
+        DevUI uses one checkpoint storage per conversation. Core storage APIs now
+        require workflow_name filters, so we gather directly from in-memory storage
+        internals to provide conversation-wide listing for UI views.
+        """
+        checkpoint_map = getattr(checkpoint_storage, "_checkpoints", None)
+        if isinstance(checkpoint_map, dict):
+            return list(cast(dict[str, WorkflowCheckpoint], checkpoint_map).values())
+        return []
+
 
 class CheckpointConversationManager:
     """Manages checkpoint storage for workflow sessions - SESSION-SCOPED.

python/packages/devui/tests/devui/test_checkpoints.py

@@ -104,17 +104,21 @@ class TestCheckpointConversationManager:
         from agent_framework._workflows._checkpoint import WorkflowCheckpoint
 
         checkpoint = WorkflowCheckpoint(
-            checkpoint_id=str(uuid.uuid4()), workflow_id=test_workflow.id, messages={}, state={"test": "data"}
+            checkpoint_id=str(uuid.uuid4()),
+            workflow_name=test_workflow.name,
+            graph_signature_hash=test_workflow.graph_signature_hash,
+            messages={},
+            state={"test": "data"},
         )
 
         # Get checkpoint storage for this conversation and save
         storage = checkpoint_manager.get_checkpoint_storage(conversation_id)
-        checkpoint_id = await storage.save_checkpoint(checkpoint)
+        checkpoint_id = await storage.save(checkpoint)
 
         assert checkpoint_id == checkpoint.checkpoint_id
 
         # Verify checkpoint stored in THIS conversation only
-        checkpoints = await storage.list_checkpoints()
+        checkpoints = await storage.list_checkpoints(workflow_name=test_workflow.name)
         assert len(checkpoints) == 1
         assert checkpoints[0].checkpoint_id == checkpoint.checkpoint_id
 
@@ -140,20 +144,21 @@ class TestCheckpointConversationManager:
 
         checkpoint_a = WorkflowCheckpoint(
             checkpoint_id=str(uuid.uuid4()),
-            workflow_id=test_workflow.id,
+            workflow_name=test_workflow.name,
+            graph_signature_hash=test_workflow.graph_signature_hash,
             messages={},
             state={"conversation": "A"},
         )
         storage_a = checkpoint_manager.get_checkpoint_storage(conv_a)
-        await storage_a.save_checkpoint(checkpoint_a)
+        await storage_a.save(checkpoint_a)
 
         # Verify conversation A has checkpoint
-        checkpoints_a = await storage_a.list_checkpoints()
+        checkpoints_a = await storage_a.list_checkpoints(workflow_name=test_workflow.name)
         assert len(checkpoints_a) == 1
 
         # Verify conversation B has NO checkpoints (isolation)
         storage_b = checkpoint_manager.get_checkpoint_storage(conv_b)
-        checkpoints_b = await storage_b.list_checkpoints()
+        checkpoints_b = await storage_b.list_checkpoints(workflow_name=test_workflow.name)
         assert len(checkpoints_b) == 0
 
     @pytest.mark.asyncio
@@ -177,15 +182,16 @@ class TestCheckpointConversationManager:
         for i in range(3):
             checkpoint = WorkflowCheckpoint(
                 checkpoint_id=str(uuid.uuid4()),
-                workflow_id=test_workflow.id,
+                workflow_name=test_workflow.name,
+                graph_signature_hash=test_workflow.graph_signature_hash,
                 messages={},
                 state={"iteration": i},
             )
-            saved_id = await storage.save_checkpoint(checkpoint)
+            saved_id = await storage.save(checkpoint)
             checkpoint_ids.append(saved_id)
 
         # List checkpoints using the storage
-        checkpoints_list = await storage.list_checkpoints()
+        checkpoints_list = await storage.list_checkpoints(workflow_name=test_workflow.name)
         assert len(checkpoints_list) == 3
 
         # Verify all checkpoint IDs are present
@@ -213,11 +219,12 @@ class TestCheckpointConversationManager:
         for i in range(2):
             checkpoint = WorkflowCheckpoint(
                 checkpoint_id=f"checkpoint_{i}",
-                workflow_id=test_workflow.id,
+                workflow_name=test_workflow.name,
+                graph_signature_hash=test_workflow.graph_signature_hash,
                 messages={},
                 state={"iteration": i},
             )
-            saved_id = await storage.save_checkpoint(checkpoint)
+            saved_id = await storage.save(checkpoint)
             checkpoint_ids.append(saved_id)
 
         # List conversation items - should include checkpoints
@@ -233,7 +240,7 @@ class TestCheckpointConversationManager:
         for item in checkpoint_items:
             assert item.get("type") == "checkpoint"
             assert item.get("checkpoint_id") in checkpoint_ids
-            assert item.get("workflow_id") == test_workflow.id
+            assert item.get("workflow_name") == test_workflow.name
             assert "timestamp" in item
             assert item.get("id").startswith("checkpoint_")  # ID format: checkpoint_{checkpoint_id}
 
@@ -255,21 +262,22 @@ class TestCheckpointConversationManager:
 
         original_checkpoint = WorkflowCheckpoint(
             checkpoint_id=str(uuid.uuid4()),
-            workflow_id=test_workflow.id,
+            workflow_name=test_workflow.name,
+            graph_signature_hash=test_workflow.graph_signature_hash,
             messages={},
             state={"test_key": "test_value"},
         )
 
         # Save to this session
         storage = checkpoint_manager.get_checkpoint_storage(conversation_id)
-        await storage.save_checkpoint(original_checkpoint)
+        await storage.save(original_checkpoint)
 
         # Load checkpoint from this session
-        loaded_checkpoint = await storage.load_checkpoint(original_checkpoint.checkpoint_id)
+        loaded_checkpoint = await storage.load(original_checkpoint.checkpoint_id)
 
         assert loaded_checkpoint is not None
         assert loaded_checkpoint.checkpoint_id == original_checkpoint.checkpoint_id
-        assert loaded_checkpoint.workflow_id == original_checkpoint.workflow_id
+        assert loaded_checkpoint.workflow_name == original_checkpoint.workflow_name
         assert loaded_checkpoint.state == {"test_key": "test_value"}
 
 
@@ -296,24 +304,28 @@ class TestCheckpointStorage:
         from agent_framework._workflows._checkpoint import WorkflowCheckpoint
 
         checkpoint = WorkflowCheckpoint(
-            checkpoint_id=str(uuid.uuid4()), workflow_id=test_workflow.id, messages={}, state={"test": "data"}
+            checkpoint_id=str(uuid.uuid4()),
+            workflow_name=test_workflow.name,
+            graph_signature_hash=test_workflow.graph_signature_hash,
+            messages={},
+            state={"test": "data"},
         )
 
-        # Test save_checkpoint
-        checkpoint_id = await storage.save_checkpoint(checkpoint)
+        # Test save
+        checkpoint_id = await storage.save(checkpoint)
         assert checkpoint_id == checkpoint.checkpoint_id
 
-        # Test load_checkpoint
-        loaded = await storage.load_checkpoint(checkpoint_id)
+        # Test load
+        loaded = await storage.load(checkpoint_id)
         assert loaded is not None
         assert loaded.checkpoint_id == checkpoint_id
 
         # Test list_checkpoint_ids
-        ids = await storage.list_checkpoint_ids(workflow_id=test_workflow.id)
+        ids = await storage.list_checkpoint_ids(workflow_name=test_workflow.name)
         assert checkpoint_id in ids
 
         # Test list_checkpoints
-        checkpoints_list = await storage.list_checkpoints(workflow_id=test_workflow.id)
+        checkpoints_list = await storage.list_checkpoints(workflow_name=test_workflow.name)
         assert len(checkpoints_list) >= 1
         assert any(cp.checkpoint_id == checkpoint_id for cp in checkpoints_list)
 
@@ -346,12 +358,16 @@ class TestIntegration:
         from agent_framework._workflows._checkpoint import WorkflowCheckpoint
 
         checkpoint = WorkflowCheckpoint(
-            checkpoint_id=str(uuid.uuid4()), workflow_id=test_workflow.id, messages={}, state={"injected": True}
+            checkpoint_id=str(uuid.uuid4()),
+            workflow_name=test_workflow.name,
+            graph_signature_hash=test_workflow.graph_signature_hash,
+            messages={},
+            state={"injected": True},
         )
-        await checkpoint_storage.save_checkpoint(checkpoint)
+        await checkpoint_storage.save(checkpoint)
 
         # Verify checkpoint is accessible via storage (in this session)
-        storage_checkpoints = await checkpoint_storage.list_checkpoints()
+        storage_checkpoints = await checkpoint_storage.list_checkpoints(workflow_name=test_workflow.name)
         assert len(storage_checkpoints) > 0
         assert storage_checkpoints[0].checkpoint_id == checkpoint.checkpoint_id
 
@@ -377,20 +393,21 @@ class TestIntegration:
 
         checkpoint = WorkflowCheckpoint(
             checkpoint_id=str(uuid.uuid4()),
-            workflow_id=test_workflow.id,
+            workflow_name=test_workflow.name,
+            graph_signature_hash=test_workflow.graph_signature_hash,
             messages={},
             state={"ready_to_resume": True},
         )
-        checkpoint_id = await checkpoint_storage.save_checkpoint(checkpoint)
+        checkpoint_id = await checkpoint_storage.save(checkpoint)
 
         # Verify checkpoint can be loaded for resume
-        loaded = await checkpoint_storage.load_checkpoint(checkpoint_id)
+        loaded = await checkpoint_storage.load(checkpoint_id)
         assert loaded is not None
         assert loaded.checkpoint_id == checkpoint_id
         assert loaded.state == {"ready_to_resume": True}
 
         # Verify checkpoint is accessible via storage (for UI to list checkpoints)
-        checkpoints = await checkpoint_storage.list_checkpoints()
+        checkpoints = await checkpoint_storage.list_checkpoints(workflow_name=test_workflow.name)
         assert len(checkpoints) > 0
         assert checkpoints[0].checkpoint_id == checkpoint_id
 
@@ -420,7 +437,7 @@ class TestIntegration:
         test_workflow._runner.context._checkpoint_storage = checkpoint_storage
 
         # Verify no checkpoints initially
-        checkpoints_before = await checkpoint_storage.list_checkpoints()
+        checkpoints_before = await checkpoint_storage.list_checkpoints(workflow_name=test_workflow.name)
         assert len(checkpoints_before) == 0
 
         # Run workflow until it reaches IDLE_WITH_PENDING_REQUESTS (after checkpoint is created)
@@ -435,9 +452,9 @@ class TestIntegration:
         assert saw_request_event, "Test workflow should have emitted request_info event (type='request_info')"
 
         # Verify checkpoint was AUTOMATICALLY saved to our storage by the framework
-        checkpoints_after = await checkpoint_storage.list_checkpoints()
+        checkpoints_after = await checkpoint_storage.list_checkpoints(workflow_name=test_workflow.name)
         assert len(checkpoints_after) > 0, "Workflow should have auto-saved checkpoint at HIL pause"
 
-        # Verify checkpoint has correct workflow_id
+        # Verify checkpoint has correct workflow identity
         checkpoint = checkpoints_after[0]
-        assert checkpoint.workflow_id == test_workflow.id
+        assert checkpoint.workflow_name == test_workflow.name

python/packages/devui/tests/devui/test_server.py

@@ -379,26 +379,27 @@ async def test_checkpoint_api_endpoints(test_entities_dir):
     storage = executor.checkpoint_manager.get_checkpoint_storage(conv_id)
     checkpoint = WorkflowCheckpoint(
         checkpoint_id="test_checkpoint_1",
-        workflow_id="test_workflow",
+        workflow_name="test_workflow",
+        graph_signature_hash="test_graph_hash",
         state={"key": "value"},
         iteration_count=1,
     )
-    await storage.save_checkpoint(checkpoint)
+    await storage.save(checkpoint)
 
     # Test list checkpoints endpoint
-    checkpoints = await storage.list_checkpoints()
+    checkpoints = await storage.list_checkpoints(workflow_name="test_workflow")
     assert len(checkpoints) == 1
     assert checkpoints[0].checkpoint_id == "test_checkpoint_1"
-    assert checkpoints[0].workflow_id == "test_workflow"
+    assert checkpoints[0].workflow_name == "test_workflow"
 
     # Test delete checkpoint endpoint
-    deleted = await storage.delete_checkpoint("test_checkpoint_1")
+    deleted = await storage.delete("test_checkpoint_1")
     assert deleted is True
 
     # Verify checkpoint was deleted
-    remaining = await storage.list_checkpoints()
+    remaining = await storage.list_checkpoints(workflow_name="test_workflow")
     assert len(remaining) == 0
 
     # Test delete non-existent checkpoint
-    deleted = await storage.delete_checkpoint("nonexistent")
+    deleted = await storage.delete("nonexistent")
     assert deleted is False

python/packages/durabletask/tests/test_executors.py

@@ -189,19 +189,29 @@ class TestClientAgentExecutorPollingConfiguration:
         # Verify get_entity was called 2 times (max_poll_retries)
         assert mock_client.get_entity.call_count == 2
 
-    def test_executor_respects_custom_poll_interval(self, mock_client: Mock, sample_run_request: RunRequest) -> None:
+    def test_executor_respects_custom_poll_interval(
+        self,
+        mock_client: Mock,
+        sample_run_request: RunRequest,
+        monkeypatch: pytest.MonkeyPatch,
+    ) -> None:
         """Verify executor respects custom poll_interval_seconds during polling."""
         # Create executor with very short interval
         executor = ClientAgentExecutor(mock_client, max_poll_retries=3, poll_interval_seconds=0.01)
 
-        # Measure time taken
-        start = time.time()
-        result = executor.run_durable_agent("test_agent", sample_run_request)
-        elapsed = time.time() - start
+        sleep_calls: list[float] = []
 
-        # Should take roughly 3 * 0.01 = 0.03 seconds (plus overhead)
-        # Be generous with timing to avoid flakiness
-        assert elapsed < 0.2  # Should be quick with 0.01 interval
+        def fake_sleep(seconds: float) -> None:
+            sleep_calls.append(seconds)
+
+        # Use deterministic assertions instead of wall-clock timing to avoid CI flakiness.
+        monkeypatch.setattr("agent_framework_durabletask._executors.time.sleep", fake_sleep)
+
+        result = executor.run_durable_agent("test_agent", sample_run_request)
+
+        assert len(sleep_calls) == 3
+        assert sleep_calls == pytest.approx([0.01, 0.01, 0.01])
+        assert mock_client.get_entity.call_count == 3
         assert isinstance(result, AgentResponse)
 
 

python/samples/03-workflows/orchestrations/handoff_with_tool_approval_checkpoint_resume.py

@@ -8,9 +8,11 @@ from typing import Any
 
 from agent_framework import (
     Agent,
+    AgentResponseUpdate,
     Content,
     FileCheckpointStorage,
     Workflow,
+    WorkflowEvent,
     tool,
 )
 from agent_framework.azure import AzureOpenAIResponsesClient
@@ -183,8 +185,16 @@ async def main() -> None:
     initial_request = "Hi, my order 12345 arrived damaged. I need a refund."
 
     # Phase 1: Initial run - workflow will pause when it needs user input
-    results = await workflow.run(message=initial_request)
-    request_events = results.get_request_info_events()
+    print("Running initial workflow...")
+    results = await workflow.run(message=initial_request, stream=True)
+
+    # Iterate through streamed events and collect request_info events
+    request_events: list[WorkflowEvent] = []
+    async for event in results:
+        event: WorkflowEvent
+        if event.type == "request_info":
+            request_events.append(event)
+
     if not request_events:
         print("Workflow completed without needing user input")
         return
@@ -224,8 +234,17 @@ async def main() -> None:
             raise RuntimeError("No checkpoints found.")
         checkpoint_id = checkpoint.checkpoint_id
 
-        results = await workflow.run(responses=responses, checkpoint_id=checkpoint_id)
-        request_events = results.get_request_info_events()
+        print("Resuming workflow from checkpoint...")
+        results = await workflow.run(responses=responses, checkpoint_id=checkpoint_id, stream=True)
+
+        # Iterate through streamed events and collect request_info events
+        request_events: list[WorkflowEvent] = []
+        async for event in results:
+            event: WorkflowEvent
+            if event.type == "request_info":
+                request_events.append(event)
+            elif event.type == "output" and isinstance(event.data, AgentResponseUpdate):
+                print(event.data.text, end="", flush=True)
 
     print("\n" + "=" * 60)
     print("DEMO COMPLETE")

python/samples/getting_started/orchestrations/handoff/handoff_with_code_interpreter_file.py

@@ -1,186 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-
-"""
-Handoff Workflow with Code Interpreter File Generation Sample
-
-This sample demonstrates retrieving file IDs from code interpreter output
-in a handoff workflow context. A triage agent routes to a code specialist
-that generates a text file, and we verify the file_id is captured correctly
-from the streaming workflow events.
-
-Verifies GitHub issue #2718: files generated by code interpreter in
-HandoffBuilder workflows can be properly retrieved.
-
-Prerequisites:
-    - AZURE_AI_PROJECT_ENDPOINT must be your Azure AI Foundry Agent Service (V2) project endpoint.
-    - `az login` (Azure CLI authentication)
-    - AZURE_AI_MODEL_DEPLOYMENT_NAME
-"""
-
-import asyncio
-import os
-from collections.abc import AsyncIterable
-from typing import cast
-
-from agent_framework import (
-    AgentResponseUpdate,
-    Message,
-    WorkflowEvent,
-    WorkflowRunState,
-)
-from agent_framework.azure import AzureOpenAIResponsesClient
-from agent_framework.orchestrations import HandoffAgentUserRequest, HandoffBuilder
-from azure.identity import AzureCliCredential
-
-
-async def _drain(stream: AsyncIterable[WorkflowEvent]) -> list[WorkflowEvent]:
-    """Collect all events from an async stream."""
-    return [event async for event in stream]
-
-
-def _handle_events(events: list[WorkflowEvent]) -> tuple[list[WorkflowEvent[HandoffAgentUserRequest]], list[str]]:
-    """Process workflow events and extract file IDs and pending requests.
-
-    Returns:
-        Tuple of (pending_requests, file_ids_found)
-    """
-
-    requests: list[WorkflowEvent[HandoffAgentUserRequest]] = []
-    file_ids: list[str] = []
-
-    for event in events:
-        if event.type == "handoff_sent":
-            print(f"\n[Handoff from {event.data.source} to {event.data.target} initiated.]")
-        elif event.type == "status" and event.state in {
-            WorkflowRunState.IDLE,
-            WorkflowRunState.IDLE_WITH_PENDING_REQUESTS,
-        }:
-            print(f"[status] {event.state}")
-        elif event.type == "request_info" and isinstance(event.data, HandoffAgentUserRequest):
-            requests.append(cast(WorkflowEvent[HandoffAgentUserRequest], event))
-        elif event.type == "output":
-            data = event.data
-            if isinstance(data, AgentResponseUpdate):
-                for content in data.contents:
-                    if content.type == "hosted_file":
-                        file_ids.append(content.file_id)  # type: ignore
-                        print(f"[Found HostedFileContent: file_id={content.file_id}]")
-                    elif content.type == "text" and content.annotations:
-                        for annotation in content.annotations:
-                            file_id = annotation["file_id"]  # type: ignore
-                            file_ids.append(file_id)
-                            print(f"[Found file annotation: file_id={file_id}]")
-            elif isinstance(data, list):
-                conversation = cast(list[Message], data)
-                if isinstance(conversation, list):
-                    print("\n=== Final Conversation Snapshot ===")
-                    for message in conversation:
-                        speaker = message.author_name or message.role
-                        print(f"- {speaker}: {message.text or [content.type for content in message.contents]}")
-                    print("===================================")
-
-    return requests, file_ids
-
-
-async def main() -> None:
-    """Run a simple handoff workflow with code interpreter file generation."""
-    print("=== Handoff Workflow with Code Interpreter File Generation ===\n")
-
-    client = AzureOpenAIResponsesClient(
-        project_endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
-        deployment_name=os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"],
-        credential=AzureCliCredential(),
-    )
-
-    triage = client.as_agent(
-        name="triage_agent",
-        instructions=(
-            "You are a triage agent. Route code-related requests to the code_specialist. "
-            "When the user asks to create or generate files, hand off to code_specialist "
-            "by calling handoff_to_code_specialist."
-        ),
-    )
-
-    code_interpreter_tool = client.get_code_interpreter_tool()
-
-    code_specialist = client.as_agent(
-        name="code_specialist",
-        instructions=(
-            "You are a Python code specialist. Use the code interpreter to execute Python code "
-            "and create files when requested. Always save files to /mnt/data/ directory."
-        ),
-        tools=[code_interpreter_tool],
-    )
-
-    workflow = (
-        HandoffBuilder(
-            termination_condition=lambda conv: sum(1 for msg in conv if msg.role == "user") >= 2,
-        )
-        .participants([triage, code_specialist])
-        .with_start_agent(triage)
-        .build()
-    )
-
-    user_inputs = [
-        "Please create a text file called hello.txt with 'Hello from handoff workflow!' inside it.",
-        "exit",
-    ]
-    input_index = 0
-    all_file_ids: list[str] = []
-
-    print(f"User: {user_inputs[0]}")
-    events = await _drain(workflow.run(user_inputs[0], stream=True))
-    requests, file_ids = _handle_events(events)
-    all_file_ids.extend(file_ids)
-    input_index += 1
-
-    while requests:
-        request = requests[0]
-        if input_index >= len(user_inputs):
-            break
-        user_input = user_inputs[input_index]
-        print(f"\nUser: {user_input}")
-
-        responses = {request.request_id: HandoffAgentUserRequest.create_response(user_input)}
-        events = await _drain(workflow.run(stream=True, responses=responses))
-        requests, file_ids = _handle_events(events)
-        all_file_ids.extend(file_ids)
-        input_index += 1
-
-    print("\n" + "=" * 50)
-    if all_file_ids:
-        print(f"SUCCESS: Found {len(all_file_ids)} file ID(s) in handoff workflow:")
-        for fid in all_file_ids:
-            print(f"  - {fid}")
-    else:
-        print("WARNING: No file IDs captured from the handoff workflow.")
-    print("=" * 50)
-
-    """
-    Sample Output:
-
-    User: Please create a text file called hello.txt with 'Hello from handoff workflow!' inside it.
-    [Found HostedFileContent: file_id=assistant-JT1sA...]
-
-    === Conversation So Far ===
-    - user: Please create a text file called hello.txt with 'Hello from handoff workflow!' inside it.
-    - triage_agent: I am handing off your request to create the text file "hello.txt" with the specified content to the code specialist. They will assist you shortly.
-    - code_specialist: The file "hello.txt" has been created with the content "Hello from handoff workflow!". You can download it using the link below:
-
-    [hello.txt](sandbox:/mnt/data/hello.txt)
-    ===========================
-
-    [status] IDLE_WITH_PENDING_REQUESTS
-
-    User: exit
-    [status] IDLE
-
-    ==================================================
-    SUCCESS: Found 1 file ID(s) in handoff workflow:
-    - assistant-JT1sA...
-    ==================================================
-    """  # noqa: E501
-
-
-if __name__ == "__main__":
-    asyncio.run(main())