fix unit tests

python/packages/core/agent_framework/__init__.py

@@ -9,7 +9,7 @@ integrations, many of which are lazy-loaded from optional packages.
 """
 
 import importlib.metadata
-from typing import Final
+from typing import TYPE_CHECKING, Any, Final
 
 try:
     _version = importlib.metadata.version(__name__)
@@ -264,6 +264,7 @@ from ._workflows._agent_executor import (
 )
 from ._workflows._agent_utils import resolve_agent_id
 from ._workflows._checkpoint import (
+    CheckpointID,
     CheckpointStorage,
     FileCheckpointStorage,
     InMemoryCheckpointStorage,
@@ -307,7 +308,6 @@ from ._workflows._functional import (
     workflow,
 )
 from ._workflows._request_info_mixin import response_handler
-from ._workflows._runner import Runner
 from ._workflows._runner_context import (
     InProcRunnerContext,
     RunnerContext,
@@ -405,6 +405,7 @@ __all__ = [
     "ChatResponse",
     "ChatResponseUpdate",
     "CheckResult",
+    "CheckpointID",
     "CheckpointStorage",
     "ClassSkill",
     "CompactionProvider",
@@ -618,3 +619,20 @@ __all__ = [
     "validate_workflow_graph",
     "workflow",
 ]
+
+if TYPE_CHECKING:
+    from ._workflows._runner import Runner
+
+
+def __getattr__(name: str) -> Any:
+    """Lazily resolve deprecated public names, emitting a ``DeprecationWarning``.
+
+    ``Runner`` remains importable from ``agent_framework`` for backward
+    compatibility but is deprecated and slated for removal from the public API.
+    """
+    if name == "Runner":
+        from ._workflows._runner import Runner, warn_runner_deprecated
+
+        warn_runner_deprecated()
+        return Runner
+    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

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

@@ -3,6 +3,7 @@
 import asyncio
 import contextlib
 import logging
+import warnings
 from collections import defaultdict
 from collections.abc import AsyncGenerator, Sequence
 from typing import Any
@@ -10,7 +11,7 @@ from typing import Any
 from ..exceptions import (
     WorkflowCheckpointException,
     WorkflowConvergenceException,
-    WorkflowRunnerException,
+    WorkflowException,
 )
 from ._checkpoint import CheckpointID, CheckpointStorage, WorkflowCheckpoint
 from ._const import EXECUTOR_STATE_KEY
@@ -27,6 +28,21 @@ from ._state import State
 logger = logging.getLogger(__name__)
 
 
+def warn_runner_deprecated() -> None:
+    """Emit a deprecation warning when ``Runner`` is accessed from the public API.
+
+    ``Runner`` remains importable from ``agent_framework`` for backward
+    compatibility, but it is intended for internal use only and will be removed
+    from the public API in a future version.
+    """
+    warnings.warn(
+        "`Runner` is deprecated and will be removed from the public API in a future version. "
+        "It is intended for internal use only.",
+        DeprecationWarning,
+        stacklevel=3,
+    )
+
+
 class Runner:
     """A class to run a workflow in Pregel supersteps."""
 
@@ -63,99 +79,123 @@ class Runner:
         self._iteration = 0
         self._max_iterations = max_iterations
         self._state = state
-        self._running = False
-        self._resumed_from_checkpoint = False  # Track whether we resumed
+
+        # Checkpointing related attributes
+        self._resumed_from_checkpoint = False
+        self._previous_checkpoint_id: CheckpointID | None = None
 
     @property
     def context(self) -> RunnerContext:
-        """Get the workflow context."""
+        """Get the runner context for message, event, and checkpoint handling."""
         return self._ctx
 
+    @property
+    def state(self) -> State:
+        """Get the shared state for the workflow."""
+        return self._state
+
     def reset_iteration_count(self) -> None:
-        """Reset the iteration count to zero."""
+        """Reset the iteration count to zero.
+
+        This is useful when the workflow resumes from a new set of messages.
+
+        Note:
+            When a workflow is resumed from a response (for a request_info_event)
+            or a checkpoint, the iteration count is normally NOT reset.
+        """
         self._iteration = 0
 
+    def reset_runtime_state(
+        self,
+        *,
+        iteration: int = 0,
+        previous_checkpoint_id: CheckpointID | None = None,
+        resumed_from_checkpoint: bool = False,
+    ) -> None:
+        """Reset runner runtime bookkeeping to a known baseline.
+
+        Args:
+            iteration: Iteration value to restore.
+            previous_checkpoint_id: Checkpoint parent pointer for subsequent saves.
+            resumed_from_checkpoint: Whether to treat next run as resumed.
+        """
+        self._iteration = iteration
+        self._previous_checkpoint_id = previous_checkpoint_id
+        self._resumed_from_checkpoint = resumed_from_checkpoint
+
     async def run_until_convergence(self) -> AsyncGenerator[WorkflowEvent, None]:
         """Run the workflow until no more messages are sent."""
-        if self._running:
-            raise WorkflowRunnerException("Runner is already running.")
+        # Emit any events already produced prior to entering loop
+        if await self._ctx.has_events():
+            logger.info("Yielding pre-loop events")
+            for event in await self._ctx.drain_events():
+                yield event
 
-        self._running = True
-        previous_checkpoint_id: CheckpointID | None = None
-        try:
-            # Emit any events already produced prior to entering loop
-            if await self._ctx.has_events():
-                logger.info("Yielding pre-loop events")
-                for event in await self._ctx.drain_events():
-                    yield event
+        # Create a checkpoint before a run starts. Checkpoints are usually considered to be created at the
+        # end of an iteration, we can think of this checkpoint as being created at the end of "superstep 0"
+        # which captures the states after which the start executor has run. Note that we execute the start
+        # executor outside of the main iteration loop.
+        if await self._ctx.has_messages() and not self._resumed_from_checkpoint:
+            await self.create_checkpoint_if_enabled()
 
-            # Create the first checkpoint. Checkpoints are usually considered to be created at the end of an iteration,
-            # we can think of the first checkpoint as being created at the end of a "superstep 0" which captures the
-            # states after which the start executor has run.  Note that we execute the start executor outside of the
-            # main iteration loop.
-            if await self._ctx.has_messages() and not self._resumed_from_checkpoint:
-                previous_checkpoint_id = await self._create_checkpoint_if_enabled(previous_checkpoint_id)
+        while self._iteration < self._max_iterations:
+            logger.info(f"Starting superstep {self._iteration + 1}")
+            yield WorkflowEvent.superstep_started(iteration=self._iteration + 1)
 
-            while self._iteration < self._max_iterations:
-                logger.info(f"Starting superstep {self._iteration + 1}")
-                yield WorkflowEvent.superstep_started(iteration=self._iteration + 1)
-
-                # Run iteration concurrently with live event streaming: we poll
-                # for new events while the iteration coroutine progresses.
-                iteration_task = asyncio.create_task(self._run_iteration())
-                try:
-                    while not iteration_task.done():
-                        try:
-                            # Wait briefly for any new event; timeout allows progress checks
-                            event = await asyncio.wait_for(self._ctx.next_event(), timeout=0.05)
-                            yield event
-                        except asyncio.TimeoutError:
-                            # Periodically continue to let iteration advance
-                            continue
-                except asyncio.CancelledError:
-                    # Propagate cancellation to the iteration task to avoid orphaned work
-                    iteration_task.cancel()
-                    with contextlib.suppress(asyncio.CancelledError):
-                        await iteration_task
-                    raise
-
-                # Propagate errors from iteration, but first surface any pending events
-                try:
+            # Run iteration concurrently with live event streaming: we poll
+            # for new events while the iteration coroutine progresses.
+            iteration_task = asyncio.create_task(self._run_iteration())
+            try:
+                while not iteration_task.done():
+                    try:
+                        # Wait briefly for any new event; timeout allows progress checks
+                        event = await asyncio.wait_for(self._ctx.next_event(), timeout=0.05)
+                        yield event
+                    except asyncio.TimeoutError:
+                        # Periodically continue to let iteration advance
+                        continue
+            except asyncio.CancelledError:
+                # Propagate cancellation to the iteration task to avoid orphaned work
+                iteration_task.cancel()
+                with contextlib.suppress(asyncio.CancelledError):
                     await iteration_task
-                except Exception:
-                    # Make sure failure-related events (like ExecutorFailedEvent) are surfaced
-                    if await self._ctx.has_events():
-                        for event in await self._ctx.drain_events():
-                            yield event
-                    raise
-                self._iteration += 1
+                raise
 
-                # Drain any straggler events emitted at tail end
+            # Propagate errors from iteration, but first surface any pending events
+            try:
+                await iteration_task
+            except Exception:
+                # Make sure failure-related events (like ExecutorFailedEvent) are surfaced
                 if await self._ctx.has_events():
                     for event in await self._ctx.drain_events():
                         yield event
+                raise
+            self._iteration += 1
 
-                logger.info(f"Completed superstep {self._iteration}")
+            # Drain any straggler events emitted at tail end
+            if await self._ctx.has_events():
+                for event in await self._ctx.drain_events():
+                    yield event
 
-                # Commit pending state changes at superstep boundary
-                self._state.commit()
+            logger.info(f"Completed superstep {self._iteration}")
 
-                # Create checkpoint after each superstep iteration
-                previous_checkpoint_id = await self._create_checkpoint_if_enabled(previous_checkpoint_id)
+            # Commit pending state changes at superstep boundary
+            self._state.commit()
 
-                yield WorkflowEvent.superstep_completed(iteration=self._iteration)
+            # Create checkpoint after each superstep iteration
+            await self.create_checkpoint_if_enabled()
 
-                # Check for convergence: no more messages to process
-                if not await self._ctx.has_messages():
-                    break
+            yield WorkflowEvent.superstep_completed(iteration=self._iteration)
 
-            if self._iteration >= self._max_iterations and await self._ctx.has_messages():
-                raise WorkflowConvergenceException(f"Runner did not converge after {self._max_iterations} iterations.")
+            # Check for convergence: no more messages to process
+            if not await self._ctx.has_messages():
+                break
 
-            logger.info(f"Workflow completed after {self._iteration} supersteps")
-            self._resumed_from_checkpoint = False  # Reset resume flag for next run
-        finally:
-            self._running = False
+        logger.info(f"Workflow completed after {self._iteration} supersteps")
+        self._resumed_from_checkpoint = False  # Reset resume flag for next run
+
+        if self._iteration >= self._max_iterations and await self._ctx.has_messages():
+            raise WorkflowConvergenceException(f"Runner did not converge after {self._max_iterations} iterations.")
 
     async def _run_iteration(self) -> None:
         """Run a single iteration of the workflow.
@@ -209,40 +249,121 @@ class Runner:
         ]
         await asyncio.gather(*tasks)
 
-    async def _create_checkpoint_if_enabled(self, previous_checkpoint_id: CheckpointID | None) -> CheckpointID | None:
+    async def _prepare_checkpoint_state(self) -> None:
+        """Persist executor snapshots into committed shared state.
+
+        This is used by checkpoint capture paths that need a complete, restorable
+        state payload without necessarily writing to a checkpoint storage backend.
+        """
+        await self._save_executor_states()
+        self._state.commit()
+
+    async def capture_checkpoint_object(self, *, metadata: dict[str, Any] | None = None) -> WorkflowCheckpoint:
+        """Capture the current runner state as an in-memory checkpoint object.
+
+        Persists executor snapshots into committed state and builds a
+        ``WorkflowCheckpoint`` from the current committed state. The checkpoint is
+        not written to any storage backend; the caller owns its lifetime (for
+        example, the workflow's captured initial checkpoint used by reset).
+
+        This is only valid when the runner is quiescent: it rejects capture when
+        in-flight executor messages or pending request_info events are present,
+        since those represent mid-run state that would not form a clean baseline.
+
+        Args:
+            metadata: Optional metadata to attach to the checkpoint.
+
+        Returns:
+            A ``WorkflowCheckpoint`` snapshot of the current runner state.
+
+        Raises:
+            WorkflowException: If in-flight messages or pending requests are present.
+        """
+        if await self._ctx.has_messages():
+            raise WorkflowException("Cannot capture checkpoint while in-flight messages are present.")
+
+        pending_requests = await self._ctx.get_pending_request_info_events()
+        if pending_requests:
+            raise WorkflowException("Cannot capture checkpoint while pending requests are present.")
+
+        await self._prepare_checkpoint_state()
+        return WorkflowCheckpoint(
+            workflow_name=self._workflow_name,
+            graph_signature_hash=self._graph_signature_hash,
+            previous_checkpoint_id=None,
+            messages={},
+            state=self._state.export_state(),
+            pending_request_info_events={},
+            iteration_count=0,
+            metadata=metadata or {},
+        )
+
+    async def create_checkpoint_if_enabled(self) -> None:
         """Create a checkpoint if checkpointing is enabled and attach a label and metadata."""
         if not self._ctx.has_checkpointing():
-            return None
+            return
 
         try:
-            # Save executor states into the shared state before creating the checkpoint,
-            # so that they are included in the checkpoint payload.
-            await self._save_executor_states()
-            # `on_checkpoint_save()` writes via State.set(), which stages values in the
-            # pending buffer. Checkpoints serialize committed state only, so commit here
-            # to ensure executor snapshots are captured in this checkpoint.
-            self._state.commit()
+            # Save executor states into committed state before creating the checkpoint.
+            await self._prepare_checkpoint_state()
 
             checkpoint_id = await self._ctx.create_checkpoint(
                 self._workflow_name,
                 self._graph_signature_hash,
                 self._state,
-                previous_checkpoint_id,
+                self._previous_checkpoint_id,
                 self._iteration,
             )
 
-            logger.info(f"Created checkpoint: {checkpoint_id}")
-            return checkpoint_id
+            logger.info(
+                "Created checkpoint: %s with parent checkpoint at iteration %d: %s",
+                checkpoint_id,
+                self._iteration,
+                self._previous_checkpoint_id,
+            )
+            self._previous_checkpoint_id = checkpoint_id
         except Exception as e:
-            logger.warning(f"Failed to create checkpoint: {e}")
-            return None
+            logger.warning(
+                "Failed to create checkpoint at iteration %d: %s. "
+                "Note that this does not fail the workflow run. "
+                "The next successfully-created checkpoint will be parented to the last successful checkpoint: %s",
+                self._iteration,
+                e,
+                self._previous_checkpoint_id,
+            )
+
+    async def restore_from_checkpoint_object(self, checkpoint: WorkflowCheckpoint) -> None:
+        """Restore runner state from an in-memory checkpoint object.
+
+        Unlike :meth:`restore_from_checkpoint`, this does not load from storage or
+        validate the graph signature; it applies a checkpoint that the caller already
+        holds (for example, the workflow's captured initial checkpoint used by reset).
+
+        This clears any runtime checkpoint storage override and resets the context for a
+        fresh run, then restores shared state, executor snapshots, and runtime bookkeeping
+        from the checkpoint.
+
+        Args:
+            checkpoint: The checkpoint whose state should be restored.
+        """
+        self._ctx.clear_runtime_checkpoint_storage()
+        self._ctx.reset_for_new_run()
+
+        self._state.clear()
+        self._state.import_state(checkpoint.state)
+        await self._restore_executor_states()
+        self.reset_runtime_state(
+            iteration=checkpoint.iteration_count,
+            previous_checkpoint_id=checkpoint.previous_checkpoint_id,
+            resumed_from_checkpoint=False,
+        )
 
     async def restore_from_checkpoint(
         self,
         checkpoint_id: CheckpointID,
         checkpoint_storage: CheckpointStorage | None = None,
     ) -> None:
-        """Restore workflow state from a checkpoint.
+        """Restore the runner from a checkpoint.
 
         Args:
             checkpoint_id: The ID of the checkpoint to restore from
@@ -290,7 +411,7 @@ class Runner:
             # Apply the checkpoint to the context
             await self._ctx.apply_checkpoint(checkpoint)
             # Mark the runner as resumed
-            self._mark_resumed(checkpoint.iteration_count)
+            self._mark_resumed(checkpoint)
 
             logger.info(f"Successfully restored workflow from checkpoint: {checkpoint_id}")
         except WorkflowCheckpointException:
@@ -356,13 +477,14 @@ class Runner:
 
         return parsed
 
-    def _mark_resumed(self, iteration: int) -> None:
+    def _mark_resumed(self, checkpoint: WorkflowCheckpoint) -> None:
         """Mark the runner as having resumed from a checkpoint.
 
         Optionally set the current iteration and max iterations.
         """
         self._resumed_from_checkpoint = True
-        self._iteration = iteration
+        self._iteration = checkpoint.iteration_count
+        self._previous_checkpoint_id = checkpoint.checkpoint_id
 
     async def _set_executor_state(self, executor_id: str, state: dict[str, Any]) -> None:
         """Store executor state in state under a reserved key.

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

@@ -403,12 +403,14 @@ class InProcRunnerContext:
     def reset_for_new_run(self) -> None:
         """Reset the context for a new workflow run.
 
-        This clears messages, events, and resets streaming flag.
-        Runtime checkpoint storage is NOT cleared here as it's managed at the workflow level.
+        Clears messages, the pending event queue, the pending request_info
+        correlation map, and the streaming flag. Runtime checkpoint storage is
+        NOT cleared here as it's managed at the workflow level.
         """
         self._messages.clear()
         # Clear any pending events (best-effort) by recreating the queue
         self._event_queue = asyncio.Queue()
+        self._pending_request_info_events.clear()
         self._streaming = False  # Reset streaming flag
 
     async def apply_checkpoint(self, checkpoint: WorkflowCheckpoint) -> None:

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

@@ -11,14 +11,16 @@ import logging
 import types
 import uuid
 import warnings
+import weakref
 from collections.abc import AsyncIterable, Awaitable, Callable, Mapping, Sequence
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, Literal, overload
 
 from .._sessions import ContextProvider
 from .._types import ResponseStream
+from ..exceptions import WorkflowException
 from ..observability import OtelAttr, capture_exception, create_workflow_span
-from ._checkpoint import CheckpointStorage
+from ._checkpoint import CheckpointStorage, WorkflowCheckpoint
 from ._const import DEFAULT_MAX_ITERATIONS, GLOBAL_KWARGS_KEY, WORKFLOW_RUN_KWARGS_KEY
 from ._edge import (
     EdgeGroup,
@@ -346,25 +348,33 @@ class Workflow(DictConvertible):
         # Store non-serializable runtime objects as private attributes
         self._runner_context = runner_context
         self._runner_context.set_yield_output_classifier(self._output_designation.classify)
-        self._state = State()
         self._runner: Runner = Runner(
             self.edge_groups,
             self.executors,
-            self._state,
+            State(),
             runner_context,
             self.name,
             self.graph_signature_hash,
             max_iterations=max_iterations,
         )
 
-        # Flag to prevent concurrent workflow executions
-        self._is_running = False
-
         # Current run-level status of this workflow instance. Updated in lockstep with
         # the status events emitted from `_run_workflow_with_tracing`. Defaults to IDLE
         # for a freshly built workflow that has not yet been run.
         self._status: WorkflowRunState = WorkflowRunState.IDLE
 
+        # Weak reference to the in-flight run's ``ResponseStream``. Used as the single
+        # concurrency lock: if the previous stream is still alive, ``run()`` rejects a
+        # new run synchronously (before any await). When the stream is fully consumed
+        # ``_run_core``'s finally clears this; if the caller drops the stream without
+        # ever iterating, the weakref dereferences to ``None`` once Python collects it,
+        # so a subsequent ``run()`` is allowed.
+        self._active_run: weakref.ref[ResponseStream[WorkflowEvent, WorkflowRunResult]] | None = None
+
+        # In-memory initial checkpoint captured from the just-built workflow state.
+        # This is internal-only and used by ``reset()``.
+        self._initial_checkpoint: WorkflowCheckpoint | None = None
+
     @property
     def status(self) -> WorkflowRunState:
         """Return the current run-level status of this workflow instance.
@@ -376,16 +386,6 @@ class Workflow(DictConvertible):
         """
         return self._status
 
-    def _ensure_not_running(self) -> None:
-        """Ensure the workflow is not already running."""
-        if self._is_running:
-            raise RuntimeError("Workflow is already running. Concurrent executions are not allowed.")
-        self._is_running = True
-
-    def _reset_running_flag(self) -> None:
-        """Reset the running flag."""
-        self._is_running = False
-
     def to_dict(self) -> dict[str, Any]:
         """Serialize the workflow definition into a JSON-ready dictionary."""
         data: dict[str, Any] = {
@@ -478,6 +478,44 @@ class Workflow(DictConvertible):
         """Get the list of executors in the workflow."""
         return list(self.executors.values())
 
+    async def _ensure_initial_checkpoint(self) -> None:
+        """Capture the in-memory initial checkpoint once for this workflow instance."""
+        if self._initial_checkpoint is not None:
+            return
+
+        self._initial_checkpoint = await self._runner.capture_checkpoint_object(
+            metadata={"kind": "initial_in_memory"},
+        )
+
+    async def reset(self) -> None:
+        """Reset the workflow instance to its captured initial checkpoint state.
+
+        The initial checkpoint is captured in memory once per workflow instance and
+        is not persisted to external checkpoint storage.
+
+        Raises:
+            WorkflowException: If called while a workflow run is active.
+        """
+        if self._is_run_active():
+            raise WorkflowException(
+                "Cannot reset workflow while a run is active. "
+                "Reset is only allowed between runs when the workflow is idle."
+            )
+
+        # Capture the baseline if it doesn't exist yet. This is idempotent: on a
+        # normal reset after one or more runs it's a no-op (the snapshot was taken
+        # before the first run); when reset is the first operation it captures the
+        # pristine just-built state so the workflow stays runnable.
+        await self._ensure_initial_checkpoint()
+        if self._initial_checkpoint is None:
+            raise WorkflowException("Workflow initial checkpoint is unavailable.")
+
+        # Restore runner state, executor snapshots, and runtime bookkeeping from the
+        # in-memory initial checkpoint.
+        await self._runner.restore_from_checkpoint_object(self._initial_checkpoint)
+
+        self._status = WorkflowRunState.IDLE
+
     async def _run_workflow_with_tracing(
         self,
         initial_executor_fn: Callable[[], Awaitable[None]] | None = None,
@@ -535,13 +573,12 @@ class Workflow(DictConvertible):
                 yield in_progress  # noqa: RUF070
 
                 # Per-run reset for fresh-message runs only. We deliberately
-                # do NOT clear shared workflow state (`_state.clear()`) or the
-                # runner context's in-flight messages (`reset_for_new_run()`)
-                # here - state and pending work persist across `run()` calls
-                # so that a `WorkflowAgent` can deliver multi-turn input on
-                # the same instance and have prior turns' context survive.
-                # Iteration counting and per-run kwargs ARE per-run though,
-                # so they're reset here.
+                # do NOT clear shared workflow state or the runner context's
+                # in-flight messages here - state and pending work persist
+                # across `run()` calls so that a `WorkflowAgent` can deliver
+                # multi-turn input on the same instance and have prior turns'
+                # context survive. Iteration counting and per-run kwargs ARE
+                # per-run though, so they're reset here.
                 if not is_continuation:
                     self._runner.reset_iteration_count()
 
@@ -564,14 +601,13 @@ class Workflow(DictConvertible):
                         combined_kwargs["client_kwargs"] = self._resolve_invocation_kwargs(
                             client_kwargs, "client_kwargs"
                         )
-                    self._state.set(WORKFLOW_RUN_KWARGS_KEY, combined_kwargs)
+                    self._runner.state.set(WORKFLOW_RUN_KWARGS_KEY, combined_kwargs)
                 elif not is_continuation:
-                    self._state.set(WORKFLOW_RUN_KWARGS_KEY, {})
-                self._state.commit()  # Commit immediately so kwargs are available
+                    self._runner.state.set(WORKFLOW_RUN_KWARGS_KEY, {})
+                self._runner.state.commit()  # Commit immediately so kwargs are available
 
-                # Set streaming mode (always set explicitly per run since
-                # reset_for_new_run() no longer runs to clear it).
-                self._runner_context.set_streaming(streaming)
+                # Explicitly set streaming mode per run
+                self._runner.context.set_streaming(streaming)
 
                 # Execute initial setup if provided
                 if initial_executor_fn:
@@ -665,7 +701,7 @@ class Workflow(DictConvertible):
             await executor.execute(
                 message,
                 [self.__class__.__name__],
-                self._state,
+                self._runner.state,
                 self._runner.context,
                 trace_contexts=None,
                 source_span_ids=None,
@@ -745,9 +781,22 @@ class Workflow(DictConvertible):
         Raises:
             ValueError: If parameter combination is invalid.
         """
-        # Validate parameters and set running flag eagerly (before any async work)
+        # Validate parameters first so misuse fails before we touch any run state.
         self._validate_run_params(message, responses, checkpoint_id)
-        self._ensure_not_running()
+
+        # Concurrency check: reject a second run synchronously - before constructing
+        # the ResponseStream or yielding control to the event loop - so a concurrent
+        # ``run`` call can't slip past the guard while the first call is suspended
+        # inside its async generator. The ``ResponseStream`` returned below is the
+        # lock: as long as the caller holds a reference to it, ``self._active_run()``
+        # resolves to a live object and a new ``run`` is rejected. When the stream is
+        # fully consumed, ``_run_core``'s finally clears the attribute. When the
+        # caller drops the stream without iterating, garbage collection invalidates
+        # the weakref, so a subsequent ``run`` is permitted.
+        if self._is_run_active():
+            raise WorkflowException(
+                "Workflow is already running; concurrent runs are not allowed on the same instance."
+            )
 
         response_stream = ResponseStream[WorkflowEvent, WorkflowRunResult](
             self._run_core(
@@ -760,10 +809,8 @@ class Workflow(DictConvertible):
                 client_kwargs=client_kwargs,
             ),
             finalizer=functools.partial(self._finalize_events, include_status_events=include_status_events),
-            cleanup_hooks=[
-                functools.partial(self._run_cleanup, checkpoint_storage),
-            ],
         )
+        self._active_run = weakref.ref(response_stream)
 
         if stream:
             return response_stream
@@ -789,51 +836,69 @@ class Workflow(DictConvertible):
         if checkpoint_storage is not None:
             self._runner.context.set_runtime_checkpoint_storage(checkpoint_storage)
 
-        # Async validation: a fresh-message run is only allowed when the
-        # runner context has fully drained from any prior run. If it still
-        # has in-flight executor messages, the prior run didn't complete -
-        # the caller must either resume from a checkpoint or wait for the
-        # prior run to drain. (Pending request_info events are intentionally
-        # NOT blocked here: a follow-up run with message=... is the normal
-        # way to deliver a response to those pending requests, e.g. via
-        # WorkflowAgent._process_pending_requests.)
-        # NOTE: _validate_run_params already enforces that ``message`` is
-        # mutually exclusive with both ``checkpoint_id`` and ``responses``,
-        # so we don't need to re-check those here.
-        if message is not None and await self._runner.context.has_messages():
-            raise RuntimeError(
-                "Cannot start a new run with 'message' while in-flight executor "
-                "messages remain from a prior run. Resume from a checkpoint "
-                "(checkpoint_id=...) or wait for the prior run to complete. "
-                "Workflows that need to recover from a mid-run failure must use "
-                "checkpointing; there is no in-process recovery path."
-            )
+        # Capture the weakref instance ``run()`` installed for *this* run. We
+        # compare by object identity in the finally so a stale finalizer (e.g.
+        # the caller dropped this stream after partial iteration, then started
+        # a new run before async-gen finalization throws ``GeneratorExit`` into
+        # us) does not clobber a successor run's freshly installed weakref.
+        # ``run()`` runs synchronously and assigns ``self._active_run`` before
+        # this generator's body is first iterated, so by the time we read it
+        # here it already points at our own ``ResponseStream``.
+        my_active_run = self._active_run
 
-        initial_executor_fn = self._resolve_execution_mode(message, responses, checkpoint_id, checkpoint_storage)
+        try:
+            # Async validation: a fresh-message run is only allowed when the
+            # runner context has fully drained from any prior run. If it still
+            # has in-flight executor messages, the prior run didn't complete -
+            # the caller must either resume from a checkpoint or wait for the
+            # prior run to drain. (Pending request_info events are intentionally
+            # NOT blocked here: a follow-up run with message=... is the normal
+            # way to deliver a response to those pending requests, e.g. via
+            # WorkflowAgent._process_pending_requests.)
+            # NOTE: _validate_run_params already enforces that ``message`` is
+            # mutually exclusive with both ``checkpoint_id`` and ``responses``,
+            # so we don't need to re-check those here.
+            if message is not None and await self._runner.context.has_messages():
+                raise RuntimeError(
+                    "Cannot start a new run with 'message' while in-flight executor "
+                    "messages remain from a prior run. Resume from a checkpoint "
+                    "(checkpoint_id=...) or wait for the prior run to complete. "
+                    "Workflows that need to recover from a mid-run failure must use "
+                    "checkpointing; there is no in-process recovery path."
+                )
 
-        async for event in self._run_workflow_with_tracing(
-            initial_executor_fn=initial_executor_fn,
-            is_continuation=(message is None),
-            streaming=streaming,
-            function_invocation_kwargs=function_invocation_kwargs,
-            client_kwargs=client_kwargs,
-        ):
-            if event.type == "request_info" and event.request_id in (responses or {}):
-                # Don't yield request_info events for which we have responses to send -
-                # these are considered "handled". This prevents the caller from seeing
-                # events for requests they are already responding to.
-                # This usually happens when responses are provided with a checkpoint
-                # (restore then send), because the request_info events are stored in the
-                # checkpoint and would be emitted on restoration by the runner regardless
-                # of if a response is provided or not.
-                continue
-            yield event
+            await self._ensure_initial_checkpoint()
 
-    async def _run_cleanup(self, checkpoint_storage: CheckpointStorage | None) -> None:
-        """Cleanup hook called after stream consumption."""
-        if checkpoint_storage is not None:
-            self._runner.context.clear_runtime_checkpoint_storage()
-        self._reset_running_flag()
+            initial_executor_fn = self._resolve_execution_mode(message, responses, checkpoint_id, checkpoint_storage)
+
+            async for event in self._run_workflow_with_tracing(
+                initial_executor_fn=initial_executor_fn,
+                is_continuation=(message is None),
+                streaming=streaming,
+                function_invocation_kwargs=function_invocation_kwargs,
+                client_kwargs=client_kwargs,
+            ):
+                if event.type == "request_info" and event.request_id in (responses or {}):
+                    # Don't yield request_info events for which we have responses to send -
+                    # these are considered "handled". This prevents the caller from seeing
+                    # events for requests they are already responding to.
+                    # This usually happens when responses are provided with a checkpoint
+                    # (restore then send), because the request_info events are stored in the
+                    # checkpoint and would be emitted on restoration by the runner regardless
+                    # of if a response is provided or not.
+                    continue
+                yield event
+        finally:
+            # Clear the active-run weakref so a subsequent ``run()`` is allowed,
+            # but only if the slot still holds *our* weakref. If the caller
+            # dropped this stream after partial iteration and a new ``run()``
+            # already installed its own weakref before our async-gen finalizer
+            # ran, ``self._active_run`` now points at the successor; clearing
+            # it would silently break the successor's concurrency guard.
+            if self._active_run is my_active_run:
+                self._active_run = None
+            if checkpoint_storage is not None:
+                self._runner.context.clear_runtime_checkpoint_storage()
 
     @staticmethod
     def _finalize_events(
@@ -935,7 +1000,7 @@ class Workflow(DictConvertible):
 
     async def _send_responses_internal(self, responses: Mapping[str, Any]) -> None:
         """Internal method to validate and send responses to the executors."""
-        pending_requests = await self._runner_context.get_pending_request_info_events()
+        pending_requests = await self._runner.context.get_pending_request_info_events()
         if not pending_requests:
             raise RuntimeError("No pending requests found in workflow context.")
 
@@ -955,7 +1020,7 @@ class Workflow(DictConvertible):
             coerced_responses[request_id] = response
 
         await asyncio.gather(*[
-            self._runner_context.send_request_info_response(request_id, response)
+            self._runner.context.send_request_info_response(request_id, response)
             for request_id, response in coerced_responses.items()
         ])
 
@@ -1151,3 +1216,12 @@ class Workflow(DictConvertible):
             context_providers=context_providers,
             **kwargs,
         )
+
+    def _is_run_active(self) -> bool:
+        """Check if a workflow run is currently active.
+
+        Returns:
+            True if a run is active, False otherwise.
+        """
+        existing_stream = self._active_run() if self._active_run is not None else None
+        return existing_stream is not None

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

@@ -517,6 +517,10 @@ class WorkflowExecutor(Executor):
         self._execution_contexts = execution_contexts
         self._request_to_execution = request_to_execution
 
+        # Reset the sub workflow to its initial state. This must be done before pumping
+        # the request info events back into the sub workflow.
+        await self.workflow.reset()
+
         # Add the `request_info_event`s back to the sub workflow.
         # This is only a temporary solution to rehydrate the sub workflow with the requests.
         # The proper way would be to rehydrate the workflow from a checkpoint on a Workflow

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

@@ -336,6 +336,97 @@ async def test_workflow_checkpoint_chaining_via_previous_checkpoint_id():
         )
 
 
+async def test_workflow_checkpoint_ancestry_preserved_after_resume():
+    """Resuming from a checkpoint must preserve ancestry: future checkpoints chain back to the resumed one."""
+    from typing_extensions import Never
+
+    from agent_framework import WorkflowBuilder, WorkflowContext, handler
+    from agent_framework._workflows._executor import Executor
+
+    class StartExecutor(Executor):
+        @handler
+        async def run(self, message: str, ctx: WorkflowContext[str]) -> None:
+            await ctx.send_message(message, target_id="middle")
+
+    class MiddleExecutor(Executor):
+        @handler
+        async def process(self, message: str, ctx: WorkflowContext[str]) -> None:
+            await ctx.send_message(message + "-processed", target_id="finish")
+
+    class FinishExecutor(Executor):
+        @handler
+        async def finish(self, message: str, ctx: WorkflowContext[Never, str]) -> None:
+            await ctx.yield_output(message + "-done")
+
+    storage = InMemoryCheckpointStorage()
+
+    def _build_workflow() -> Any:
+        start = StartExecutor(id="start")
+        middle = MiddleExecutor(id="middle")
+        finish = FinishExecutor(id="finish")
+        return (
+            WorkflowBuilder(
+                name="resume-ancestry-test",
+                max_iterations=10,
+                start_executor=start,
+                checkpoint_storage=storage,
+            )
+            .add_edge(start, middle)
+            .add_edge(middle, finish)
+            .build()
+        )
+
+    # First run: produce an initial chain of checkpoints
+    workflow = _build_workflow()
+    workflow_name = workflow.name
+    _ = [event async for event in workflow.run("hello", stream=True)]
+
+    initial_checkpoints = sorted(await storage.list_checkpoints(workflow_name=workflow_name), key=lambda c: c.timestamp)
+    assert len(initial_checkpoints) >= 3, (
+        f"Need at least 3 initial checkpoints to pick a middle one, got {len(initial_checkpoints)}"
+    )
+    initial_ids = {cp.checkpoint_id for cp in initial_checkpoints}
+
+    # Pick an intermediate checkpoint to resume from (not the first, not the last)
+    resume_from = initial_checkpoints[len(initial_checkpoints) // 2]
+
+    # Resume on a fresh workflow instance (same graph signature) and run to completion
+    resumed_workflow = _build_workflow()
+    assert resumed_workflow.name == workflow_name
+    _ = [event async for event in resumed_workflow.run(checkpoint_id=resume_from.checkpoint_id, stream=True)]
+
+    # Inspect new checkpoints created after resuming
+    all_checkpoints = sorted(await storage.list_checkpoints(workflow_name=workflow_name), key=lambda c: c.timestamp)
+    new_checkpoints = [cp for cp in all_checkpoints if cp.checkpoint_id not in initial_ids]
+    assert new_checkpoints, "Resuming from an intermediate checkpoint should produce new checkpoints"
+
+    # The very first checkpoint created after resuming must chain back to the resumed checkpoint
+    assert new_checkpoints[0].previous_checkpoint_id == resume_from.checkpoint_id, (
+        "First post-resume checkpoint must chain to the checkpoint that was resumed from; "
+        f"got previous_checkpoint_id={new_checkpoints[0].previous_checkpoint_id!r}, "
+        f"expected {resume_from.checkpoint_id!r}"
+    )
+
+    # Subsequent post-resume checkpoints must continue chaining
+    for i in range(1, len(new_checkpoints)):
+        assert new_checkpoints[i].previous_checkpoint_id == new_checkpoints[i - 1].checkpoint_id, (
+            f"Post-resume checkpoint {i} should chain to checkpoint {i - 1}"
+        )
+
+    # Walking the chain backwards from the most recent checkpoint must reach the original root
+    # without breaks (i.e. the full ancestry across the resume boundary is intact).
+    checkpoints_by_id = {cp.checkpoint_id: cp for cp in all_checkpoints}
+    chain: list[str] = []
+    cursor: str | None = new_checkpoints[-1].checkpoint_id
+    while cursor is not None:
+        chain.append(cursor)
+        cursor = checkpoints_by_id[cursor].previous_checkpoint_id
+    # Chain must include the resumed-from checkpoint and terminate at the original root
+    assert resume_from.checkpoint_id in chain
+    assert chain[-1] == initial_checkpoints[0].checkpoint_id
+    assert checkpoints_by_id[chain[-1]].previous_checkpoint_id is None
+
+
 async def test_memory_checkpoint_storage_roundtrip_json_native_types():
     """Test that JSON-native types (str, int, float, bool, None) roundtrip correctly."""
     storage = InMemoryCheckpointStorage()

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

@@ -17,7 +17,6 @@ from agent_framework import (
     WorkflowContext,
     WorkflowConvergenceException,
     WorkflowEvent,
-    WorkflowRunnerException,
     WorkflowRunState,
     handler,
 )
@@ -305,40 +304,62 @@ async def test_fanout_edge_runner_delivers_to_multiple_targets_concurrently() ->
     assert probe_target.call_count == 1
 
 
-async def test_runner_already_running():
-    """Test that running the runner while it is already running raises an error."""
+async def test_runner_run_until_convergence_runs_sequentially():
+    """run_until_convergence can be invoked back-to-back on the same Runner.
+
+    The Runner itself does not enforce concurrency; that responsibility lives on
+    :class:`Workflow`. This test simply confirms the Runner is reusable across
+    sequential runs.
+    """
+    runner = _make_runner()
+    async for _ in runner.run_until_convergence():
+        pass
+    async for _ in runner.run_until_convergence():
+        pass
+
+
+def _make_runner() -> Runner:
+    """Build a minimal runner for runner-level tests."""
+    return Runner(
+        [],
+        {},
+        State(),
+        InProcRunnerContext(),
+        "test_name",
+        graph_signature_hash="test_hash",
+    )
+
+
+async def test_runner_accepts_new_run_after_previous_failure():
+    """A failed run must not leave the Runner unable to start a new run.
+
+    After the first run raises, ``run_until_convergence()`` must be callable
+    again and not surface any lifecycle-related rejection.
+    """
     executor_a = MockExecutor(id="executor_a")
     executor_b = MockExecutor(id="executor_b")
-
-    # Create a loop
     edges = [
         SingleEdgeGroup(executor_a.id, executor_b.id),
         SingleEdgeGroup(executor_b.id, executor_a.id),
     ]
-
-    executors: dict[str, Executor] = {
-        executor_a.id: executor_a,
-        executor_b.id: executor_b,
-    }
+    executors: dict[str, Executor] = {executor_a.id: executor_a, executor_b.id: executor_b}
     state = State()
     ctx = InProcRunnerContext()
+    runner = Runner(edges, executors, state, ctx, "test_name", graph_signature_hash="test_hash", max_iterations=2)
 
-    runner = Runner(edges, executors, state, ctx, "test_name", graph_signature_hash="test_hash")
+    await executor_a.execute(MockMessage(data=0), ["START"], state, ctx)
 
-    await executor_a.execute(
-        MockMessage(data=0),
-        ["START"],  # source_executor_ids
-        state,  # state
-        ctx,  # runner_context
-    )
+    with pytest.raises(WorkflowConvergenceException):
+        async for _ in runner.run_until_convergence():
+            pass
 
-    with pytest.raises(WorkflowRunnerException, match="Runner is already running."):
-
-        async def _run():
-            async for _ in runner.run_until_convergence():
-                pass
-
-        await asyncio.gather(_run(), _run())
+    # A second run on the same Runner must not be blocked by stale lifecycle
+    # state from the failed run.
+    try:
+        async for _ in runner.run_until_convergence():
+            pass
+    except Exception as exc:
+        assert "Runner is already running" not in str(exc), "Runner stayed locked after a failed run"
 
 
 async def test_runner_emits_runner_completion_for_agent_response_without_targets():
@@ -862,7 +883,13 @@ async def test_runner_checkpoint_with_resumed_flag():
     state = State()
 
     runner = Runner(edges, executors, state, ctx, "test_name", graph_signature_hash="test_hash")
-    runner._mark_resumed(5)  # pyright: ignore[reportPrivateUsage]
+    resumed_checkpoint = WorkflowCheckpoint(
+        checkpoint_id="resumed-cp",
+        workflow_name="test_name",
+        graph_signature_hash="test_hash",
+        iteration_count=5,
+    )
+    runner._mark_resumed(resumed_checkpoint)  # pyright: ignore[reportPrivateUsage]
 
     # Add a message to trigger the checkpoint creation path
     await ctx.send_message(WorkflowMessage(data=MockMessage(data=8), source_id="START"))
@@ -882,6 +909,86 @@ async def test_runner_checkpoint_with_resumed_flag():
     assert runner._resumed_from_checkpoint is False  # pyright: ignore[reportPrivateUsage]
 
 
+async def test_runner_mark_resumed_sets_previous_checkpoint_id():
+    """_mark_resumed must populate _previous_checkpoint_id so future checkpoints chain back to the resume point."""
+    runner = Runner(
+        [],
+        {},
+        State(),
+        InProcRunnerContext(),
+        "test_name",
+        graph_signature_hash="test_hash",
+    )
+
+    # Pre-condition: nothing to chain back to
+    assert runner._previous_checkpoint_id is None  # pyright: ignore[reportPrivateUsage]
+
+    resumed_checkpoint = WorkflowCheckpoint(
+        checkpoint_id="resumed-cp-id",
+        workflow_name="test_name",
+        graph_signature_hash="test_hash",
+        iteration_count=3,
+    )
+    runner._mark_resumed(resumed_checkpoint)  # pyright: ignore[reportPrivateUsage]
+
+    assert runner._resumed_from_checkpoint is True  # pyright: ignore[reportPrivateUsage]
+    assert runner._iteration == 3  # pyright: ignore[reportPrivateUsage]
+    assert runner._previous_checkpoint_id == "resumed-cp-id"  # pyright: ignore[reportPrivateUsage]
+
+
+async def test_runner_post_resume_checkpoint_chains_to_resumed_checkpoint():
+    """After resuming, the next checkpoint created must reference the resumed checkpoint as its parent."""
+    storage = InMemoryCheckpointStorage()
+    ctx = CheckpointingContext(storage)
+    executor_a = MockExecutor(id="executor_a")
+    executor_b = MockExecutor(id="executor_b")
+
+    edges = [
+        SingleEdgeGroup(executor_a.id, executor_b.id),
+        SingleEdgeGroup(executor_b.id, executor_a.id),
+    ]
+
+    executors: dict[str, Executor] = {
+        executor_a.id: executor_a,
+        executor_b.id: executor_b,
+    }
+    state = State()
+
+    runner = Runner(edges, executors, state, ctx, "test_name", graph_signature_hash="test_hash")
+
+    # Simulate having resumed from a prior checkpoint
+    resumed_checkpoint = WorkflowCheckpoint(
+        checkpoint_id="parent-checkpoint-id",
+        workflow_name="test_name",
+        graph_signature_hash="test_hash",
+        iteration_count=1,
+    )
+    runner._mark_resumed(resumed_checkpoint)  # pyright: ignore[reportPrivateUsage]
+
+    # Seed a message so the runner has work to do (and creates checkpoints at superstep boundaries)
+    await ctx.send_message(WorkflowMessage(data=MockMessage(data=8), source_id=executor_a.id))
+
+    async for _ in runner.run_until_convergence():
+        pass
+
+    # Find the first checkpoint created after the resume point (across all workflows tracked by storage)
+    new_checkpoints = sorted(
+        await storage.list_checkpoints(workflow_name="test_name"),
+        key=lambda c: c.timestamp,
+    )
+    assert new_checkpoints, "Resuming and running should produce at least one new checkpoint"
+
+    # The first new checkpoint must chain to the resumed-from checkpoint, not to None
+    assert new_checkpoints[0].previous_checkpoint_id == "parent-checkpoint-id", (
+        "First post-resume checkpoint must chain to the resumed checkpoint id; "
+        f"got {new_checkpoints[0].previous_checkpoint_id!r}"
+    )
+
+    # Subsequent post-resume checkpoints continue the chain
+    for i in range(1, len(new_checkpoints)):
+        assert new_checkpoints[i].previous_checkpoint_id == new_checkpoints[i - 1].checkpoint_id
+
+
 class ExecutorThatFailsWithEvents(Executor):
     """An executor that emits events and then raises an exception after receiving messages."""
 

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

@@ -0,0 +1,72 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+"""Tests for `InProcRunnerContext`."""
+
+import pytest
+
+from agent_framework import (
+    InProcRunnerContext,
+    WorkflowEvent,
+    WorkflowMessage,
+)
+
+
+def _make_request_info_event(request_id: str, source_executor_id: str = "executor") -> WorkflowEvent[str]:
+    return WorkflowEvent.request_info(
+        request_id=request_id,
+        source_executor_id=source_executor_id,
+        request_data="please respond",
+        response_type=str,
+    )
+
+
+class TestInProcRunnerContextResetForNewRun:
+    """Verify `reset_for_new_run` clears per-run state, including pending request_info events."""
+
+    async def test_reset_clears_pending_request_info_events(self) -> None:
+        ctx = InProcRunnerContext()
+
+        await ctx.add_request_info_event(_make_request_info_event("req-1"))
+        await ctx.add_request_info_event(_make_request_info_event("req-2"))
+
+        assert set((await ctx.get_pending_request_info_events()).keys()) == {"req-1", "req-2"}
+
+        ctx.reset_for_new_run()
+
+        assert await ctx.get_pending_request_info_events() == {}
+
+    async def test_reset_clears_pending_request_info_events_when_already_empty(self) -> None:
+        ctx = InProcRunnerContext()
+
+        assert await ctx.get_pending_request_info_events() == {}
+
+        ctx.reset_for_new_run()
+
+        assert await ctx.get_pending_request_info_events() == {}
+
+    async def test_reset_after_pending_event_blocks_response_correlation(self) -> None:
+        """After `reset_for_new_run`, prior request ids must no longer correlate to a response."""
+        ctx = InProcRunnerContext()
+        await ctx.add_request_info_event(_make_request_info_event("req-1"))
+
+        ctx.reset_for_new_run()
+
+        with pytest.raises(ValueError, match="No pending request found for request_id: req-1"):
+            await ctx.send_request_info_response("req-1", "answer")
+
+    async def test_reset_clears_messages_events_and_streaming_flag(self) -> None:
+        """Sanity-check the other state `reset_for_new_run` is documented to clear."""
+        ctx = InProcRunnerContext()
+        await ctx.send_message(WorkflowMessage(data="hello", source_id="executor"))
+        await ctx.add_event(WorkflowEvent("status", data="running"))
+        ctx.set_streaming(True)
+
+        assert await ctx.has_messages() is True
+        assert await ctx.has_events() is True
+        assert ctx.is_streaming() is True
+
+        ctx.reset_for_new_run()
+
+        assert await ctx.has_messages() is False
+        assert await ctx.has_events() is False
+        assert ctx.is_streaming() is False

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

@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.
 
 import asyncio
+import gc
 import tempfile
 from collections.abc import AsyncIterable, Awaitable, Sequence
 from dataclasses import dataclass, field
@@ -26,6 +27,7 @@ from agent_framework import (
     WorkflowContext,
     WorkflowConvergenceException,
     WorkflowEvent,
+    WorkflowException,
     WorkflowMessage,
     WorkflowRunState,
     handler,
@@ -759,8 +761,7 @@ async def test_workflow_concurrent_execution_prevention():
 
     # Try to start a second concurrent execution - this should fail
     with pytest.raises(
-        RuntimeError,
-        match="Workflow is already running. Concurrent executions are not allowed.",
+        WorkflowException, match="Workflow is already running; concurrent runs are not allowed on the same instance."
     ):
         await workflow.run(NumberMessage(data=0))
 
@@ -795,8 +796,7 @@ async def test_workflow_concurrent_execution_prevention_streaming():
 
     # Try to start a second concurrent execution - this should fail
     with pytest.raises(
-        RuntimeError,
-        match="Workflow is already running. Concurrent executions are not allowed.",
+        WorkflowException, match="Workflow is already running; concurrent runs are not allowed on the same instance."
     ):
         await workflow.run(NumberMessage(data=0))
 
@@ -828,14 +828,12 @@ async def test_workflow_concurrent_execution_prevention_mixed_methods():
 
     # Try different execution methods - all should fail
     with pytest.raises(
-        RuntimeError,
-        match="Workflow is already running. Concurrent executions are not allowed.",
+        WorkflowException, match="Workflow is already running; concurrent runs are not allowed on the same instance."
     ):
         await workflow.run(NumberMessage(data=0))
 
     with pytest.raises(
-        RuntimeError,
-        match="Workflow is already running. Concurrent executions are not allowed.",
+        WorkflowException, match="Workflow is already running; concurrent runs are not allowed on the same instance."
     ):
         async for _ in workflow.run(NumberMessage(data=0), stream=True):
             break
@@ -848,6 +846,154 @@ async def test_workflow_concurrent_execution_prevention_mixed_methods():
     assert result.get_final_state() == WorkflowRunState.IDLE
 
 
+async def test_workflow_sequential_runs_after_completion() -> None:
+    """A completed run must release the runner so the next ``run`` succeeds.
+
+    This is the happy-path counterpart to the concurrent-run guard tests:
+    those tests verify that a *concurrent* run is rejected, but they do not
+    verify that the lock is actually released afterwards. This test
+    exercises that release path explicitly across the three call shapes
+    (non-streaming, streaming-iterated, streaming-via-get_final_response)
+    and across multiple consecutive turns to catch lock leaks.
+    """
+    executor = IncrementExecutor(id="seq_executor", limit=3, increment=1)
+    workflow = WorkflowBuilder(start_executor=executor).build()
+
+    # Non-streaming -> non-streaming
+    r1 = await workflow.run(NumberMessage(data=0))
+    assert r1.get_final_state() == WorkflowRunState.IDLE
+
+    r2 = await workflow.run(NumberMessage(data=0))
+    assert r2.get_final_state() == WorkflowRunState.IDLE
+
+    # Non-streaming -> streaming-iterated
+    stream_events: list[WorkflowEvent] = []
+    async for event in workflow.run(NumberMessage(data=0), stream=True):
+        stream_events.append(event)
+    assert any(e.type == "status" and e.state == WorkflowRunState.IDLE for e in stream_events)
+
+    # Streaming -> streaming via get_final_response (no manual iteration)
+    r3 = await workflow.run(NumberMessage(data=0), stream=True).get_final_response()
+    assert r3.get_final_state() == WorkflowRunState.IDLE
+
+    # Streaming -> non-streaming (back to the start)
+    r4 = await workflow.run(NumberMessage(data=0))
+    assert r4.get_final_state() == WorkflowRunState.IDLE
+
+
+async def test_workflow_unconsumed_stream_releases_run_lock() -> None:
+    """An unconsumed stream must not leak the run lock.
+
+    ``Workflow.run`` reserves the runner *synchronously* so that concurrent
+    callers are rejected immediately. The reservation is normally released
+    by ``_run_core``'s ``finally`` once the stream is iterated. If the
+    caller never iterates the stream, a GC-time finalizer must release the
+    reservation instead - otherwise every subsequent ``Workflow.run`` call
+    on this instance would fail with the concurrent-run error.
+    """
+    executor = IncrementExecutor(id="unconsumed_stream_exec", limit=3, increment=1)
+    workflow = WorkflowBuilder(start_executor=executor).build()
+
+    # Build a stream and immediately drop it without iterating.
+    stream = workflow.run(NumberMessage(data=0), stream=True)
+    assert stream is not None  # silence unused-variable warnings; stream is GC'd below
+    del stream
+    gc.collect()
+    # Yield to the event loop so any scheduled finalizer work can run.
+    await asyncio.sleep(0)
+
+    # The runner should be back to IDLE; a fresh run must succeed.
+    result = await workflow.run(NumberMessage(data=0))
+    assert result.get_final_state() == WorkflowRunState.IDLE
+
+
+async def test_workflow_unawaited_run_coroutine_releases_run_lock() -> None:
+    """An un-awaited non-streaming ``run()`` coroutine must also not leak the lock.
+
+    ``Workflow.run`` (non-streaming) returns a coroutine produced by
+    ``ResponseStream.get_final_response``. The underlying ResponseStream is
+    held alive by that coroutine, so dropping the coroutine without
+    awaiting it must still release the reservation via the same GC-time
+    fallback used for unconsumed streams.
+    """
+    executor = IncrementExecutor(id="unawaited_run_exec", limit=3, increment=1)
+    workflow = WorkflowBuilder(start_executor=executor).build()
+
+    coro = workflow.run(NumberMessage(data=0))
+    # Closing suppresses the "coroutine was never awaited" warning. We cast to
+    # ``Any`` because the typed return is ``Awaitable[...]``; in practice it is
+    # a coroutine that exposes ``close``.
+    cast(Any, coro).close()
+    del coro
+    gc.collect()
+    await asyncio.sleep(0)
+
+    result = await workflow.run(NumberMessage(data=0))
+    assert result.get_final_state() == WorkflowRunState.IDLE
+
+
+async def test_workflow_partial_stream_does_not_clobber_successor_active_run() -> None:
+    """A stale ``_run_core`` finalizer must not clear a successor's run lock.
+
+    Repro for the GC-finalizer race the user reported:
+
+    1. Start stream A and consume one event so its body is suspended at a
+       ``yield``. Its ``finally`` is now armed and will run when the
+       generator is closed.
+    2. Drop stream A and ``gc.collect``. The ``_active_run`` weakref's
+       referent is gone, so a subsequent ``run()`` will pass the
+       concurrency guard - but stream A's async-gen finalizer hasn't
+       actually executed yet (``aclose`` is scheduled on the loop).
+    3. Synchronously start stream B; ``run()`` installs a fresh weakref
+       in ``_active_run``.
+    4. Yield to the loop so stream A's stale ``finally`` runs. Without
+       the identity check it unconditionally writes
+       ``self._active_run = None``, silently disabling the concurrency
+       guard for stream B.
+    """
+    executor = IncrementExecutor(id="stale_finalizer_exec", limit=100, increment=1)
+    workflow = WorkflowBuilder(start_executor=executor).build()
+
+    # Step 1: drive stream A's body until it's suspended at its first yield.
+    stream_a = workflow.run(NumberMessage(data=0), stream=True)
+    aiter_a = stream_a.__aiter__()
+    await aiter_a.__anext__()
+
+    # Step 2: drop stream A; GC invalidates the weakref and schedules
+    # async-gen close, but does not run the close inline.
+    del stream_a
+    del aiter_a
+    gc.collect()
+
+    # Step 3: synchronously start stream B *before* yielding to the loop,
+    # so the stale ``aclose`` for stream A hasn't fired yet.
+    stream_b = workflow.run(NumberMessage(data=0), stream=True)
+    ref_b = workflow._active_run  # type: ignore[attr-defined]
+    assert ref_b is not None and ref_b() is stream_b
+
+    # Step 4: yield enough times for stream A's scheduled aclose to drive
+    # its body through ``GeneratorExit`` and into its ``finally``.
+    for _ in range(5):
+        await asyncio.sleep(0)
+
+    # With the fix, stream B's reservation is still in place. Without it,
+    # ``_active_run`` was clobbered to ``None`` and a concurrent run would
+    # be (incorrectly) accepted.
+    assert workflow._active_run is ref_b  # type: ignore[attr-defined]
+    with pytest.raises(
+        WorkflowException,
+        match="Workflow is already running; concurrent runs are not allowed on the same instance.",
+    ):
+        await workflow.run(NumberMessage(data=0))
+
+    # Tear down stream B without iterating it (its body never started, so
+    # closing it is a no-op for workflow state).
+    del stream_b
+    del ref_b
+    gc.collect()
+    await asyncio.sleep(0)
+
+
 class _StreamingTestAgent(BaseAgent):
     """Test agent that supports both streaming and non-streaming modes."""
 
@@ -1269,3 +1415,85 @@ async def test_output_executors_filtering_with_run_responses_streaming() -> None
 
 
 # endregion
+
+
+# region Workflow.reset
+
+
+class CounterStateExecutor(Executor):
+    """Executor with local mutable state used to verify checkpoint-based reset."""
+
+    def __init__(self, id: str) -> None:
+        super().__init__(id=id)
+        self.counter = 0
+
+    @handler
+    async def handle(self, message: str, ctx: WorkflowContext[str, int]) -> None:
+        self.counter += 1
+        await ctx.yield_output(self.counter)
+
+    async def on_checkpoint_save(self) -> dict[str, Any]:
+        return {"counter": self.counter}
+
+    async def on_checkpoint_restore(self, state: dict[str, Any]) -> None:
+        self.counter = int(state.get("counter", 0))
+
+
+class TestWorkflowReset:
+    """Tests for :meth:`Workflow.reset`."""
+
+    async def test_reset_restores_initial_shared_state(self) -> None:
+        """Reset clears accumulated workflow state back to the initial baseline."""
+        executor = StateTrackingExecutor(id="state_executor")
+        workflow = WorkflowBuilder(start_executor=executor).add_edge(executor, executor).build()
+
+        result1 = await workflow.run(StateTrackingMessage(data="message1", run_id="run1"))
+        assert result1.get_outputs()[0] == ["run1:message1"]
+
+        result2 = await workflow.run(StateTrackingMessage(data="message2", run_id="run2"))
+        assert result2.get_outputs()[0] == ["run1:message1", "run2:message2"]
+
+        await workflow.reset()
+
+        result3 = await workflow.run(StateTrackingMessage(data="message3", run_id="run3"))
+        assert result3.get_outputs()[0] == ["run3:message3"]
+
+    async def test_reset_restores_executor_checkpoint_state(self) -> None:
+        """Reset restores per-executor local state captured in the initial checkpoint."""
+        executor = CounterStateExecutor(id="counter_executor")
+        workflow = WorkflowBuilder(start_executor=executor).add_edge(executor, executor).build()
+
+        result1 = await workflow.run("one")
+        assert result1.get_outputs() == [1]
+
+        result2 = await workflow.run("two")
+        assert result2.get_outputs() == [2]
+
+        await workflow.reset()
+
+        result3 = await workflow.run("three")
+        assert result3.get_outputs() == [1]
+
+    async def test_reset_before_first_run_is_allowed(self, simple_executor: Executor) -> None:
+        """Reset can be called before the first run and leaves workflow runnable."""
+        workflow = WorkflowBuilder(start_executor=simple_executor).add_edge(simple_executor, simple_executor).build()
+
+        await workflow.reset()
+
+        result = await workflow.run("hello")
+        assert result.get_final_state() == WorkflowRunState.IDLE
+
+    async def test_reset_raises_while_run_active(self, simple_executor: Executor) -> None:
+        """Reset must reject while a workflow run is active."""
+        workflow = WorkflowBuilder(start_executor=simple_executor).add_edge(simple_executor, simple_executor).build()
+
+        active_stream = workflow.run(WorkflowMessage(data="hi", source_id="test"), stream=True)
+        try:
+            with pytest.raises(WorkflowException, match="Cannot reset workflow while a run is active"):
+                await workflow.reset()
+        finally:
+            async for _ in active_stream:
+                pass
+
+
+# endregion

python/packages/declarative/tests/test_http_request_executor.py

@@ -90,7 +90,7 @@ async def _run(yaml_def: dict[str, Any], handler: HttpRequestHandler) -> Any:
 
 def _state(workflow: Any, events: Any) -> dict[str, Any]:
     """Read declarative state out of the workflow after run completes."""
-    return workflow._state.get(DECLARATIVE_STATE_KEY) or {}
+    return workflow._runner.state.get(DECLARATIVE_STATE_KEY) or {}
 
 
 # Helper used by parametrised path tests
@@ -151,7 +151,7 @@ class TestSuccessPath:
         workflow = factory.create_workflow_from_definition(_yaml(_action(method="GET", response="Local.Result")))
         await workflow.run({})
 
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] == {"key": "value", "number": 42}
         assert handler.last_info is not None
         assert handler.last_info.method == "GET"
@@ -164,7 +164,7 @@ class TestSuccessPath:
         workflow = factory.create_workflow_from_definition(_yaml(_action(response="Local.Result")))
         await workflow.run({})
 
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] == "not-json content"
 
     @pytest.mark.asyncio
@@ -174,7 +174,7 @@ class TestSuccessPath:
         workflow = factory.create_workflow_from_definition(_yaml(_action(response="Local.Result")))
         await workflow.run({})
 
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] is None
 
     @pytest.mark.asyncio
@@ -184,7 +184,7 @@ class TestSuccessPath:
         workflow = factory.create_workflow_from_definition(_yaml(_action(response={"path": "Local.Result"})))
         await workflow.run({})
 
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] == {"x": 1}
 
     @pytest.mark.asyncio
@@ -517,7 +517,7 @@ class TestResponseHeaders:
         factory = WorkflowFactory(http_request_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(response_headers="Local.H")))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         h = decl["Local"]["H"]
         assert h["Content-Type"] == "application/json"
         assert h["Set-Cookie"] == "a=1,b=2"
@@ -528,7 +528,7 @@ class TestResponseHeaders:
         factory = WorkflowFactory(http_request_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(response_headers="Local.H")))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["H"] is None
 
     @pytest.mark.asyncio
@@ -538,7 +538,7 @@ class TestResponseHeaders:
         workflow = factory.create_workflow_from_definition(_yaml(_action(response_headers="Local.H")))
         with pytest.raises(DeclarativeActionError):
             await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["H"] == {"X-Trace": "abc"}
 
 
@@ -559,7 +559,7 @@ class TestConversationAppend:
             )
         )
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         conv = decl["System"]["conversations"].get("conv-test-1")
         assert conv is not None
         assert len(conv["messages"]) == 1
@@ -570,7 +570,7 @@ class TestConversationAppend:
         factory = WorkflowFactory(http_request_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(response="Local.Result", conversation_id="")))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         # Auto-init creates an entry for the System.ConversationId conversation,
         # but it should NOT have HTTP-appended messages from us.
         for _cid, conv in decl["System"]["conversations"].items():
@@ -582,7 +582,7 @@ class TestConversationAppend:
         factory = WorkflowFactory(http_request_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(conversation_id="conv-test-1")))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         # No conversation entry should have been created either.
         assert "conv-test-1" not in decl["System"]["conversations"]
 

python/packages/declarative/tests/test_http_request_yaml_integration.py

@@ -73,8 +73,8 @@ async def test_http_request_yaml_roundtrip() -> None:
     workflow = factory.create_workflow_from_yaml_path(FIXTURE_PATH)
     await workflow.run({})
 
-    decl: dict[str, Any] = workflow._state.get(DECLARATIVE_STATE_KEY) or {}
-    local: dict[str, Any] = decl.get("Local") or {}
+    decl: dict[str, Any] = workflow._runner.state.get(DECLARATIVE_STATE_KEY) or {}
+    local = decl.get("Local") or {}
 
     assert local.get("RepoOwner") == "dotnet"
     repo_info = local.get("RepoInfo")

python/packages/declarative/tests/test_invoke_mcp_tool_executor.py

@@ -244,7 +244,7 @@ class TestOutput:
         factory = WorkflowFactory(mcp_tool_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(output={"result": "Local.Result"})))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] == [{"k": "v", "n": 1}]
 
     @pytest.mark.asyncio
@@ -253,7 +253,7 @@ class TestOutput:
         factory = WorkflowFactory(mcp_tool_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(output={"result": "Local.Result"})))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] == ["plain text not json"]
 
     @pytest.mark.asyncio
@@ -262,7 +262,7 @@ class TestOutput:
         factory = WorkflowFactory(mcp_tool_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(output={"messages": "Local.Messages"})))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         msg = decl["Local"]["Messages"]
         # Single Tool-role message containing both contents (parity with .NET).
         assert isinstance(msg, Message)
@@ -276,7 +276,7 @@ class TestOutput:
         factory = WorkflowFactory(mcp_tool_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(output={"result": "Local.Result"})))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] == ["https://example.com/file.txt"]
 
     @pytest.mark.asyncio
@@ -285,7 +285,7 @@ class TestOutput:
         factory = WorkflowFactory(mcp_tool_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(output={"result": {"path": "Local.Result"}})))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] == ["ok"]
 
 
@@ -306,7 +306,7 @@ class TestConversation:
             )
         )
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         conv = decl["System"]["conversations"]["conv-42"]
         msgs = conv["messages"] if isinstance(conv, dict) else conv.messages
         assert len(msgs) == 1
@@ -328,7 +328,7 @@ class TestConversation:
             )
         )
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         # Empty conversation id must not produce a `""` entry under System.conversations.
         conversations = decl.get("System", {}).get("conversations", {})
         assert "" not in conversations
@@ -529,7 +529,7 @@ class TestErrorHandling:
         factory = WorkflowFactory(mcp_tool_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(output={"result": "Local.Result"})))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] == "Error: server down"
 
     @pytest.mark.asyncio
@@ -538,7 +538,7 @@ class TestErrorHandling:
         factory = WorkflowFactory(mcp_tool_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(output={"result": "Local.Result"})))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert decl["Local"]["Result"] == "Error: invalid arguments"
 
     @pytest.mark.asyncio
@@ -547,7 +547,7 @@ class TestErrorHandling:
         factory = WorkflowFactory(mcp_tool_handler=handler)
         workflow = factory.create_workflow_from_definition(_yaml(_action(output={"result": "Local.Result"})))
         await workflow.run({})
-        decl = workflow._state.get(DECLARATIVE_STATE_KEY)
+        decl = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         result = decl["Local"]["Result"]
         assert isinstance(result, str)
         assert result.startswith("Error:")

python/packages/declarative/tests/test_workflow_factory.py

@@ -291,11 +291,11 @@ actions:
         # Stamp a marker into the declarative state between turns. The
         # continuation branch must preserve it; a state-clearing run would
         # wipe ``DECLARATIVE_STATE_KEY`` and force re-initialization.
-        state_data = workflow._state.get(DECLARATIVE_STATE_KEY)
+        state_data = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert isinstance(state_data, dict), "Expected declarative state to be initialized after turn 1"
         state_data["Local"] = {"persisted_marker": "kept-from-turn-1"}
-        workflow._state.set(DECLARATIVE_STATE_KEY, state_data)
-        workflow._state.commit()
+        workflow._runner.state.set(DECLARATIVE_STATE_KEY, state_data)
+        workflow._runner.state.commit()
 
         second = await agent.run("turn-2-msg")
         assert second.text == "turn-2-msg", (
@@ -305,7 +305,7 @@ actions:
         # The continuation branch in ``_ensure_state_initialized`` must:
         # 1. preserve the cross-turn marker we stamped above
         # 2. refresh Inputs.input and System.LastMessage* to the new turn
-        post_state = workflow._state.get(DECLARATIVE_STATE_KEY)
+        post_state = workflow._runner.state.get(DECLARATIVE_STATE_KEY)
         assert isinstance(post_state, dict), "declarative state vanished between turns"
         local = post_state.get("Local", {})
         assert local.get("persisted_marker") == "kept-from-turn-1", (

python/packages/foundry_hosting/agent_framework_foundry_hosting/_responses.py

@@ -386,7 +386,6 @@ class ResponsesHostServer(ResponsesAgentServerHost):
             )
 
         self._is_workflow_agent = False
-        self._checkpoint_storage_path = None
         if isinstance(agent, WorkflowAgent):
             if agent.workflow._runner_context.has_checkpointing():  # pyright: ignore[reportPrivateUsage]
                 raise RuntimeError(
@@ -580,8 +579,6 @@ class ResponsesHostServer(ResponsesAgentServerHost):
 
             # The following should never happen due to the checks above.
             # This is for type safety and defensive programming.
-            if self._checkpoint_storage_path is None:
-                raise RuntimeError("Checkpoint storage path is not configured for workflow agent.")
             if not isinstance(self._agent, WorkflowAgent):
                 raise RuntimeError("Agent is not a workflow agent.")
 
@@ -599,43 +596,27 @@ class ResponsesHostServer(ResponsesAgentServerHost):
             # the only place that state lives is the workflow checkpoint, so
             # on every turn we restore the latest checkpoint and feed the new
             # input back into the start executor as a continuation rather than
-            # a fresh run.
+            # a fresh run. If no conversation_id or previous_response_id is
+            # supplied (or no checkpoint exists for that context), reset the
+            # workflow to its in-memory initial baseline to avoid context bleed
+            # between requests.
             latest_checkpoint_id: str | None = None
             restore_storage: FileCheckpointStorage | None = None
             if context_id is not None:
-                restore_storage = _checkpoint_storage_for_context(self._checkpoint_storage_path, context_id)
-                latest_checkpoint = await restore_storage.get_latest(workflow_name=self._agent.workflow.name)
+                context_storage = _checkpoint_storage_for_context(self._checkpoint_storage_path, context_id)
+                latest_checkpoint = await context_storage.get_latest(workflow_name=self._agent.workflow.name)
                 if latest_checkpoint is not None:
                     latest_checkpoint_id = latest_checkpoint.checkpoint_id
+                    restore_storage = context_storage
 
-            # Storage that will receive checkpoints written during this turn.
-            # When the caller chains with previous_response_id, the next turn
-            # will reference the current response_id as its previous_response_id,
-            # so new checkpoints must land under the current response_id (or the
-            # conversation_id when set). When conversation_id is set, this
-            # matches restore_storage; when only previous_response_id was
-            # supplied, restore_storage points at the *prior* response's
-            # directory and write_storage points at the *current* response's.
-            write_context_id = context.conversation_id or context.response_id
-            write_storage = _checkpoint_storage_for_context(self._checkpoint_storage_path, write_context_id)
-
-            # Multi-turn pattern: when we have a prior checkpoint, restore it
-            # first (drive the workflow back to idle with prior state intact),
-            # then make a separate call that delivers the new user input. This
-            # depends on Workflow.run preserving shared state across calls. The
-            # restore-only call may yield events from any pending in-flight
-            # work in the checkpoint; we consume those internally here so they
-            # don't surface to the response stream as duplicates.
-            #
-            # If the restored checkpoint had pending request_info events, the
-            # restore-only call replays them through
-            # ``WorkflowAgent._convert_workflow_event_to_agent_response_updates``
-            # and populates ``self._agent.pending_requests``. That is the correct
-            # state: those requests are genuinely outstanding, and the next
-            # ``run(input_messages, ...)`` call may contain ``function_call_output``
-            # items (carried as FunctionResult/FunctionApprovalResponse content)
-            # that fulfill them via :meth:`WorkflowAgent._process_pending_requests`.
-            if latest_checkpoint_id is not None:
+            # Restore the workflow to the latest checkpoint and run it with the
+            # new input. Events (including request info events) will not be emitted
+            # during restoration (in streaming) or after restoration (in non-streaming)
+            # since we assume the client had already seen those events and we don't want
+            # to emit duplicates.
+            if latest_checkpoint_id is None or restore_storage is None:
+                await self._agent.workflow.reset()
+            else:
                 if is_streaming_request:
                     async for _ in self._agent.run(
                         stream=True,
@@ -650,6 +631,17 @@ class ResponsesHostServer(ResponsesAgentServerHost):
                         checkpoint_storage=restore_storage,
                     )
 
+            # Storage that will receive checkpoints written during this turn.
+            # When the caller chains with previous_response_id, the next turn
+            # will reference the current response_id as its previous_response_id,
+            # so new checkpoints must land under the current response_id (or the
+            # conversation_id when set). When conversation_id is set, this
+            # matches restore_storage; when only previous_response_id was
+            # supplied, restore_storage points at the *prior* response's
+            # directory and write_storage points at the *current* response's.
+            write_context_id = context.conversation_id or context.response_id
+            write_storage = _checkpoint_storage_for_context(self._checkpoint_storage_path, write_context_id)
+
             if not is_streaming_request:
                 # Run the agent in non-streaming mode with the new user input.
                 response = await self._agent.run(

python/packages/foundry_hosting/tests/test_responses.py

@@ -3062,6 +3062,7 @@ class TestCheckpointContextPathValidation:
         agent.workflow = MagicMock()
         agent.workflow.name = "wf"
         agent.workflow._runner_context.has_checkpointing = MagicMock(return_value=False)
+        agent.workflow.reset = AsyncMock()
         agent.run = AsyncMock(
             side_effect=[
                 AgentResponse(messages=[]),
@@ -3092,6 +3093,136 @@ class TestCheckpointContextPathValidation:
         assert new_turn_messages[0].text == "next turn"
         assert new_turn_call.kwargs["checkpoint_storage"].storage_path == (root / response_id).resolve()
 
+    async def test_handle_inner_workflow_resets_when_no_context_id(self, tmp_path: Any) -> None:
+        """When no context id is supplied, the workflow resets to its initial in-memory state."""
+        from agent_framework import WorkflowAgent
+        from azure.ai.agentserver.responses import ResponseContext
+        from azure.ai.agentserver.responses.models import CreateResponse, ItemMessage
+
+        response_id = "resp_current"
+        root = tmp_path / "root"
+        root.mkdir()
+
+        agent = MagicMock(spec=WorkflowAgent)
+        agent.id = "wf-agent"
+        agent.name = "wf"
+        agent.description = ""
+        agent.context_providers = []
+        agent.workflow = MagicMock()
+        agent.workflow.name = "wf"
+        agent.workflow._runner_context.has_checkpointing = MagicMock(return_value=False)
+        agent.workflow.reset = AsyncMock()
+        agent.run = AsyncMock(
+            return_value=AgentResponse(messages=[Message(role="assistant", contents=[Content.from_text("ok")])])
+        )
+        server = ResponsesHostServer(agent, store=InMemoryResponseProvider())
+        server._checkpoint_storage_path = str(root)  # pyright: ignore[reportPrivateUsage]
+
+        # No previous_response_id and no conversation_id.
+        request = CreateResponse(model="m", input="hi")
+        context = ResponseContext(response_id=response_id, mode_flags=MagicMock())
+        input_item = ItemMessage({"type": "message", "role": "user", "content": "fresh turn"})
+
+        with patch.object(ResponseContext, "get_input_items", new=AsyncMock(return_value=[input_item])):
+            async for _ in server._handle_inner_workflow(request, context):  # pyright: ignore[reportPrivateUsage]
+                pass
+
+        # No checkpoint restore is attempted; workflow resets in memory.
+        assert agent.workflow.reset.await_count == 1
+        assert agent.run.call_count == 1
+
+        # The single run() call delivers the new input; checkpoints land under response_id
+        # (the write-sink directory keyed by the current response id).
+        new_turn_call = agent.run.call_args_list[0]
+        new_turn_messages = new_turn_call.args[0]
+        assert len(new_turn_messages) == 1
+        assert new_turn_messages[0].text == "fresh turn"
+        assert new_turn_call.kwargs["checkpoint_storage"].storage_path == (root / response_id).resolve()
+
+    async def test_handle_inner_workflow_resets_each_request_without_context_id(self, tmp_path: Any) -> None:
+        """Requests without context ids reset workflow state per request."""
+        from agent_framework import WorkflowAgent
+        from azure.ai.agentserver.responses import ResponseContext
+        from azure.ai.agentserver.responses.models import CreateResponse, ItemMessage
+
+        root = tmp_path / "root"
+        root.mkdir()
+
+        agent = MagicMock(spec=WorkflowAgent)
+        agent.id = "wf-agent"
+        agent.name = "wf"
+        agent.description = ""
+        agent.context_providers = []
+        agent.workflow = MagicMock()
+        agent.workflow.name = "wf"
+        agent.workflow._runner_context.has_checkpointing = MagicMock(return_value=False)
+        agent.workflow.reset = AsyncMock()
+        # Two run() calls total: one new turn per request.
+        agent.run = AsyncMock(return_value=AgentResponse(messages=[]))
+
+        server = ResponsesHostServer(agent, store=InMemoryResponseProvider())
+        server._checkpoint_storage_path = str(root)  # pyright: ignore[reportPrivateUsage]
+
+        request1 = CreateResponse(model="m", input="hi")
+        context1 = ResponseContext(response_id="resp_first", mode_flags=MagicMock())
+        request2 = CreateResponse(model="m", input="hi again")
+        context2 = ResponseContext(response_id="resp_second", mode_flags=MagicMock())
+        input_item = ItemMessage({"type": "message", "role": "user", "content": "turn"})
+
+        with patch.object(ResponseContext, "get_input_items", new=AsyncMock(return_value=[input_item])):
+            async for _ in server._handle_inner_workflow(request1, context1):  # pyright: ignore[reportPrivateUsage]
+                pass
+            async for _ in server._handle_inner_workflow(request2, context2):  # pyright: ignore[reportPrivateUsage]
+                pass
+
+        assert agent.workflow.reset.await_count == 2
+        assert agent.run.call_count == 2
+
+    async def test_handle_inner_workflow_resets_when_context_dir_is_empty(self, tmp_path: Any) -> None:
+        """When previous_response_id has no checkpoint, workflow resets instead of restoring."""
+        from agent_framework import WorkflowAgent
+        from azure.ai.agentserver.responses import ResponseContext
+        from azure.ai.agentserver.responses.models import CreateResponse, ItemMessage
+
+        previous_response_id = "resp_previous"
+        response_id = "resp_current"
+        root = tmp_path / "root"
+        root.mkdir()
+        # The per-context storage exists but contains no checkpoints.
+        (root / previous_response_id).mkdir()
+
+        agent = MagicMock(spec=WorkflowAgent)
+        agent.id = "wf-agent"
+        agent.name = "wf"
+        agent.description = ""
+        agent.context_providers = []
+        agent.workflow = MagicMock()
+        agent.workflow.name = "wf"
+        agent.workflow._runner_context.has_checkpointing = MagicMock(return_value=False)
+        agent.workflow.reset = AsyncMock()
+        agent.run = AsyncMock(
+            return_value=AgentResponse(messages=[Message(role="assistant", contents=[Content.from_text("ok")])])
+        )
+        server = ResponsesHostServer(agent, store=InMemoryResponseProvider())
+        server._checkpoint_storage_path = str(root)  # pyright: ignore[reportPrivateUsage]
+
+        request = CreateResponse(model="m", input="hi", previous_response_id=previous_response_id)
+        context = ResponseContext(
+            response_id=response_id, previous_response_id=previous_response_id, mode_flags=MagicMock()
+        )
+        input_item = ItemMessage({"type": "message", "role": "user", "content": "next turn"})
+
+        with patch.object(ResponseContext, "get_input_items", new=AsyncMock(return_value=[input_item])):
+            async for _ in server._handle_inner_workflow(request, context):  # pyright: ignore[reportPrivateUsage]
+                pass
+
+        assert agent.workflow.reset.await_count == 1
+        assert agent.run.call_count == 1
+
+        # The new turn writes checkpoints under the current response id.
+        new_turn_call = agent.run.call_args_list[0]
+        assert new_turn_call.kwargs["checkpoint_storage"].storage_path == (root / response_id).resolve()
+
     @pytest.mark.parametrize(
         "bad_id",
         [
@@ -3185,6 +3316,8 @@ class TestCheckpointContextPathValidation:
         agent.workflow = MagicMock()
         agent.workflow.name = "wf"
         agent.workflow._runner_context.has_checkpointing = MagicMock(return_value=False)
+        agent.workflow.reset = AsyncMock()
+        agent.run = AsyncMock(return_value=AgentResponse(messages=[]))
 
         # Constructor inspects WorkflowAgent.workflow internals; bypass setup
         # by feeding a configured mock through a normal init.