From fbafe1f0d3b172a698b3b406200f519c44580ad7 Mon Sep 17 00:00:00 2001
From: Greyson LaLonde <greyson.r.lalonde@gmail.com>
Date: Wed, 10 Jun 2026 20:40:08 -0700
Subject: [PATCH] fix(flow): gate restore on a flag so live snapshots don't
 replay as resume

Checkpoint serialization stamps checkpoint_completed_methods onto every live
Flow in RuntimeState.root, including the agent executor reused across a crew's
tasks. kickoff_async read that stamp as a restore signal, so the second task
replayed the first task's completed methods and never reached a final answer.

Gate is_restoring on _restored_from_checkpoint, set only by
_restore_from_checkpoint, and consume it single-shot.
---
 lib/crewai/src/crewai/flow/runtime.py |  8 +++-
 lib/crewai/tests/test_checkpoint.py   | 60 +++++++++++++++++++++++++++
 2 files changed, 67 insertions(+), 1 deletion(-)

diff --git a/lib/crewai/src/crewai/flow/runtime.py b/lib/crewai/src/crewai/flow/runtime.py
index a6acc0b83..6a9dfeda7 100644
--- a/lib/crewai/src/crewai/flow/runtime.py
+++ b/lib/crewai/src/crewai/flow/runtime.py
@@ -862,6 +862,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             self._completed_methods = {
                 FlowMethodName(m) for m in self.checkpoint_completed_methods
             }
+            self._restored_from_checkpoint = True
         if self.checkpoint_method_outputs is not None:
             self._method_outputs = list(self.checkpoint_method_outputs)
         if self.checkpoint_method_counts is not None:
@@ -897,6 +898,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
     _completed_methods: set[FlowMethodName] = PrivateAttr(default_factory=set)
     _method_call_counts: dict[FlowMethodName, int] = PrivateAttr(default_factory=dict)
     _is_execution_resuming: bool = PrivateAttr(default=False)
+    _restored_from_checkpoint: bool = PrivateAttr(default=False)
     _event_futures: list[Future[None]] = PrivateAttr(default_factory=list)
     _pending_feedback_context: PendingFeedbackContext | None = PrivateAttr(default=None)
     _human_feedback_method_outputs: dict[str, Any] = PrivateAttr(default_factory=dict)
@@ -2058,7 +2060,7 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
             # Reset flow state for fresh execution unless restoring from persistence
             is_restoring = (
                 inputs and "id" in inputs and self.persistence is not None
-            ) or self.checkpoint_completed_methods is not None
+            ) or self._restored_from_checkpoint
             if not is_restoring:
                 # Clear completed methods and outputs for a fresh start
                 self._completed_methods.clear()
@@ -2075,6 +2077,10 @@ class Flow(BaseModel, Generic[T], metaclass=FlowMeta):
                 if self._completed_methods:
                     self._is_execution_resuming = True
 
+            # Restore is single-shot: a later kickoff on the same instance
+            # starts fresh.
+            self._restored_from_checkpoint = False
+
             # Fork hydration: when restore_from_state_id is set and persistence is
             # available, hydrate self._state from the source UUID's latest snapshot
             # and reassign state.id to a fresh value so subsequent @persist writes
diff --git a/lib/crewai/tests/test_checkpoint.py b/lib/crewai/tests/test_checkpoint.py
index d3b2f9a97..9113a6669 100644
--- a/lib/crewai/tests/test_checkpoint.py
+++ b/lib/crewai/tests/test_checkpoint.py
@@ -16,6 +16,7 @@ from pydantic import BaseModel
 from crewai.agent.core import Agent
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.crew import Crew
+from crewai.llms.base_llm import BaseLLM
 from crewai.flow.flow import _INITIAL_STATE_CLASS_MARKER, Flow, start
 from crewai.state.checkpoint_config import CheckpointConfig
 from crewai.state.checkpoint_listener import (
@@ -682,3 +683,62 @@ class TestAgentCheckpoint:
             cfg = CheckpointConfig(restore_from=loc)
             restored = Agent.from_checkpoint(cfg)
             assert restored._kickoff_event_id == "evt-456"
+
+
+class _FinalAnswerLLM(BaseLLM):
+    """Stub LLM that always returns a final answer without any API calls."""
+
+    def __init__(self) -> None:
+        super().__init__(model="stub")
+
+    def call(
+        self,
+        messages,
+        tools=None,
+        callbacks=None,
+        available_functions=None,
+        from_task=None,
+        from_agent=None,
+        response_model=None,
+    ):
+        return "Final Answer: done."
+
+    def supports_function_calling(self) -> bool:
+        return False
+
+    def supports_stop_words(self) -> bool:
+        return False
+
+    def get_context_window_size(self) -> int:
+        return 4096
+
+    async def acall(self, *args, **kwargs):
+        raise NotImplementedError
+
+
+class TestCheckpointReusedExecutor:
+    """Checkpoint serialization stamps every live Flow's completed methods.
+
+    The agent executor is a Flow reused across a crew's tasks, so the stamp
+    must not be read back as a restore signal on the next task — otherwise the
+    second task replays as a resume and never reaches a final answer.
+    """
+
+    def test_second_task_runs_with_checkpointing_enabled(self) -> None:
+        agent = Agent(role="r", goal="g", backstory="b", llm=_FinalAnswerLLM())
+        task1 = Task(description="first", expected_output="x", agent=agent)
+        task2 = Task(description="second", expected_output="y", agent=agent)
+        with tempfile.TemporaryDirectory() as d:
+            crew = Crew(
+                agents=[agent],
+                tasks=[task1, task2],
+                verbose=False,
+                checkpoint=CheckpointConfig(
+                    provider=JsonProvider(location=d),
+                    on_events=["task_started", "task_completed"],
+                ),
+            )
+            result = crew.kickoff()
+
+        assert len(result.tasks_output) == 2
+        assert result.tasks_output[1].raw