fix(graph_engine): error strategy fall. (#26078)

Signed-off-by: -LAN- <laipz8200@outlook.com>

api/core/workflow/graph_engine/domain/graph_execution.py

     completed: bool = Field(default=False)
     aborted: bool = Field(default=False)
     error: GraphExecutionErrorState | None = Field(default=None)
-    node_executions: list[NodeExecutionState] = Field(default_factory=list)
+    exceptions_count: int = Field(default=0)
+    node_executions: list[NodeExecutionState] = Field(default_factory=list[NodeExecutionState])
 
 
 def _serialize_error(error: Exception | None) -> GraphExecutionErrorState | None:
     completed: bool = False
     aborted: bool = False
     error: Exception | None = None
-    node_executions: dict[str, NodeExecution] = field(default_factory=dict)
+    node_executions: dict[str, NodeExecution] = field(default_factory=dict[str, NodeExecution])
+    exceptions_count: int = 0
 
     def start(self) -> None:
         """Mark the graph execution as started."""
             completed=self.completed,
             aborted=self.aborted,
             error=_serialize_error(self.error),
+            exceptions_count=self.exceptions_count,
             node_executions=node_states,
         )
 
         self.completed = state.completed
         self.aborted = state.aborted
         self.error = _deserialize_error(state.error)
+        self.exceptions_count = state.exceptions_count
         self.node_executions = {
             item.node_id: NodeExecution(
                 node_id=item.node_id,
             )
             for item in state.node_executions
         }
+
+    def record_node_failure(self) -> None:
+        """Increment the count of node failures encountered during execution."""
+        self.exceptions_count += 1

api/core/workflow/graph_engine/event_management/event_handlers.py

 """
 
 import logging
+from collections.abc import Mapping
 from functools import singledispatchmethod
 from typing import TYPE_CHECKING, final
 
 from core.workflow.entities import GraphRuntimeState
-from core.workflow.enums import NodeExecutionType
+from core.workflow.enums import ErrorStrategy, NodeExecutionType
 from core.workflow.graph import Graph
 from core.workflow.graph_events import (
     GraphNodeEventBase,
         """
         # Track execution in domain model
         node_execution = self._graph_execution.get_or_create_node_execution(event.node_id)
+        is_initial_attempt = node_execution.retry_count == 0
         node_execution.mark_started(event.id)
 
         # Track in response coordinator for stream ordering
         self._response_coordinator.track_node_execution(event.node_id, event.id)
 
-        # Collect the event
-        self._event_collector.collect(event)
+        # Collect the event only for the first attempt; retries remain silent
+        if is_initial_attempt:
+            self._event_collector.collect(event)
 
     @_dispatch.register
     def _(self, event: NodeRunStreamChunkEvent) -> None:
         node_execution.mark_taken()
 
         # Store outputs in variable pool
-        self._store_node_outputs(event)
+        self._store_node_outputs(event.node_id, event.node_run_result.outputs)
 
         # Forward to response coordinator and emit streaming events
         streaming_events = self._response_coordinator.intercept_event(event)
 
         # Handle response node outputs
         if node.execution_type == NodeExecutionType.RESPONSE:
-            self._update_response_outputs(event)
+            self._update_response_outputs(event.node_run_result.outputs)
 
         # Collect the event
         self._event_collector.collect(event)
         # Update domain model
         node_execution = self._graph_execution.get_or_create_node_execution(event.node_id)
         node_execution.mark_failed(event.error)
+        self._graph_execution.record_node_failure()
 
         result = self._error_handler.handle_node_failure(event)
 
         Args:
             event: The node exception event
         """
-        # Node continues via fail-branch, so it's technically "succeeded"
+        # Node continues via fail-branch/default-value, treat as completion
         node_execution = self._graph_execution.get_or_create_node_execution(event.node_id)
         node_execution.mark_taken()
 
+        # Persist outputs produced by the exception strategy (e.g. default values)
+        self._store_node_outputs(event.node_id, event.node_run_result.outputs)
+
+        node = self._graph.nodes[event.node_id]
+
+        if node.error_strategy == ErrorStrategy.DEFAULT_VALUE:
+            ready_nodes, edge_streaming_events = self._edge_processor.process_node_success(event.node_id)
+        elif node.error_strategy == ErrorStrategy.FAIL_BRANCH:
+            ready_nodes, edge_streaming_events = self._edge_processor.handle_branch_completion(
+                event.node_id, event.node_run_result.edge_source_handle
+            )
+        else:
+            raise NotImplementedError(f"Unsupported error strategy: {node.error_strategy}")
+
+        for edge_event in edge_streaming_events:
+            self._event_collector.collect(edge_event)
+
+        for node_id in ready_nodes:
+            self._state_manager.enqueue_node(node_id)
+            self._state_manager.start_execution(node_id)
+
+        # Update response outputs if applicable
+        if node.execution_type == NodeExecutionType.RESPONSE:
+            self._update_response_outputs(event.node_run_result.outputs)
+
+        self._state_manager.finish_execution(event.node_id)
+
+        # Collect the exception event for observers
+        self._event_collector.collect(event)
+
     @_dispatch.register
     def _(self, event: NodeRunRetryEvent) -> None:
         """
         node_execution = self._graph_execution.get_or_create_node_execution(event.node_id)
         node_execution.increment_retry()
 
-    def _store_node_outputs(self, event: NodeRunSucceededEvent) -> None:
+        # Finish the previous attempt before re-queuing the node
+        self._state_manager.finish_execution(event.node_id)
+
+        # Emit retry event for observers
+        self._event_collector.collect(event)
+
+        # Re-queue node for execution
+        self._state_manager.enqueue_node(event.node_id)
+        self._state_manager.start_execution(event.node_id)
+
+    def _store_node_outputs(self, node_id: str, outputs: Mapping[str, object]) -> None:
         """
         Store node outputs in the variable pool.
 
         Args:
             event: The node succeeded event containing outputs
         """
-        for variable_name, variable_value in event.node_run_result.outputs.items():
-            self._graph_runtime_state.variable_pool.add((event.node_id, variable_name), variable_value)
+        for variable_name, variable_value in outputs.items():
+            self._graph_runtime_state.variable_pool.add((node_id, variable_name), variable_value)
 
-    def _update_response_outputs(self, event: NodeRunSucceededEvent) -> None:
+    def _update_response_outputs(self, outputs: Mapping[str, object]) -> None:
         """Update response outputs for response nodes."""
         # TODO: Design a mechanism for nodes to notify the engine about how to update outputs
         # in runtime state, rather than allowing nodes to directly access runtime state.
-        for key, value in event.node_run_result.outputs.items():
+        for key, value in outputs.items():
             if key == "answer":
                 existing = self._graph_runtime_state.get_output("answer", "")
                 if existing:

api/core/workflow/graph_engine/graph_engine.py

     GraphNodeEventBase,
     GraphRunAbortedEvent,
     GraphRunFailedEvent,
+    GraphRunPartialSucceededEvent,
     GraphRunStartedEvent,
     GraphRunSucceededEvent,
 )
                 if self._graph_execution.error:
                     raise self._graph_execution.error
             else:
-                yield GraphRunSucceededEvent(
-                    outputs=self._graph_runtime_state.outputs,
-                )
+                outputs = self._graph_runtime_state.outputs
+                exceptions_count = self._graph_execution.exceptions_count
+                if exceptions_count > 0:
+                    yield GraphRunPartialSucceededEvent(
+                        exceptions_count=exceptions_count,
+                        outputs=outputs,
+                    )
+                else:
+                    yield GraphRunSucceededEvent(
+                        outputs=outputs,
+                    )
 
         except Exception as e:
-            yield GraphRunFailedEvent(error=str(e))
+            yield GraphRunFailedEvent(
+                error=str(e),
+                exceptions_count=self._graph_execution.exceptions_count,
+            )
             raise
 
         finally:

api/core/workflow/graph_engine/layers/debug_logging.py

     GraphEngineEvent,
     GraphRunAbortedEvent,
     GraphRunFailedEvent,
+    GraphRunPartialSucceededEvent,
     GraphRunStartedEvent,
     GraphRunSucceededEvent,
     NodeRunExceptionEvent,
             if self.include_outputs and event.outputs:
                 self.logger.info("  Final outputs: %s", self._format_dict(event.outputs))
 
+        elif isinstance(event, GraphRunPartialSucceededEvent):
+            self.logger.warning("⚠️ Graph run partially succeeded")
+            if event.exceptions_count > 0:
+                self.logger.warning("  Total exceptions: %s", event.exceptions_count)
+            if self.include_outputs and event.outputs:
+                self.logger.info("  Final outputs: %s", self._format_dict(event.outputs))
+
         elif isinstance(event, GraphRunFailedEvent):
             self.logger.error("❌ Graph run failed: %s", event.error)
             if event.exceptions_count > 0:

api/core/workflow/nodes/iteration/iteration_node.py

 from core.workflow.graph_events import (
     GraphNodeEventBase,
     GraphRunFailedEvent,
+    GraphRunPartialSucceededEvent,
     GraphRunSucceededEvent,
 )
 from core.workflow.node_events import (
             if isinstance(event, GraphNodeEventBase):
                 self._append_iteration_info_to_event(event=event, iter_run_index=current_index)
                 yield event
-            elif isinstance(event, GraphRunSucceededEvent):
+            elif isinstance(event, (GraphRunSucceededEvent, GraphRunPartialSucceededEvent)):
                 result = variable_pool.get(self._node_data.output_selector)
                 if result is None:
                     outputs.append(None)

api/tests/unit_tests/core/workflow/graph_engine/event_management/test_event_handlers.py

+"""Tests for graph engine event handlers."""
+
+from __future__ import annotations
+
+from datetime import datetime
+
+from core.workflow.entities import GraphRuntimeState, VariablePool
+from core.workflow.enums import NodeExecutionType, NodeState, NodeType, WorkflowNodeExecutionStatus
+from core.workflow.graph import Graph
+from core.workflow.graph_engine.domain.graph_execution import GraphExecution
+from core.workflow.graph_engine.event_management.event_handlers import EventHandler
+from core.workflow.graph_engine.event_management.event_manager import EventManager
+from core.workflow.graph_engine.graph_state_manager import GraphStateManager
+from core.workflow.graph_engine.ready_queue.in_memory import InMemoryReadyQueue
+from core.workflow.graph_engine.response_coordinator.coordinator import ResponseStreamCoordinator
+from core.workflow.graph_events import NodeRunRetryEvent, NodeRunStartedEvent
+from core.workflow.node_events import NodeRunResult
+from core.workflow.nodes.base.entities import RetryConfig
+
+
+class _StubEdgeProcessor:
+    """Minimal edge processor stub for tests."""
+
+
+class _StubErrorHandler:
+    """Minimal error handler stub for tests."""
+
+
+class _StubNode:
+    """Simple node stub exposing the attributes needed by the state manager."""
+
+    def __init__(self, node_id: str) -> None:
+        self.id = node_id
+        self.state = NodeState.UNKNOWN
+        self.title = "Stub Node"
+        self.execution_type = NodeExecutionType.EXECUTABLE
+        self.error_strategy = None
+        self.retry_config = RetryConfig()
+        self.retry = False
+
+
+def _build_event_handler(node_id: str) -> tuple[EventHandler, EventManager, GraphExecution]:
+    """Construct an EventHandler with in-memory dependencies for testing."""
+
+    node = _StubNode(node_id)
+    graph = Graph(nodes={node_id: node}, edges={}, in_edges={}, out_edges={}, root_node=node)
+
+    variable_pool = VariablePool()
+    runtime_state = GraphRuntimeState(variable_pool=variable_pool, start_at=0.0)
+    graph_execution = GraphExecution(workflow_id="test-workflow")
+
+    event_manager = EventManager()
+    state_manager = GraphStateManager(graph=graph, ready_queue=InMemoryReadyQueue())
+    response_coordinator = ResponseStreamCoordinator(variable_pool=variable_pool, graph=graph)
+
+    handler = EventHandler(
+        graph=graph,
+        graph_runtime_state=runtime_state,
+        graph_execution=graph_execution,
+        response_coordinator=response_coordinator,
+        event_collector=event_manager,
+        edge_processor=_StubEdgeProcessor(),
+        state_manager=state_manager,
+        error_handler=_StubErrorHandler(),
+    )
+
+    return handler, event_manager, graph_execution
+
+
+def test_retry_does_not_emit_additional_start_event() -> None:
+    """Ensure retry attempts do not produce duplicate start events."""
+
+    node_id = "test-node"
+    handler, event_manager, graph_execution = _build_event_handler(node_id)
+
+    execution_id = "exec-1"
+    node_type = NodeType.CODE
+    start_time = datetime.utcnow()
+
+    start_event = NodeRunStartedEvent(
+        id=execution_id,
+        node_id=node_id,
+        node_type=node_type,
+        node_title="Stub Node",
+        start_at=start_time,
+    )
+    handler.dispatch(start_event)
+
+    retry_event = NodeRunRetryEvent(
+        id=execution_id,
+        node_id=node_id,
+        node_type=node_type,
+        node_title="Stub Node",
+        start_at=start_time,
+        error="boom",
+        retry_index=1,
+        node_run_result=NodeRunResult(
+            status=WorkflowNodeExecutionStatus.FAILED,
+            error="boom",
+            error_type="TestError",
+        ),
+    )
+    handler.dispatch(retry_event)
+
+    # Simulate the node starting execution again after retry
+    second_start_event = NodeRunStartedEvent(
+        id=execution_id,
+        node_id=node_id,
+        node_type=node_type,
+        node_title="Stub Node",
+        start_at=start_time,
+    )
+    handler.dispatch(second_start_event)
+
+    collected_types = [type(event) for event in event_manager._events]  # type: ignore[attr-defined]
+
+    assert collected_types == [NodeRunStartedEvent, NodeRunRetryEvent]
+
+    node_execution = graph_execution.get_or_create_node_execution(node_id)
+    assert node_execution.retry_count == 1

api/tests/unit_tests/core/workflow/graph_engine/test_graph_engine.py

 from hypothesis import HealthCheck, given, settings
 from hypothesis import strategies as st
 
+from core.workflow.enums import ErrorStrategy
 from core.workflow.graph_engine import GraphEngine
 from core.workflow.graph_engine.command_channels import InMemoryChannel
-from core.workflow.graph_events import GraphRunStartedEvent, GraphRunSucceededEvent
+from core.workflow.graph_events import (
+    GraphRunPartialSucceededEvent,
+    GraphRunStartedEvent,
+    GraphRunSucceededEvent,
+)
+from core.workflow.nodes.base.entities import DefaultValue, DefaultValueType
 
 # Import the test framework from the new module
+from .test_mock_config import MockConfigBuilder
 from .test_table_runner import TableTestRunner, WorkflowRunner, WorkflowTestCase
 
 
         else:
             assert result.event_sequence_match is True
         assert result.success, f"Test {i + 1} failed: {result.event_mismatch_details or result.error}"
+
+
+def test_graph_run_emits_partial_success_when_node_failure_recovered():
+    runner = TableTestRunner()
+
+    fixture_data = runner.workflow_runner.load_fixture("basic_chatflow")
+    mock_config = MockConfigBuilder().with_node_error("llm", "mock llm failure").build()
+
+    graph, graph_runtime_state = runner.workflow_runner.create_graph_from_fixture(
+        fixture_data=fixture_data,
+        query="hello",
+        use_mock_factory=True,
+        mock_config=mock_config,
+    )
+
+    llm_node = graph.nodes["llm"]
+    base_node_data = llm_node.get_base_node_data()
+    base_node_data.error_strategy = ErrorStrategy.DEFAULT_VALUE
+    base_node_data.default_value = [DefaultValue(key="text", value="fallback response", type=DefaultValueType.STRING)]
+
+    engine = GraphEngine(
+        workflow_id="test_workflow",
+        graph=graph,
+        graph_runtime_state=graph_runtime_state,
+        command_channel=InMemoryChannel(),
+    )
+
+    events = list(engine.run())
+
+    assert isinstance(events[-1], GraphRunPartialSucceededEvent)
+
+    partial_event = next(event for event in events if isinstance(event, GraphRunPartialSucceededEvent))
+    assert partial_event.exceptions_count == 1
+    assert partial_event.outputs.get("answer") == "fallback response"
+
+    assert not any(isinstance(event, GraphRunSucceededEvent) for event in events)

api/tests/unit_tests/core/workflow/nodes/test_retry.py

-import pytest
-
-pytest.skip(
-    "Retry functionality is part of Phase 2 enhanced error handling - not implemented in MVP of queue-based engine",
-    allow_module_level=True,
-)
-
-DEFAULT_VALUE_EDGE = [
-    {
-        "id": "start-source-node-target",
-        "source": "start",
-        "target": "node",
-        "sourceHandle": "source",
-    },
-    {
-        "id": "node-source-answer-target",
-        "source": "node",
-        "target": "answer",
-        "sourceHandle": "source",
-    },
-]
-
-
-def test_retry_default_value_partial_success():
-    """retry default value node with partial success status"""
-    graph_config = {
-        "edges": DEFAULT_VALUE_EDGE,
-        "nodes": [
-            {"data": {"title": "start", "type": "start", "variables": []}, "id": "start"},
-            {"data": {"title": "answer", "type": "answer", "answer": "{{#node.result#}}"}, "id": "answer"},
-            ContinueOnErrorTestHelper.get_http_node(
-                "default-value",
-                [{"key": "result", "type": "string", "value": "http node got error response"}],
-                retry_config={"retry_config": {"max_retries": 2, "retry_interval": 1000, "retry_enabled": True}},
-            ),
-        ],
-    }
-
-    graph_engine = ContinueOnErrorTestHelper.create_test_graph_engine(graph_config)
-    events = list(graph_engine.run())
-    assert sum(1 for e in events if isinstance(e, NodeRunRetryEvent)) == 2
-    assert events[-1].outputs == {"answer": "http node got error response"}
-    assert any(isinstance(e, GraphRunPartialSucceededEvent) for e in events)
-    assert len(events) == 11
-
-
-def test_retry_failed():
-    """retry failed with success status"""
-    graph_config = {
-        "edges": DEFAULT_VALUE_EDGE,
-        "nodes": [
-            {"data": {"title": "start", "type": "start", "variables": []}, "id": "start"},
-            {"data": {"title": "answer", "type": "answer", "answer": "{{#node.result#}}"}, "id": "answer"},
-            ContinueOnErrorTestHelper.get_http_node(
-                None,
-                None,
-                retry_config={"retry_config": {"max_retries": 2, "retry_interval": 1000, "retry_enabled": True}},
-            ),
-        ],
-    }
-    graph_engine = ContinueOnErrorTestHelper.create_test_graph_engine(graph_config)
-    events = list(graph_engine.run())
-    assert sum(1 for e in events if isinstance(e, NodeRunRetryEvent)) == 2
-    assert any(isinstance(e, GraphRunFailedEvent) for e in events)
-    assert len(events) == 8