feat(graph_engine): add ready_queue state persistence to GraphRuntimeState

- Add ReadyQueueState TypedDict for type-safe queue serialization
- Add ready_queue attribute to GraphRuntimeState for initializing with pre-existing queue state
- Update GraphEngine to load ready_queue from GraphRuntimeState on initialization
- Implement proper type hints using ReadyQueueState for better type safety
- Add comprehensive tests for ready_queue loading functionality

The ready_queue is read-only after initialization and allows resuming workflow
execution with a pre-populated queue of nodes ready to execute.

api/core/workflow/entities/graph_runtime_state.py

@@ -1,5 +1,5 @@
 from copy import deepcopy
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 from pydantic import BaseModel, PrivateAttr
 
@@ -7,6 +7,9 @@ from core.model_runtime.entities.llm_entities import LLMUsage
 
 from .variable_pool import VariablePool
 
+if TYPE_CHECKING:
+    from core.workflow.graph_engine.ready_queue import ReadyQueueState
+
 
 class GraphRuntimeState(BaseModel):
     # Private attributes to prevent direct modification
@@ -16,6 +19,7 @@ class GraphRuntimeState(BaseModel):
     _llm_usage: LLMUsage = PrivateAttr(default_factory=LLMUsage.empty_usage)
     _outputs: dict[str, Any] = PrivateAttr(default_factory=dict)
     _node_run_steps: int = PrivateAttr(default=0)
+    _ready_queue: "ReadyQueueState | dict[str, object]" = PrivateAttr(default_factory=dict)
 
     def __init__(
         self,
@@ -25,6 +29,7 @@ class GraphRuntimeState(BaseModel):
         llm_usage: LLMUsage | None = None,
         outputs: dict[str, Any] | None = None,
         node_run_steps: int = 0,
+        ready_queue: "ReadyQueueState | dict[str, object] | None" = None,
         **kwargs: object,
     ):
         """Initialize the GraphRuntimeState with validation."""
@@ -51,6 +56,10 @@ class GraphRuntimeState(BaseModel):
             raise ValueError("node_run_steps must be non-negative")
         self._node_run_steps = node_run_steps
 
+        if ready_queue is None:
+            ready_queue = {}
+        self._ready_queue = deepcopy(ready_queue)
+
     @property
     def variable_pool(self) -> VariablePool:
         """Get the variable pool."""
@@ -133,3 +142,8 @@ class GraphRuntimeState(BaseModel):
         if tokens < 0:
             raise ValueError("tokens must be non-negative")
         self._total_tokens += tokens
+
+    @property
+    def ready_queue(self) -> "ReadyQueueState | dict[str, object]":
+        """Get a copy of the ready queue state."""
+        return deepcopy(self._ready_queue)

api/core/workflow/graph_engine/graph_engine.py

@@ -106,6 +106,16 @@ class GraphEngine:
         # === Execution Queues ===
         # Queue for nodes ready to execute
         self._ready_queue = InMemoryReadyQueue()
+        # Load ready queue state from GraphRuntimeState if not empty
+        ready_queue_state = self._graph_runtime_state.ready_queue
+        if ready_queue_state:
+            # Import ReadyQueueState here to avoid circular imports
+            from .ready_queue import ReadyQueueState
+
+            # Ensure we have a ReadyQueueState object
+            if isinstance(ready_queue_state, dict):
+                ready_queue_state = ReadyQueueState(**ready_queue_state)  # type: ignore
+            self._ready_queue.loads(ready_queue_state)
         # Queue for events generated during execution
         self._event_queue: queue.Queue[GraphNodeEventBase] = queue.Queue()
 

api/core/workflow/graph_engine/ready_queue/__init__.py

@@ -6,6 +6,6 @@ the queue of nodes ready for execution.
 """
 
 from .in_memory import InMemoryReadyQueue
-from .protocol import ReadyQueue
+from .protocol import ReadyQueue, ReadyQueueState
 
-__all__ = ["InMemoryReadyQueue", "ReadyQueue"]
+__all__ = ["InMemoryReadyQueue", "ReadyQueue", "ReadyQueueState"]

api/core/workflow/graph_engine/ready_queue/in_memory.py

@@ -8,6 +8,8 @@ serialization capabilities for state storage.
 import queue
 from typing import final
 
+from .protocol import ReadyQueueState
+
 
 @final
 class InMemoryReadyQueue:
@@ -80,12 +82,12 @@ class InMemoryReadyQueue:
         """
         return self._queue.qsize()
 
-    def dumps(self) -> dict[str, object]:
+    def dumps(self) -> ReadyQueueState:
         """
         Serialize the queue state for storage.
 
         Returns:
-            A dictionary containing the serialized queue state
+            A ReadyQueueState dictionary containing the serialized queue state
         """
         # Extract all items from the queue without removing them
         items: list[str] = []
@@ -104,14 +106,14 @@ class InMemoryReadyQueue:
         for item in temp_items:
             self._queue.put(item)
 
-        return {
-            "type": "InMemoryReadyQueue",
-            "version": "1.0",
-            "items": items,
-            "maxsize": self._queue.maxsize,
-        }
+        return ReadyQueueState(
+            type="InMemoryReadyQueue",
+            version="1.0",
+            items=items,
+            maxsize=self._queue.maxsize,
+        )
 
-    def loads(self, data: dict[str, object]) -> None:
+    def loads(self, data: ReadyQueueState) -> None:
         """
         Restore the queue state from serialized data.
 

api/core/workflow/graph_engine/ready_queue/protocol.py

@@ -5,7 +5,21 @@ This protocol defines the interface for managing the queue of nodes ready
 for execution, supporting both in-memory and persistent storage scenarios.
 """
 
-from typing import Protocol
+from typing import Protocol, TypedDict
+
+
+class ReadyQueueState(TypedDict):
+    """
+    TypedDict for serialized ready queue state.
+
+    This defines the structure of the dictionary returned by dumps()
+    and expected by loads() for ready queue serialization.
+    """
+
+    type: str  # Queue implementation type (e.g., "InMemoryReadyQueue")
+    version: str  # Serialization format version
+    items: list[str]  # List of node IDs in the queue
+    maxsize: int  # Maximum queue size (0 for unlimited)
 
 
 class ReadyQueue(Protocol):
@@ -68,17 +82,17 @@ class ReadyQueue(Protocol):
         """
         ...
 
-    def dumps(self) -> dict[str, object]:
+    def dumps(self) -> ReadyQueueState:
         """
         Serialize the queue state for storage.
 
         Returns:
-            A dictionary containing the serialized queue state
+            A ReadyQueueState dictionary containing the serialized queue state
             that can be persisted and later restored
         """
         ...
 
-    def loads(self, data: dict[str, object]) -> None:
+    def loads(self, data: ReadyQueueState) -> None:
         """
         Restore the queue state from serialized data.
 

api/tests/unit_tests/core/workflow/entities/test_graph_runtime_state.py

@@ -4,6 +4,7 @@ import pytest
 
 from core.workflow.entities.graph_runtime_state import GraphRuntimeState
 from core.workflow.entities.variable_pool import VariablePool
+from core.workflow.graph_engine.ready_queue import ReadyQueueState
 
 
 class TestGraphRuntimeState:
@@ -109,3 +110,30 @@ class TestGraphRuntimeState:
 
         # Original should remain unchanged
         assert state.get_output("nested")["level1"]["level2"]["value"] == "test"
+
+    def test_ready_queue_property(self):
+        variable_pool = VariablePool()
+
+        # Test default empty ready_queue
+        state = GraphRuntimeState(variable_pool=variable_pool, start_at=time())
+        assert state.ready_queue == {}
+
+        # Test initialization with ready_queue data as ReadyQueueState
+        queue_data = ReadyQueueState(type="InMemoryReadyQueue", version="1.0", items=["node1", "node2"], maxsize=0)
+        state = GraphRuntimeState(variable_pool=variable_pool, start_at=time(), ready_queue=queue_data)
+        assert state.ready_queue == queue_data
+
+        # Test with different ready_queue data at initialization
+        another_queue_data = ReadyQueueState(
+            type="InMemoryReadyQueue",
+            version="1.0",
+            items=["node3", "node4", "node5"],
+            maxsize=0,
+        )
+        another_state = GraphRuntimeState(variable_pool=variable_pool, start_at=time(), ready_queue=another_queue_data)
+        assert another_state.ready_queue == another_queue_data
+
+        # Test immutability - modifying retrieved queue doesn't affect internal state
+        retrieved_queue = state.ready_queue
+        retrieved_queue["items"].append("node6")
+        assert len(state.ready_queue["items"]) == 2  # Should still be 2, not 3

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

@@ -744,3 +744,78 @@ def test_event_sequence_validation_with_table_tests():
         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_ready_queue_state_loading():
+    """
+    Test that the ready_queue state is properly loaded from GraphRuntimeState
+    during GraphEngine initialization.
+    """
+    # Use the TableTestRunner to create a proper workflow instance
+    runner = TableTestRunner()
+
+    # Create a simple workflow
+    test_case = WorkflowTestCase(
+        fixture_path="simple_passthrough_workflow",
+        inputs={"query": "test"},
+        expected_outputs={"query": "test"},
+        description="Test ready_queue loading",
+    )
+
+    # Load the workflow fixture
+    workflow_runner = runner.workflow_runner
+    fixture_data = workflow_runner.load_fixture("simple_passthrough_workflow")
+
+    # Create graph and runtime state with pre-populated ready_queue
+    ready_queue_data = {
+        "type": "InMemoryReadyQueue",
+        "version": "1.0",
+        "items": ["node1", "node2", "node3"],
+        "maxsize": 0,
+    }
+
+    # We need to create the graph first, then create a new GraphRuntimeState with ready_queue
+    graph, original_runtime_state = workflow_runner.create_graph_from_fixture(fixture_data, query="test")
+
+    # Create a new GraphRuntimeState with the ready_queue data
+    from core.workflow.entities import GraphRuntimeState
+    from core.workflow.graph_engine.ready_queue import ReadyQueueState
+
+    # Convert ready_queue_data to ReadyQueueState
+    ready_queue_state = ReadyQueueState(**ready_queue_data)
+
+    graph_runtime_state = GraphRuntimeState(
+        variable_pool=original_runtime_state.variable_pool,
+        start_at=original_runtime_state.start_at,
+        ready_queue=ready_queue_state,
+    )
+
+    # Update all nodes to use the new GraphRuntimeState
+    for node in graph.nodes.values():
+        node.graph_runtime_state = graph_runtime_state
+
+    # Create GraphEngine
+    command_channel = InMemoryChannel()
+    engine = GraphEngine(
+        tenant_id="test-tenant",
+        app_id="test-app",
+        workflow_id="test-workflow",
+        user_id="test-user",
+        user_from=UserFrom.ACCOUNT,
+        invoke_from=InvokeFrom.DEBUGGER,
+        call_depth=0,
+        graph=graph,
+        graph_config={},
+        graph_runtime_state=graph_runtime_state,
+        command_channel=command_channel,
+    )
+
+    # Verify that the ready_queue was loaded from GraphRuntimeState
+    assert engine._ready_queue.qsize() == 3
+
+    # Verify the initial state matches what was provided
+    initial_queue_state = engine.graph_runtime_state.ready_queue
+    assert initial_queue_state["type"] == "InMemoryReadyQueue"
+    assert initial_queue_state["version"] == "1.0"
+    assert len(initial_queue_state["items"]) == 3
+    assert initial_queue_state["items"] == ["node1", "node2", "node3"]