Fix bug with running graphs in temporal workflows

Author: dmontagu

pydantic_ai_slim/pydantic_ai/run.py

@@ -7,7 +7,7 @@
 from typing import TYPE_CHECKING, Any, Generic, Literal, overload
 
 from pydantic_graph import BaseNode, End, GraphRunContext
-from pydantic_graph.beta.graph import EndMarker, GraphRun, GraphTask, JoinItem
+from pydantic_graph.beta.graph import EndMarker, GraphRun, GraphTaskRequest, JoinItem
 from pydantic_graph.beta.step import NodeStep
 
 from . import (
@@ -181,7 +181,7 @@ async def __anext__(
         return self._task_to_node(task)
 
     def _task_to_node(
-        self, task: EndMarker[FinalResult[OutputDataT]] | JoinItem | Sequence[GraphTask]
+        self, task: EndMarker[FinalResult[OutputDataT]] | JoinItem | Sequence[GraphTaskRequest]
     ) -> _agent_graph.AgentNode[AgentDepsT, OutputDataT] | End[FinalResult[OutputDataT]]:
         if isinstance(task, Sequence) and len(task) == 1:
             first_task = task[0]
@@ -197,8 +197,8 @@ def _task_to_node(
             return End(task.value)
         raise exceptions.AgentRunError(f'Unexpected node: {task}')  # pragma: no cover
 
-    def _node_to_task(self, node: _agent_graph.AgentNode[AgentDepsT, OutputDataT]) -> GraphTask:
-        return GraphTask(NodeStep(type(node)).id, inputs=node, fork_stack=())
+    def _node_to_task(self, node: _agent_graph.AgentNode[AgentDepsT, OutputDataT]) -> GraphTaskRequest:
+        return GraphTaskRequest(NodeStep(type(node)).id, inputs=node, fork_stack=())
 
     async def next(
         self,

pydantic_graph/pydantic_graph/beta/graph.py

@@ -8,8 +8,7 @@
 from __future__ import annotations as _annotations
 
 import sys
-import uuid
-from collections.abc import AsyncGenerator, AsyncIterable, AsyncIterator, Iterable, Sequence
+from collections.abc import AsyncGenerator, AsyncIterable, AsyncIterator, Callable, Iterable, Sequence
 from contextlib import AbstractContextManager, AsyncExitStack, ExitStack, asynccontextmanager, contextmanager
 from dataclasses import dataclass, field
 from typing import TYPE_CHECKING, Any, Generic, Literal, TypeGuard, cast, get_args, get_origin, overload
@@ -22,7 +21,7 @@
 from pydantic_graph import exceptions
 from pydantic_graph._utils import AbstractSpan, get_traceparent, infer_obj_name, logfire_span
 from pydantic_graph.beta.decision import Decision
-from pydantic_graph.beta.id_types import ForkID, ForkStack, ForkStackItem, GraphRunID, JoinID, NodeID, NodeRunID, TaskID
+from pydantic_graph.beta.id_types import ForkID, ForkStack, ForkStackItem, JoinID, NodeID, NodeRunID, TaskID
 from pydantic_graph.beta.join import Join, JoinNode, JoinState, ReducerContext
 from pydantic_graph.beta.node import (
     EndNode,
@@ -306,14 +305,13 @@ def __str__(self) -> str:
 
 
 @dataclass
-class GraphTask:
-    """A single task representing the execution of a node in the graph.
+class GraphTaskRequest:
+    """A request to run a task representing the execution of a node in the graph.
 
-    GraphTask encapsulates all the information needed to execute a specific
+    GraphTaskRequest encapsulates all the information needed to execute a specific
     node, including its inputs and the fork context it's executing within.
     """
 
-    # With our current BaseNode thing, next_node_id and next_node_inputs are merged into `next_node` itself
     node_id: NodeID
     """The ID of the node to execute."""
 
@@ -326,7 +324,29 @@ class GraphTask:
     Used by the GraphRun to decide when to proceed through joins.
     """
 
-    task_id: TaskID = field(default_factory=lambda: TaskID(str(uuid.uuid4())), repr=False)
+
+@dataclass
+class GraphTask(GraphTaskRequest):
+    """A task representing the execution of a node in the graph.
+
+    GraphTask encapsulates all the information needed to execute a specific
+    node, including its inputs and the fork context it's executing within,
+    and has a unique ID to identify the task within the graph run.
+    """
+
+    node_id: NodeID
+    """The ID of the node to execute."""
+
+    inputs: Any
+    """The input data for the node."""
+
+    fork_stack: ForkStack = field(repr=False)
+    """Stack of forks that have been entered.
+
+    Used by the GraphRun to decide when to proceed through joins.
+    """
+
+    task_id: TaskID
     """Unique identifier for this task."""
 
 
@@ -378,12 +398,20 @@ def __init__(
         self._next: EndMarker[OutputT] | Sequence[GraphTask] | None = None
         """The next item to be processed."""
 
-        run_id = GraphRunID(str(uuid.uuid4()))
-        initial_fork_stack: ForkStack = (ForkStackItem(StartNode.id, NodeRunID(run_id), 0),)
-        self._first_task = GraphTask(node_id=StartNode.id, inputs=inputs, fork_stack=initial_fork_stack)
+        self._next_task_id = 0
+        self._next_node_run_id = 0
+        initial_fork_stack: ForkStack = (ForkStackItem(StartNode.id, self._get_next_node_run_id(), 0),)
+        self._first_task = GraphTask(
+            node_id=StartNode.id, inputs=inputs, fork_stack=initial_fork_stack, task_id=self._get_next_task_id()
+        )
         self._iterator_task_group = create_task_group()
         self._iterator_instance = _GraphIterator[StateT, DepsT, OutputT](
-            self.graph, self.state, self.deps, self._iterator_task_group
+            self.graph,
+            self.state,
+            self.deps,
+            self._iterator_task_group,
+            self._get_next_node_run_id,
+            self._get_next_task_id,
         )
         self._iterator = self._iterator_instance.iter_graph(self._first_task)
 
@@ -449,7 +477,7 @@ async def __anext__(self) -> EndMarker[OutputT] | Sequence[GraphTask]:
         return self._next
 
     async def next(
-        self, value: EndMarker[OutputT] | Sequence[GraphTask] | None = None
+        self, value: EndMarker[OutputT] | Sequence[GraphTaskRequest] | None = None
     ) -> EndMarker[OutputT] | Sequence[GraphTask]:
         """Advance the graph execution by one step.
 
@@ -467,7 +495,10 @@ async def next(
             # if `next` is called before the `first_node` has run.
             await anext(self)
         if value is not None:
-            self._next = value
+            if isinstance(value, EndMarker):
+                self._next = value
+            else:
+                self._next = [GraphTask(gt.node_id, gt.inputs, gt.fork_stack, self._get_next_task_id()) for gt in value]
         return await anext(self)
 
     @property
@@ -490,6 +521,16 @@ def output(self) -> OutputT | None:
             return self._next.value
         return None
 
+    def _get_next_task_id(self) -> TaskID:
+        next_id = TaskID(f'task:{self._next_task_id}')
+        self._next_task_id += 1
+        return next_id
+
+    def _get_next_node_run_id(self) -> NodeRunID:
+        next_id = NodeRunID(f'task:{self._next_node_run_id}')
+        self._next_node_run_id += 1
+        return next_id
+
 
 @dataclass
 class _GraphTaskAsyncIterable:
@@ -510,6 +551,8 @@ class _GraphIterator(Generic[StateT, DepsT, OutputT]):
     state: StateT
     deps: DepsT
     task_group: TaskGroup
+    get_next_node_run_id: Callable[[], NodeRunID]
+    get_next_task_id: Callable[[], TaskID]
 
     cancel_scopes: dict[TaskID, CancelScope] = field(init=False)
     active_tasks: dict[TaskID, GraphTask] = field(init=False)
@@ -522,6 +565,7 @@ def __post_init__(self):
         self.active_tasks = {}
         self.active_reducers = {}
         self.iter_stream_sender, self.iter_stream_receiver = create_memory_object_stream[_GraphTaskResult]()
+        self._next_node_run_id = 1
 
     async def iter_graph(  # noqa C901
         self, first_task: GraphTask
@@ -782,12 +826,12 @@ def _handle_node(
         fork_stack: ForkStack,
     ) -> Sequence[GraphTask] | JoinItem | EndMarker[OutputT]:
         if isinstance(next_node, StepNode):
-            return [GraphTask(next_node.step.id, next_node.inputs, fork_stack)]
+            return [GraphTask(next_node.step.id, next_node.inputs, fork_stack, self.get_next_task_id())]
         elif isinstance(next_node, JoinNode):
             return JoinItem(next_node.join.id, next_node.inputs, fork_stack)
         elif isinstance(next_node, BaseNode):
             node_step = NodeStep(next_node.__class__)
-            return [GraphTask(node_step.id, next_node, fork_stack)]
+            return [GraphTask(node_step.id, next_node, fork_stack, self.get_next_task_id())]
         elif isinstance(next_node, End):
             return EndMarker(next_node.data)
         else:
@@ -821,7 +865,7 @@ def _handle_path(self, path: Path, inputs: Any, fork_stack: ForkStack) -> Sequen
             'These markers should be removed from paths during graph building'
         )
         if isinstance(item, DestinationMarker):
-            return [GraphTask(item.destination_id, inputs, fork_stack)]
+            return [GraphTask(item.destination_id, inputs, fork_stack, self.get_next_task_id())]
         elif isinstance(item, TransformMarker):
             inputs = item.transform(StepContext(state=self.state, deps=self.deps, inputs=inputs))
             return self._handle_path(path.next_path, inputs, fork_stack)
@@ -853,7 +897,7 @@ def _handle_fork_edges(
         )  # this should have already been ensured during graph building
 
         new_tasks: list[GraphTask] = []
-        node_run_id = NodeRunID(str(uuid.uuid4()))
+        node_run_id = self.get_next_node_run_id()
         if node.is_map:
             # If the map specifies a downstream join id, eagerly create a join state for it
             if (join_id := node.downstream_join_id) is not None:

pydantic_graph/pydantic_graph/beta/id_types.py

@@ -24,9 +24,6 @@
 ForkID = NodeID
 """Alias for NodeId when referring to fork nodes."""
 
-GraphRunID = NewType('GraphRunID', str)
-"""Unique identifier for a complete graph execution run."""
-
 TaskID = NewType('TaskID', str)
 """Unique identifier for a task within the graph execution."""
 

tests/graph/beta/test_graph_iteration.py

@@ -8,7 +8,7 @@
 import pytest
 
 from pydantic_graph.beta import GraphBuilder, StepContext
-from pydantic_graph.beta.graph import EndMarker, GraphTask
+from pydantic_graph.beta.graph import EndMarker, GraphTask, GraphTaskRequest
 from pydantic_graph.beta.id_types import NodeID
 from pydantic_graph.beta.join import reduce_list_append
 
@@ -400,7 +400,7 @@ async def second_step(ctx: StepContext[IterState, None, int]) -> int:
                 # Get the fork_stack from the EndMarker's source
                 fork_stack = run.next_task[0].fork_stack if isinstance(run.next_task, list) else ()
 
-                new_task = GraphTask(
+                new_task = GraphTaskRequest(
                     node_id=NodeID('second_step'),
                     inputs=event.value,
                     fork_stack=fork_stack,

tests/test_temporal.py

@@ -49,6 +49,8 @@
 from pydantic_ai.run import AgentRunResult
 from pydantic_ai.tools import DeferredToolRequests, DeferredToolResults, ToolDefinition
 from pydantic_ai.usage import RequestUsage
+from pydantic_graph.beta import GraphBuilder, StepContext
+from pydantic_graph.beta.join import reduce_list_append
 
 try:
     from temporalio import workflow
@@ -2228,3 +2230,108 @@ async def test_fastmcp_toolset(allow_model_requests: None, client: Client):
         assert output == snapshot(
             'The `pydantic/pydantic-ai` repository is a Python agent framework crafted for developing production-grade Generative AI applications. It emphasizes type safety, model-agnostic design, and extensibility. The framework supports various LLM providers, manages agent workflows using graph-based execution, and ensures structured, reliable LLM outputs. Key packages include core framework components, graph execution engines, evaluation tools, and example applications.'
         )
+
+
+# ============================================================================
+# Beta Graph API Tests - Tests for running pydantic-graph beta API in Temporal
+# ============================================================================
+
+
+@dataclass
+class GraphState:
+    """State for the graph execution test."""
+
+    values: list[int] = field(default_factory=list)
+
+
+# Create a graph with parallel execution using the beta API
+graph_builder = GraphBuilder(
+    name='parallel_test_graph',
+    state_type=GraphState,
+    input_type=int,
+    output_type=list[int],
+)
+
+
+@graph_builder.step
+async def source(ctx: StepContext[GraphState, None, int]) -> int:
+    """Source step that passes through the input value."""
+    return ctx.inputs
+
+
+@graph_builder.step
+async def multiply_by_two(ctx: StepContext[GraphState, None, int]) -> int:
+    """Multiply input by 2."""
+    return ctx.inputs * 2
+
+
+@graph_builder.step
+async def multiply_by_three(ctx: StepContext[GraphState, None, int]) -> int:
+    """Multiply input by 3."""
+    return ctx.inputs * 3
+
+
+@graph_builder.step
+async def multiply_by_four(ctx: StepContext[GraphState, None, int]) -> int:
+    """Multiply input by 4."""
+    return ctx.inputs * 4
+
+
+# Create a join to collect results
+result_collector = graph_builder.join(reduce_list_append, initial_factory=list[int])
+
+# Build the graph with parallel edges (broadcast pattern)
+graph_builder.add(
+    graph_builder.edge_from(graph_builder.start_node).to(source),
+    # Broadcast: send value to all three parallel steps
+    graph_builder.edge_from(source).to(multiply_by_two, multiply_by_three, multiply_by_four),
+    # Collect all results
+    graph_builder.edge_from(multiply_by_two, multiply_by_three, multiply_by_four).to(result_collector),
+    graph_builder.edge_from(result_collector).to(graph_builder.end_node),
+)
+
+parallel_test_graph = graph_builder.build()
+
+
+@workflow.defn
+class ParallelGraphWorkflow:
+    """Workflow that executes a graph with parallel task execution."""
+
+    @workflow.run
+    async def run(self, input_value: int) -> list[int]:
+        """Run the parallel graph workflow.
+
+        Args:
+            input_value: The input number to process
+
+        Returns:
+            List of results from parallel execution
+        """
+        result = await parallel_test_graph.run(
+            state=GraphState(),
+            inputs=input_value,
+        )
+        return result
+
+
+async def test_beta_graph_parallel_execution_in_workflow(client: Client):
+    """Test that beta graph API with parallel execution works in Temporal workflows.
+
+    This test verifies the fix for the bug where parallel task execution in graphs
+    wasn't working properly with Temporal workflows due to GraphTask/GraphTaskRequest
+    serialization issues.
+    """
+    async with Worker(
+        client,
+        task_queue=TASK_QUEUE,
+        workflows=[ParallelGraphWorkflow],
+    ):
+        output = await client.execute_workflow(  # pyright: ignore[reportUnknownMemberType]
+            ParallelGraphWorkflow.run,
+            args=[10],
+            id=ParallelGraphWorkflow.__name__,
+            task_queue=TASK_QUEUE,
+        )
+        # Results can be in any order due to parallel execution
+        # 10 * 2 = 20, 10 * 3 = 30, 10 * 4 = 40
+        assert sorted(output) == [20, 30, 40]