Class Workflow

src/google/adk/workflow/_workflow.py:146–876

A graph-based workflow node. _run_impl() IS the graph orchestration loop: - SETUP: build graph, seed triggers - LOOP: schedule ready nodes via NodeRunner, handle completions - FINALIZE: collect terminal outputs

class Workflow(BaseNode):
  """A graph-based workflow node.

  _run_impl() IS the graph orchestration loop:
  - SETUP: build graph, seed triggers
  - LOOP: schedule ready nodes via NodeRunner, handle completions
  - FINALIZE: collect terminal outputs
  """

  rerun_on_resume: bool = Field(default=True)

  edges: list[EdgeItem] = Field(
      description='Edges to build the workflow graph.',
      default_factory=list,
  )

  max_concurrency: int | None = None
  """Maximum parallel graph-scheduled nodes. None means unlimited.

  Only applies to nodes triggered by graph edges. Dynamic nodes
  (via ctx.run_node()) are excluded — they are awaited inline by
  their parent and throttling them would cause deadlock.
  """

  graph: Graph | None = Field(
      description='The compiled workflow graph.',
      default=None,
  )

  # --- Construction ---

  def model_post_init(self, context: Any) -> None:
    super().model_post_init(context)
    if self.edges and self.graph is None:
      self.graph = self._build_graph()
    self._validate_state_schema()
    self._validate_no_task_mode_graph_nodes()

  def _build_graph(self) -> Graph:
    """Convert edge definitions to a validated Graph."""
    graph = Graph.from_edge_items(self.edges)
    graph.validate_graph()
    return graph

  def _validate_no_task_mode_graph_nodes(self) -> None:
    """Reject ``mode='task'`` LlmAgents that appear as static graph nodes.

    Task-mode agents are multi-turn — they pause for user replies and
    expect the original ``node_input`` (the task brief) to remain visible
    across re-dispatches.  The workflow scheduler currently overwrites
    ``node_input`` with the latest user message on every re-entry, so the
    task brief is lost and the agent loses context.  Until the scheduler
    preserves the originating ``node_input`` on resume, task agents may
    only be used:

      * as chat sub-agents of an LlmAgent coordinator (FC delegation
        via ``_TaskAgentTool`` / ``_dispatch_task_fc``), or
      * dispatched dynamically via ``ctx.run_node`` from a custom