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182 | class SwarmAgent[StateT: AgentState]:
"""Swarm pattern: dispatch to many workers, collect, then reach consensus.
Notes:
- The underlying engine executes nodes sequentially; true parallelism isn't
performed at the graph level. For concurrency, worker/collector nodes can
internally use BackgroundTaskManager or async to fan-out.
- This pattern wires a linear broadcast-collect chain ending in CONSENSUS.
Nodes:
- optional DISPATCH: prepare/plan the swarm task
- WORKER_i: a set of worker nodes (Callable or ToolNode)
- optional COLLECT: consolidate each worker's result into shared state
- CONSENSUS: aggregate all collected results and produce final output
"""
def __init__(
self,
state: StateT | None = None,
context_manager: BaseContextManager[StateT] | None = None,
publisher: BasePublisher | None = None,
id_generator: BaseIDGenerator = DefaultIDGenerator(),
container: InjectQ | None = None,
):
self._graph = StateGraph[StateT](
state=state,
context_manager=context_manager,
publisher=publisher,
id_generator=id_generator,
container=container,
)
def compile(
self,
workers: dict[str, Callable | ToolNode | tuple[Callable | ToolNode, str]],
consensus_node: Callable | tuple[Callable, str],
options: dict | None = None,
checkpointer: BaseCheckpointer[StateT] | None = None,
store: BaseStore | None = None,
interrupt_before: list[str] | None = None,
interrupt_after: list[str] | None = None,
callback_manager: CallbackManager = CallbackManager(),
) -> CompiledGraph:
resolved_workers = self._add_worker_nodes(workers)
worker_sequence = resolved_workers
options = options or {}
dispatch_node = options.get("dispatch")
collect_node = options.get("collect")
followup_condition = options.get("followup_condition")
dispatch_name = self._resolve_dispatch(dispatch_node)
collect_info = self._resolve_collect(collect_node)
consensus_name = self._resolve_consensus(consensus_node)
entry = dispatch_name or worker_sequence[0]
self._graph.set_entry_point(entry)
if dispatch_name:
self._graph.add_edge(dispatch_name, worker_sequence[0])
self._wire_edges(worker_sequence, collect_info, consensus_name)
if followup_condition is None:
def _cond(_: AgentState) -> str:
return END
followup_condition = _cond
self._graph.add_conditional_edges(
consensus_name,
followup_condition,
{entry: entry, END: END},
)
return self._graph.compile(
checkpointer=checkpointer,
store=store,
interrupt_before=interrupt_before,
interrupt_after=interrupt_after,
callback_manager=callback_manager,
)
# ---- helpers ----
def _add_worker_nodes(
self,
workers: dict[str, Callable | ToolNode | tuple[Callable | ToolNode, str]],
) -> list[str]:
if not workers:
raise ValueError("workers must be a non-empty dict")
names: list[str] = []
for key, fn in workers.items():
if isinstance(fn, tuple):
func, name = fn
else:
func, name = fn, key
if not (callable(func) or isinstance(func, ToolNode)):
raise ValueError(f"Worker '{key}' must be a callable or ToolNode")
self._graph.add_node(name, func)
names.append(name)
return names
def _resolve_dispatch(self, node: Callable | tuple[Callable, str] | None) -> str | None:
if not node:
return None
if isinstance(node, tuple):
func, name = node
else:
func, name = node, "DISPATCH"
if not callable(func):
raise ValueError("dispatch node must be callable")
self._graph.add_node(name, func)
return name
def _resolve_collect(
self,
node: Callable | tuple[Callable, str] | None,
) -> tuple[Callable, str] | None:
if not node:
return None
if isinstance(node, tuple):
func, name = node
else:
func, name = node, "COLLECT"
if not callable(func):
raise ValueError("collect node must be callable")
# Do not add a single shared collect node to avoid ambiguous routing.
# We'll create per-worker collect nodes during wiring using this (func, base_name).
return func, name
def _resolve_consensus(self, node: Callable | tuple[Callable, str]) -> str:
if isinstance(node, tuple):
func, name = node
else:
func, name = node, "CONSENSUS"
if not callable(func):
raise ValueError("consensus node must be callable")
self._graph.add_node(name, func)
return name
def _wire_edges(
self,
worker_sequence: list[str],
collect_info: tuple[Callable, str] | None,
consensus_name: str,
) -> None:
for i, wname in enumerate(worker_sequence):
is_last = i == len(worker_sequence) - 1
target = consensus_name if is_last else worker_sequence[i + 1]
if collect_info:
cfunc, base = collect_info
cname = f"{base}_{i + 1}"
# Create a dedicated collect node per worker to prevent loops
self._graph.add_node(cname, cfunc)
self._graph.add_edge(wname, cname)
self._graph.add_edge(cname, target)
else:
self._graph.add_edge(wname, target)
|