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strands.multiagent.swarm

Swarm Multi-Agent Pattern Implementation.

This module provides a collaborative agent orchestration system where agents work together as a team to solve complex tasks, with shared context and autonomous coordination.

Key Features:

  • Self-organizing agent teams with shared working memory
  • Tool-based coordination
  • Autonomous agent collaboration without central control
  • Dynamic task distribution based on agent capabilities
  • Collective intelligence through shared context
  • Human input via user interrupts raised in BeforeNodeCallEvent hooks and agent nodes

SwarmNode

@dataclass
class SwarmNode()

Defined in: src/strands/multiagent/swarm.py:63

Represents a node (e.g. Agent) in the swarm.

__post_init__

def __post_init__() -> None

Defined in: src/strands/multiagent/swarm.py:72

Capture initial executor state after initialization.

__hash__

def __hash__() -> int

Defined in: src/strands/multiagent/swarm.py:78

Return hash for SwarmNode based on node_id.

__eq__

def __eq__(other: Any) -> bool

Defined in: src/strands/multiagent/swarm.py:82

Return equality for SwarmNode based on node_id.

__str__

def __str__() -> str

Defined in: src/strands/multiagent/swarm.py:88

Return string representation of SwarmNode.

__repr__

def __repr__() -> str

Defined in: src/strands/multiagent/swarm.py:92

Return detailed representation of SwarmNode.

reset_executor_state

def reset_executor_state() -> None

Defined in: src/strands/multiagent/swarm.py:96

Reset SwarmNode executor state to initial state when swarm was created.

If Swarm is resuming from an interrupt, we reset the executor state from the interrupt context.

SharedContext

@dataclass
class SharedContext()

Defined in: src/strands/multiagent/swarm.py:113

Shared context between swarm nodes.

add_context

def add_context(node: SwarmNode, key: str, value: Any) -> None

Defined in: src/strands/multiagent/swarm.py:118

Add context.

SwarmState

@dataclass
class SwarmState()

Defined in: src/strands/multiagent/swarm.py:162

Current state of swarm execution.

current_node

The agent currently executing

task

The original task from the user that is being executed

completion_status

Current swarm execution status

shared_context

Context shared between agents

node_history

Complete history of agents that have executed

start_time

When swarm execution began

results

Results from each agent execution

execution_time

Total execution time in milliseconds

handoff_node

The agent to execute next

handoff_message

Message passed during agent handoff

should_continue

def should_continue(
        *, max_handoffs: int, max_iterations: int, execution_timeout: float,
        repetitive_handoff_detection_window: int,
        repetitive_handoff_min_unique_agents: int) -> tuple[bool, str]

Defined in: src/strands/multiagent/swarm.py:180

Check if the swarm should continue.

Returns: (should_continue, reason)

SwarmResult

@dataclass
class SwarmResult(MultiAgentResult)

Defined in: src/strands/multiagent/swarm.py:223

Result from swarm execution - extends MultiAgentResult with swarm-specific details.

Swarm

class Swarm(MultiAgentBase)

Defined in: src/strands/multiagent/swarm.py:229

Self-organizing collaborative agent teams with shared working memory.

__init__

def __init__(
        nodes: list[Agent],
        *,
        entry_point: Agent | None = None,
        max_handoffs: int = 20,
        max_iterations: int = 20,
        execution_timeout: float = 900.0,
        node_timeout: float = 300.0,
        repetitive_handoff_detection_window: int = 0,
        repetitive_handoff_min_unique_agents: int = 0,
        session_manager: SessionManager | None = None,
        hooks: list[HookProvider] | None = None,
        id: str = _DEFAULT_SWARM_ID,
        trace_attributes: Mapping[str, AttributeValue] | None = None) -> None

Defined in: src/strands/multiagent/swarm.py:232

Initialize Swarm with agents and configuration.

Arguments:

  • id - Unique swarm id (default: “default_swarm”)
  • nodes - List of nodes (e.g. Agent) to include in the swarm
  • entry_point - Agent to start with. If None, uses the first agent (default: None)
  • max_handoffs - Maximum handoffs to agents and users (default: 20)
  • max_iterations - Maximum node executions within the swarm (default: 20)
  • execution_timeout - Total execution timeout in seconds (default: 900.0)
  • node_timeout - Individual node timeout in seconds (default: 300.0)
  • repetitive_handoff_detection_window - Number of recent nodes to check for repetitive handoffs Disabled by default (default: 0)
  • repetitive_handoff_min_unique_agents - Minimum unique agents required in recent sequence Disabled by default (default: 0)
  • session_manager - Session manager for persisting graph state and execution history (default: None)
  • hooks - List of hook providers for monitoring and extending graph execution behavior (default: None)
  • trace_attributes - Custom trace attributes to apply to the agent’s trace span (default: None)

__call__

def __call__(task: MultiAgentInput,
             invocation_state: dict[str, Any] | None = None,
             **kwargs: Any) -> SwarmResult

Defined in: src/strands/multiagent/swarm.py:303

Invoke the swarm synchronously.

Arguments:

  • task - The task to execute
  • invocation_state - Additional state/context passed to underlying agents. Defaults to None to avoid mutable default argument issues.
  • **kwargs - Keyword arguments allowing backward compatible future changes.

invoke_async

async def invoke_async(task: MultiAgentInput,
                       invocation_state: dict[str, Any] | None = None,
                       **kwargs: Any) -> SwarmResult

Defined in: src/strands/multiagent/swarm.py:318

Invoke the swarm asynchronously.

This method uses stream_async internally and consumes all events until completion, following the same pattern as the Agent class.

Arguments:

  • task - The task to execute
  • invocation_state - Additional state/context passed to underlying agents. Defaults to None to avoid mutable default argument issues.
  • **kwargs - Keyword arguments allowing backward compatible future changes.

stream_async

async def stream_async(task: MultiAgentInput,
                       invocation_state: dict[str, Any] | None = None,
                       **kwargs: Any) -> AsyncIterator[dict[str, Any]]

Defined in: src/strands/multiagent/swarm.py:342

Stream events during swarm execution.

Arguments:

  • task - The task to execute
  • invocation_state - Additional state/context passed to underlying agents. Defaults to None to avoid mutable default argument issues.
  • **kwargs - Keyword arguments allowing backward compatible future changes.

Yields:

Dictionary events during swarm execution, such as:

  • multi_agent_node_start: When a node begins execution
  • multi_agent_node_stream: Forwarded agent events with node context
  • multi_agent_handoff: When control is handed off between agents
  • multi_agent_node_stop: When a node stops execution
  • result: Final swarm result

serialize_state

def serialize_state() -> dict[str, Any]

Defined in: src/strands/multiagent/swarm.py:949

Serialize the current swarm state to a dictionary.

deserialize_state

def deserialize_state(payload: dict[str, Any]) -> None

Defined in: src/strands/multiagent/swarm.py:979

Restore swarm state from a session dict and prepare for execution.

This method handles two scenarios:

  1. If the persisted status is COMPLETED, FAILED resets all nodes and graph state to allow re-execution from the beginning.
  2. Otherwise, restores the persisted state and prepares to resume execution from the next ready nodes.

Arguments:

  • payload - Dictionary containing persisted state data including status, completed nodes, results, and next nodes to execute.