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Documentation Index

Fetch the complete documentation index at: https://docs.swarms.world/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

The SwarmRouter class dynamically routes tasks to different swarm architectures based on user selection or automatic matching. It provides a unified interface for managing multiple swarm types and enables flexible task execution with logging, type validation, and metadata capture.

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Class Definition

from swarms.structs.swarm_router import SwarmRouter

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Parameters

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id
str
default:"generate_api_key(prefix='swarm-router')"
Unique identifier for the SwarmRouter instance
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name
str
default:"swarm-router"
Name identifier for the SwarmRouter instance
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description
str
default:"Routes your task to the desired swarm"
Description of the SwarmRouter’s purpose
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max_loops
int
default:"1"
Maximum number of execution loops
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agents
List[Union[Agent, Callable]]
default:"[]"
List of Agent objects or callables to use in the swarm
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swarm_type
SwarmType
default:"SequentialWorkflow"
Type of swarm to use. Options: “AgentRearrange”, “MixtureOfAgents”, “SequentialWorkflow”, “ConcurrentWorkflow”, “GroupChat”, “MultiAgentRouter”, “HierarchicalSwarm”, “HeavySwarm”, “MajorityVoting”, “CouncilAsAJudge”, “BatchedGridWorkflow”, “LLMCouncil”, “DebateWithJudge”, “RoundRobin”
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autosave
bool
default:"False"
Whether to enable autosaving of swarm configuration, state, and metadata to workspace_dir/swarms/SwarmRouter/-/
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autosave_use_timestamp
bool
default:"True"
If True, use timestamp in directory name; if False, use UUID
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rearrange_flow
str
Flow configuration string for AgentRearrange swarm type
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return_json
bool
default:"False"
Whether to return results as JSON
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auto_generate_prompts
bool
default:"False"
Whether to auto-generate agent prompts
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shared_memory_system
Any
Shared memory system for agents
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rules
str
Rules to inject into every agent’s system prompt
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documents
List[str]
default:"[]"
List of document file paths to use
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output_type
OutputType
default:"dict-all-except-first"
Output format type. Supported: ‘str’, ‘string’, ‘list’, ‘json’, ‘dict’, ‘yaml’, ‘xml’
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multi_agent_collab_prompt
bool
default:"True"
Whether to enable multi-agent collaboration prompts
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verbose
bool
default:"False"
Enable verbose logging output

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Available Swarm Types

  • AgentRearrange: Optimizes agent arrangement for task execution with custom flow configuration
  • MixtureOfAgents: Combines multiple agent types for diverse task perspectives
  • SequentialWorkflow: Executes tasks sequentially through agents in order
  • ConcurrentWorkflow: Executes tasks in parallel across agents
  • GroupChat: Enables collaborative discussion and consensus-building
  • MultiAgentRouter: Intelligent routing and load balancing across agents
  • HierarchicalSwarm: Layered decision-making with director and worker agents
  • HeavySwarm: High-capacity processing with specialized question generation and synthesis
  • MajorityVoting: Democratic decision-making with voting mechanisms
  • CouncilAsAJudge: Deliberative decision-making with expert evaluation
  • BatchedGridWorkflow: Batched task processing across agent grids
  • LLMCouncil: Council-based deliberation with chairman coordination
  • DebateWithJudge: Structured debate with pro, con, and judge agents
  • RoundRobin: Cycles through agents in round-robin fashion

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Methods

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reliability_check()

def reliability_check(self) -> None
Performs reliability checks on swarm configuration to validate essential parameters. Raises:
  • SwarmRouterConfigError: If swarm configuration is invalid

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run()

def run(
    self,
    task: Optional[str] = None,
    img: Optional[str] = None,
    tasks: Optional[List[str]] = None,
    *args,
    **kwargs,
) -> Any
Executes a task on the selected swarm type. Parameters:
​
task
str
The task to be executed by the swarm
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img
str
Optional image input for the task
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tasks
List[str]
Optional list of tasks for batch processing (used by BatchedGridWorkflow)
Returns:
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result
Any
The result of the swarm’s execution, format depends on output_type setting
Raises:
  • SwarmRouterRunError: If task execution fails

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batch_run()

def batch_run(
    self,
    tasks: List[str],
    img: Optional[str] = None,
    imgs: Optional[List[str]] = None,
    *args,
    **kwargs,
) -> List[Any]
Executes multiple tasks in sequence. Parameters:
​
tasks
List[str]
required
A list of tasks to be executed by the swarm
​
img
str
Optional image input for all tasks
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imgs
List[str]
Optional list of images corresponding to each task
Returns:
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results
List[Any]
A list of results from the swarm’s execution for each task

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concurrent_run()

def concurrent_run(
    self,
    task: str,
    img: Optional[str] = None,
    imgs: Optional[List[str]] = None,
    *args,
    **kwargs,
) -> Any
Executes a task using concurrent execution with ThreadPoolExecutor. Parameters:
​
task
str
required
The task to be executed by the swarm
​
img
str
Optional image input for the task
Returns:
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result
Any
The result of the swarm’s concurrent execution

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to_dict()

def to_dict(self) -> Dict[str, Any]
Converts all attributes of the SwarmRouter to a dictionary for serialization. Returns:
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config
Dict[str, Any]
A dictionary representation of the SwarmRouter’s configuration and state

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Usage Example

from swarms.structs.agent import Agent
from swarms.structs.swarm_router import SwarmRouter

# Create agents
agent1 = Agent(
    agent_name="Research-Agent",
    system_prompt="Research specialist",
    model_name="claude-sonnet-4-6"
)

agent2 = Agent(
    agent_name="Analysis-Agent",
    system_prompt="Analysis specialist",
    model_name="claude-sonnet-4-6"
)

# Create SwarmRouter with SequentialWorkflow
router = SwarmRouter(
    name="research-analysis-swarm",
    description="A swarm for research and analysis tasks",
    agents=[agent1, agent2],
    swarm_type="SequentialWorkflow",
    max_loops=1,
    autosave=True,
    verbose=True
)

# Execute a task
result = router.run(
    task="Research the latest trends in AI and provide analysis"
)

print(result)

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Autosave Configuration

When autosave=True, the SwarmRouter automatically saves:
  1. config.json: Initial configuration on initialization
  2. state.json: Current state after each run
  3. metadata.json: Execution metadata including results
Files are saved to: workspace_dir/swarms/SwarmRouter/{swarm-name}-{timestamp}/

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Source Code

View the source code on GitHub