> ## 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.

# SwarmRouter

> A dynamic router that routes tasks to different swarm types based on user selection or automatic matching

## 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.

## Class Definition

```python theme={null}
from swarms.structs.swarm_router import SwarmRouter
```

## Parameters

<ParamField path="id" type="str" default="generate_api_key(prefix='swarm-router')">
  Unique identifier for the SwarmRouter instance
</ParamField>

<ParamField path="name" type="str" default="swarm-router">
  Name identifier for the SwarmRouter instance
</ParamField>

<ParamField path="description" type="str" default="Routes your task to the desired swarm">
  Description of the SwarmRouter's purpose
</ParamField>

<ParamField path="max_loops" type="int" default="1">
  Maximum number of execution loops
</ParamField>

<ParamField path="agents" type="List[Union[Agent, Callable]]" default="[]">
  List of Agent objects or callables to use in the swarm
</ParamField>

<ParamField path="swarm_type" type="SwarmType" default="SequentialWorkflow">
  Type of swarm to use. Options: "AgentRearrange", "MixtureOfAgents", "SequentialWorkflow", "ConcurrentWorkflow", "GroupChat", "MultiAgentRouter", "HierarchicalSwarm", "HeavySwarm", "MajorityVoting", "CouncilAsAJudge", "BatchedGridWorkflow", "LLMCouncil", "DebateWithJudge", "RoundRobin"
</ParamField>

<ParamField path="autosave" type="bool" default="False">
  Whether to enable autosaving of swarm configuration, state, and metadata to workspace\_dir/swarms/SwarmRouter/{swarm-name}-{timestamp}/
</ParamField>

<ParamField path="autosave_use_timestamp" type="bool" default="True">
  If True, use timestamp in directory name; if False, use UUID
</ParamField>

<ParamField path="rearrange_flow" type="str" optional>
  Flow configuration string for AgentRearrange swarm type
</ParamField>

<ParamField path="return_json" type="bool" default="False">
  Whether to return results as JSON
</ParamField>

<ParamField path="auto_generate_prompts" type="bool" default="False">
  Whether to auto-generate agent prompts
</ParamField>

<ParamField path="shared_memory_system" type="Any" optional>
  Shared memory system for agents
</ParamField>

<ParamField path="rules" type="str" optional>
  Rules to inject into every agent's system prompt
</ParamField>

<ParamField path="documents" type="List[str]" default="[]">
  List of document file paths to use
</ParamField>

<ParamField path="output_type" type="OutputType" default="dict-all-except-first">
  Output format type. Supported: 'str', 'string', 'list', 'json', 'dict', 'yaml', 'xml'
</ParamField>

<ParamField path="multi_agent_collab_prompt" type="bool" default="True">
  Whether to enable multi-agent collaboration prompts
</ParamField>

<ParamField path="verbose" type="bool" default="False">
  Enable verbose logging output
</ParamField>

## 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

## Methods

### `reliability_check()`

```python theme={null}
def reliability_check(self) -> None
```

Performs reliability checks on swarm configuration to validate essential parameters.

**Raises:**

* `SwarmRouterConfigError`: If swarm configuration is invalid

***

### `run()`

```python theme={null}
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:**

<ParamField path="task" type="str" optional>
  The task to be executed by the swarm
</ParamField>

<ParamField path="img" type="str" optional>
  Optional image input for the task
</ParamField>

<ParamField path="tasks" type="List[str]" optional>
  Optional list of tasks for batch processing (used by BatchedGridWorkflow)
</ParamField>

**Returns:**

<ResponseField name="result" type="Any">
  The result of the swarm's execution, format depends on output\_type setting
</ResponseField>

**Raises:**

* `SwarmRouterRunError`: If task execution fails

***

### `batch_run()`

```python theme={null}
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:**

<ParamField path="tasks" type="List[str]" required>
  A list of tasks to be executed by the swarm
</ParamField>

<ParamField path="img" type="str" optional>
  Optional image input for all tasks
</ParamField>

<ParamField path="imgs" type="List[str]" optional>
  Optional list of images corresponding to each task
</ParamField>

**Returns:**

<ResponseField name="results" type="List[Any]">
  A list of results from the swarm's execution for each task
</ResponseField>

***

### `concurrent_run()`

```python theme={null}
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:**

<ParamField path="task" type="str" required>
  The task to be executed by the swarm
</ParamField>

<ParamField path="img" type="str" optional>
  Optional image input for the task
</ParamField>

**Returns:**

<ResponseField name="result" type="Any">
  The result of the swarm's concurrent execution
</ResponseField>

***

### `to_dict()`

```python theme={null}
def to_dict(self) -> Dict[str, Any]
```

Converts all attributes of the SwarmRouter to a dictionary for serialization.

**Returns:**

<ResponseField name="config" type="Dict[str, Any]">
  A dictionary representation of the SwarmRouter's configuration and state
</ResponseField>

## Usage Example

```python theme={null}
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)
```

## 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}/`

## Source Code

View the [source code on GitHub](https://github.com/kyegomez/swarms/blob/master/swarms/structs/swarm_router.py)