Overview
The MultiAgentRouter uses a boss agent powered by LLMs to intelligently route tasks to the most appropriate specialized agent(s). The boss agent analyzes task requirements and agent capabilities to make routing decisions, supporting both single and multiple agent assignments.
Installation
Attributes
name
str
default:"swarm-router"
The name of the router
A description of the router’s purpose
agents
List[Agent]
default:"None"
A list of agents to be managed by the router
The model to use for the boss agent
The temperature for the boss agent’s model
A shared memory system for agents to query
The type of output expected from the agents
Whether to print the boss agent’s decision
Custom system prompt for the router
Whether to skip executing agents when their assigned task is null or None
Methods
route_task()
Routes a task to the appropriate agent(s) and returns their response.
def route_task(self, task: str) -> dict
task (str): The task to be routed
Returns: Dictionary containing the routing result, including selected agent(s), reasoning, and response
run()
Alias for route_task().
def run(self, task: str) -> dict
batch_run()
Batch route tasks to the appropriate agents sequentially.
def batch_run(self, tasks: List[str] = []) -> list
tasks (List[str]): List of tasks to route
Returns: List of routing results
concurrent_batch_run()
Concurrently route tasks to the appropriate agents.
def concurrent_batch_run(self, tasks: List[str] = []) -> list
tasks (List[str]): List of tasks to route
Returns: List of routing results from concurrent execution
Usage Examples
Basic Routing
from swarms import Agent, MultiAgentRouter
# Define specialized agents
agents = [
Agent(
agent_name="ResearchAgent",
description="Specializes in researching topics and providing detailed, factual information",
system_prompt="You are a research specialist. Provide detailed, well-researched information.",
model_name="openai/gpt-4o",
),
Agent(
agent_name="CodeExpertAgent",
description="Expert in writing, reviewing, and explaining code",
system_prompt="You are a coding expert. Write and review code with best practices.",
model_name="openai/gpt-4o",
),
Agent(
agent_name="WritingAgent",
description="Skilled in creative and technical writing",
system_prompt="You are a writing specialist. Create and edit content.",
model_name="openai/gpt-4o",
),
]
# Initialize router
router = MultiAgentRouter(agents=agents)
# Route a task
task = "Write a Python function to calculate fibonacci numbers"
result = router.route_task(task)
print(result)
Custom System Prompt
router = MultiAgentRouter(
name="custom-router",
agents=agents,
system_prompt="You are an expert task router. Always select the most qualified agent.",
model="gpt-4o",
temperature=0.2
)
result = router.run("Explain quantum computing")
Batch Processing
tasks = [
"Research the latest AI developments",
"Write a function to sort an array",
"Create a blog post about technology trends"
]
# Sequential batch processing
results = router.batch_run(tasks)
# Concurrent batch processing
results_concurrent = router.concurrent_batch_run(tasks)
Multiple Agent Assignment
# The router can assign complex tasks to multiple agents
task = "Research quantum computing and write a tutorial with code examples"
# The boss agent will intelligently split this across multiple agents:
# - ResearchAgent for the research
# - CodeExpertAgent for the code examples
# - WritingAgent for the tutorial writing
result = router.route_task(task)
Skip Null Tasks
router = MultiAgentRouter(
agents=agents,
skip_null_tasks=True # Skip agents with null/None tasks
)
# If boss agent assigns null task to an agent, it will be skipped
result = router.route_task("Simple task")
{
"handoffs": [
{
"reasoning": "This agent is best suited because...",
"agent_name": "ResearchAgent",
"task": "Research the latest AI developments in detail"
}
]
}
{
"handoffs": [
{
"reasoning": "Research capabilities needed for background",
"agent_name": "ResearchAgent",
"task": "Research quantum computing fundamentals"
},
{
"reasoning": "Code expertise for implementation examples",
"agent_name": "CodeExpertAgent",
"task": "Create code examples demonstrating quantum algorithms"
}
]
}
Features
- Intelligent Routing: Boss agent analyzes task requirements and agent capabilities
- Single or Multiple Agents: Automatically determines if task requires one or multiple agents
- Custom Routing Logic: Override system prompt to customize routing behavior
- Batch Processing: Process multiple tasks sequentially or concurrently
- Flexible Output: Support for various output formats (dict, string, json, etc.)
- Null Task Handling: Option to skip agents with null/empty task assignments