MultiAgentRouter

pip install -U swarms

def route_task(self, task: str) -> dict

def run(self, task: str) -> dict

def batch_run(self, tasks: List[str] = []) -> list

def concurrent_batch_run(self, tasks: List[str] = []) -> list

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)

router = MultiAgentRouter(
    name="custom-router",
    agents=agents,
    system_prompt="You are an expert task router. Always select the most qualified agent.",
    model="claude-sonnet-4-6",
    temperature=0.2
)

result = router.run("Explain quantum computing")

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)

# 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)

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"
    }
  ]
}