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Different models excel at different tasks. GPT handles reasoning and tool use well. Claude writes better prose. Specialized models exist for code, math, and domain-specific work. Handoffs let agents route subtasks to the right model.

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How It Works

Pass a list of models instead of a single model. The agent can hand off to any model in the list based on what the task requires: 
import asyncio
from dedalus_labs import AsyncDedalus, DedalusRunner
from dotenv import load_dotenv

load_dotenv()

async def main():
    client = AsyncDedalus()
    runner = DedalusRunner(client)

    result = await runner.run(
        input="Research the latest SpaceX launch, then write a creative blog post about it",
        model=["openai/gpt-4o-mini", "anthropic/claude-sonnet-4-20250514"],
        mcp_servers=["tsion/brave-search-mcp"]
    )

    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())
The first model handles research (tool calling, information gathering). When it’s time to write, it can hand off to Claude for the creative work.

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When to Use Handoffs

Handoffs shine when a task has distinct phases requiring different capabilities:
  • Research → Writing: GPT gathers information, Claude writes the final piece
  • Analysis → Code: A reasoning model plans the approach, a code model implements it
  • Triage → Specialist: A general model routes to domain-specific models
For simple tasks where one model handles everything, stick to a single model.

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Model Strengths

A rough guide to model selection:
TaskGood Models
Tool calling, reasoningopenai/gpt-4o-mini, openai/gpt-4.1
Writing, creative workanthropic/claude-sonnet-4-20250514
Code generationanthropic/claude-sonnet-4-20250514, openai/gpt-4o
Fast, cheap responsesopenai/gpt-4o-mini
Claude models use the format anthropic/claude-sonnet-4-20250514. Check the providers guide for available models.

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Next Steps

  • Policies — Control handoff logic dynamically
  • Use Cases — See multi-capability workflows