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This example demonstrates multi-model routing where the agent intelligently selects the best model based on task complexity, with model attributes for optimization.
Claude (anthropic/claude-sonnet-4-20250514) is great at writing and creative tasks. Experiment with different models for different use-cases!
import os
from dedalus_labs import AsyncDedalus, DedalusRunner
from dotenv import load_dotenv
from dedalus_labs.utils.streaming import stream_async
import asyncio

load_dotenv()

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

    result = await runner.run(
        input="Find the year GPT-5 released, and handoff to Claude to write a haiku about Elon Musk. Output this haiku. Use your tools.",
        model=["openai/gpt-5", "claude-sonnet-4-20250514"],
        mcp_servers=["tsion/brave-search-mcp"],
        stream=False
    )

    print(result.final_output)

if __name__ == "__main__":
    asyncio.run(main())
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