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Streaming shows output token-by-token instead of waiting for the complete response. Users see progress immediately, which matters for longer outputs or interactive applications.

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Basic Streaming

Set stream=True and use the streaming helper: 
import asyncio
from dedalus_labs import AsyncDedalus, DedalusRunner
from dedalus_labs.utils.stream import stream_async
from dotenv import load_dotenv

load_dotenv()

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

    result = runner.run(
        input="Explain how neural networks learn",
        model="openai/gpt-4o-mini",
        stream=True
    )

    await stream_async(result)

if __name__ == "__main__":
    asyncio.run(main())
Output appears character-by-character as the model generates it.

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Sync Streaming (Python)

For Python scripts that don’t need async: 
from dedalus_labs import Dedalus, DedalusRunner
from dedalus_labs.utils.stream import stream_sync
from dotenv import load_dotenv

load_dotenv()

def main():
    client = Dedalus()
    runner = DedalusRunner(client)

    result = runner.run(
        input="Explain how neural networks learn",
        model="openai/gpt-4o-mini",
        stream=True
    )

    stream_sync(result)

if __name__ == "__main__":
    main()

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When to Stream

Stream when:
  • Building chat interfaces where perceived latency matters
  • Generating long-form content (articles, code, analysis)
  • Running in terminals or logs where progress feedback helps
Don’t stream when:
  • You need to parse the complete response before displaying
  • Using structured outputs with .parse()
  • Response time is already fast enough

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Streaming with Tools

Streaming works with tool-calling workflows. You’ll see the model’s reasoning and tool results as they happen: 
result = runner.run(
    input="Search for AI news and summarize the top story",
    model="openai/gpt-4o-mini",
    mcp_servers=["tsion/brave-search-mcp"],
    stream=True
)

await stream_async(result)

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Next Steps