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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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Stream in one line

Set stream=True so users see progress as the agent works. 
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)

    stream = runner.run(
        input="Find me the nearest basketball games in January in San Francisco (stream your work).",
        model="anthropic/claude-opus-4-5",
        mcp_servers=["windsor/ticketmaster-mcp"],  # Discover events via Ticketmaster
        stream=True,
    )

    await stream_async(stream)

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

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Streaming with Tools

Streaming works with tool-calling workflows. You can stream while the agent calls local tools, MCPs, or both. 
import asyncio

from dedalus_labs import AsyncDedalus, DedalusRunner
from dedalus_labs.utils.stream import stream_async
from dotenv import load_dotenv

load_dotenv()

def summarize_headlines(headlines: list[str]) -> str:
    """Format headlines as a short bullet list."""
    return "\n".join(f"• {h}" for h in headlines[:3])

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

    stream = runner.run(
        input=(
            "Search for AI news. Extract 3 headlines. "
            "Then call summarize_headlines(headlines) and stream your final answer."
        ),
        model="openai/gpt-5.2",
        mcp_servers=["windsor/brave-search-mcp"],  # Web search via Brave Search MCP
        tools=[summarize_headlines],
        stream=True,
    )

    await stream_async(stream)

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

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Compare: non-streaming vs streaming (same scenario)

The scenario below is the same in both snippets. The only difference is whether you set stream=True and iterate over the stream.
In Python, non-streaming refers to stream=False, not “sync”. If you use AsyncDedalus, you’ll still write async code and use asyncio.run(...). If you prefer fully synchronous code, use the Dedalus client (example below).

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Python

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="Find me the nearest basketball games in January in San Francisco.",
        model="anthropic/claude-opus-4-5",
        mcp_servers=["windsor/ticketmaster-mcp"],  # Discover events via Ticketmaster
    )

    # You only see output after the full run completes.
    print(result.final_output)

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

​
Python (sync client)

from dedalus_labs import Dedalus, DedalusRunner
from dotenv import load_dotenv

load_dotenv()

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

    result = runner.run(
        input="Find me the nearest basketball games in January in San Francisco.",
        model="anthropic/claude-opus-4-5",
        mcp_servers=["windsor/ticketmaster-mcp"],  # Discover events via Ticketmaster
    )

    print(result.final_output)

if __name__ == "__main__":
    main()

​
TypeScript

import Dedalus from "dedalus-labs";
import { DedalusRunner } from "dedalus-labs";

const client = new Dedalus();
const runner = new DedalusRunner(client, true);

async function main() {
	const result = await runner.run({
		input: "Find me the nearest basketball games in January in San Francisco.",
		model: "anthropic/claude-opus-4-5",
		mcpServers: ["windsor/ticketmaster-mcp"], // Discover events via Ticketmaster
	});

	console.log((result as any).finalOutput);
}

main();

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How the user experience differs

  • Progressive rendering: you can display text as it arrives (“typing”), instead of waiting for a complete response.
  • Visible work: in tool/MCP workflows, you can show status updates (e.g., “Searching Ticketmaster…”) while the agent is calling tools.
  • Interruptibility: you can stop early (client-side) if the user already has what they need, instead of paying for a full completion.

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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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Next steps

  • Route across models: Handoffs — Use fast/strong models by phase
  • Add images last: Images & Vision — Add multimodality when your text workflow is solid
  • See patterns: Use Cases — More streaming agent examples
Connect these docs programmatically to Claude, VSCode, and more via MCP for real-time answers.
Last modified on February 28, 2026