Sequential Workflow Streaming

SequentialWorkflow exposes two streaming methods that yield tokens from each agent in pipeline order, in real time. Each agent’s tokens are streamed the moment the LLM produces them; once an agent finishes, its full output is handed off to the next agent — same hand-off as run(), just streamed.

workflow.run_stream(task) — sync generator
workflow.arun_stream(task) — async generator
Pass with_events=True to either to receive structured agent_start / token / agent_end events instead of plain token strings.

Building the Pipeline

from swarms import Agent, SequentialWorkflow


def make_agent(name: str, system_prompt: str) -> Agent:
    return Agent(
        agent_name=name,
        system_prompt=system_prompt,
        model_name="gpt-5.4-mini",
        max_loops=1,
        persistent_memory=False,
        print_on=False,
    )


workflow = SequentialWorkflow(
    agents=[
        make_agent(
            "Researcher",
            "Research the topic and produce a concise factual brief.",
        ),
        make_agent(
            "Analyst",
            "Take the brief and produce sharp analytical insights.",
        ),
        make_agent(
            "Writer",
            "Take the analysis and produce a polished, reader-friendly summary.",
        ),
    ],
    autosave=False,
)

Sync Streaming

Plain token strings, yielded in pipeline order. Agent 1’s tokens stream first, then Agent 2’s, then Agent 3’s.

for token in workflow.run_stream("the rise of solid-state batteries"):
    print(token, end="", flush=True)

Async Streaming

import asyncio


async def main():
    async for token in workflow.arun_stream(
        "the rise of solid-state batteries"
    ):
        print(token, end="", flush=True)


asyncio.run(main())

Structured Events with `with_events=True`

By default the stream yields plain token strings. Pass with_events=True to receive event dicts instead — useful when you want to render a separate panel per agent, attribute every token to the emitting agent, or know exactly when each agent starts and finishes.

import asyncio


async def main():
    async for evt in workflow.arun_stream(
        "the rise of solid-state batteries",
        with_events=True,
    ):
        if evt["type"] == "agent_start":
            print(f"\n--- {evt['agent']} starting ---")
        elif evt["type"] == "token":
            print(evt["token"], end="", flush=True)
        elif evt["type"] == "agent_end":
            print(
                f"\n--- {evt['agent']} finished "
                f"({len(evt['output'])} chars) ---"
            )


asyncio.run(main())

The three event types are:

Type	Fields	When emitted
`agent_start`	`agent`	Right before an agent begins streaming
`token`	`agent`, `token`	For every token the agent emits
`agent_end`	`agent`, `output`	After the agent finishes; carries the full output

max_loops > 1 and drift_detection are not applied in streaming mode. Use workflow.run() if you need those.

SequentialWorkflow — the underlying architecture
Agent Streaming — single-agent streaming with run_stream / arun_stream
Streaming — full overview of every streaming mode

Index

Basic Examples

Model Providers

Single Agent

Tree of Thoughts

Multi-Agent Examples

Auto Agent Builder

Scheduled Agents

Applications

Research

Technical Analysis

Use Cases

Finance

Voice Agents

Integrations

MCP Tool Integrations

Deployment

CLI

Sequential Workflow Streaming

Building the Pipeline

Sync Streaming

Async Streaming

Structured Events with `with_events=True`

​Building the Pipeline

​Sync Streaming

​Async Streaming

​Structured Events with with_events=True

​Related

Building the Pipeline

Sync Streaming

Async Streaming

Structured Events with `with_events=True`

Related