WorkflowAgent streaming returns large chunks instead of token-level events. Please help 🙏 🙏 🙏 🙏

[kmoyya]

Issue

When using workflow.as_agent().run(stream=True), workflow agents buffer responses into large text chunks instead of streaming individual tokens like single agents.

Code Comparison

Single Agent (✅ Token-level streaming works)

from agent_framework import Agent

agent = Agent(
    name="single_streaming_agent",
    client=AnthropicClient(api_key="...", base_url="..."),
    instructions="You are a creative copywriter."
)

async for event in agent.run("Write a short tagline for electric bike.", stream=True):
    if event.contents:
        print(f"AgentResponseUpdate: '{event.contents[0].text}' ({len(event.contents[0].text)} chars)")

Workflow Agent (❌ Buffering issue)

  from agent_framework import SequentialBuilder

  workflow = SequentialBuilder().add_agent(agent).build()
  workflow_agent = workflow.as_agent()

  async for event in workflow_agent.run("Write a short tagline for electric bike.", stream=True):
      if event.contents:
          print(f"AgentResponseUpdate: '{event.contents[0].text}' ({len(event.contents[0].text)} chars)")

Comparison

Single Agent :

  AgentResponseUpdate: '"' (1 char)
  AgentResponseUpdate: 'Ride' (4 chars)
  AgentResponseUpdate: ' sm' (3 chars)
  AgentResponseUpdate: 'arter, spen' (11 chars)
  AgentResponseUpdate: 'd less,' (7 chars)
  AgentResponseUpdate: ' arrive happy' (13 chars)
  AgentResponseUpdate: '."' (2 chars)

Workflow Agent (Current Issue):

  AgentResponseUpdate: 'Write a short tagline for a budget-friendly electric bike targeting urban commuters.' (84 chars)
  AgentResponseUpdate: '**"City Miles, Penny Smiles."**\n\n---\n\nAlternative options:\n\n- "Commute Smart. Spend Less. Ride
  Happy."\n- "Your City. Your Ride. Your Budget."' (180 chars)

Question

The workflow loses token-level granularity essential for real time streaming UIs (For AGUI)

How can we preserve token-by-token streaming when using workflow.as_agent().run(stream=True)? 🙏

[moonbox3]

Thanks for raising this! The good news is that AG-UI now natively supports MAF workflows, so you no longer need to wrap your workflow with .as_agent() to integrate with AG-UI. This preserves token-level streaming granularity.

Use AgentFrameworkWorkflow and add_agent_framework_fastapi_endpoint to expose your workflow directly:

from agent_framework.ag_ui import AgentFrameworkWorkflow, add_agent_framework_fastapi_endpoint
from fastapi import FastAPI

app = FastAPI()

demo_workflow = AgentFrameworkWorkflow(
    workflow_factory=lambda _thread_id: workflow,  # your built workflow
    name="my_workflow",
    description="Streaming workflow demo",
)

add_agent_framework_fastapi_endpoint(
    app=app,
    agent=demo_workflow,
    path="/my-endpoint",
)

The .as_agent() wrapper was designed for composability (embedding a workflow inside another agent), not for streaming fidelity — so the buffering behavior you observed is expected in that path.

For a full working example, check out the AG-UI Workflow Handoff demo.