Agentic Generative UI

Render the agent's live state

Some work does not fit into a single tool call. A research task, a multi-step plan, a long-running job: the interesting thing to show the user is not one result, but progress. Agentic generative UI renders the agent's state and re-renders it every time that state changes.

The pattern has two halves:

1. Your Flow writes progress into its own state as it works.
2. Your frontend reads that state with useAgent and paints it, re-rendering as the state streams in.

The Flow's state reaches the frontend over AG-UI without you wiring up any transport. A state snapshot is emitted automatically at each step (method) boundary of the Flow, and you can push intermediate updates during a long-running step by calling copilotkit_emit_state explicitly. You subclass the state to add your own fields, update them in the Flow, and read them in React.

Build a live task planner

This example builds a planner that breaks a request into about ten steps and streams them to the UI as a checklist. It assumes you already have a CrewAI server and a CopilotKit frontend wired up. If you do not, start with the Frontend Overview.

Subclass CopilotKitState to declare the state your UI needs. CopilotKitState already carries the conversation (messages); you add whatever else you want to render, here a list of task steps.

from typing import List, Literal
from pydantic import BaseModel, Field
from ag_ui_crewai.sdk import CopilotKitState


class TaskStep(BaseModel):
    description: str
    status: Literal["enabled", "disabled"]


class AgentState(CopilotKitState):
    steps: List[TaskStep] = Field(default_factory=list)

Everything on AgentState is included in the state snapshot the frontend receives. A snapshot is emitted automatically at each step boundary, so writing to self.state is enough for the UI to pick it up between steps. To update the UI during a long step, emit explicitly (shown below).

Type your Flow with the custom state (Flow[AgentState]) and let the model fill it in. Here the LLM calls a generate_task_steps tool; the streamed tool call lands in the conversation and the steps become visible in state.

from crewai.flow.flow import Flow, start
from litellm import acompletion
from ag_ui_crewai.sdk import copilotkit_stream

GENERATE_TASK_STEPS_TOOL = {
    "type": "function",
    "function": {
        "name": "generate_task_steps",
        "description": "Break a task into about 10 short imperative steps.",
        "parameters": {
            "type": "object",
            "properties": {
                "steps": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "description": {"type": "string"},
                            "status": {"type": "string", "enum": ["enabled"]},
                        },
                        "required": ["description", "status"],
                    },
                },
            },
            "required": ["steps"],
        },
    },
}


class TaskPlannerFlow(Flow[AgentState]):
    @start()
    async def chat(self):
        response = await copilotkit_stream(
            await acompletion(
                model="openai/gpt-4o",
                messages=[
                    {"role": "system", "content": "Plan the task the user asks for."},
                    *self.state.messages,
                ],
                tools=[GENERATE_TASK_STEPS_TOOL],
                parallel_tool_calls=False,
                stream=True,
            )
        )
        message = response.choices[0].message
        self.state.messages.append(message)

Wrapping the LLM call in copilotkit_stream streams the assistant's tokens and tool call to the frontend as they are produced. The steps you write to self.state are sent in the state snapshot emitted at the end of this step.

The automatic snapshot fires at step boundaries. If a single step does substantial work and you want the checklist to fill in as it happens, emit intermediate state yourself with copilotkit_emit_state. Each call pushes the current state to the frontend immediately.

from ag_ui_crewai.sdk import copilotkit_emit_state

class TaskPlannerFlow(Flow[AgentState]):
    @start()
    async def execute(self):
        for step in self.state.steps:
            step.status = "disabled"          # mark done as you go
            await copilotkit_emit_state(self.state)   # push update now
            await do_work(step)

Import copilotkit_emit_state from ag_ui_crewai.sdk. It requires the CopilotKit SDK (pip install "copilotkit[crewai]"). Reach for it only when a step is long enough that waiting for its boundary snapshot would feel unresponsive.

Register the Flow exactly as any other, on its own path:

# server.py
from fastapi import FastAPI
from ag_ui_crewai.endpoint import add_crewai_flow_fastapi_endpoint
from my_agents.task_planner import TaskPlannerFlow

app = FastAPI(title="CrewAI Agent Server")

add_crewai_flow_fastapi_endpoint(
    app=app,
    flow=TaskPlannerFlow(),
    path="/task_planner",
)

See the Frontend Overview for the full server, runtime, and provider setup, and remember to register the agent (here task_planner) in your CopilotKit runtime route.

On the frontend, useAgent gives you the agent's live state. Subscribe to state changes so your component re-renders every time the Flow writes an update.

"use client";
import { useAgent, UseAgentUpdate } from "@copilotkit/react-core/v2";

function TaskPlan() {
  const { agent } = useAgent({
    agentId: "task_planner",
    updates: [UseAgentUpdate.OnStateChanged],
  });

  const steps = agent?.state?.steps ?? [];

  return (
    <ul>
      {steps.map((s, i) => (
        <li key={i}>{s.description}</li>
      ))}
    </ul>
  );
}

useAgent returns { agent }. A few things to know:

• agent.state is the live Flow state. Its shape matches the fields you added to AgentState, so agent.state.steps is your list of task steps.
• agent.isRunning tells you when the agent is actively working, useful for showing a spinner or disabling input.
• updates: [UseAgentUpdate.OnStateChanged] re-renders the component whenever state changes, so the checklist fills in as the Flow streams its steps.

Where this goes next

Reading state is the foundation. Two guides build directly on it:

• Shared State adds the other direction: editing the agent's state from the UI and having the Flow pick up the change.
• Predictive State streams a tool's in-progress arguments into state so the UI reflects work before it is committed.

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