Display components

"""LangGraph agent backing the Tool-Based Generative UI demo.The frontend registers `render_bar_chart` and `render_pie_chart` tools via`useComponent`. CopilotKit's LangGraph middleware injects those tools intothe model request at runtime so the agent can call them."""from langchain.agents import create_agentfrom langchain_openai import ChatOpenAIfrom copilotkit import CopilotKitMiddlewareSYSTEM_PROMPT = """You are a data visualization assistant.When the user asks for a chart, call `render_bar_chart` or `render_pie_chart`with a concise title, short description, and a `data` array of`{label, value}` items. Pick bar for comparisons over a small set ofcategories; pick pie for composition / share-of-whole.If the user names a chart subject but does NOT supply concrete numbers(e.g. "show me a pie chart of website traffic by source"), do NOT askthem for data. Invent plausible illustrative sample values yourself,call the appropriate `render_*` tool immediately, and briefly note inthe follow-up that the values are illustrative samples. Always renderthe chart on the first turn -- never reply with a clarifying questionasking for the data.Keep chat responses brief -- let the chart do the talking."""graph = create_agent(    model=ChatOpenAI(model="gpt-5.4"),    tools=[],    middleware=[CopilotKitMiddleware()],    system_prompt=SYSTEM_PROMPT,)

What is this?#

Render-only generative UI lets you register React components as tools your agent can invoke. When the agent calls the tool, CopilotKit renders your component directly in the chat with the tool's arguments as props; no handler logic or user interaction required.


useComponent({
name: "showChart",
description: "Populate data and show the user a chart",
parameters: ChartProps,
render: Chart
});


export const ChartProps = z.object({
  title: z.string(),
  data: z.array(z.object({ label: z.string(), value: z.number() })),
});

export function Chart({ title, data }: z.infer<typeof ChartProps>) {
  return (
    <div>
      <h3>{title}</h3>
      <ResponsiveContainer width="100%" height={300}>
        <BarChart data={data}>
          <XAxis dataKey="label" /><YAxis /><Tooltip />
          <Bar dataKey="value" fill="#6366f1" />
        </BarChart>
      </ResponsiveContainer>
    </div>
  );
}

When should I use this?#

Use render-only generative UI when you want to:

Display rich UI (cards, charts, tables) inline in the chat
Show structured data from agent responses
Render previews, status indicators, or visual feedback
Let the agent present information beyond plain text

How it works in code#

Install the LangGraph Python SDK

uv add copilotkit

poetry add copilotkit

pip install copilotkit --extra-index-url https://copilotkit.gateway.scarf.sh/simple/

conda install copilotkit -c copilotkit-channel

Wire CopilotKit middleware into your graph

Frontend tools registered with useFrontendTool are forwarded to your agent at runtime. CopilotKitMiddleware is the bridge — drop it into create_agent's middleware list and the LLM sees the forwarded tool definitions on every turn.

frontend_tools.py

from langchain.agents import create_agent
from langchain_openai import ChatOpenAI
from copilotkit import CopilotKitMiddleware

graph = create_agent(
    model=ChatOpenAI(model="gpt-5.4"),
    tools=[],
    middleware=[CopilotKitMiddleware()],
    system_prompt="You are a helpful, concise assistant.",
)

The renderer component receives the tool's arguments as typed props and mounts inline in the chat. Below is the chart renderer wired up in the canonical demo — the agent emits the data, the component draws it.

page.tsx

  useComponent({    name: "render_bar_chart",    description: "Display a bar chart with labeled numeric values.",    parameters: barChartPropsSchema,    render: BarChart,  });