Components as Tools

"""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?#

Tool-based Generative UI is the simplest form of Generative UI: you register a React component with useComponent, and CopilotKit exposes it to the agent as a tool. When the agent calls the tool, CopilotKit renders your component inline in the chat, passing the tool's arguments straight through as typed props.

Unlike tool rendering, which wraps a real backend tool in a custom UI, tool-based GenUI is the component. There is no handler, no user interaction, no server-side execution. The agent decides when to show it, populates the data, and CopilotKit paints it.

When should I use this?#

Use useComponent when you want to:

Display rich UI (cards, charts, tables, dashboards) inline in the chat
Show structured data the agent has derived from its reasoning
Render previews, status indicators, or visual summaries
Let the agent present information beyond plain text

For components that need user interaction, see Human-in-the-loop. For operational transparency around a real backend tool, see Tool rendering.

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.",
)

Import the React hook and Zod in the component that registers the tool. This also applies to the built-in agent, which needs no backend tool-registration step.

import { useComponent } from "@copilotkit/react-core/v2";
import { z } from "zod";

useComponent takes a name, a Zod schema for its props, and the component to render. The runtime registers it as a frontend tool so the agent can discover it, and the schema becomes that tool's parameter definition — it is what tells the model which arguments to send.

parameters is optional, but leaving it out advertises the tool with an empty parameter schema ({ "type": "object", "properties": {} }). The model then has nothing to fill in, so it calls the tool with no arguments and your component renders with no props. Pass a schema for any component that needs data.

page.tsx

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

The component itself is ordinary React: it reads only its props and can stream in as the agent fills the payload. The example above uses Recharts for the bar chart; it doesn't know anything about CopilotKit.

The name you pass to useComponent is what the agent sees as the tool name. Make it a verb like render_bar_chart or show_weather so the LLM reliably picks it when the user asks for that visualization.

Rendering in a headless chat#

CopilotKit's built-in chat components paint registered components for you. A headless or custom chat renders the message list itself, so nothing paints a tool call unless you render it — the component is registered and the agent calls it, but the chat stays empty.

Render the tool calls on each assistant message with CopilotChatToolCallsView:

import { CopilotChatToolCallsView } from "@copilotkit/react-core/v2";

<CopilotChatToolCallsView message={assistantMessage} messages={allMessages} />;

It looks up the sibling tool-role message for each tool call and hands both to the registered renderer. For finer placement, call useRenderToolCall() and paint each tool call yourself — see Headless UI.