Components as Tools

"""LlamaIndex agent for the Tool-Based Generative UI demo.The frontend registers `render_bar_chart` and `render_pie_chart` tools via`useComponent`. The AG-UI protocol forwards those tool definitions to theagent at request time, so the backend agent itself declares no bespoketools — the LLM sees the frontend tools through the AG-UI request payloadand picks one to call when the user asks for a chart.Mirrors `langgraph-python/src/agents/gen_ui_tool_based.py`."""from __future__ import annotationsimport osfrom llama_index.llms.openai import OpenAIfrom llama_index.protocols.ag_ui.router import get_ag_ui_workflow_routerSYSTEM_PROMPT = """You are a data visualization and creative 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.When the user asks for a haiku, call `generate_haiku` with the Japanesetext, English translation, an image name, and a gradient color.Keep chat responses brief -- let the visual output do the talking."""_openai_kwargs = {}if os.environ.get("OPENAI_BASE_URL"):    _openai_kwargs["api_base"] = os.environ["OPENAI_BASE_URL"]gen_ui_tool_based_router = get_ag_ui_workflow_router(    llm=OpenAI(model="gpt-5-mini", **_openai_kwargs),    frontend_tools=[],    backend_tools=[],    system_prompt=SYSTEM_PROMPT,    initial_state={},)

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#

Nothing to wire on the agent

The AG-UI protocol forwards frontend tool definitions to the agent at request time, so the backend agent declares no bespoke tools. The LLM sees a component registered with useComponent through the AG-UI request payload and picks it when the user asks for what it draws.

src/agents/chart_agent.py

from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-5-mini")

Tell the model when to call it

The tool arrives on every run, but a model with no instruction about it will answer in prose instead of calling it. Name the tool in the system prompt.

src/agents/chart_agent.py

SYSTEM_PROMPT = """You are a data visualization assistant.

When the user asks for a chart, call `render_bar_chart` with a concise
title and a `data` array of `{label, value}` items."""

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.