showcase/shell-docs/src/content/docs/generative-ui/a2ui/index.mdx
A2UI (Agent-to-UI) is a declarative Generative UI specification, led by Google with CopilotKit as a launch and design partner. It lets your agent render structured UI components through a JSON-based schema instead of plain text: cards, rows, columns, badges, and price tags, all composed from a catalog of components you (or the platform) define.
You can design and preview A2UI schemas visually using the A2UI Composer.
<Callout type="info"> **Free course:** See this pattern built end-to-end in [Build Interactive Agents with Generative UI](https://www.deeplearning.ai/short-courses/build-interactive-agents-with-generative-ui/), a free DeepLearning.AI short course taught by CopilotKit's CEO covering the full Generative UI spectrum (Controlled, Declarative, and Open-Ended). </Callout>CopilotKit ships two complementary A2UI approaches:
Dynamic Schema: a secondary LLM generates both the schema and the data. Maximum flexibility, so the agent can produce any UI for any prompt.
Fixed Schema: the component tree is authored ahead of time as JSON. The agent only streams data into the data model at runtime. Fastest, with no LLM schema generation.
Both approaches share the same A2UI wire protocol and the same frontend renderer. The difference is where the schema comes from and how data reaches the client.
Every A2UI surface is assembled from three kinds of operations emitted by the agent and consumed by the frontend renderer:
createSurface / surfaceUpdate declares (or pins) the
component tree (the schema) for a surface.updateComponents / dataModelUpdate supplies the data that
populates the components via JSON Pointer paths.beginRendering tells the client the first frame is ready.The CopilotKit Python SDK provides helpers for each:
from copilotkit import a2ui
a2ui.create_surface(surface_id, catalog_id=...)
a2ui.update_components(surface_id, components)
a2ui.update_data_model(surface_id, {"items": data})
a2ui.begin_rendering(surface_id, root_id)
a2ui.render(operations=[...]) # wraps and serializes for a tool result
A2UI turns on in one of two ways. Most apps use the first.
Pass an A2UI catalog to the <CopilotKit> provider. The catalog tells
the renderer which components your agent can draw, and it does two
things for you automatically: it enables A2UI and it injects the A2UI
tool into your agent. No a2ui block on the runtime is required.
import { CopilotKit } from "@copilotkit/react-core/v2";
<CopilotKit runtimeUrl="/api/copilotkit" a2ui={{ catalog: myCatalog }}>
{children}
</CopilotKit>;
If you do not pass a catalog, enable A2UI on the runtime instead.
a2ui: true applies the middleware so the surfaces your agent emits
are rendered; add injectA2UITool: true to also inject the render
tool so the agent can generate surfaces dynamically:
import { CopilotRuntime } from "@copilotkit/runtime";
const runtime = new CopilotRuntime({
agents: { default: myAgent },
a2ui: { injectA2UITool: true },
});
The middleware handles the wire protocol automatically, intercepting tool results that contain A2UI operations and rendering them as rich surfaces in the chat.
<Callout type="warn"> With neither a catalog on the provider nor `a2ui` on the runtime, A2UI surfaces will not render. The operations fall through as plain tool results. </Callout>When injection is on, the runtime adds a tool named generate_a2ui to
your agent. Calling it runs a secondary LLM, a subagent, that designs
the full A2UI surface (components, layout, and data) from your catalog,
then streams it into the chat. You do not write this tool yourself. See
Dynamic Schema for the full flow.
To take control instead of relying on auto-inject, set
injectA2UITool: false and provide the tool yourself with the AG-UI
factory (get_a2ui_tools() in Python, getA2UITools() in TypeScript),
where you set the model, default catalog id, and more. An explicit
injectA2UITool: false always wins, even when a catalog is present.
This is also how the Fixed Schema approach
works: your agent owns a data-only tool and no subagent is injected.