Fixed Schema A2UI

In the fixed-schema approach, you design the UI schema once (by hand, or using the A2UI Composer) and keep it on the agent side. The agent tool only provides the data; the surface appears instantly when the tool returns because nothing has to be generated at runtime.

How the schema is delivered to the runtime is the only thing that varies between integrations:

• Schema-loading (langgraph-python, langgraph-typescript, langgraph-fastapi, llamaindex, crewai-crews, pydantic-ai, ms-agent-python, google-adk) — the schema is saved as a .json file next to the agent and loaded once at startup.
• Schema-inline (spring-ai, ms-agent-dotnet) — the schema is declared inline as a typed literal in source. The host language doesn't ship a load_schema JSON loader, so the structure is compiled in directly.
• LLM-driven (mastra, strands) — the agent runs a secondary LLM call to produce the operations container per-request. The catalog is still fixed; the schema is generated on demand.

Ask about a flight and the agent renders a fully structured card from a pre-defined schema:

How it works

1. The schema is made available to the agent — either loaded from a JSON file at startup, declared inline, or generated per-request, depending on the integration.
2. The agent's display_flight tool receives data from the primary LLM (origin / destination / airline / price).
3. The tool returns a2ui.render(...) with createSurface + updateComponents + updateDataModel operations.
4. The A2UI middleware intercepts the tool result and the frontend renders the surface using the matching 5-component client catalog (Title, Airport, Arrow, AirlineBadge, PriceTag, plus the built-ins).

Compositional schemas

The example below ships a flight card assembled compositionally from small sub-components rather than one monolithic FlightCard:

Card
 └─ Column
     ├─ Title        ("Flight Details")
     ├─ Row          (Airport → Arrow → Airport)
     ├─ Row          (AirlineBadge · PriceTag)
     └─ Button       (Book)

That tree lives backend-side — as a JSON file, an inline literal, or a per-request LLM output, depending on the integration. Components without data bindings (like Title or Arrow) carry their value inline; components bound to the LLM's data (like Airport) reference fields via JSON Pointer paths such as { "path": "/origin" }. The A2UI binder resolves those paths before the React renderer runs, so renderer props are typed as their resolved values (plain z.string(), not a path-or-literal union).

The 5-component custom catalog

The frontend catalog declares just the domain-specific primitives (Title, Airport, Arrow, AirlineBadge, PriceTag) and merges in CopilotKit's basic catalog (Card, Column, Row, Text, Button, …) via includeBasicCatalog: true.

Each component declares its props as a Zod schema. Props are the resolved values, never the path expressions:

TypeScript enforces that the renderer map's keys and prop shapes match the definitions exactly, so refactors stay safe:

createCatalog(..., { includeBasicCatalog: true }) merges the custom renderers with CopilotKit's built-ins so the schema can reference Card, Column, Row, Button alongside the domain primitives:

a2ui.load_schema(path) (or the framework's equivalent thin json.load wrapper) parses the schema file once at module-import time. The sibling booked_schema.json is kept ready for the button-click "booked" optimistic swap (see the note on action handlers below):

The agent tool returns a2ui.render(operations=[…]). The A2UI middleware detects the operations container in the tool result and forwards it to the frontend renderer. The LLM only generates the four data fields (origin, destination, airline, price); the schema does the rest:

Spring AI / .NET don't ship a load_schema JSON helper, so the component tree is declared inline as a typed literal in source — equivalent to deserialising a flight_schema.json but compiled into the agent class. The structure is identical to the JSON form; only the surface syntax changes:

The agent tool builds the same createSurface + updateComponents + updateDataModel operations container and returns it. The A2UI middleware detects the operations in the tool result and forwards them to the frontend renderer; the LLM only supplies the four data fields:

Mastra and Strands take a different route: the agent tool runs a secondary LLM call with a forced tool choice that produces the operations container per-request. The frontend catalog is still fixed (same Title/Airport/Arrow/AirlineBadge/PriceTag primitives), but the schema is built on the fly. Schema construction and render emission happen in the same tool call:

Why compositional beats monolithic

A single big FlightCard component would be faster to write but would lock the design in place. Assembling the card from Card / Column / Row / Title / Airport / Arrow / AirlineBadge / PriceTag gives you:

• Reusable primitives — the same Airport renderer works in search results, booking confirmations, and future seat maps.
• Schema-level design iteration — re-arranging rows or swapping a badge requires only a JSON edit; the renderer code is untouched.
• A2UI Composer compatibility — hand-written and Composer-built schemas share the same primitive vocabulary.

Registering the runtime

On the TypeScript side, A2UI's middleware auto-detects the operations in any tool result, so even with a fixed schema, the minimum setup is a2ui: {}. The a2ui-fixed-schema cell happens to also keep injectA2UITool: true so the same agent can be pointed at dynamic-schema workflows later without re-configuring.

const runtime = new CopilotRuntime({
  agents: { "a2ui-fixed-schema": agent },
  a2ui: { injectA2UITool: true, agents: ["a2ui-fixed-schema"] },
});

Action handlers (reference)

The canonical reference pairs fixed schemas with action_handlers={...} to declare optimistic UI swaps (e.g. replacing the flight schema with BOOKED_SCHEMA when the user clicks "Book"). The Python SDK's a2ui.render does not yet accept action_handlers, so the cell omits them; the booked_schema.json sibling is retained so the swap can be wired up the moment the SDK exposes the handler kwarg.

When available, a button declares its action like this:

{
  "Button": {
    "label": "Book",
    "action": {
      "name": "book_flight",
      "context": [
        { "key": "flightNumber", "value": { "path": "/flightNumber" } },
        { "key": "price", "value": { "path": "/price" } }
      ]
    }
  }
}

And the Python tool matches it with a handler keyed by the action name (plus a "*" catch-all). Until the SDK lands, see the reference fixed-schema guide for the full pattern.

When should I use fixed schemas?

• The surface is well-known: flight cards, product tiles, order summaries, dashboards.
• You want deterministic, designer-controlled UI. No LLM schema drift.
• You want the fastest possible first paint; no secondary LLM call.

If the UI must adapt per prompt, reach for dynamic schemas instead.