Dynamic Schema A2UI

LLM-generated A2UI — a secondary LLM creates both the schema and data from any prompt.


"""LangGraph agent for the Declarative Generative UI (A2UI — Dynamic Schema) demo."""from __future__ import annotationsimport osfrom copilotkit import CopilotKitMiddlewarefrom langchain.agents import create_agentfrom langchain.tools import toolfrom langchain_openai import ChatOpenAI# Cross-reference: showcase/integrations/google-adk/src/agents/declarative_gen_ui_agent.py# Both integrations register the same a2ui catalog (Card / Row / Column /# Text / Metric / PieChart / BarChart / DataTable / StatusBadge / InfoRow /# PrimaryButton — see each integration's# src/app/demos/declarative-gen-ui/a2ui/definitions.ts, which are# byte-identical across LP and ADK).## The fictional sales dataset and the per-question composition rules# are injected via App Context from# showcase/integrations/langgraph-python/src/app/demos/declarative-gen-ui/sales-context.ts# (a frontend file shared byte-for-byte with the ADK integration — see# its DUPLICATION NOTICE).## Keep this SYSTEM_PROMPT and the ADK `_INSTRUCTION` aligned in spirit.# Minor wording differences are tolerated (e.g. this prompt uses shape# words — "table"/"pie"/"bar" — as question-category descriptors, while# ADK names the rendered components — "DataTable"/"PieChart"/"BarChart"# — in the analogous slot), but the structural rules and the component# name set must match the catalog above.SYSTEM_PROMPT = (    "You are the embedded sales analyst for Vantage Threads, the fictional "    "B2B apparel company described in your App Context. Answer every "    "business question by calling `generate_a2ui` to draw a rich visual "    "surface, and keep the chat reply to one short sentence.\n"    "\n"    "Ground every number in the sales dataset from App Context — never "    "invent figures that contradict it. Follow the dashboard composition "    "rules from App Context when choosing components: pick the component "    "by the shape of the question (snapshot → composed KPI dashboard with "    "charts; team performance → table; risk → status badges; single "    "account → info rows; part-of-whole → pie; trend/comparison → bar). "    "Never ask the user which chart they want. `generate_a2ui` takes no "    "arguments and handles the rendering automatically. Compose "    "generously — a dashboard should feel like a real analytics product, "    "not a single widget.")@tooldef generate_a2ui() -> dict:    """Generate a dynamic A2UI dashboard surface from the current conversation.    Takes no arguments. The CopilotKit runtime middleware    (`a2ui.injectA2UITool: true`) intercepts the call and drives a    secondary-LLM `render_a2ui` planner to emit the surface ops; this    Python body should NEVER execute in normal operation. It exists only    so the LP agent's declared `tools=` list mirrors the ADK sibling    (`declarative_gen_ui_agent.py`) and the SYSTEM_PROMPT's    `generate_a2ui` reference resolves to a registered tool name.    If this body actually runs, the CopilotKit a2ui middleware is    misconfigured and silently returning an empty surface would hide the    real bug — fail loud per `fail-loud-discipline`.    """    raise RuntimeError(        "generate_a2ui called directly — CopilotKit a2ui.injectA2UITool "        "middleware should intercept this call before it reaches the "        "agent. Check the route configuration at "        "app/api/copilotkit-declarative-gen-ui/route.ts."    )graph = create_agent(    model=ChatOpenAI(model=os.getenv("OPENAI_MODEL", "gpt-4o")),    tools=[generate_a2ui],    middleware=[CopilotKitMiddleware()],    system_prompt=SYSTEM_PROMPT,)

In the dynamic-schema approach, a secondary LLM generates the entire UI (schema, data, and layout) based on the conversation context. It's the most flexible A2UI flavor; the agent can render any UI for any request without pre-defined schemas.

How it works#

First, add CopilotKitMiddleware to your create_agent call. The middleware is what makes every CopilotKit feature on the frontend — frontend tools, shared state, agent context, and generative UI components — visible to your LangGraph agent 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.",
)
Install the SDK

If copilotkit isn't already in your project, add it so the import above resolves. Pick the package manager that matches your project:

uv add copilotkit
poetry add copilotkit
pip install copilotkit --extra-index-url https://copilotkit.gateway.scarf.sh/simple/
conda install copilotkit -c copilotkit-channel
  1. The agent calls the A2UI tool to draw a surface — made available when injectA2UITool: true.
  2. The runtime serializes your client-side catalog (component names + Zod prop schemas) into the agent's copilotkit.context so the LLM knows which components it may emit.
  3. The tool call streams through LangGraph as TOOL_CALL_ARGS events.
  4. The A2UI middleware intercepts the stream and renders cards progressively as data items arrive.

The 3-file split#

The canonical Bring-Your-Own-Catalog (BYOC) layout keeps three files side-by-side under frontend/src/app/a2ui/:

FileWhat lives there
definitions.tsZod props schema + human-readable descriptions for each custom component. Platform-agnostic, so the runtime can serialise it to the LLM.
renderers.tsxReact implementations keyed by the same names. TypeScript enforces that every definition has a renderer.
catalog.tscreateCatalog(definitions, renderers, { includeBasicCatalog: true }): merges your custom components with CopilotKit's built-in primitives.

Declare your custom component definitions#

Each entry pairs a Zod prop schema with a description. The description is crucial; the LLM reads it to decide which component to emit. The example below ships a small dashboard catalog (Card / StatusBadge / Metric / InfoRow / PrimaryButton):

definitions.ts
import { z } from "zod";import type { CatalogDefinitions } from "@copilotkit/a2ui-renderer";export const myDefinitions = {  // Override the basic catalog's Row/Column so `gap` is honoured — the  // built-in versions ignore it, which makes composed dashboards cramped.  Row: {    description:      "Horizontal layout container. Children share the width evenly. Use `gap` (px) to space dashboard tiles.",    props: z.object({      gap: z.number().optional(),      // Enum mirrors the keys the renderer actually maps to CSS. Anything      // outside this set silently falls back at render time, so we reject      // it at schema-parse time to surface LLM typos early.      align: z        .enum(["start", "center", "end", "stretch", "baseline"])        .optional(),      justify: z.enum(["start", "center", "end", "spaceBetween"]).optional(),      children: z.array(z.string()),    }),  },  Column: {    description:      "Vertical layout container. Use `gap` (px) to space stacked sections.",    props: z.object({      gap: z.number().optional(),      align: z        .enum(["start", "center", "end", "stretch", "baseline"])        .optional(),      children: z.array(z.string()),    }),  },  // Override the basic catalog's Text so it aligns flush with sibling  // components (the built-in version carries an 8px outer margin).  Text: {    description: "A plain text line. Use for short explanations inside cards.",    props: z.object({      text: z.string(),    }),  },  Card: {    description:      "A titled card container with an optional subtitle and a single child slot. Use it to group related content (metrics, rows, buttons).",    props: z.object({      title: z.string(),      subtitle: z.string().optional(),      child: z.string().optional(),    }),  },  StatusBadge: {    description:      "A small coloured pill communicating the state of something (healthy/degraded/at-risk, on-track/behind). Choose `variant` to match the intent.",    props: z.object({      text: z.string(),      variant: z.enum(["success", "warning", "error", "info"]).optional(),    }),  },  Metric: {    description:      "A key/value KPI tile with an optional trend indicator and trend delta. Ideal for dashboard KPI rows (e.g. 'Revenue • $4.2M • up 12%').",    props: z.object({      label: z.string(),      value: z.string(),      trend: z.enum(["up", "down", "neutral"]).optional(),      trendValue: z.string().optional(),    }),  },  InfoRow: {    description:      "A compact two-column 'label: value' row. Good for stacks of facts inside a Card (owner, region, ARR, renewal date, etc.).",    props: z.object({      label: z.string(),      value: z.string(),    }),  },  DataTable: {    description:      "A data table with column headers and rows. Ideal for rankings and per-person/per-item breakdowns (rep performance vs quota, deal lists). Each row's keys MUST appear in `columns[].key`; unknown row keys render as blank cells and indicate model/schema drift.",    // NOTE on B12 (row-keys ⊆ columns[].key): we'd normally enforce this    // with `z.object(...).refine(...)`, but the host catalog package's    // `CatalogComponentDefinition` type requires `props: ZodObject<…>`    // (it inspects `.shape` at runtime), and `.refine` returns a    // `ZodEffects` that breaks both the `satisfies CatalogDefinitions`    // type assertion and the runtime `.shape` access. Until the host    // type is broadened, we encode the constraint in the description    // above so the LLM sees the rule, and leave hard enforcement to    // the rendering pipeline (which already shows the empty cell —    // detection is the gap, not behaviour).    props: z.object({      columns: z.array(z.object({ key: z.string(), label: z.string() })),      // Cells may be strings or numbers — the renderer stringifies at      // render time, but accepting both lets the LLM emit raw numerics      // (e.g. attainment 124) instead of being forced to stringify.      rows: z.array(z.record(z.union([z.string(), z.number()]))),    }),  },  PrimaryButton: {    description:      "A styled primary call-to-action button. Attach an optional `action` that will be dispatched back to the agent when the user clicks it.",    props: z.object({      label: z.string(),      // The renderer hands `action` opaquely to the A2UI `dispatch` helper,      // which forwards it back to the agent. We don't constrain the shape      // (different demos use different action payloads), but `z.unknown()`      // is strictly better than `z.any()` here because it forces any      // consumer that touches the value to narrow it explicitly.      action: z.unknown().optional(),    }),  },  PieChart: {    description:      "A pie/donut chart with a brand-coloured legend. Provide `title`, `description`, and `data` as an array of `{ label, value }` objects. Great for part-of-whole breakdowns (revenue by region, pipeline by stage).",    props: z.object({      title: z.string(),      description: z.string(),      data: z.array(        z.object({          label: z.string(),          value: z.number(),        }),      ),    }),  },  BarChart: {    description:      "A vertical bar chart built on Recharts. Provide `title`, `description`, and `data` as an array of `{ label, value }` objects. Great for comparing series across categories or time (monthly revenue, signups per month).",    props: z.object({      title: z.string(),      description: z.string(),      data: z.array(        z.object({          label: z.string(),          value: z.number(),        }),      ),    }),  },} satisfies CatalogDefinitions;

Implement the React renderers#

Every key in myDefinitions must have a matching renderer. Props are statically typed against the Zod schema, so refactors stay safe:

renderers.tsx
export const myRenderers: CatalogRenderers<MyDefinitions> = {  Row: ({ props, children }) => {    const justifyMap: Record<string, string> = {      start: "flex-start",      center: "center",      end: "flex-end",      spaceBetween: "space-between",    };    const items = Array.isArray(props.children) ? props.children : [];    return (      <div        style={{          display: "flex",          flexDirection: "row",          gap: `${props.gap ?? 16}px`,          alignItems: props.align ?? "stretch",          justifyContent: justifyMap[props.justify ?? "start"] ?? "flex-start",          flexWrap: "wrap",          width: "100%",        }}      >        {items.map((id, i) => (          <div key={`${id}-${i}`} style={{ flex: "1 1 0", minWidth: 0 }}>            {children(id)}          </div>        ))}      </div>    );  },  Column: ({ props, children }) => {    const items = Array.isArray(props.children) ? props.children : [];    return (      <div        style={{          display: "flex",          flexDirection: "column",          gap: `${props.gap ?? 12}px`,          width: "100%",        }}      >        {items.map((id, i) => (          <React.Fragment key={`${id}-${i}`}>{children(id)}</React.Fragment>        ))}      </div>    );  },  Text: ({ props }) => (    <span style={{ fontSize: "0.85rem", color: c.cardFg, lineHeight: 1.5 }}>      {props.text}    </span>  ),  Card: ({ props, children }) => (    // `data-testid="declarative-card"` stays shared so existing e2e selectors    // still find every card; `data-card-id={props.title}` disambiguates    // sibling cards (e.g. the at-risk pill's 3 severity cards) so test    // assertions can target a specific card by title.    <CardShell      title={props.title}      subtitle={props.subtitle}      testid="declarative-card"      cardId={props.title}    >      {props.child && children(props.child)}    </CardShell>  ),  StatusBadge: ({ props }) => {    const variant = props.variant ?? "info";    const Icon = {      error: TriangleAlert,      warning: CircleAlert,      success: CircleCheck,      info: Info,    }[variant];    return (      // `alignSelf: flex-start` keeps the pill content-sized — flex parents      // (our Column override) default to stretch, which inflates it into a      // full-width banner.      <Badge        variant={variant}        style={{ alignSelf: "flex-start" }}        data-testid="declarative-status-badge"      >        <Icon size={12} strokeWidth={2.5} style={{ marginRight: 4 }} />        {props.text}      </Badge>    );  },  Metric: ({ props }) => {    const trendColors: Record<string, string> = {      up: "#059669",      down: "#dc2626",      neutral: c.muted,    };    const trendIcons: Record<string, string> = {      up: "↑",      down: "↓",      neutral: "→",    };    return (      <div        data-testid="declarative-metric"        style={{          display: "flex",          flexDirection: "column",          gap: "4px",          minWidth: "120px",        }}      >        <span          style={{            fontSize: "0.75rem",            color: c.muted,            fontWeight: 500,            textTransform: "uppercase",            letterSpacing: "0.05em",          }}        >          {props.label}        </span>        <div style={{ display: "flex", alignItems: "baseline", gap: "8px" }}>          <span            style={{              fontSize: "1.5rem",              fontWeight: 700,              color: c.cardFg,              letterSpacing: "-0.02em",            }}          >            {props.value}          </span>          {props.trend && (            <span              style={{                fontSize: "0.8rem",                fontWeight: 500,                color: trendColors[props.trend] ?? c.muted,              }}            >              {trendIcons[props.trend]}              {props.trendValue ? ` ${props.trendValue}` : ""}            </span>          )}        </div>      </div>    );  },  InfoRow: ({ props }) => (    // Divider via `border-b last:border-b-0` so the final row doesn't dangle    // a trailing line, regardless of whether the agent wraps these in a    // Column or drops them directly into a Card's child slot.    <div      data-testid="declarative-info-row"      className="flex items-baseline justify-between gap-4 py-2 border-b border-[var(--border)] last:border-b-0 last:pb-0 first:pt-0"    >      <span className="text-sm text-[var(--muted-foreground)]">        {props.label}      </span>      <span className="text-sm font-medium text-[var(--foreground)] text-right tabular-nums">        {props.value}      </span>    </div>  ),  DataTable: ({ props }) => {    const cols = Array.isArray(props.columns) ? props.columns : [];    const rows = Array.isArray(props.rows) ? props.rows : [];    return (      <div        data-testid="declarative-data-table"        className="w-full overflow-x-auto"      >        <table className="w-full border-collapse text-sm">          <thead>            <tr>              {cols.map((col) => (                <th                  key={col.key}                  className="border-b-2 border-[var(--border)] px-3 py-2 text-left text-xs font-semibold uppercase tracking-wider text-[var(--muted-foreground)]"                >                  {col.label}                </th>              ))}            </tr>          </thead>          <tbody>            {rows.map((row, i) => {              // Stable row key: prefer the first column's value (primary-key-ish),              // suffix with index in case values repeat, fall back to a JSON              // stringify of the row when columns is empty. Stable keys prevent              // React from re-mounting every row when the agent re-emits a              // slightly different table.              const pk = cols.length > 0 ? row[cols[0].key] : undefined;              const rowKey =                pk !== undefined ? `${pk}-${i}` : JSON.stringify(row);              return (                <tr                  key={rowKey}                  className="border-b border-[var(--border)] last:border-b-0"                >                  {cols.map((col) => (                    <td                      key={col.key}                      className="px-3 py-2 tabular-nums text-[var(--foreground)]"                    >                      {String(row[col.key] ?? "")}                    </td>                  ))}                </tr>              );            })}          </tbody>        </table>      </div>    );  },  PrimaryButton: ({ props, dispatch }) => (    <Button      onClick={() => {        if (props.action && dispatch) dispatch(props.action);      }}    >      {props.label}    </Button>  ),  PieChart: ({ props }) => {    // Coerce values to numbers — the LLM sometimes emits them as strings.    // Use a strict finite check so null/undefined/NaN/non-numeric strings are    // surfaced via console.warn rather than silently collapsed to 0 (which    // masks schema/data drift). Recharts requires a numeric value to render,    // so we fall back to 0 only after logging.    const data = (Array.isArray(props.data) ? props.data : []).map((d) => {      const raw = (d as { value?: unknown }).value;      const n = typeof raw === "number" ? raw : parseFloat(raw as string);      let value: number;      if (Number.isFinite(n)) {        value = n;      } else {        console.warn("Invalid chart value", {          component: "PieChart",          key: "value",          raw,        });        value = 0;      }      return { ...d, value };    });    return (      <CardShell        title={props.title}        subtitle={props.description}        testid="declarative-pie-chart"      >        {data.length === 0 ? (          <div className="py-8 text-center text-sm text-[var(--muted-foreground)]">            No data available          </div>        ) : (          <div style={{ width: "100%", height: 200 }}>            <ResponsiveContainer>              <RechartsPieChart>                <Pie                  data={data}                  dataKey="value"                  nameKey="label"                  cx="50%"                  cy="50%"                  innerRadius={40}                  outerRadius={80}                  paddingAngle={2}                >                  {data.map((_, i) => (                    <Cell                      key={i}                      fill={CHART_COLORS[i % CHART_COLORS.length]}                    />                  ))}                </Pie>                <Tooltip />              </RechartsPieChart>            </ResponsiveContainer>          </div>        )}      </CardShell>    );  },  BarChart: ({ props }) => {    // Coerce values to numbers — the LLM sometimes emits them as strings,    // which recharts treats as categorical (unordered Y-axis ticks). Use a    // strict finite check so null/undefined/NaN/non-numeric strings are    // surfaced via console.warn rather than silently collapsed to 0 (which    // masks schema/data drift). Recharts requires a numeric value to render,    // so we fall back to 0 only after logging.    const data = (Array.isArray(props.data) ? props.data : []).map((d) => {      const raw = (d as { value?: unknown }).value;      const n = typeof raw === "number" ? raw : parseFloat(raw as string);      let value: number;      if (Number.isFinite(n)) {        value = n;      } else {        console.warn("Invalid chart value", {          component: "BarChart",          key: "value",          raw,        });        value = 0;      }      return { ...d, value };    });    return (      <CardShell        title={props.title}        subtitle={props.description}        testid="declarative-bar-chart"      >        {data.length === 0 ? (          <div className="py-8 text-center text-sm text-[var(--muted-foreground)]">            No data available          </div>        ) : (          <div style={{ width: "100%", height: 200 }}>            <ResponsiveContainer>              <RechartsBarChart data={data}>                <CartesianGrid strokeDasharray="3 3" stroke={c.divider} />                <XAxis dataKey="label" tick={{ fontSize: 11, fill: c.muted }} />                <YAxis tick={{ fontSize: 11, fill: c.muted }} />                <Tooltip />                <Bar dataKey="value" fill="#3b82f6" radius={[4, 4, 0, 0]} />              </RechartsBarChart>            </ResponsiveContainer>          </div>        )}      </CardShell>    );  },};

Wire definitions × renderers into a catalog#

createCatalog is what you hand to the provider. Flip includeBasicCatalog: true to merge CopilotKit's built-ins (Column, Row, Text, Image, Card, Button, List, Tabs, …), so the LLM can compose custom + basic components interchangeably:

catalog.ts
import { createCatalog } from "@copilotkit/a2ui-renderer";import { myDefinitions } from "./definitions";import { myRenderers } from "./renderers";export const myCatalog = createCatalog(myDefinitions, myRenderers, {  catalogId: "declarative-gen-ui-catalog",  includeBasicCatalog: true,});

Pass the catalog to the provider#

A single prop (a2ui={{ catalog }}) is all the frontend needs; the provider registers the catalog and wires up the built-in A2UI activity-message renderer:

page.tsx
import React from "react";import { CopilotKit } from "@copilotkit/react-core/v2";import { myCatalog } from "./a2ui/catalog";import { Chat } from "./chat";export default function DeclarativeGenUIDemo() {  return (    <CopilotKit      runtimeUrl="/api/copilotkit-declarative-gen-ui"      agent="declarative-gen-ui"      a2ui={{ catalog: myCatalog }}    >      <div className="flex justify-center items-center h-screen w-full">        <div className="h-full w-full max-w-4xl">          <Chat />        </div>      </div>    </CopilotKit>

Turn A2UI on (runtime)#

On the TypeScript runtime, injectA2UITool: true turns A2UI on: CopilotKit renders A2UI output, serialises your client catalog into the agent's copilotkit.context, and makes the A2UI tool available to the agent. The frontend wiring is identical across integrations:

app/api/copilotkit/route.ts
const runtime = new CopilotRuntime({
  agents: { default: myAgent },
  a2ui: {
    injectA2UITool: true,
  },
});

Progressive streaming#

The secondary LLM's render_a2ui tool call streams through LangGraph as TOOL_CALL_ARGS events. The A2UI middleware:

  1. Waits for the full components array before emitting anything — the schema must be complete before rendering starts.
  2. Extracts surfaceId + root from the partial JSON.
  3. Emits createSurface + updateComponents once the schema is complete.
  4. Extracts complete items objects progressively and emits an updateDataModel for each, so cards appear one by one as data streams in.

A built-in progress indicator shows while the schema is still generating and hides automatically once data items start arriving.

When should I use dynamic schemas?#

  • You don't know the UI shape ahead of time; the agent decides what to show based on the user's request.
  • You want to prototype A2UI without committing to a schema file yet.
  • You're building a conversational dashboard where the layout varies per turn.

If the surface is well-known (e.g. a product card, a flight result), prefer a fixed schema; it's faster, cheaper, and the UI is deterministic.