Fully Headless UI
Build any UI — chat or not — on top of the CopilotKit primitives with zero UI opinions.
/** * Agent factories for the Strands TypeScript showcase backend. * * `buildShowcaseAgent` is the single shared agent that serves the vast * majority of demos (the frontend differentiates each demo via * useFrontendTool / useRenderTool / useHumanInTheLoop / useAgentContext). * It mirrors the Python sibling's `build_showcase_agent` minus A2UI. * * The tool-free specialized agents (voice, byoc-hashbrown, byoc-json-render) * are mounted on dedicated sub-paths by `server.ts`. */import { readFileSync } from "node:fs";import { dirname, join } from "node:path";import { fileURLToPath } from "node:url";import { Agent, tool } from "@strands-agents/sdk";import { z } from "zod";import { StrandsAgent } from "@ag-ui/aws-strands";import type { StrandsAgentConfig } from "@ag-ui/aws-strands";import { A2UI_OPERATIONS_KEY, createSurface, updateComponents, updateDataModel,} from "@ag-ui/a2ui-toolkit";import { createModel } from "./model-factory";import { SHOWCASE_TOOLS } from "./tools";import { buildStatePrompt, salesStateFromArgs, notesStateFromArgs, stepsStateFromArgs, documentStateFromArgs, makeSubagentStateFromResult,} from "./state";import { SYSTEM_PROMPT, VOICE_SYSTEM_PROMPT, BYOC_HASHBROWN_SYSTEM_PROMPT, BYOC_JSON_RENDER_SYSTEM_PROMPT,} from "./prompts";export async function buildShowcaseAgent(): Promise<StrandsAgent> { const config: StrandsAgentConfig = { stateContextBuilder: buildStatePrompt, toolBehaviors: { // Sales pipeline lives in shared state; emit the snapshot from args. manage_sales_todos: { skipMessagesSnapshot: true, stateFromArgs: salesStateFromArgs, }, // Shared State (Read + Write) — notes panel. set_notes: { stateFromArgs: notesStateFromArgs }, // gen-ui-agent — live progress card driven by set_steps transitions. set_steps: { stateFromArgs: stepsStateFromArgs }, // shared-state-streaming — stream the document string into state. write_document: { stateFromArgs: documentStateFromArgs }, // Sub-agents — append a delegation entry carrying the actual output. research_agent: { stateFromResult: makeSubagentStateFromResult("research_agent"), }, writing_agent: { stateFromResult: makeSubagentStateFromResult("writing_agent"), }, critique_agent: { stateFromResult: makeSubagentStateFromResult("critique_agent"), }, }, }; const strandsAgent = new Agent({ model: await createModel(), systemPrompt: SYSTEM_PROMPT, tools: SHOWCASE_TOOLS, }); return new StrandsAgent({ agent: strandsAgent, name: "strands_agent", description: "A polished CopilotKit demo assistant: chat, tools, shared state, HITL, sub-agents.", config, });}/** Tool-free agent for the voice demo (transcription + basic chat). */export async function buildVoiceAgent(): Promise<StrandsAgent> { const strandsAgent = new Agent({ model: await createModel(), systemPrompt: VOICE_SYSTEM_PROMPT, tools: [], }); return new StrandsAgent({ agent: strandsAgent, name: "voice_agent", description: "Simple assistant for the voice demo — no tools.", });}/** Tool-free hashbrown UI-kit envelope generator (declarative-hashbrown). */export async function buildByocHashbrownAgent(): Promise<StrandsAgent> { const strandsAgent = new Agent({ model: await createModel(), systemPrompt: BYOC_HASHBROWN_SYSTEM_PROMPT, tools: [], }); return new StrandsAgent({ agent: strandsAgent, name: "byoc_hashbrown", description: "Hashbrown UI-kit envelope generator for the declarative-hashbrown demo.", });}/** Tool-free json-render flat-spec generator (declarative-json-render). */export async function buildByocJsonRenderAgent(): Promise<StrandsAgent> { const strandsAgent = new Agent({ model: await createModel(), systemPrompt: BYOC_JSON_RENDER_SYSTEM_PROMPT, tools: [], }); return new StrandsAgent({ agent: strandsAgent, name: "byoc_json_render", description: "json-render flat-spec generator for the declarative-json-render demo.", });}// ---------------------------------------------------------------------------// A2UI Fixed Schema (declarative-generative-ui) — dedicated backend tool.// ---------------------------------------------------------------------------//// Unlike the dynamic A2UI demo (which relies on the adapter auto-injecting a// `generate_a2ui` tool to *generate* a surface), the fixed-schema demo wires a// single plain backend tool — `display_flight` — that returns the// `a2ui_operations` envelope (createSurface -> updateComponents ->// updateDataModel). The component tree is fixed and authored ahead of time// (./a2ui_schemas/flight_schema.json); only the *data* changes per call. The// runtime A2UIMiddleware detects the envelope in the tool result and paints.// No sub-agent, no generation, no `generate_a2ui` injection.//// The schema's component names + data paths must match the showcase frontend// catalog at src/app/demos/a2ui-fixed-schema/a2ui/{definitions,renderers,// catalog}.ts — catalog id `copilotkit://flight-fixed-catalog`. This mirrors// the canonical langgraph-python demo (src/agents/a2ui_fixed.py).const _A2UI_DIR = dirname(fileURLToPath(import.meta.url));const A2UI_FIXED_CATALOG_ID = "copilotkit://flight-fixed-catalog";const A2UI_FIXED_SURFACE_ID = "flight-fixed-schema";// Fixed, pre-authored component layout. Loaded from JSON so it can be authored// and reviewed independently of the agent code.const FLIGHT_SCHEMA: Array<Record<string, unknown>> = JSON.parse( readFileSync(join(_A2UI_DIR, "a2ui_schemas", "flight_schema.json"), "utf-8"),);const A2UI_FIXED_SYSTEM_PROMPT = "You help users find flights. When asked about a flight, call " + "`display_flight` exactly ONCE with origin, destination, airline, and " + 'price. Use short airport codes (e.g. "SFO", "JFK") for ' + 'origin/destination and a price string like "$289". The tool\'s return ' + "value is an A2UI surface descriptor — the flight card is already rendered " + "to the user; do NOT call `display_flight` again for the same trip and do " + "NOT repeat the flight details in text. After the tool returns, reply with " + "one short confirmation sentence and stop.";/** * Dedicated agent for the A2UI fixed-schema demo. Returns the envelope as a * plain OBJECT (not a JSON string): the Strands TS SDK wraps an object * tool-return in a `json` content block the adapter reads and re-stringifies * into the TOOL_CALL_RESULT the client A2UIMiddleware scans for * `a2ui_operations`. (A bare string return lands in no content block and the * result comes through empty — unlike the Python SDK, which wraps strings.) */export async function buildA2uiFixedSchemaAgent(): Promise<StrandsAgent> { const displayFlight = tool({ name: "display_flight", description: "Show a flight card for the given trip. Use short airport codes " + '(e.g. "SFO", "JFK") for origin/destination and a price string like ' + '"$289". After this tool returns, the flight card is already rendered ' + "to the user via the A2UI surface — do NOT call it again for the same " + "flight; reply with one short confirmation sentence and stop.", inputSchema: z.object({ origin: z.string().describe('Origin airport code, e.g. "SFO".'), destination: z.string().describe('Destination airport code, e.g. "JFK".'), airline: z.string().describe('Airline name, e.g. "United".'), price: z.string().describe('Price string, e.g. "$289".'), }), callback: ({ origin, destination, airline, price }) => ({ [A2UI_OPERATIONS_KEY]: [ createSurface(A2UI_FIXED_SURFACE_ID, A2UI_FIXED_CATALOG_ID), updateComponents(A2UI_FIXED_SURFACE_ID, FLIGHT_SCHEMA), updateDataModel(A2UI_FIXED_SURFACE_ID, { origin, destination, airline, price, }), ], }), }); const strandsAgent = new Agent({ // Chat Completions API: the Responses adapter buffers tool-call argument // deltas, which would defeat A2UI's progressive surface streaming. model: await createModel({ openaiApi: "chat" }), systemPrompt: A2UI_FIXED_SYSTEM_PROMPT, tools: [displayFlight], }); return new StrandsAgent({ agent: strandsAgent, name: "a2ui_fixed_schema", description: "A2UI surface from a fixed, pre-authored schema (direct backend tool)", });}// ---------------------------------------------------------------------------// A2UI Dynamic Schema (declarative-gen-ui) — adapter auto-injects generate_a2ui.// ---------------------------------------------------------------------------//// Unlike the fixed-schema demo (which wires a `display_flight` tool returning a// pre-authored envelope), the dynamic demo lets the agent *generate* the// surface layout on the fly. The Next.js route// (app/api/copilotkit-declarative-gen-ui/route.ts) sets// `a2ui: { injectA2UITool: true, defaultCatalogId: "declarative-gen-ui-catalog" }`;// the runtime forwards the flag, the Strands adapter auto-injects a// `generate_a2ui` tool and drives a secondary render planner. The// `config.a2ui` block below supplies the catalog id stamped into generated// surfaces and the composition guide that teaches the planner the page's// catalog. Mirrors the ag-ui dynamic-schema reference example.//// The compositionGuide MUST describe the catalog the page registers at// src/app/demos/declarative-gen-ui/a2ui/{definitions,renderers,catalog}.ts// (catalog id `declarative-gen-ui-catalog`): Card / StatusBadge / Metric /// InfoRow / PrimaryButton / PieChart / BarChart / DataTable, composed inside// the basic catalog's Row / Column / Text (`includeBasicCatalog: true`).//// Grounding dataset + composition rules are kept in spirit with the frontend// `sales-context.ts` (SALES_DATASET + COMPOSITION_RULES) the page registers via// `useAgentContext`. The frontend context steers the PRIMARY agent; this// compositionGuide is the channel the adapter feeds to the secondary// `render_a2ui` planner (it gets `guidelines`, not the frontend App Context),// so the planner is self-contained.const A2UI_DYNAMIC_CATALOG_ID = "declarative-gen-ui-catalog";const A2UI_DYNAMIC_SALES_DATASET = `Vantage Threads (fictional B2B apparel company) — Q2 sales data. Ground every visual in these numbers; invent only plausible details consistent with them.- Quarterly revenue: $4.2M (up 12% QoQ). New customers: 186 (up 8%). Win rate: 31% (down 2pts). Avg deal size: $22.6k (up 5%).- Revenue by region: North America $1.9M, EMEA $1.3M, APAC $720k, LATAM $280k.- Monthly revenue: Jan $1.21M, Feb $1.34M, Mar $1.65M, Apr $1.38M, May $1.42M, Jun $1.40M.- Reps (vs quota): Dana Whitfield 124%, Marcus Lee 108%, Priya Sharma 97%, Tom Okafor 88%, Elena Vasquez 71%.- At-risk: total $615k ARR across 3 accounts — Northwind Retail ($340k renewal, no contact 6 weeks; severity high), Cascadia Outfitters ($180k, champion left; severity medium), Atlas Goods ($95k, stalled legal review; severity medium).- Biggest account: Meridian Apparel Group — owner Dana Whitfield, region North America, ARR $612k, renewal Sep 30, last contact 3 days ago, health green, 4 open opportunities worth $210k.- Meridian revenue by product line: Outerwear $260k, Footwear $180k, Accessories $112k, Custom $60k.`;const A2UI_DYNAMIC_COMPOSITION_RULES = `Use ONLY these exact component names (the registered catalog — any other name fails to render): Card, Column, Row, Text, Metric, PieChart, BarChart, DataTable, StatusBadge, InfoRow, PrimaryButton. The single-value KPI tile component is named exactly "Metric" (NOT "MetricTile" or "MetricCard").Pick A2UI components by the shape of the question — never ask which chart the user wants:1. Overall snapshot / "sales dashboard" → a Column (gap 16) whose first child is a Row (gap 16) of 4 Metric components (each with trend + trendValue), followed by a Row with a PieChart (revenue by region) next to a BarChart (monthly revenue, all six months Jan-Jun). Do NOT wrap the dashboard in a surrounding Card — the charts carry their own card chrome. Do NOT use StatusBadge, DataTable, or InfoRow here.2. Rep / team performance → a Column (gap 16) with a Card containing a DataTable (columns: rep, attainment, pipeline) next to or above a BarChart of quota attainment % per rep — no StatusBadge or InfoRow.3. Risk / health checks → a Column (gap 16): first a Row (gap 16) of 3 Metric components (ARR at risk $615k trend down, accounts at risk 3, biggest exposure Northwind $340k), then a Row (gap 16) with one compact Card per at-risk account (title = account name, subtitle = ARR at stake) containing a StatusBadge (error for high severity, warning otherwise) above a one-line Text with the reason and the recommended next action — no DataTable or InfoRow.4. Single account/entity details → a Row (gap 16) with a Card of InfoRow facts (owner, region, ARR, renewal date, last contact) next to a PieChart of that account's revenue by product line — no DataTable or StatusBadge.5. Part-of-whole follow-ups → PieChart; trends or comparisons over time/categories → BarChart.Compose generously — a dashboard should feel like a real analytics product, not a single widget.`;const A2UI_DYNAMIC_COMPOSITION_GUIDE = `${A2UI_DYNAMIC_SALES_DATASET}\n\n${A2UI_DYNAMIC_COMPOSITION_RULES}`;// Mirrors the langgraph-python demo's a2ui_dynamic.py SYSTEM_PROMPT.const A2UI_DYNAMIC_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.";/** * Dedicated agent for the A2UI dynamic-schema demo. Wires NO `generate_a2ui` * tool — the runtime's `injectA2UITool: true` makes the adapter auto-inject it * and drive a secondary render planner to GENERATE the surface. */export async function buildA2uiDynamicAgent(): Promise<StrandsAgent> { const strandsAgent = new Agent({ // Chat Completions API: the Responses adapter buffers tool-call argument // deltas, which would defeat A2UI's progressive surface streaming. model: await createModel({ openaiApi: "chat" }), systemPrompt: A2UI_DYNAMIC_SYSTEM_PROMPT, }); const config: StrandsAgentConfig = { a2ui: { defaultCatalogId: A2UI_DYNAMIC_CATALOG_ID, guidelines: { compositionGuide: A2UI_DYNAMIC_COMPOSITION_GUIDE }, }, }; return new StrandsAgent({ agent: strandsAgent, name: "a2ui_dynamic_schema", description: "Dynamic A2UI surfaces generated on the fly (auto-injected tool)", config, });}// ---------------------------------------------------------------------------// A2UI Error Recovery (a2ui-recovery) — adapter auto-injects + runs recovery.// ---------------------------------------------------------------------------//// Same auto-injected dynamic-schema setup as buildA2uiDynamicAgent, but the// aimock fixtures force the inner render_a2ui to emit free-form/sloppy args// (heal pill) or a structurally-invalid surface on every attempt (exhaust// pill). The Strands adapter runs the toolkit validate->retry recovery loop on// its auto-inject path (default 3 attempts) and returns the// a2ui_recovery_exhausted hard-fail envelope when the cap is hit — so this// agent wires NO tool, unlike the langgraph/ADK siblings (which own the tool// explicitly via getA2UITools + injectA2UITool:false). Mirrors the ag-ui dojo// aws-strands recovery example./** * Dedicated agent for the A2UI error-recovery demo. Wires NO `generate_a2ui` * tool — the runtime's `injectA2UITool: true` makes the adapter auto-inject it, * drive the secondary render planner, and run the recovery loop. */export async function buildA2uiRecoveryAgent(): Promise<StrandsAgent> { const strandsAgent = new Agent({ // Chat Completions API: the Responses adapter buffers tool-call argument // deltas, which would defeat A2UI's progressive surface streaming. model: await createModel({ openaiApi: "chat" }), systemPrompt: A2UI_DYNAMIC_SYSTEM_PROMPT, }); const config: StrandsAgentConfig = { a2ui: { defaultCatalogId: A2UI_DYNAMIC_CATALOG_ID, guidelines: { compositionGuide: A2UI_DYNAMIC_COMPOSITION_GUIDE }, }, }; return new StrandsAgent({ agent: strandsAgent, name: "a2ui_recovery", description: "Dynamic A2UI with automatic error recovery (auto-injected tool)", config, });}What is this?#
A headless UI gives you full control over the chat experience. You bring your own components, layout, and styling while CopilotKit handles agent communication, message management, tool-call rendering, and streaming. No <CopilotChat>, no slot overrides, just your components composed on top of the low-level hooks.
When should I use this?#
Use headless UI when:
- The slot system isn't enough: you need a completely different layout.
- You're embedding chat into an existing UI with its own patterns.
- You're building a non-chat surface that still talks to an agent (a dashboard, a canvas, an inspector) and want
useRenderToolCall/useRenderActivityMessageon their own. - You want to render generative UI primitives outside of a chat entirely.
The core hooks#
Three hooks power it, and they're the same ones <CopilotChat> uses internally.
useAgent({ agentId })— exposes the current conversation (messages,isRunning) and the run-state object.useCopilotKit()— returns the runtime handle you callrunAgent({ agent })on.useRenderToolCall()— returns a function that paints any registered tool call inline.
Minimal example#
Start with a hand-rolled message list and composer built from useAgent + useCopilotKit:
const { agent } = useAgent({ agentId: "headless-simple" }); const { copilotkit } = useCopilotKit(); const [input, setInput] = useState(""); const send = (text: string) => { const trimmed = text.trim(); if (!trimmed || agent.isRunning) return; agent.addMessage({ id: generateMessageId(), role: "user", content: trimmed, }); setInput(""); void copilotkit.runAgent({ agent }).catch((err) => { // The Headless Simple demo is the canonical "two hooks, your // design system" example users copy-paste as a starting point. // Silently swallowing errors here would model broken practice; // log so a network failure / runtime error / transport disconnect // surfaces in the console for the developer. console.error("[langgraph-python:headless-simple] runAgent failed", err); }); };The message list is a plain .map() over agent.messages: user messages render as right-aligned bubbles, assistant messages render streamed text plus inline tool calls via renderToolCall({ toolCall }):
{visible.map((m) => m.role === "user" ? ( <UserBubble key={m.id} content={m.content} /> ) : ( <AssistantBubble key={m.id} content={m.content} /> ), )}No <CopilotChat />, no slots. The trade-off: you only get text and tool calls. Reasoning messages, activity messages, and custom before/after slots won't show up unless you wire them in yourself, which is exactly what the complete example covers.
Complete example#
The headless-complete cell rebuilds the full generative-UI composition from the low-level hooks directly, without importing <CopilotChatMessageView>: text, tool calls, reasoning cards, A2UI + MCP Apps activity messages, and custom before/after message slots.
The useRenderedMessages hook#
The cell's central piece is a hand-rolled useRenderedMessages(messages, isRunning) that returns the same flat list of messages, each augmented with a renderedContent: ReactNode field. This hook is a manual recreation of what <CopilotChatMessageView> does:
const renderToolCall = useRenderToolCall(); const { renderActivityMessage } = useRenderActivityMessage(); // Index tool results by their originating tool-call id so each tool-call // card can hand the matching ToolMessage to `useRenderToolCall`. // Without this the renderer can't see a result and the card stays in the // "in-progress" state forever. const toolMessagesByCallId = useMemo(() => { const map = new Map<string, ToolMessage>(); for (const m of messages) { if (m.role === "tool" && "toolCallId" in m && m.toolCallId) { map.set(m.toolCallId, m as ToolMessage); } } return map; }, [messages]);Three low-level hooks feed it:
useRenderToolCall()— returns the renderer for any registered tool call (per-tool viauseRenderTool/useComponent, plus the wildcard fromuseDefaultRenderTool).useRenderActivityMessage()— renders A2UI + MCP Apps activity messages for the current agent scope.useRenderCustomMessages()— invokesrenderCustomMessagehooks registered against the activeCopilotChatConfigurationProvider, emitting"before"and"after"slots around every message.
Per-role dispatch#
The role-switch mirrors CopilotChatMessageView's renderMessageBlock exactly: assistant bodies get text and tool calls, user bodies get their text content, reasoning messages go through the <CopilotChatReasoningMessage> leaf, and activity messages route through renderActivityMessage:
{messages.map((m) => { if (m.role === "user") { // Cast through the local input shape — UserBubble accepts a // simplified version of the ag-ui content union. return ( <UserBubble key={m.id} content={m.content as Parameters<typeof UserBubble>[0]["content"]} /> ); } if (m.role === "assistant") { const toolCalls = "toolCalls" in m && Array.isArray(m.toolCalls) ? m.toolCalls : []; return ( <AssistantBubble key={m.id} content={typeof m.content === "string" ? m.content : undefined} > {toolCalls.map((tc) => { const toolMessage = toolMessagesByCallId.get(tc.id); const node = renderToolCall({ toolCall: tc, toolMessage, }); return node ? <div key={tc.id}>{node}</div> : null; })} </AssistantBubble> ); } if (m.role === "activity") { const node = renderActivityMessage(m); if (!node) return null; return <ActivityWrapper key={m.id}>{node}</ActivityWrapper>; } return null; })}Tool-call composition#
For each toolCall on an assistant message, we look up the sibling tool-role message (keyed by toolCallId) and hand both to renderToolCall:
{toolCalls.map((tc) => { const toolMessage = toolMessagesByCallId.get(tc.id); const node = renderToolCall({ toolCall: tc, toolMessage, }); return node ? <div key={tc.id}>{node}</div> : null; })}Bubble chrome#
The UserBubble and AssistantBubble components are pure chrome: they receive the pre-rendered node from useRenderedMessages and drop it into a styled container. No chat primitives are imported here:
export function AssistantBubble({ content, children,}: { content?: string; children?: React.ReactNode;}) { const hasText = typeof content === "string" && content.trim().length > 0; const hasChildren = React.Children.count(children) > 0; if (!hasText && !hasChildren) return null; return ( <div data-testid="headless-message-assistant" data-message-role="assistant" className="flex w-full items-start gap-3" > <Avatar className="h-8 w-8 shrink-0 border bg-muted text-muted-foreground"> <AvatarFallback className="bg-muted text-muted-foreground"> <Bot className="h-4 w-4" /> </AvatarFallback> </Avatar> <div className="flex max-w-[calc(100%-2.75rem)] flex-1 flex-col items-start gap-2"> {hasText && ( <div className={cn( "max-w-[90%] rounded-2xl rounded-tl-sm px-4 py-2.5 text-sm leading-relaxed shadow-sm", "bg-muted text-foreground", )} > <ReactMarkdown remarkPlugins={[remarkGfm]} components={{ p: ({ children }) => ( <p className="my-1 first:mt-0 last:mb-0">{children}</p> ), ul: ({ children }) => ( <ul className="my-1 list-disc pl-5">{children}</ul> ), ol: ({ children }) => ( <ol className="my-1 list-decimal pl-5">{children}</ol> ), li: ({ children }) => <li className="my-0.5">{children}</li>, code: ({ children, className }) => { const isBlock = (className ?? "").includes("language-"); if (isBlock) { return <code className={className}>{children}</code>; } return ( <code className="rounded bg-background px-1 py-0.5 font-mono text-[0.85em]"> {children} </code> ); }, pre: ({ children }) => ( <pre className="my-2 overflow-x-auto rounded-md bg-background p-3 font-mono text-xs"> {children} </pre> ), a: ({ children, href }) => ( <a href={href} target="_blank" rel="noreferrer noopener" className="text-primary underline underline-offset-2 hover:opacity-80" > {children} </a> ), strong: ({ children }) => ( <strong className="font-semibold">{children}</strong> ), h1: ({ children }) => ( <h1 className="my-2 text-base font-semibold">{children}</h1> ), h2: ({ children }) => ( <h2 className="my-2 text-base font-semibold">{children}</h2> ), h3: ({ children }) => ( <h3 className="my-2 text-sm font-semibold">{children}</h3> ), blockquote: ({ children }) => ( <blockquote className="my-2 border-l-2 border-border pl-3 italic text-muted-foreground"> {children} </blockquote> ), }} > {content as string} </ReactMarkdown> </div> )} {hasChildren && ( <div className="flex w-full max-w-full flex-col gap-2"> {children} </div> )} </div> </div> );}export function UserBubble({ content,}: { content: string | MultimodalPart[];}) { const { text, attachments } = splitContent(content); const hasText = text.trim().length > 0; const hasAttachments = attachments.length > 0; if (!hasText && !hasAttachments) return null; return ( <div data-testid="headless-message-user" data-message-role="user" className="flex w-full items-start gap-3 flex-row-reverse" > <Avatar className="h-8 w-8 shrink-0 border bg-primary text-primary-foreground"> <AvatarFallback className="bg-primary text-primary-foreground"> <User className="h-4 w-4" /> </AvatarFallback> </Avatar> <div className="flex max-w-[80%] flex-col items-end gap-2"> {hasAttachments && ( <div className="flex flex-wrap justify-end gap-2"> {attachments.map((a) => ( <AttachmentChip key={a.id} attachment={a} /> ))} </div> )} {hasText && ( <div className={cn( "rounded-2xl rounded-tr-sm px-4 py-2.5 text-sm leading-relaxed shadow-sm", "bg-primary text-primary-foreground", )} > <p className="whitespace-pre-wrap break-words">{text}</p> </div> )} </div> </div> );}function splitContent(content: string | MultimodalPart[]): { text: string; attachments: Attachment[];} { if (typeof content === "string") { return { text: content, attachments: [] }; } let text = ""; const attachments: Attachment[] = []; let i = 0; for (const part of content) { if (part.type === "text") { text += part.text; continue; } const meta = (part.metadata ?? {}) as { filename?: string; size?: number; }; attachments.push({ id: `${part.type}-${i++}`, type: part.type, source: part.source, filename: meta.filename, size: meta.size, status: "ready", }); } return { text, attachments };}Next steps#
- Slots — less work than going fully headless, often enough.
- CSS customization — when you just need to re-skin the defaults.