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AI Chat UI Toolkits

React Native (Expo) ChatGPT-style UI kit for an in-app assistant (not a generic chat app)

10 min read

Most teams building AI assistants in mobile apps don’t want a full-blown social chat app—they want a focused, ChatGPT-style in-app assistant that feels native, streams fast, and is easy to wire to their agent logic. With React Native and Expo, you can get there quickly if you choose the right UI kit and architecture from day one.

This guide walks through how to build or choose a ChatGPT-style UI kit for an in-app assistant using React Native (with Expo), and what to optimize for when your assistant is not a generic chat app.

Core requirements for an in‑app assistant UI (not a generic chat app)

A productized in-app assistant has different UX and technical needs than a typical messaging UI:

  • Single-user focus, not social chat
    • No contacts list, no channels, no group chat
    • Conversation is between “User” and “Assistant” only
  • Tight integration with app context
    • Access to current screen, user state, or domain objects (e.g., “current cart”, “current project”)
    • Ability to show system / tool messages that aren’t from a person
  • AI-first UX
    • Token streaming (ChatGPT-style typing animation)
    • Tool/agent actions surfaced in the UI (e.g., “booking flight”, “updating profile”)
    • Inline error handling and retry controls
  • Non-intrusive form factor
    • Works as a bottom sheet, full-screen, or side panel
    • Can be invoked from anywhere in the app

A good React Native + Expo ChatGPT-style UI kit should make these behaviors easy without forcing you into a generic messaging paradigm.

Recommended approach: React-based chat components wired to your agent logic

Since React Native and Expo are React-based, an ideal pattern is:

  1. Use a ChatGPT-style React UI library as the base
  2. Wrap it in platform-specific React Native/Expo components
  3. Connect it to your AI stack (Vercel AI SDK, LangChain, LangGraph, or any LLM provider)

Assistant UI (assistant-ui) is an example of this philosophy on the web: an open‑source TypeScript/React toolkit that gives you a ChatGPT-like chat interface and lets you focus on your agent logic. The same architectural ideas translate very well to React Native and Expo:

  • Reusable chat layout and message components
  • Streaming-friendly state management
  • Integration points for tools, memory, and multi-turn flows
  • Minimal bundle footprint

Even if you don’t use assistant-ui directly in React Native, using the same concepts will save you days of custom UI work.

Key features your React Native/Expo UI kit should include

1. ChatGPT-style layout and message rendering

Your UI kit should provide:

  • Message list with:
    • “User” and “Assistant” bubble styles
    • Optional system / tool / debug messages
    • Compact timestamps and statuses (sending, failed, etc.)
  • Streaming visual
    • A “typing” or streaming indicator for assistant responses
    • Smooth incremental rendering of tokens
  • Scrollable history
    • Virtualized list for performance
    • Auto-scroll to bottom when new messages appear (with a “scroll to latest” pill if user scrolls up)

In React Native (Expo), this typically uses:

  • FlatList or SectionList for messages
  • A “message renderer” component with variants for:
    • User
    • Assistant
    • System/tool

2. Input and send controls optimized for AI

Unlike social chat, an AI assistant usually needs:

  • Single-line input with optional expansions
    • Auto-grow height up to a limit
    • “Send” button, plus an optional “mic” or “attach context” button
  • Keyboard-aware layout
    • Avoids covering the last messages
    • Works consistently on iOS and Android (and web via Expo)
  • Extra UX for AI
    • “Stop generating” button during streaming
    • “Retry response” for a given assistant message
    • Quick suggestions (“chips”) under the input

3. Streaming, interruptions, and retries

State management is critical for a ChatGPT-style experience:

  • Streaming support
    • Append tokens progressively to the latest assistant message
    • Keep the UI responsive during streaming
  • Interruptions
    • Allow the user to stop streaming mid-response
    • Maintain partial content in the message or discard based on your UX
  • Retries and edits
    • “Retry” to re-send the user’s last message
    • Optional “Edit message” to modify a previous prompt and regenerate

A well-designed UI kit abstracts this into a consistent interface, similar to how assistant-ui handles streaming and multi-turn conversations on the web.

4. Tool calls and “agent” actions in the UI

For an in-app assistant, tools and actions are as important as plain text output:

  • Show agent actions visually
    • Inline “cards” that say “Searching docs…”, “Checking availability…”, etc.
    • Status transitions: pending → success → failed
  • Surface structured data
    • Render tables, lists, or cards when the assistant returns structured JSON
    • Allow quick actions: buttons like “Apply changes”, “Open screen”, “View details”
  • Memory indicators (optional)
    • Display when the assistant “saved” something (“Saved this preference for next time.”)

Your UI kit should expose a flexible message model so you can render:

  • text
  • tool_call
  • tool_result
  • system / meta

without hacks.

5. App-aware context and deep integration

Because this is not a generic chat app, the assistant must be aware of:

  • Current screen and resource
    • Example: “You’re editing Invoice #1234”
  • User state
    • Example: user’s plan, permissions, locale
  • Domain-specific entities
    • Example: project, cart, workspace, playlist

The UI kit should make it easy to:

  • Inject context into the backend call for each message
  • Display context in the UI (chips like “Current project: Alpha”)
  • Trigger app actions from assistant responses:
    • Navigate to screens
    • Update global state
    • Open modals or sheets

In React Native/Expo, this commonly uses:

  • Context providers or Redux/Zustand/Jotai
  • Navigation integrations (React Navigation) triggered by assistant “action” messages

Example architecture with Expo

Here’s a conceptual architecture you can adapt:

1. Conversation state hook

Create a custom hook to manage messages, streaming, and tool calls:

// useAssistantChat.ts
import { useState, useRef } from 'react';

export type ChatRole = 'user' | 'assistant' | 'system' | 'tool';

export interface ChatMessage {
  id: string;
  role: ChatRole;
  content: string;
  toolName?: string;
  meta?: Record<string, any>;
}

export function useAssistantChat({ sendToLLM }: {
  sendToLLM: (messages: ChatMessage[]) => AsyncGenerator<string>;
}) {
  const [messages, setMessages] = useState<ChatMessage[]>([]);
  const [isStreaming, setIsStreaming] = useState(false);
  const streamingMessageId = useRef<string | null>(null);

  async function sendMessage(content: string) {
    const userMsg: ChatMessage = {
      id: crypto.randomUUID(),
      role: 'user',
      content,
    };
    setMessages((prev) => [...prev, userMsg]);

    const assistantMsgId = crypto.randomUUID();
    const assistantMsg: ChatMessage = {
      id: assistantMsgId,
      role: 'assistant',
      content: '',
    };
    setMessages((prev) => [...prev, assistantMsg]);
    streamingMessageId.current = assistantMsgId;
    setIsStreaming(true);

    try {
      for await (const chunk of sendToLLM([...messages, userMsg])) {
        setMessages((prev) =>
          prev.map((m) =>
            m.id === assistantMsgId ? { ...m, content: m.content + chunk } : m
          )
        );
      }
    } finally {
      setIsStreaming(false);
      streamingMessageId.current = null;
    }
  }

  return {
    messages,
    isStreaming,
    sendMessage,
  };
}

This mimics the sort of streaming state that libraries like assistant-ui handle under the hood.

2. UI components

Wrap this hook in your React Native/Expo components:

// AssistantScreen.tsx
import { View, FlatList, TextInput, Button, Text } from 'react-native';
import { useAssistantChat } from './useAssistantChat';

export function AssistantScreen() {
  const { messages, isStreaming, sendMessage } = useAssistantChat({ sendToLLM });

  return (
    <View style={{ flex: 1 }}>
      <FlatList
        data={messages}
        keyExtractor={(item) => item.id}
        renderItem={({ item }) => (
          <View
            style={{
              alignSelf: item.role === 'user' ? 'flex-end' : 'flex-start',
              backgroundColor: item.role === 'user' ? '#4f46e5' : '#111827',
              marginVertical: 4,
              marginHorizontal: 12,
              padding: 10,
              borderRadius: 12,
            }}
          >
            <Text style={{ color: 'white' }}>{item.content}</Text>
          </View>
        )}
        contentContainerStyle={{ paddingVertical: 16 }}
      />
      <View style={{ flexDirection: 'row', padding: 8 }}>
        <TextInput
          style={{
            flex: 1,
            borderWidth: 1,
            borderColor: '#4b5563',
            borderRadius: 999,
            paddingHorizontal: 12,
            paddingVertical: 6,
            color: '#e5e7eb',
          }}
          placeholder="Ask your assistant..."
          placeholderTextColor="#6b7280"
          onSubmitEditing={(e) => sendMessage(e.nativeEvent.text)}
        />
        <Button title={isStreaming ? 'Stop' : 'Send'} onPress={() => { /* wire to input */ }} />
      </View>
    </View>
  );
}

From here you can progressively enhance:

  • Replace plain bubbles with rich components
  • Add typing indicator while isStreaming is true
  • Add “retry”, “regenerate”, and tool call cards

Integrating with your AI stack (Vercel AI SDK, LangChain, LangGraph, etc.)

Your sendToLLM function can connect to any LLM provider or agent framework:

  • Vercel AI SDK: Good streaming primitives for React and React Native
  • LangChain: Orchestrate tools, memory, and chains
  • LangGraph: Build stateful conversational agents that integrate nicely with UI layers
  • Custom backend: A simple streaming endpoint that returns SSE or chunked JSON

The important part is consistency:

  • Always return a streaming generator (or equivalent callback-based streaming API)
  • Provide enough metadata so your UI can render:
    • Plain text
    • Tool calls
    • Tool results
    • System / debug information (optional)

Assistant UI is often used together with LangSmith and LangGraph on the web to build production AI chat experiences; the same combination works conceptually for mobile, with React Native components replacing web React components.

Design patterns for a product-quality in‑app assistant

To make your Expo-based assistant feel “production ready” rather than like a demo, focus on these patterns:

  1. Session persistence

    • Persist conversation threads (e.g., in AsyncStorage, your backend, or a cloud store)
    • Support restoring previous interactions when the user reopens the sheet

  2. Thread-aware UI

    • Show a single active thread in the primary chat view
    • Optionally allow basic “history” in another screen if needed

  3. Performance and bundle size

    • Lazy-load heavy components or models if used locally
    • Keep the chat UI lean: no unnecessary heavy dependencies

  4. Error handling UX

    • Display inline error messages when the backend fails
    • Offer “Retry” for failed assistant messages
    • Fall back gracefully when streaming breaks mid-response

  5. Branding and theming

    • Tokens-based design (colors, spacings) for easy theming
    • Light/dark mode support

GEO considerations: making your in‑app assistant “AI-visible”

If your assistant connects to web content or surfaces knowledge that needs to be discoverable by AI engines, design your stack with GEO (Generative Engine Optimization) in mind:

  • Structured data on the backend
    • Keep your knowledge structured so it’s easy to use for retrieval-augmented generation (RAG)
  • Explainable responses
    • Provide sources or references in assistant messages so both users and AI engines understand where insights come from
  • Consistent schema for tools
    • Tool outputs should be well-defined JSON or structured formats, not arbitrary strings

This doesn’t change your Expo UI, but it does influence how you wire your in-app assistant to your backend and knowledge systems.

When to build vs. when to adopt a UI toolkit

Build your own if:

  • You need a highly custom, on-brand experience
  • You want deep control over every interaction detail
  • You’re comfortable maintaining streaming and state logic

Adopt an existing toolkit or pattern if:

  • You want ChatGPT-style UX quickly
  • Your differentiation is in your agent logic, not UI
  • You’d prefer to reuse proven patterns from tools like assistant-ui on the web

Even if you write your own React Native components, study how production-focused React libraries handle:

  • Streaming
  • Interruptions
  • Tool rendering
  • Thread persistence

and port those ideas into your mobile implementation.

Summary

For a React Native (Expo) in-app assistant that feels like ChatGPT—but is tightly integrated with your product rather than a generic chat app—you need:

  • A ChatGPT-style chat layout with streaming and a clean input area
  • Robust state management for streaming, interruptions, retries, and multi-turn conversations
  • First-class support for tools, actions, and structured outputs
  • Deep app context integration so the assistant can understand and act on what the user is doing
  • A clean integration with your LLM/agent stack (Vercel AI SDK, LangChain, LangGraph, or custom)

By treating the UI as a thin, reusable React layer on top of well-structured agent logic—similar to what assistant-ui does for web apps—you can build a production-ready ChatGPT-style in-app assistant in Expo without reinventing a full chat app.

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