How to Add Streaming AI Chat to Any Next.js App

Adding AI chat to a Next.js app has a few non-obvious pieces: streaming responses, client-side rendering of chunks, error handling for API failures, and not exposing your API key to the client.

This is the complete pattern.

---

Architecture

Client (React) → POST /api/chat → Server (Route Handler) → Claude/OpenAI API → Stream back

The API key lives on the server. The client never sees it. Responses stream chunk by chunk for a responsive feel.

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1. Route Handler (Server Side)

app/api/chat/route.ts:

import { NextRequest } from "next/server";
import Anthropic from "@anthropic-ai/sdk";

const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY!,
});

export async function POST(req: NextRequest) {
  const { messages } = await req.json();

  // Validate input
  if (!Array.isArray(messages) || messages.length === 0) {
    return new Response("Invalid messages", { status: 400 });
  }

  // Create streaming response
  const stream = await anthropic.messages.stream({
    model: "claude-sonnet-4-6",
    max_tokens: 1024,
    messages: messages.map((m: { role: string; content: string }) => ({
      role: m.role as "user" | "assistant",
      content: m.content,
    })),
  });

  // Return as ReadableStream
  const readable = new ReadableStream({
    async start(controller) {
      for await (const chunk of stream) {
        if (
          chunk.type === "content_block_delta" &&
          chunk.delta.type === "text_delta"
        ) {
          controller.enqueue(new TextEncoder().encode(chunk.delta.text));
        }
      }
      controller.close();
    },
  });

  return new Response(readable, {
    headers: {
      "Content-Type": "text/plain; charset=utf-8",
      "Transfer-Encoding": "chunked",
    },
  });
}

---

2. React Hook for Streaming

hooks/useChat.ts:

import { useState, useCallback } from "react";

interface Message {
  role: "user" | "assistant";
  content: string;
}

export function useChat() {
  const [messages, setMessages] = useState<Message[]>([]);
  const [isLoading, setIsLoading] = useState(false);
  const [error, setError] = useState<string | null>(null);

  const sendMessage = useCallback(async (userMessage: string) => {
    const newMessages = [
      ...messages,
      { role: "user" as const, content: userMessage },
    ];

    setMessages(newMessages);
    setIsLoading(true);
    setError(null);

    // Add empty assistant message to stream into
    setMessages((prev) => [
      ...prev,
      { role: "assistant", content: "" },
    ]);

    try {
      const res = await fetch("/api/chat", {
        method: "POST",
        headers: { "Content-Type": "application/json" },
        body: JSON.stringify({ messages: newMessages }),
      });

      if (!res.ok) throw new Error(`API error: ${res.status}`);
      if (!res.body) throw new Error("No response body");

      const reader = res.body.getReader();
      const decoder = new TextDecoder();

      while (true) {
        const { done, value } = await reader.read();
        if (done) break;

        const chunk = decoder.decode(value, { stream: true });

        // Append chunk to last message
        setMessages((prev) => {
          const updated = [...prev];
          updated[updated.length - 1] = {
            role: "assistant",
            content: updated[updated.length - 1].content + chunk,
          };
          return updated;
        });
      }
    } catch (err) {
      setError(err instanceof Error ? err.message : "Something went wrong");
      // Remove the empty assistant message on error
      setMessages((prev) => prev.slice(0, -1));
    } finally {
      setIsLoading(false);
    }
  }, [messages]);

  return { messages, sendMessage, isLoading, error };
}

---

3. Chat Component

components/Chat.tsx:

"use client";

import { useState, useRef, useEffect } from "react";
import { useChat } from "@/hooks/useChat";

export default function Chat() {
  const { messages, sendMessage, isLoading, error } = useChat();
  const [input, setInput] = useState("");
  const bottomRef = useRef<HTMLDivElement>(null);

  // Auto-scroll to bottom
  useEffect(() => {
    bottomRef.current?.scrollIntoView({ behavior: "smooth" });
  }, [messages]);

  const handleSubmit = async (e: React.FormEvent) => {
    e.preventDefault();
    if (!input.trim() || isLoading) return;
    const message = input;
    setInput("");
    await sendMessage(message);
  };

  return (
    <div className="flex flex-col h-[600px] border rounded-lg">
      {/* Messages */}
      <div className="flex-1 overflow-y-auto p-4 space-y-4">
        {messages.map((msg, i) => (
          <div
            key={i}
            className={`flex ${msg.role === "user" ? "justify-end" : "justify-start"}`}
          >
            <div
              className={`max-w-[80%] rounded-lg p-3 text-sm ${
                msg.role === "user"
                  ? "bg-blue-600 text-white"
                  : "bg-gray-100 text-gray-900"
              }`}
            >
              {msg.content || (isLoading && i === messages.length - 1 ? "▋" : "")}
            </div>
          </div>
        ))}
        {error && (
          <div className="text-red-500 text-sm text-center">{error}</div>
        )}
        <div ref={bottomRef} />
      </div>

      {/* Input */}
      <form onSubmit={handleSubmit} className="border-t p-4 flex gap-2">
        <input
          value={input}
          onChange={(e) => setInput(e.target.value)}
          placeholder="Type a message..."
          disabled={isLoading}
          className="flex-1 border rounded px-3 py-2 text-sm focus:outline-none focus:ring-2 focus:ring-blue-500"
        />
        <button
          type="submit"
          disabled={isLoading || !input.trim()}
          className="bg-blue-600 text-white px-4 py-2 rounded text-sm disabled:opacity-50"
        >
          {isLoading ? "..." : "Send"}
        </button>
      </form>
    </div>
  );
}

---

4. Environment Setup

.env.local:

ANTHROPIC_API_KEY=sk-ant-...

Or for OpenAI:

OPENAI_API_KEY=sk-...

For OpenAI, replace the route handler's Anthropic call with:

import OpenAI from "openai";
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY! });

const stream = await openai.chat.completions.create({
  model: "gpt-4o",
  stream: true,
  messages,
});

---

What's Not Covered Here

This pattern handles the happy path well. Production additions:

• Rate limiting — prevent abuse (use @upstash/ratelimit + Vercel KV)
• Auth check — verify user is logged in before allowing API calls
• Message persistence — save conversation history to DB
• Token counting — track and limit usage per user

The AI SaaS Starter Kit has all of this pre-built — streaming chat, auth-gated route, usage tracking, and a dashboard to manage it.

AI SaaS Starter Kit — $99

---

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