Artificial intelligence is well past theoretical potential, and it is already making real-world impact. And nowhere is this potential more evident and exciting than in the world of smart assistants. Thanks to frameworks like Next.js and libraries like LangChain, developers can now build intelligent, reactive, and personalized assistants right in the browser.
Users today want more than static systems. They want intelligent assistants that provide them with interactive experiences. The combination of Next.js and LanChain can help in that by building AI-powered assistants that can talk back.
In this blog, we’ll walk through how Next.js and LangChain can be combined to create assistants that are not just smart, but also production-ready, scalable, and incredibly interactive.
What is LangChain?
LangChain is an open-source framework that helps developers build applications using large language models (LLMs), such as GPT-4, Claude, or DeepSeek. It connects them to external data, tools, and memory in a modular and composable way.
Developers use LangChain to go beyond basic prompts and enable LLM-based apps to interact with APIs, documents, databases, and even the internet in a structured and intelligent manner.
LLMs are powerful but alone they have no external data access, which inhibits their reasoning abilities. LangChain solves this problem by allowing LLMs to dynamically choose external tools or actions based on user input. This allows developers to create LLM-powered applications that can understand context in real-time.
Why combine Next.js with LangChain?
Before diving into the how, let’s talk about the why.
- Next.js is a powerful React framework ideal for building dynamic, performant, and SEO-friendly web applications.
- LangChain is a framework designed to help developers build context-aware LLM-powered applications, offering integrations with vector stores, memory, prompt templates, tools, and agents.
Combining Next.js and LangChain together is the perfect full-stack recipe for:
- Frontend: Built using React and styled with Tailwind or shadcn/ui.
- Backend API routes: Handle LLM queries and LangChain pipelines.
- Edge-ready deployment: Scale with Vercel or other serverless platforms.
What type of AI assistants can you build using LangChain and Next.js?
The LangChain and Next.js combination opens up a wide range of possibilities for building intelligent, interactive, and production-ready web applications.
Below are just some of the most compelling use cases and applications that you can build by merging these technologies.
1. Answer questions based on internal documentation
You can build a retrieval-augmented generation (RAG) system where users get accurate answers for their queries related to your company’s internal documents, such as PDFs, policies, and memos.
2. Extract insights from PDFs and websites
LangChain and Next.js let users upload documents or paste URLs, and the smart assistant will return key takeaways from those sources like summaries or structured data.
3. Handle customer support queries
For customer facing businesses, these technologies are a great way to deploy chatbots or support assistants that can answer customer questions based on your business knowledge, which expedites customer support to provide a smooth user experience.
4. Act as research copilots using live data
Academicians can greatly benefit from smart assistants made by LangChain and Next.js. Researchers, educators, and scientists can input their questions and get curated insights from real-time sources like news, academic papers, or APIs connected with research repositories.
Moreover, such assistants can be further enhanced by emerging standards like the Model Context Protocol (MCP) for interoperability.
5. Automate internal workflows
Creating AI assistants using LangChain with Next.js can automate your repetitive backend tasks, such as filling out forms, sending Slack updates, and syncing calendars. Automating your internal workflows will free up your business teams’ time that can be spent on more productive tasks.
Let’s walk through how to get started.
Step-by-step guide: Building a smart assistant with Next.js and LangChain
1. Initialize your Next.js project
First, set up a new Next.js app.
npx create-next-app@latest smart-assistant
cd smart-assistant
npm install
This creates a basic folder structure with React and file-based routing powered by Next.js. Your assistant will live here, both on the UI and server side.
2. Install dependencies
Next, install the necessary packages. For this project, you’ll need LangChain and OpenAI packages:
bash
npm install langchain openai dotenv
Installing dependencies equips your app with the core AI functionality you’ll need for smart assistant behavior. 3
3. Set up environment variables
In your .env.local file, add your API key:
OPENAI_API_KEY=sk-…
These credentials allow your server to securely access OpenAI’s models through the LangChain interface. Never expose this key on the client side.
4. Create a LangChain API route
Inside the pages/api folder, create a file like assistant.ts:
import { NextApiRequest, NextApiResponse } from ‘next’;
import { OpenAI } from ‘langchain/llms/openai’;
import { PromptTemplate } from ‘langchain/prompts’;
const model = new OpenAI({ temperature: 0.7 });
const prompt = new PromptTemplate({
inputVariables: [‘question’],
template: ‘You are a helpful assistant. Answer this question: {question}’,
});
export default async function handler(req: NextApiRequest, res: NextApiResponse) {
const { question } = req.body;
const promptText = await prompt.format({ question });
const response = await model.call(promptText);
res.status(200).json({ result: response });
}
This route acts as a serverless function that receives questions, processes them using LangChain and OpenAI, and returns a response. This keeps your API key hidden and allows for secure AI calls.
5. Create a simple UI
Now create a basic interface in pages/index.tsx
import { useState } from ‘react’;
export default function Home() {
const [question, setQuestion] = useState(”);
const [response, setResponse] = useState(”);
const handleAsk = async () => {
const res = await fetch(‘/api/ask’, {
method: ‘POST’,
headers: { ‘Content-Type’: ‘application/json’ },
body: JSON.stringify({ question }),
});
const data = await res.json();
setResponse(data.result);
};
return (
<main className=”p-8 max-w-xl mx-auto”>
<h1 className=”text-2xl font-bold mb-4″>Smart Assistant</h1>
<input
type=”text”
placeholder=”Ask something…”
value={question}
onChange={(e) => setQuestion(e.target.value)}
className=”border p-2 w-full mb-4″
/>
<button
onClick={handleAsk}
className=”bg-blue-500 text-white px-4 py-2 rounded”
>
Ask
</button>
<p className=”mt-6″>{response}</p>
</main>
);
}
A minimal UI allows users to type questions, which is then sent to your LangChain API route as input. The AI’s result to that input is displayed instantly.
Going further with LangChain: Advanced capabilities
LangChain supports much more than basic prompts. You can utilize its advanced features to build dynamic and context-aware applications powered by the latest LLMs.
1. Retrieval-augmented generation
You can use LangChain’s RAG features to integrate with Pinecone, Chroma, or Supabase to fetch documents and answer questions using real context. LangChain will search of these vectore databases for relevant document chunks and pass the retireved context to the LLM to generate a grounded, accurate answer.
2. Memory
LangChain memory modules Maintain conversational history. They allow the LLM to retain information across turns, so it can maintain context of the conversation and improve interaction quality.
3. Agents
You can use LangChain to Create assistants that choose tools or APIs dynamically. Such AI agents enable your app to decide which tools to use based on the user query. As a result, LLM thinks, chooses, and acts instead of giving a static response.
4. Tool integrations
LangChain supports easy integration of external tools and APIs that LLM can call during execution. From Google Search to WolframAlpha, your assistant can pull in real-time data.
Here’s a simple example using document QA with a vector store:
import { RetrievalQAChain } from ‘langchain/chains’;
import { Chroma } from ‘langchain/vectorstores/chroma’;
// Configure embedding, vector store, and LLM
Deployment to Vercel
Vercel is a cloud-based platform for developing and deploying frontend applications quickly and efficiently. The platform takes your code and automatically deploys it to a global edge network, which is a system of servers located around the world to deliver content to users.
As the creator of Next.js, Vercel offers particular support for the framework. If you want to use LangChain with Next.js, Vercel can help you deploy the frontend of your smart assistant.
Here is how can deploy LangChain easily to Vercel:
bash
vercel deploy
Vercel seamlessly handles API routes and serverless functions, which is perfect for LangChain-backed apps.
Useful resources
Here are some additional resources that will help you in developing smart assistants using LangChain and Next.js.
Final thoughts
Integrating Next.js with LangChain unlocks powerful possibilities for building smart assistants that are fast, scalable, and intelligent. Next.js handles the frontend and backend, while LangChain provides the tools and abstraction needed to interact meaningfully with large language models like GPT-5.
Whether you’re a solo developer experimenting with LLMs or a startup building the next-gen AI product, this stack is a winning combo. What’s more, LangChain’s expanding ecosystem allows you to plug in memory, databases, file systems, and third-party tools, meaning you can build context-aware assistants that grow smarter with every interaction.
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