Vercel AI SDK vs LangChain: Which Should You Use?

Both tools help you build LLM-powered apps, but they solve different problems. Picking the wrong one adds real friction. Here's the honest breakdown.

TL;DR

	Vercel AI SDK	LangChain
Best for	Streaming UI, Next.js apps	Complex pipelines, agents, RAG
Learning curve	Low	High
Bundle size	Small	Large
Streaming	First-class	Supported but clunky
Provider support	OpenAI, Anthropic, Mistral, etc.	Everything
Abstractions	Thin	Heavy
Production stability	Solid	Inconsistent

Vercel AI SDK

The AI SDK is purpose-built for frontend-connected AI. Its killer feature is the useChat and useCompletion hooks — they handle streaming, state, and loading out of the box.

npm install ai openai

// app/api/chat/route.ts
import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";

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

  const result = await streamText({
    model: openai("gpt-4o-mini"),
    messages,
  });

  return result.toDataStreamResponse();
}

// app/chat/page.tsx
'use client'
import { useChat } from 'ai/react'

export default function Chat() {
  const { messages, input, handleInputChange, handleSubmit } = useChat()

  return (
    <div>
      {messages.map((m) => (
        <div key={m.id}>{m.content}</div>
      ))}
      <form onSubmit={handleSubmit}>
        <input value={input} onChange={handleInputChange} />
        <button type="submit">Send</button>
      </form>
    </div>
  )
}

That's a complete streaming chat UI. The SDK handles reconnection, error states, and the streaming protocol. Switching providers is a one-line change.

Use the AI SDK when:

You're building a Next.js app with a chat or completion UI
Streaming UX matters
You want minimal abstraction and full control
You're just getting started

LangChain

LangChain is a framework for building LLM pipelines. It shines when your AI logic is complex — multiple steps, memory, tool use, document retrieval.

npm install langchain @langchain/openai

import { ChatOpenAI } from "@langchain/openai";
import { ConversationChain } from "langchain/chains";
import { BufferMemory } from "langchain/memory";

const model = new ChatOpenAI({ model: "gpt-4o-mini" });
const memory = new BufferMemory();

const chain = new ConversationChain({ llm: model, memory });

const res1 = await chain.call({ input: "My name is Alex." });
const res2 = await chain.call({ input: "What is my name?" });
// Correctly returns "Alex" because of memory

Where LangChain really earns its keep is RAG pipelines:

import { OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { RetrievalQAChain } from "langchain/chains";
import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 500,
  chunkOverlap: 50,
});

const docs = await splitter.createDocuments([yourLongDocument]);
const vectorStore = await MemoryVectorStore.fromDocuments(
  docs,
  new OpenAIEmbeddings(),
);

const chain = RetrievalQAChain.fromLLM(
  new ChatOpenAI({ model: "gpt-4o-mini" }),
  vectorStore.asRetriever(),
);

const result = await chain.call({
  query: "What does the document say about X?",
});

That's chunking, embedding, storing, and retrieval in ~15 lines.

Use LangChain when:

You're building RAG pipelines
You need conversation memory across sessions
You're building agents with multiple tools
You need to chain multiple LLM calls together

The Honest Downsides

Vercel AI SDK is thin by design. If you need agents, memory, or document pipelines, you're writing it yourself. It's also Vercel-ecosystem-flavored — works great with Next.js, less natural elsewhere.

LangChain has a reputation for a reason. The abstractions can leak badly, docs lag behind breaking changes, and debugging a chain gone wrong is painful. Many teams use it to get started and gradually replace it with direct API calls as they understand their use case better.

Can You Use Both?

Yes, and it's actually common. Use the AI SDK for your streaming chat UI, and call a LangChain-powered RAG endpoint for document Q&A. They don't conflict.

// The AI SDK handles streaming to the browser
// LangChain handles the retrieval logic on the server

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

  // LangChain does the RAG work
  const result = await ragChain.call({ query });

  // AI SDK streams the response
  const stream = await streamText({
    model: openai("gpt-4o-mini"),
    prompt: `Answer based on this context: ${result.text}\n\nQuestion: ${query}`,
  });

  return stream.toDataStreamResponse();
}

My Recommendation

Start with the Vercel AI SDK. It's simpler, the DX is better, and it covers 80% of use cases cleanly. Add LangChain only when you hit something it genuinely can't do — usually that means RAG or stateful agents. By that point you'll know exactly why you need it.