LangChain vs LlamaIndex vs Vercel AI SDK: Choosing the Right AI Framework in 2026

You're ready to build an AI application. You've picked your LLM, designed the user experience, and now you need a framework to wire everything together. You open your browser and immediately find three names competing for your attention: LangChain, LlamaIndex, and Vercel AI SDK.

Each one promises to simplify AI development. Each one has a different philosophy, different strengths, and different trade-offs. Picking the wrong one means rewriting code weeks into your project. Picking the right one means building faster and shipping sooner.

This guide compares all three frameworks head to head so you can make the right choice for your specific project.

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Quick Comparison Table

	LangChain	LlamaIndex	Vercel AI SDK
Primary focus	General-purpose AI orchestration	Data indexing and RAG	Streaming AI UI for web apps
Languages	Python, TypeScript/JS	Python, TypeScript/JS	TypeScript/JS only
RAG support	Good (via integrations)	Excellent (core strength)	Basic (bring your own)
Agent framework	LangGraph (advanced)	Agent workflows	AI SDK (lightweight)
Streaming	Supported	Supported	Excellent (core strength)
UI components	None built-in	None built-in	React hooks and components
Learning curve	Steep	Moderate	Low
Best for	Complex agent workflows	Data-heavy RAG applications	Next.js and React AI apps

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LangChain: The Swiss Army Knife

LangChain is the most comprehensive AI framework available. It provides tools for virtually every AI application pattern — chatbots, RAG pipelines, agents, structured output, evaluation, and more. If there's a way to use an LLM, LangChain probably has an abstraction for it.

Core Philosophy

LangChain's approach is composability through chains. You build applications by connecting modular components — prompts, models, output parsers, retrievers, tools — into pipelines. Each component has a standardized interface, so you can swap out parts without rewriting the whole system.

The framework is built around LangChain Expression Language (LCEL), a declarative syntax for composing chains:

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a helpful assistant that answers questions about {topic}."],
  ["human", "{question}"],
]);

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

const chain = prompt.pipe(model).pipe(new StringOutputParser());

const response = await chain.invoke({
  topic: "machine learning",
  question: "What is gradient descent?",
});

RAG with LangChain

LangChain supports RAG through a rich set of integrations. You can connect to dozens of vector stores (Pinecone, Weaviate, Chroma, pgvector), use various document loaders (PDF, web, databases), and customize every step of the retrieval pipeline.

import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
import { OpenAIEmbeddings } from "@langchain/openai";
import { SupabaseVectorStore } from "@langchain/community/vectorstores/supabase";

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
});

const docs = await splitter.splitDocuments(rawDocs);

const vectorStore = await SupabaseVectorStore.fromDocuments(
  docs,
  new OpenAIEmbeddings(),
  { client: supabaseClient, tableName: "documents" }
);

const retriever = vectorStore.asRetriever({ k: 4 });

The flexibility is a double-edged sword. LangChain gives you complete control over every component, but that means you need to understand how each piece fits together.

Agent Capabilities

This is where LangChain stands out. LangGraph, LangChain's agent framework, is the most advanced option of the three for building complex, stateful agent workflows.

LangGraph models agents as graphs where nodes are actions (LLM calls, tool executions, human-in-the-loop checkpoints) and edges define the flow between them. This allows you to build agents that:

• Execute multi-step reasoning with branching logic
• Maintain persistent state across interactions
• Include human approval steps
• Handle errors and retries gracefully
• Coordinate multiple agents working together

import { StateGraph } from "@langchain/langgraph";

const workflow = new StateGraph({
  channels: {
    messages: { value: (a, b) => [...a, ...b], default: () => [] },
    nextStep: { value: (a, b) => b, default: () => "agent" },
  },
})
  .addNode("agent", callModel)
  .addNode("tools", callTools)
  .addEdge("__start__", "agent")
  .addConditionalEdges("agent", shouldContinue, {
    continue: "tools",
    end: "__end__",
  })
  .addEdge("tools", "agent");

If you're building AI agents that need to make decisions, call APIs, manage complex state, or coordinate multiple LLMs, LangGraph is the most mature option.

The LangChain Ecosystem

LangChain isn't just a framework — it's an ecosystem:

• LangSmith — Observability, tracing, and evaluation platform for debugging and monitoring AI apps
• LangGraph — Agent orchestration framework with persistence, streaming, and human-in-the-loop
• LangServe — Deploy chains as REST APIs
• LangChain Hub — Share and discover prompts

Strengths

• Most comprehensive feature set. If you need it, LangChain probably supports it.
• Strongest agent framework. LangGraph handles complex, multi-step workflows that other frameworks can't match.
• Massive ecosystem. Hundreds of integrations with LLM providers, vector stores, document loaders, and tools.
• Python and TypeScript. Full support for both languages, though the Python ecosystem is larger.
• Production observability. LangSmith provides tracing and debugging tools that other frameworks lack.

Weaknesses

• Steep learning curve. The abstraction layers are deep, and the API surface is large. Simple tasks can require understanding multiple concepts.
• Frequent breaking changes. The framework evolves rapidly. Code that works today may need updates in a few months.
• Over-abstraction risk. For simple use cases, LangChain adds unnecessary complexity. You might write more code than calling the LLM API directly.
• Documentation can lag. With frequent updates, documentation sometimes falls behind the actual API.

---

LlamaIndex: The Data Expert

LlamaIndex started as a framework specifically for connecting LLMs with external data. While it has expanded to support agents and other use cases, its core strength remains data indexing, retrieval, and RAG.

Core Philosophy

LlamaIndex's approach is data-first. The framework is built around the idea that the hardest part of building AI applications isn't calling an LLM — it's getting the right data to the LLM at the right time. Every design decision prioritizes making data retrieval easy, accurate, and scalable.

The fundamental building blocks are:

• Documents — your raw data (PDFs, web pages, databases, APIs)
• Nodes — chunked pieces of documents with metadata and relationships
• Indexes — organized structures for efficient retrieval
• Query Engines — interfaces for asking questions against your data

import {
  VectorStoreIndex,
  SimpleDirectoryReader,
} from "llamaindex";

const documents = await new SimpleDirectoryReader().loadData("./data");

const index = await VectorStoreIndex.fromDocuments(documents);

const queryEngine = index.asQueryEngine();

const response = await queryEngine.query("What is the refund policy?");
console.log(response.toString());

That's a complete RAG pipeline in five lines. LlamaIndex handles chunking, embedding, indexing, retrieval, and response synthesis behind the scenes.

RAG with LlamaIndex

RAG is LlamaIndex's superpower. The framework provides specialized tools that go far beyond basic vector search:

Advanced retrieval strategies:

• Hybrid search — combine vector similarity with keyword matching for better results
• Recursive retrieval — start with a high-level search, then drill into the most relevant sections
• Metadata filtering — narrow results by date, author, category, or any custom metadata
• Auto-merging retriever — automatically combines small chunks back into larger context when multiple related chunks are retrieved
• Re-ranking — apply a second model to re-score and re-order results

Specialized indexes:

• VectorStoreIndex — standard semantic search
• SummaryIndex — for summarization tasks over entire document collections
• KnowledgeGraphIndex — builds and queries knowledge graphs from your documents
• TreeIndex — hierarchical tree structure for multi-level summarization

import {
  VectorStoreIndex,
  MetadataFilters,
  MetadataFilter,
  FilterOperator,
} from "llamaindex";

const queryEngine = index.asQueryEngine({
  similarityTopK: 5,
  preFilters: new MetadataFilters({
    filters: [
      new MetadataFilter({
        key: "category",
        value: "engineering",
        operator: FilterOperator.EQ,
      }),
    ],
  }),
});

Agent Capabilities

LlamaIndex has expanded its agent capabilities significantly. It now offers agent workflows — a framework for building multi-step agent pipelines with tool use, reasoning, and state management.

import { OpenAIAgent } from "llamaindex";

const agent = new OpenAIAgent({
  tools: [searchTool, calculatorTool, emailTool],
});

const response = await agent.chat(
  "Find the quarterly revenue, calculate year-over-year growth, and email the summary to the team."
);

LlamaIndex agents work well for data-centric tasks — querying multiple data sources, synthesizing information from different indexes, and answering questions that require looking up and combining information. For general-purpose agent workflows with complex branching logic, LangGraph is still more capable.

The LlamaIndex Ecosystem

• LlamaCloud — Managed parsing, indexing, and retrieval service. Handles document processing at scale without managing infrastructure.
• LlamaParse — Advanced document parser that handles PDFs, tables, images, and complex layouts far better than basic text extraction.
• LlamaHub — Community repository of data loaders, tools, and integrations.

Strengths

• Best-in-class RAG. No other framework matches LlamaIndex's depth of retrieval strategies, indexing options, and data handling.
• Fastest path to a working RAG app. Minimal boilerplate gets you from documents to a queryable system in minutes.
• LlamaParse and LlamaCloud. Managed services that solve the hardest parts of document processing and indexing at scale.
• Growing agent support. Agent workflows are increasingly capable for data-centric use cases.
• Python and TypeScript. Both are well-supported, with the Python library being more mature.

Weaknesses

• Less flexible for non-RAG use cases. If your app doesn't involve data retrieval, much of LlamaIndex's value proposition doesn't apply.
• Agents are less mature than LangGraph. For complex, branching agent workflows, LlamaIndex's agent framework is still catching up.
• Smaller integration ecosystem. Fewer third-party integrations compared to LangChain, though the gap is narrowing.
• Opinionated defaults. The "just works" approach means less control over individual pipeline stages unless you dig deeper.

---

Vercel AI SDK: The Frontend-First Framework

Vercel AI SDK takes a completely different approach from LangChain and LlamaIndex. Instead of focusing on backend AI orchestration, it's built for creating AI-powered user interfaces — specifically with React, Next.js, and other modern web frameworks.

Core Philosophy

Vercel AI SDK's philosophy is UI-first AI. The framework assumes you're building a web application where users interact with AI through a chat interface, form, or other UI component. It prioritizes streaming, real-time updates, and seamless integration with React's component model.

The SDK has two main layers:

1. AI SDK Core — Provider-agnostic API for calling any LLM (OpenAI, Anthropic, Google, Mistral, etc.)
2. AI SDK UI — React hooks (useChat, useCompletion, useObject) that handle streaming, state management, and UI updates

// app/api/chat/route.ts (Next.js API route)
import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";

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

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

  return result.toDataStreamResponse();
}

// app/page.tsx (React component)
"use client";
import { useChat } from "@ai-sdk/react";

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

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

That's a complete streaming chat application. The useChat hook manages the entire conversation lifecycle — sending messages, streaming responses, handling errors, and updating the UI in real time.

RAG with Vercel AI SDK

Vercel AI SDK doesn't provide built-in RAG infrastructure — no document loaders, no chunking, no vector store integrations. It's intentionally lightweight on the data side. You bring your own retrieval pipeline and feed the results into the SDK's generation functions.

import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";

export async function POST(req: Request) {
  const { messages } = await req.json();
  const lastMessage = messages[messages.length - 1];

  // You implement retrieval yourself
  const relevantDocs = await searchVectorStore(lastMessage.content);
  const context = relevantDocs.map((d) => d.content).join("\n");

  const result = streamText({
    model: openai("gpt-4o"),
    system: `Answer based on this context:\n${context}`,
    messages,
  });

  return result.toDataStreamResponse();
}

This means more work if you need a RAG system, but also more flexibility. You can use any retrieval method — a simple database query, a vector search, or even LlamaIndex as the retrieval layer — and pipe the results through Vercel AI SDK for the frontend experience.

Agent Capabilities

Vercel AI SDK supports tool calling and multi-step agent interactions through its core API:

import { openai } from "@ai-sdk/openai";
import { generateText, tool } from "ai";
import { z } from "zod";

const result = await generateText({
  model: openai("gpt-4o"),
  tools: {
    weather: tool({
      description: "Get the weather for a location",
      parameters: z.object({
        location: z.string().describe("City name"),
      }),
      execute: async ({ location }) => {
        return await fetchWeather(location);
      },
    }),
  },
  maxSteps: 5,
  messages: [{ role: "user", content: "What's the weather in London?" }],
});

The maxSteps parameter enables multi-step agent loops — the model can call tools, get results, reason about them, and call more tools. For many web applications, this is enough. But for complex agent architectures with branching logic, persistent state, or multi-agent coordination, you'll need LangGraph or a custom solution.

Streaming and UI Integration

This is where Vercel AI SDK is unmatched. The framework provides:

• Streaming text — Token-by-token streaming with automatic UI updates
• Streaming structured objects — Stream JSON objects as they're generated, with partial updates
• Generative UI — Stream React components from the server to the client
• Multi-modal support — Handle image, audio, and file inputs/outputs
• Client-side state management — Hooks that handle loading states, errors, abort signals, and message history

import { openai } from "@ai-sdk/openai";
import { streamObject } from "ai";
import { z } from "zod";

const result = streamObject({
  model: openai("gpt-4o"),
  schema: z.object({
    recipe: z.object({
      name: z.string(),
      ingredients: z.array(z.string()),
      steps: z.array(z.string()),
    }),
  }),
  prompt: "Generate a recipe for chocolate chip cookies.",
});

// Stream partial objects as they arrive
for await (const partialObject of result.partialObjectStream) {
  console.log(partialObject); // { recipe: { name: "...", ingredients: ["flour"... }
}

The Vercel Ecosystem

• Next.js integration — First-class support for App Router, Server Actions, and Edge Runtime
• Provider adapters — Unified API across OpenAI, Anthropic, Google, Mistral, Cohere, and more
• v0 — Vercel's AI app builder uses the same SDK, proving it at scale
• AI SDK RSC — Experimental support for streaming React Server Components

Strengths

• Lowest learning curve. If you know React and Next.js, you can build a streaming AI app in minutes.
• Best streaming experience. No other framework handles streaming text, objects, and UI components as well.
• Provider-agnostic. Switch between OpenAI, Anthropic, Google, or any supported provider by changing one line.
• Lightweight. Adds minimal overhead. You're not importing a massive framework — just the pieces you need.
• React-native to the core. Hooks, components, and patterns that feel natural in a React codebase.

Weaknesses

• TypeScript/JavaScript only. No Python support. If your AI pipeline is in Python, Vercel AI SDK can't help.
• No built-in RAG. You need to build or integrate your own retrieval pipeline.
• Limited agent capabilities. Tool calling works, but complex agent architectures need custom code or a separate framework.
• Web-focused. Built for web applications. Less useful for backend-only services, data pipelines, or CLI tools.
• Tied to the Vercel ecosystem. While it works outside Vercel, the best experience is with Next.js deployed on Vercel.

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Head-to-Head Comparison

Data and RAG

Factor	LangChain	LlamaIndex	Vercel AI SDK
Document loaders	80+ integrations	100+ via LlamaHub	None (bring your own)
Chunking strategies	Multiple splitters	Advanced node parsing	None
Vector store support	30+ stores	20+ stores	None
Retrieval strategies	Standard + custom	Advanced (hybrid, recursive, re-ranking)	None
RAG complexity	Medium setup	Minimal setup	DIY

Agents and Tool Use

Factor	LangChain	LlamaIndex	Vercel AI SDK
Agent framework	LangGraph (most advanced)	Agent workflows	Basic tool calling
Multi-step reasoning	Excellent	Good	Basic (maxSteps)
State management	Built-in persistence	In-memory	Client-side hooks
Human-in-the-loop	First-class support	Basic	Not built-in
Multi-agent	Supported	Limited	Not supported

Developer Experience

Factor	LangChain	LlamaIndex	Vercel AI SDK
Time to hello world	30–60 minutes	15–30 minutes	5–10 minutes
Learning curve	Steep	Moderate	Low
Documentation quality	Extensive but scattered	Well-organized	Excellent
TypeScript support	Good	Good	Excellent (primary language)
Python support	Excellent (primary)	Excellent (primary)	None

Streaming and UI

Factor	LangChain	LlamaIndex	Vercel AI SDK
Text streaming	Supported	Supported	Excellent
Object streaming	Manual	Manual	Built-in
React hooks	None	None	useChat, useCompletion, useObject
Generative UI	Not supported	Not supported	Supported
Loading/error states	Manual	Manual	Automatic

Community and Ecosystem

Factor	LangChain	LlamaIndex	Vercel AI SDK
GitHub stars	100K+ (Python + JS)	40K+ (Python + JS)	15K+
NPM weekly downloads	~500K	~100K	~300K
Third-party integrations	Most extensive	Strong (data-focused)	Growing
Commercial support	LangSmith, LangGraph Cloud	LlamaCloud, LlamaParse	Vercel platform
Release cadence	Very frequent	Frequent	Frequent

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The Decision Framework

Choose LangChain When

• You're building complex agent workflows. LangGraph is the most capable agent framework for multi-step, branching, stateful AI agents.
• You need maximum flexibility. LangChain's modular design lets you customize every piece of the pipeline.
• Your project spans Python and JavaScript. LangChain has strong support for both ecosystems.
• You need production observability. LangSmith provides tracing, evaluation, and monitoring that other frameworks can't match.
• You're building backend services. API servers, data pipelines, or agent services that don't have a frontend.

Choose LlamaIndex When

• RAG is your primary use case. If your app is fundamentally about searching, retrieving, and synthesizing information from documents, LlamaIndex is purpose-built for this.
• You have complex data sources. PDFs with tables, images, mixed-format documents — LlamaParse handles these better than anything else.
• You want the fastest path to a working RAG app. LlamaIndex's defaults get you from zero to a queryable system with minimal code.
• You need advanced retrieval strategies. Hybrid search, recursive retrieval, knowledge graphs, auto-merging — LlamaIndex has these built in.
• You're building a knowledge base or Q&A system. This is exactly what LlamaIndex was designed for.

Choose Vercel AI SDK When

• You're building a Next.js or React application. The SDK integrates seamlessly with the React component model.
• Streaming UX is critical. If your users expect real-time, token-by-token responses with smooth UI updates, Vercel AI SDK handles this best.
• You want minimal complexity. The SDK is lightweight and focused. No massive framework to learn — just hooks and functions.
• You're prototyping quickly. From idea to working chat interface in minutes, not hours.
• Your AI features are part of a larger web app. The SDK fits into an existing Next.js project without taking over the architecture.

Quick Decision Guide

Your Situation	Best Choice
"I need to build a RAG chatbot over company documents"	LlamaIndex
"I'm building an AI agent that makes decisions and calls APIs"	LangChain (LangGraph)
"I want to add a chat interface to my Next.js app"	Vercel AI SDK
"I need to process and index thousands of PDFs"	LlamaIndex
"I'm building a multi-agent system with human oversight"	LangChain (LangGraph)
"I want streaming AI responses in a React app"	Vercel AI SDK
"My app needs to query multiple data sources and synthesize answers"	LlamaIndex
"I need complex branching logic in my AI pipeline"	LangChain
"I want the simplest possible setup for an AI feature"	Vercel AI SDK
"I need strong Python support"	LangChain or LlamaIndex

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Combining Frameworks

The best approach is often to use more than one. These frameworks aren't mutually exclusive — they solve different problems and combine well.

LlamaIndex + Vercel AI SDK (Most Common Combo)

Use LlamaIndex for your data pipeline and retrieval logic. Use Vercel AI SDK for the streaming frontend experience. This gives you best-in-class RAG with best-in-class UI.

// API route: LlamaIndex handles retrieval, Vercel AI SDK handles streaming
import { VectorStoreIndex } from "llamaindex";
import { openai } from "@ai-sdk/openai";
import { streamText } from "ai";

export async function POST(req: Request) {
  const { messages } = await req.json();
  const query = messages[messages.length - 1].content;

  // LlamaIndex retrieval
  const queryEngine = index.asQueryEngine();
  const context = await queryEngine.retrieve(query);

  // Vercel AI SDK streaming
  const result = streamText({
    model: openai("gpt-4o"),
    system: `Answer based on this context:\n${context.map(n => n.node.text).join("\n")}`,
    messages,
  });

  return result.toDataStreamResponse();
}

LangChain + Vercel AI SDK

Use LangChain (or LangGraph) for backend agent logic and Vercel AI SDK for the frontend streaming layer. LangChain handles the complex orchestration; Vercel AI SDK handles the user experience.

LangChain + LlamaIndex

Use LlamaIndex as the retrieval component within a LangChain pipeline. LlamaIndex provides the superior data indexing and retrieval, while LangChain orchestrates the broader agent workflow around it.

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Framework Maturity and Stability

All three frameworks are actively developed and evolving. Here's what to expect:

LangChain has the longest track record but also the most frequent breaking changes. The core API has stabilized significantly since the 0.2/0.3 releases, but expect ongoing evolution, especially in LangGraph. The large team at LangChain Inc. ensures consistent development and commercial support.

LlamaIndex has matured rapidly. The TypeScript version is now feature-rich enough for production use. LlamaCloud and LlamaParse provide managed services that reduce the operational burden. The API is more stable than LangChain's but still evolving.

Vercel AI SDK benefits from Vercel's large engineering team and its tight integration with Next.js. The core API (streamText, generateText, useChat) is stable and well-designed. As a younger framework, it has fewer features but what it has is polished.

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Frequently Asked Questions

Which framework should a beginner start with?

Start with Vercel AI SDK if you're building web applications. It has the lowest learning curve and gets you to a working app fastest. If your focus is RAG, start with LlamaIndex — its defaults handle the complexity for you. Only start with LangChain if you specifically need agent capabilities from day one.

Can I switch frameworks later?

Yes, but it's easier to switch some components than others. LLM calls are the easiest to migrate because all three frameworks use similar abstractions. RAG pipelines are harder to migrate because each framework structures retrieval differently. Agent logic is the hardest because each framework models agents with different paradigms.

Which framework has the best TypeScript support?

Vercel AI SDK was built TypeScript-first and it shows — the types are excellent, the DX is smooth, and the documentation targets TypeScript developers. LangChain.js and LlamaIndex.TS are both good but are ports of Python-first libraries, which occasionally shows in API design and documentation gaps.

Do I need a framework at all?

For simple use cases — a single LLM call, basic chat with no retrieval — you can call the LLM provider's API directly. Frameworks add value when you need streaming, tool calling, RAG, agents, or provider abstraction. If your app is just sending messages to the OpenAI API and displaying responses, you might not need one.

How do these frameworks handle costs?

None of the frameworks charge for usage — they're all open source. Your costs come from the LLM API calls, vector database hosting, and compute infrastructure. LlamaCloud and LangSmith are paid services, but the core frameworks are free. Vercel charges for hosting but the AI SDK itself is free.

Which framework is best for production?

All three are used in production. LangChain has the most production deployments and the best observability (LangSmith). LlamaIndex has LlamaCloud for managed RAG infrastructure. Vercel AI SDK benefits from Vercel's production-grade hosting. The "best" depends on your specific requirements, not the framework itself.

Can I use these with any LLM provider?

LangChain supports the most providers through its integration packages. LlamaIndex supports major providers (OpenAI, Anthropic, Google, local models). Vercel AI SDK supports all major providers through its adapter system and has an active community creating new adapters. All three work with OpenAI, Anthropic, and Google out of the box.

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Key Takeaways

• LangChain is the most comprehensive framework with the strongest agent capabilities (LangGraph), but comes with a steep learning curve and frequent API changes.
• LlamaIndex is the best choice for RAG and data-heavy applications, offering advanced retrieval strategies and the fastest path from documents to a working query system.
• Vercel AI SDK is the best choice for web developers building AI-powered React and Next.js applications, with unmatched streaming UX and the lowest learning curve.
• Start with what matches your primary need. RAG → LlamaIndex. Agents → LangChain. Web UI → Vercel AI SDK.
• Combine frameworks when your app needs strengths from multiple tools. LlamaIndex for retrieval + Vercel AI SDK for streaming is a particularly powerful combination.
• You don't always need a framework. For simple LLM calls, the provider's SDK may be enough. Add a framework when complexity demands it.

The AI framework landscape is evolving fast, but these three have established themselves as the leading options for TypeScript and JavaScript developers. Pick the one that aligns with your project's core need, and don't hesitate to bring in a second framework when you hit its limits.

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Learn More

Want to go deeper into building AI applications? Check out these FreeAcademy resources:

• What is RAG? — Understand Retrieval Augmented Generation from the ground up
• How to Build a RAG Chatbot — Hands-on tutorial with Next.js and Supabase
• What Are Vector Databases? — The technology powering modern RAG systems
• RAG vs Fine-Tuning vs Prompt Engineering — When to use each approach for customizing LLMs
• Building AI Agents with Node.js — Deep dive into building production AI agents
• Python vs JavaScript for AI Development — Choosing the right language for your AI stack
• Building AI Agents with Node.js & TypeScript Course — Full course on building production AI apps