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LangChain vs LlamaIndex vs NIM: A Beginner-Friendly Comparison
- Authors
- Name
- Nguyen Phuc Cuong
If you're just stepping into the world of Large Language Models (LLMs), you've probably heard of LangChain, LlamaIndex, and NIM. All three are powerful frameworks that help developers build applications on top of LLMs, but they approach the problem differently.
In this post, I'll break down what each tool does, how they compare, and when you might want to use one over the others.
🧠 TL;DR
| Framework | Best For | Style | Popular Use Cases |
|---|---|---|---|
| LangChain | Workflow orchestration + agents | Modular / Composable | Chatbots, agents, RAG |
| LlamaIndex | Data ingestion + querying | Document-centric | RAG, knowledge retrieval |
| NIM | Developer ergonomics + performance | Functional / Typed | High-perf RAG, TypeScript |
🔗 LangChain: The Swiss Army Knife for LLMs
LangChain is all about building chains of operations—parsing inputs, calling LLMs, managing tools, and more.
"I want to take this user input → analyze it → search a database → summarize it → and send it back."
LangChain is great for:
- Composing multi-step LLM workflows
- Building agents that use tools (like a calculator or web search)
- Fine-grained control over the flow of logic
Pros
- Highly modular
- Tons of integrations
- Active community and documentation
Cons
- Can get complex fast
- Verbose code for simple tasks
from langchain.agents import initialize_agent
agent = initialize_agent(
tools=[search_tool, math_tool],
llm=llm_model,
agent_type="zero-shot-react-description"
)
📚 LlamaIndex: Turning Documents into LLM-Ready Data
LlamaIndex, formerly known as GPT Index, focuses on data ingestion and retrieval. It shines when you're trying to build a RAG (Retrieval-Augmented Generation) system from your own docs, PDFs, or database.
"How do I feed my Notion notes / company docs into ChatGPT?"
LlamaIndex is your friend.
Pros
- Easy document parsing & chunking
- Built-in vector store support
- Fast to prototype a doc-based chatbot
Cons
- Less control over complex workflows (though improving)
- Limited agent-based features (compared to LangChain)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
docs = SimpleDirectoryReader("docs/").load_data()
index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine()
⚡️ NIM: The Newcomer with Type Safety and Speed
NIM (not to be confused with the language Nim) is a TypeScript-first LLM toolkit designed for ergonomics and performance. It's newer, leaner, and focused on making LLM workflows easy and type-safe for frontend/backend devs alike.
"If you love building in TypeScript and want type-safe LLM flows, NIM could be a game-changer."
Pros
- Type-safe by default
- Fast and clean DX
- Great for fullstack/TS devs
Cons
- Smaller community (for now)
- Still evolving in features
import { pipeline } from 'nim'
const qaBot = pipeline()
.ingest('data/\*.md')
.ask('What is NIM?')
🧭 When to Use What?
Here’s a rough guide based on what you're trying to build:
- Chatbot using company data? → LlamaIndex + LangChain
- Need agents that make decisions? → LangChain
- Want a fast, modern, typed stack? → NIM
- Just exploring? → LlamaIndex is easiest to start
👨💻 Final Thoughts
Each of these tools has its own flavor, and they’re not mutually exclusive. In fact, LangChain and LlamaIndex are often used together, and NIM could fit in where you want clean, modern TypeScript-based logic.
Whichever you choose, you're in good company. The LLM ecosystem is moving fast, but with the right tools, you’ll be able to build something magical.
🚀 Get Started
- Try LangChain's quickstart guide
- Build your first LlamaIndex app
- Explore NIM's TypeScript examples