Choosing between LangChain and LlamaIndex is a fundamental decision for any developer building with LLMs.
If you're building an AI agent or a complex, multi-step application pipeline, you're likely evaluating LangChain vs LlamaIndex. Both are powerful Python frameworks that simplify LLM integration, but they solve different problems. LangChain is your toolkit for orchestrating actions and logic, while LlamaIndex is your specialist for connecting data to LLMs. Picking the wrong one can lead to unnecessary complexity or a lack of critical features. Let's break down where each one shines.
LangChain vs LlamaIndex: The Key Differences
The core difference is architectural: LangChain is an agent and orchestration framework, whereas LlamaIndex is a data framework for LLMs. Think of LangChain as a general-purpose workshop with tools for building complex machinery (agents that can use calculators, search the web, and execute code). LlamaIndex is a dedicated library for one job: creating a searchable knowledge interface between your private data and an LLM.
LangChain provides abstractions for chains, agents, and tools, enabling you to wire together multiple LLM calls, external APIs, and custom logic. LlamaIndex provides abstractions for indexing, retrieving, and synthesizing information from your data sources. It's exceptionally good at turning your documents into queryable vector stores and managing the retrieval-augmented generation (RAG) pipeline.
When to Use LangChain
Use LangChain when your application's intelligence comes from orchestrating steps and interacting with the outside world. Its strength is building agents that can reason and act.
Classic use cases include chatbots that need to fetch live data (like weather or stock prices), coding assistants that can run shell commands, or automated workflows that decide which tool to use next based on the last output. If your design involves "if this, then do that" logic between LLM calls, you want LangChain.
Here's a minimal example of a LangChain agent using a simple math tool. This orchestration—where the LLM decides to use a tool—is LangChain's home turf.
from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI
from langchain.chains import LLMMathChain
llm = OpenAI(temperature=0)
llm_math = LLMMathChain(llm=llm)
tools = [
Tool(
name="Calculator",
func=llm_math.run,
description="Useful for answering math questions"
),
]
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)
agent.run("What is 15 raised to the power of 2?")
When to Use LlamaIndex
Use LlamaIndex when your application's core value is answering questions based on private or domain-specific data. It's the superior choice for any RAG (Retrieval-Augmented Generation) application.
If you're building a document Q&A system, a chatbot over your company's internal wiki, or a research assistant that synthesizes information from multiple PDFs, start with LlamaIndex. It offers more sophisticated and fine-tuned data connectors, indexing strategies, and query engines out of the box. Its "router" and "sub-question" query engines handle complex, multi-document queries with less manual setup than LangChain's equivalent RAG constructs.
from llama_index import VectorStoreIndex, SimpleDirectoryReader
# Load your data
documents = SimpleDirectoryReader("./your_data_folder").load_data()
# Create a vector index - this is LlamaIndex's specialty
index = VectorStoreIndex.from_documents(documents)
# Create a query engine that handles retrieval and synthesis
query_engine = index.as_query_engine()
response = query_engine.query("What was the main outcome of the Q4 project?")
print(response)
LangChain or LlamaIndex: Which One Should You Pick?
Choose based on your primary task. Ask this question: Is my app mostly about querying private data, or is it about performing tasks and making decisions?
For querying private data (RAG), pick LlamaIndex. It's more focused, often requires less code for this specific task, and its abstractions are optimized for high-quality retrieval and synthesis. For task automation and multi-step reasoning (agents), pick LangChain. Its agent-executor model and vast library of integrations are built for this.
If you need both, you can use them together—LlamaIndex can serve as a superior retrieval tool within a LangChain agent. However, for most projects, one framework will cover 90% of your needs cleanly.
My Take
For the majority of projects I see, developers reach for LangChain when they should start with LlamaIndex. The hype around agents is huge, but the most immediate, valuable business application for LLMs is unlocking insights from internal data—a pure RAG problem. LlamaIndex is the sharper tool for that job.
Unless you are explicitly building an agent that must use tools and make decisions, begin your project with LlamaIndex for its cleaner, more powerful data pipeline. You'll get a better retrieval system with less friction. Only introduce LangChain if you later need to add agentic capabilities on top of that solid data foundation.
The decision becomes obvious once you admit whether you're building a library search engine or a robot butler.