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LangChain LlamaIndex Comparison 9 min read

LangChain vs LlamaIndex: Which AI Framework Should You Use? (2025)

Both LangChain and LlamaIndex help you build LLM-powered applications — but they're optimized for different things. LangChain is the go-to for agents, chains, and complex multi-step workflows with 600+ integrations. LlamaIndex is the specialist: best-in-class RAG, simpler document Q&A setup, and deeper retrieval defaults. Here's how to choose — and when to use both together.

Key differences at a glance

Feature LangChain LlamaIndex
Primary focus Agents + general LLM apps RAG + document retrieval
RAG quality Good ✓ Best (specialized)
Agent capabilities ✓ Best (LangGraph) Basic agents
Integrations 600+ (broader) 160+ (deeper for RAG)
Learning curve Steeper Gentler for RAG use cases
Streaming support ✓ ✓
TypeScript support ✓ LangChain.js ✓ TypeScript SDK
Observability LangSmith ($) Llamatrace (free)
Use with Claude ✓ ✓
License MIT MIT

What is LangChain?

LangChain is a Python/TypeScript framework for building LLM-powered applications. It's designed around composable building blocks that let you wire together models, tools, memory, and retrieval in complex sequences.

Best for

Chains (sequence of LLM calls), agents (autonomous AI that uses tools), memory management, and applications that integrate many components across many providers.

Core concepts

  • Chain — a sequence of steps (prompt → LLM → output parser → next step)
  • Agent — an LLM that decides which tools to call and in what order
  • Tools — functions the agent can invoke (search, code runner, database query)
  • Memory — conversation state that persists across turns

LangGraph

LangChain's newer framework for building stateful, multi-agent workflows with graph-based execution — multiple agents collaborating with conditional branching, cycles, and human-in-the-loop steps. This is where LangChain has the clearest lead over LlamaIndex.

GitHub: 90k+ stars — one of the most-starred AI frameworks on GitHub.

What is LlamaIndex?

LlamaIndex (formerly GPT Index) is a Python/TypeScript framework specialized for RAG (Retrieval-Augmented Generation) — connecting LLMs to your own data. Where LangChain is a general-purpose orchestration framework, LlamaIndex is purpose-built for the retrieval layer.

Best for

Building search over documents, question-answering from your data, building knowledge bases, and indexing and retrieving private documents at scale.

Core concepts

  • Document — what you index (PDF, text file, database row, web page)
  • Index — the searchable structure built from your documents
  • QueryEngine — retrieve relevant chunks and answer with an LLM
  • VectorStore — the semantic search backend (Pinecone, Chroma, Weaviate, etc.)

GitHub: 36k+ stars — the dominant RAG-focused framework in the Python ecosystem.

RAG: code comparison

Both frameworks support RAG. LlamaIndex's API is simpler and has better defaults out of the box. LangChain gives you more control but requires more boilerplate.

LlamaIndex RAG — simpler, better defaults:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What are the main findings?")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader("./data").load_data() index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() response = query_engine.query("What are the main findings?")

LangChain RAG — more verbose, more control:

from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)
retriever = vectorstore.as_retriever()
qa_chain = RetrievalQA.from_chain_type(llm=ChatOpenAI(), retriever=retriever)
result = qa_chain.invoke({"query": "What are the main findings?"})
from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings from langchain.chains import RetrievalQA from langchain_openai import ChatOpenAI embeddings = OpenAIEmbeddings() vectorstore = Chroma(embedding_function=embeddings) retriever = vectorstore.as_retriever() qa_chain = RetrievalQA.from_chain_type(llm=ChatOpenAI(), retriever=retriever) result = qa_chain.invoke({"query": "What are the main findings?"})
from langchain_community.vectorstores import Chroma from langchain_openai import OpenAIEmbeddings from langchain.chains import RetrievalQA from langchain_openai import ChatOpenAI embeddings = OpenAIEmbeddings() vectorstore = Chroma(embedding_function=embeddings) retriever = vectorstore.as_retriever() qa_chain = RetrievalQA.from_chain_type(llm=ChatOpenAI(), retriever=retriever) result = qa_chain.invoke({"query": "What are the main findings?"})from llama_index.core import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader("./data").load_data() index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() response = query_engine.query("What are the main findings?")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader("./data").load_data() index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() response = query_engine.query("What are the main findings?")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader("./data").load_data() index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() response = query_engine.query("What are the main findings?")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader documents = SimpleDirectoryReader("./data").load_data() index = VectorStoreIndex.from_documents(documents) query_engine = index.as_query_engine() response = query_engine.query("What are the main findings?")