Comparison Intermediate · 4 min read

LangChain vs LlamaIndex comparison

Q: LangChain vs LlamaIndex comparison

Use LangChain for building flexible, modular AI applications with extensive integrations and prompt management. Use LlamaIndex (also known as GPT Index) for efficient document ingestion, indexing, and retrieval augmented generation workflows.

Quick answer

Use LangChain for building flexible, modular AI applications with extensive integrations and prompt management. Use LlamaIndex (also known as GPT Index) for efficient document ingestion, indexing, and retrieval augmented generation workflows.

VERDICT

Use LangChain for general-purpose AI app development with broad tooling; use LlamaIndex for specialized long-document indexing and retrieval tasks.

Tool	Key strength	Pricing	API access	Best for
LangChain	Modular AI app framework with multi-LLM support	Free (open-source)	Yes, via SDKs	Complex AI workflows and prompt chaining
LlamaIndex	Document ingestion and vector indexing	Free (open-source)	Yes, via SDKs	Long document retrieval and RAG pipelines
LangChain	Rich integrations with APIs and data sources	Free (open-source)	Yes	Multi-modal and multi-source AI apps
LlamaIndex	Optimized for knowledge base construction	Free (open-source)	Yes	Knowledge retrieval and summarization

Key differences

LangChain is a comprehensive framework designed for building AI applications with modular components like prompt templates, chains, agents, and memory. It supports multiple LLM providers and integrates with various data sources and APIs. LlamaIndex focuses primarily on document ingestion, indexing, and retrieval augmented generation (RAG), providing efficient vector store management and query interfaces optimized for long documents.

While LangChain emphasizes flexible AI workflows and chaining, LlamaIndex specializes in knowledge base construction and retrieval from large text corpora.

Side-by-side example

Here is a simple example of querying a document using LangChain with a vector store and an LLM.

python

from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import FAISS
from langchain_core.document_loaders import TextLoader
from langchain_core.chains import RetrievalQA
from langchain_openai import OpenAIEmbeddings
import os

# Load documents
loader = TextLoader("example.txt")
docs = loader.load()

# Create embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(docs, embeddings)

# Setup LLM
llm = ChatOpenAI(model="gpt-4o", api_key=os.environ["OPENAI_API_KEY"])

# Create retrieval QA chain
qa = RetrievalQA(llm=llm, retriever=vectorstore.as_retriever())

# Query
query = "What is the main topic of the document?"
answer = qa.run(query)
print(answer)

output

The main topic of the document is ...

LlamaIndex equivalent

Using LlamaIndex to load documents, build an index, and query it with an LLM.

python

from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, ServiceContext
from openai import OpenAI
import os

# Load documents from directory
documents = SimpleDirectoryReader('docs').load_data()

# Setup LLM client
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
service_context = ServiceContext.from_defaults(llm=client)

# Build vector index
index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)

# Query index
query_engine = index.as_query_engine()
response = query_engine.query("Summarize the key points.")
print(response.response)

output

The key points are ...

When to use each

LangChain is ideal when you need to build complex AI workflows involving multiple LLM calls, prompt management, agents, and integration with APIs or databases. It excels in multi-step reasoning and chaining tasks.

LlamaIndex is best when your primary need is to ingest large volumes of documents, create efficient vector indexes, and perform retrieval augmented generation for knowledge-heavy applications.

Use case	Recommended tool
Multi-step AI workflows and prompt chaining	LangChain
Long document ingestion and retrieval	LlamaIndex
Integrating multiple data sources and APIs	LangChain
Building knowledge bases and RAG pipelines	LlamaIndex

Pricing and access

Both LangChain and LlamaIndex are open-source and free to use. They require API keys for LLM providers like OpenAI or Anthropic to function. Both provide SDKs for easy integration.

Option	Free	Paid	API access
LangChain	Yes	No	Yes, via SDKs
LlamaIndex	Yes	No	Yes, via SDKs
OpenAI LLMs	Limited free quota	Yes	Yes
Anthropic LLMs	Limited free quota	Yes	Yes

✅

Key Takeaways

Use LangChain for building modular, multi-step AI applications with broad integrations.
LlamaIndex excels at document ingestion, indexing, and retrieval augmented generation.
Both tools are open-source and require LLM API keys for full functionality.
Choose based on whether your focus is AI workflow complexity (LangChain) or document-centric retrieval (LlamaIndex).

Verified 2026-04 · gpt-4o, claude-3-5-sonnet-20241022

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