LlamaIndex vs LangChain for RAG: A Practical Comparison
By Diesel
toolsllamaindexlangchainragcomparison
If you're building a Retrieval Augmented Generation system, you've probably narrowed it down to LlamaIndex or LangChain. Both can do it. Both have tutorials. Both have active communities telling you theirs is better.
I've shipped RAG systems with both, and the truth is less about which one is "better" and more about what kind of RAG system you're building.
## The Fundamental Difference
LlamaIndex was built for data. Its entire architecture is organized around indexing, retrieving, and querying over documents. RAG isn't a feature of LlamaIndex. It's the reason LlamaIndex exists.
LangChain was built for chaining LLM operations. RAG is one of many things you can build with it. Retrieval is a step in a chain, alongside prompt formatting, model calls, tool use, and output parsing.
This shapes everything. The APIs, the defaults, the abstractions, the documentation. LlamaIndex thinks about your data first. LangChain thinks about your workflow first. It is worth reading about [LangChain vs LangGraph](/blog/langchain-vs-langgraph) alongside this.
## LlamaIndex: Data-First RAG
```python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
# Load documents
documents = SimpleDirectoryReader("./data").load_data()
# Build index (handles chunking, embedding, and storage)
index = VectorStoreIndex.from_documents(documents)
# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is our refund policy?")
print(response)
```
Five lines from documents to working RAG. LlamaIndex handles document loading, text splitting, embedding generation, vector storage, retrieval, prompt construction, and LLM synthesis. The defaults are sensible. For a quick prototype, this is unbeatable.
But the real power is in the customization. LlamaIndex gives you fine-grained control over every stage of the RAG pipeline when you need it.
```python
from llama_index.core import VectorStoreIndex, Settings
from llama_index.core.node_parser import SentenceSplitter
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.anthropic import Anthropic
from llama_index.vector_stores.pgvector import PGVectorStore
# Configure each component
Settings.llm = Anthropic(model="claude-sonnet-4-20250514")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
# Custom chunking
parser = SentenceSplitter(chunk_size=512, chunk_overlap=50)
# Custom vector store
vector_store = PGVectorStore.from_params(
database="mydb",
host="localhost",
table_name="documents",
embed_dim=1536,
)
# Build with custom components
index = VectorStoreIndex.from_documents(
documents,
vector_store=vector_store,
transformations=[parser],
)
```
Every component is swappable. Different embedding models, different vector stores, different chunking strategies, different retrieval algorithms. The composability is excellent.
## LangChain: Workflow-First RAG
```python
from langchain_community.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import PGVector
from langchain_anthropic import ChatAnthropic
from langchain.chains import RetrievalQA
# Load and split
loader = DirectoryLoader("./data")
documents = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=50)
chunks = splitter.split_documents(documents)
# Embed and store
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = PGVector.from_documents(chunks, embeddings, connection_string=CONN)
# Build chain
llm = ChatAnthropic(model="claude-sonnet-4-20250514")
chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
)
result = chain.invoke({"query": "What is our refund policy?"})
```
More explicit. You see every step. Document loading, splitting, embedding, storing, retrieving, generating. Each one is a separate operation you configure individually.
This explicitness is LangChain's strength and weakness. You understand exactly what's happening. You also write more code. For teams that want full control over each stage and might want to insert custom logic between steps, LangChain's approach is natural.
## Where LlamaIndex Wins
**Advanced retrieval strategies.** LlamaIndex has the deepest retrieval toolkit I've seen. Recursive retrieval (documents referencing other documents), auto-merging retrieval (small chunks that merge into larger context when needed), knowledge graph retrieval, SQL retrieval, and hybrid approaches that combine multiple strategies.
```python
from llama_index.core.retrievers import AutoMergingRetriever
from llama_index.core.node_parser import HierarchicalNodeParser
# Hierarchical chunks: 2048 -> 512 -> 128
node_parser = HierarchicalNodeParser.from_defaults(
chunk_sizes=[2048, 512, 128]
)
# Auto-merging: retrieves small chunks, merges to parent if threshold met
retriever = AutoMergingRetriever(
vector_retriever,
storage_context,
simple_ratio_thresh=0.4 # merge if 40%+ of children match
)
```
Auto-merging retrieval is brilliant for real-world documents. You index at a fine granularity (small chunks for precise matching) but retrieve at the right granularity (merging to larger chunks when the context needs it). I haven't seen an equivalent in LangChain that's as clean.
**Structured data querying.** LlamaIndex can query SQL databases, pandas DataFrames, and knowledge graphs using natural language. The model translates the question into a query, executes it, and synthesizes the answer. For enterprise RAG where "documents" include databases, this is a significant advantage.
**Evaluation built in.** LlamaIndex includes evaluation modules for faithfulness (does the answer match the retrieved context?), relevancy (are the retrieved documents actually relevant?), and correctness (is the answer right?). These aren't afterthoughts. They're core features.
```python
from llama_index.core.evaluation import FaithfulnessEvaluator, RelevancyEvaluator
faithfulness = FaithfulnessEvaluator()
relevancy = RelevancyEvaluator()
# Evaluate a response
faith_result = faithfulness.evaluate_response(response=response)
rel_result = relevancy.evaluate_response(query="...", response=response)
```
## Where LangChain Wins
**Ecosystem breadth.** LangChain integrates with more tools, more vector stores, more document loaders, and more LLM providers than LlamaIndex. If you need to load data from an obscure source or store vectors in a niche database, LangChain probably has an integration.
**Post-retrieval workflows.** Once you've retrieved your documents, LangChain shines at what happens next. Maybe you need to classify the query first, route to different retrieval strategies, apply reranking, check the answer against guardrails, and format the output. That's a chain, and chains are LangChain's whole thing.
```python
from langchain_core.runnables import RunnablePassthrough
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
# Custom RAG chain with reranking and formatting
chain = (
{"context": retriever | rerank | format_docs, "question": RunnablePassthrough()}
| ChatPromptTemplate.from_template(
"Answer based on context:\n{context}\n\nQuestion: {question}"
)
| llm
| StrOutputParser()
)
```
The LCEL pipe syntax makes composition clean. You can insert any function or runnable at any point in the chain. For complex post-retrieval logic, this is powerful.
**Agent-augmented RAG.** When your RAG system needs to do more than retrieve and generate (maybe it needs to call APIs, perform calculations, or search the web when local documents aren't sufficient), LangChain's agent framework integrates naturally. The retriever becomes one tool among many. This connects directly to [hybrid search implementations](/blog/hybrid-search-rag-production).
**LangGraph for complex flows.** When your RAG pipeline needs cycles (retrieve, generate, evaluate, re-retrieve if quality is low), LangGraph gives you that loop. LlamaIndex has query pipelines, but they don't support cycles natively.
## The Performance Angle
For pure RAG performance (retrieval quality and answer accuracy), the differences between the frameworks are smaller than the differences between your choices within either framework. Which embedding model you use, how you chunk your documents, what retrieval strategy you apply, and how you construct your synthesis prompt matter way more than whether you're using LlamaIndex or LangChain.
I've benchmarked identical configurations (same chunks, same embeddings, same vector store, same model, same prompt) in both frameworks. The results are nearly identical. The framework is the plumbing. The components are what determine quality.
## My Recommendation
**Building a RAG-focused application:** LlamaIndex. It does more of what you need out of the box, the retrieval strategies are more advanced, and the evaluation tools are built in. If RAG is the core of your product, LlamaIndex is the purpose-built tool.
**Building an application where RAG is one feature:** LangChain. Your application also has tool use, classification, multi-step workflows, and agent loops. RAG is a capability, not the whole product. LangChain's composability serves this better. For a deeper look, see [vector database backends](/blog/vector-database-comparison-2025).
**Prototyping quickly:** LlamaIndex. Five lines to working RAG. Hard to beat for speed of iteration.
**Need maximum integration flexibility:** LangChain. More connectors, more loaders, more stores. If your data source is unusual, LangChain probably supports it.
**Both at once:** Completely valid. I've built systems where LlamaIndex handles the indexing and retrieval layer, and LangChain orchestrates the broader workflow that includes that retrieval. They're not mutually exclusive. Use the best tool for each layer.
The worst choice is spending three weeks evaluating frameworks instead of building your RAG pipeline. Pick one, build it, measure the results, and switch if you need to. The retrieval quality depends on your data, your chunking, and your embedding model. Not on which Python package is calling the vector database.