LlamaIndex vs LangChain: Which One to Use for Building RAG

Let me give you the conclusion first so you don't have to scroll down: For pure retrieval-based Q&A or complex data ingestion, choose LlamaIndex; for multi-step, multi-tool, or Agent workflows, choose LangChain. The two can be mixed, and in practice, they often are.

Here's why.

One-Line Positioning

LlamaIndex: Built from the start to "feed your data to the LLM." Indexing, chunking, and retrieval are its strong suits.

LangChain: More like a "glue layer" that strings together models, tools, memory, and Agents. RAG is just one small part of what it can do.

So the question isn't "which is stronger," but "which side does your work lean toward."

Direct Comparison Table

DimensionLlamaIndexLangChain

Core StrengthData indexing & retrievalWorkflow orchestration / Agent Ease of UseLower for retrieval scenariosMore concepts, steeper learning curve Data ConnectorsExtremely rich (hundreds on LlamaHub)Available but not as comprehensive Retrieval StrategiesMany built-in, ready to useYou need to assemble them yourself Agent CapabilityAvailable but not its forteHome turf, largest ecosystem Documentation QualityClear for retrieval partsVast, version changes quickly

When to Choose LlamaIndex

If your need is: "I have a bunch of PDFs / Notion / databases, and I want users to ask questions in natural language, with accurate answers and citations" — go with LlamaIndex without hesitation.

Its VectorStoreIndex runs in just a few lines:

python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
docs = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(docs)
qe = index.as_query_engine(similarity_top_k=5)
print(qe.query("What is our refund policy?"))

More importantly, advanced retrieval strategies like sentence-window retrieval and auto-merging retrieval are built into LlamaIndex — just swap the retriever class. Doing the same in LangChain requires manual assembly. If you're building enterprise knowledge bases or customer service Q&A, the time saved is significant.

For vector stores, I usually pair it with pgvector or Qdrant. See pgvector Vector Search in Practice for details.

When to Choose LangChain

As soon as your workflow goes beyond "retrieve → answer" and starts involving branches, tool calls, or multi-turn decisions, LangChain's value becomes clear.

Here's a real scenario: An Agent first determines whether the user is asking about "orders" or "product inquiries." For orders, it queries the database; for product inquiries, it uses RAG; if it can't find an answer, it escalates to a human. This kind of stateful, branching workflow is much cleaner to implement with LangChain (more precisely, LangGraph) than with hard-coded if-else. See LangGraph Stateful Agent Guide for more.

python
from langchain.agents import create_react_agent, AgentExecutor
Wrap retrieval, database query, and human escalation as tools, and let the Agent decide which to call
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

Don't Overlook These Pitfalls

LangChain version issues: It iterates so fast that a tutorial from last year might have different import paths this year. For production projects, always pin the version — never use latest. This was the most painful lesson we learned: a minor version upgrade broke the entire pipeline.

Don't force LlamaIndex's Agent: It has an Agent module, but its ecosystem and stability can't compare with LangChain's. If you need complex Agents, don't try to make LlamaIndex work.

Performance difference is negligible: The real bottleneck is vector retrieval and LLM calls; the framework overhead is negligible. Don't get hung up on this.

Can You Use Them Together?

Yes, and it's recommended. A common combination is: LlamaIndex handles data indexing and retrieval, wraps it as a LangChain tool, and then LangChain's Agent orchestrates the workflow. Each plays to its strengths — this is a mature production architecture.

Quick Selection Guide

Only document Q&A or knowledge base → LlamaIndex

Need Agents, multiple tools, or complex workflows → LangChain

Need both good retrieval and complex orchestration → Use both, with LlamaIndex as the retrieval layer

Team is new and wants a quick demo → Pick the one that fits your scenario better; don't learn both

When choosing a framework, don't be swayed by "which is more popular." First, think about what your project will look like three months from now, then decide.