LlamaIndex vs LangChain for RAG: Which Framework to Choose?

TL;DR

Use LlamaIndex if: RAG is your primary use case and you need fine-grained control over indexing, retrieval, and query pipelines.

Use LangChain if: You need agents with tool use alongside RAG, or you want one framework for your entire LLM stack.

What Is RAG?

Retrieval-Augmented Generation is the practice of retrieving relevant documents at query time and including them in the LLM’s context. Instead of relying on the model’s training data:

1. Index your documents as vector embeddings
2. At query time, embed the question
3. Retrieve the top-k most similar document chunks
4. Pass chunks + question to the LLM for a grounded answer

Both LlamaIndex and LangChain implement this pattern, but with different philosophies.

Philosophy Difference

LlamaIndex was designed specifically for the data ingestion → indexing → retrieval pipeline. It has a rich vocabulary for this: Document, Node, Index, Retriever, QueryEngine, ResponseSynthesizer. These abstractions give you precise control over each stage.

LangChain was designed for composable chains and agents, with RAG added as an important use case. Its VectorStoreRetriever + RetrievalQAChain pattern is simpler but less configurable for complex retrieval scenarios.

Side-by-Side: Basic RAG

LlamaIndex

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding

Settings.llm = OpenAI(model="gpt-4o-mini")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

documents = SimpleDirectoryReader("data/").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=5)

response = query_engine.query("What are the main topics in these documents?")
print(response)

LangChain

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA

loader = DirectoryLoader("data/")
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
splits = splitter.split_documents(docs)

vectorstore = FAISS.from_documents(splits, OpenAIEmbeddings(model="text-embedding-3-small"))
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o-mini"),
    retriever=retriever,
)

result = qa_chain.invoke({"query": "What are the main topics in these documents?"})
print(result["result"])

Both accomplish the same thing. LlamaIndex is slightly more concise for pure RAG; LangChain requires explicit text splitting.

Advanced Retrieval: Where LlamaIndex Pulls Ahead

For complex retrieval, LlamaIndex has more built-in options:

LlamaIndex: Multiple Index Types

from llama_index.core import (
    VectorStoreIndex,
    SummaryIndex,
    KeywordTableIndex,
)

# Vector index: best for semantic search
vector_index = VectorStoreIndex.from_documents(documents)

# Summary index: best for summarization queries
summary_index = SummaryIndex.from_documents(documents)

# Keyword index: best for exact term matching
keyword_index = KeywordTableIndex.from_documents(documents)

LangChain has one primary index type (vector store). LlamaIndex has a richer index hierarchy.

LlamaIndex: Recursive Retrieval

from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine

# Retrieve chunk → then retrieve parent document for more context
recursive_retriever = RecursiveRetriever(
    "vector",
    retriever_dict={"vector": vector_retriever},
    node_dict=nodes_by_id,
    verbose=True,
)

query_engine = RetrieverQueryEngine.from_args(recursive_retriever)

This “small-to-big” retrieval — retrieve precise small chunks, then include their parent nodes for full context — significantly improves answer quality for long documents.

LlamaIndex: Query Routing

from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import LLMSingleSelector
from llama_index.core.tools import QueryEngineTool

# Route different question types to different indexes
tools = [
    QueryEngineTool.from_defaults(
        query_engine=vector_engine,
        description="Use for semantic search questions.",
    ),
    QueryEngineTool.from_defaults(
        query_engine=summary_engine,
        description="Use for questions requiring document summary.",
    ),
]

router_engine = RouterQueryEngine(
    selector=LLMSingleSelector.from_defaults(),
    query_engine_tools=tools,
)

# Automatically routes to the right engine
response = router_engine.query("Summarize this document")

Implementing equivalent routing in LangChain requires more custom code.

Agent Capabilities

LangChain Agents (Mature)

LangChain’s agent ecosystem is more mature. create_react_agent, AgentExecutor, LangGraph — all production-ready with extensive documentation and community support.

from langchain.agents import create_react_agent, AgentExecutor
from langchain.tools.retriever import create_retriever_tool

# Convert retriever into an agent tool
retriever_tool = create_retriever_tool(
    vectorstore.as_retriever(),
    "search_docs",
    "Search our documentation for relevant information.",
)

agent = create_react_agent(llm, [retriever_tool, search_tool], prompt)
executor = AgentExecutor(agent=agent, tools=[retriever_tool, search_tool])

LlamaIndex Agents (Improving)

LlamaIndex has its own agent system built around FunctionCallingAgent and ReActAgent. It’s fully functional and improves with each release, but has fewer community examples than LangChain’s agent ecosystem.

from llama_index.core.agent import FunctionCallingAgent
from llama_index.core.tools import QueryEngineTool

query_tool = QueryEngineTool.from_defaults(
    query_engine=index.as_query_engine(),
    name="knowledge_base",
    description="Search the internal knowledge base.",
)

agent = FunctionCallingAgent.from_tools([query_tool], verbose=True)
response = agent.chat("What does the documentation say about authentication?")

Winner for agents: LangChain

Data Loading

LlamaIndex has 700+ data loaders via llama-hub: PDF, Word, PowerPoint, Notion, Confluence, Jira, GitHub, Google Drive, and more. The SimpleDirectoryReader handles multiple formats automatically.

LangChain has 100+ document loaders in langchain-community. Covers the most common formats but not as extensive as LlamaIndex’s hub.

Winner for data loading: LlamaIndex

Streaming Responses

Both support streaming. LangChain’s streaming is more integrated into the chain/LCEL architecture:

# LangChain streaming
for chunk in qa_chain.stream({"query": "..."}):
    print(chunk, end="", flush=True)

# LlamaIndex streaming
streaming_response = query_engine.query("...")
for token in streaming_response.response_gen:
    print(token, end="", flush=True)

Community and Ecosystem

LangChain has a larger community, but LlamaIndex’s community is very active for RAG-specific questions.

Choosing the Right Framework

Use LlamaIndex when:

• RAG is your primary use case and you need production-grade retrieval
• You have complex documents (PDFs, tables, multi-modal content)
• You need advanced retrieval strategies (recursive, hybrid, routing)
• You’re ingesting data from many different sources (700+ connectors)
• You want purpose-built abstractions for the entire RAG pipeline

Use LangChain when:

• You need agents that use tools alongside RAG
• RAG is one component of a larger LLM application
• You want a single framework for chains, agents, memory, AND retrieval
• You prefer the LCEL composable pipeline style
• Your team is already familiar with LangChain

Use Both Together

Many production applications use both:

• LlamaIndex for data ingestion, indexing, and retrieval
• LangChain for agent orchestration, memory, and tool integration

LlamaIndex provides a retriever, LangChain wraps it as an agent tool:

from langchain.tools.retriever import create_retriever_tool

# Use LlamaIndex retriever as a LangChain tool
llama_retriever = index.as_retriever(similarity_top_k=5)
langchain_retriever = LlamaIndexRetriever(retriever=llama_retriever)
tool = create_retriever_tool(langchain_retriever, "knowledge_base", "Search docs")

Frequently Asked Questions

Which is faster for production RAG?

Speed depends on your vector database, not the framework. Both LlamaIndex and LangChain are thin wrappers around your vector store. Choose the vector DB (Pinecone, Weaviate, Qdrant) based on your scale requirements; the framework choice has minimal latency impact.

Can LlamaIndex replace LangChain completely?

For RAG-focused applications: yes, LlamaIndex can handle everything. For broader LLM applications needing complex agent chains, LangGraph integrations, or extensive third-party tool support, LangChain is still the more complete framework.

Which is better for multi-document RAG across different sources?

LlamaIndex, primarily due to its broader connector ecosystem and ComposableGraph for querying across multiple indexes simultaneously.

How do I update documents in the index when the source changes?

LlamaIndex has a RefreshSimpleDirectoryReader for incremental updates without reindexing everything. LangChain requires manual delete/re-add of changed document embeddings.

What about LangChain’s RAG improvements in 2025?

LangChain has significantly improved RAG with LCEL composable chains, better metadata filtering, and multi-vector retrievers. The gap has narrowed, but LlamaIndex still has more RAG-specific primitives for complex use cases.

Next Steps

• Getting Started with LlamaIndex — Build your first LlamaIndex RAG pipeline
• RAG with Pinecone — LangChain RAG with a production vector database
• Pinecone vs Weaviate — Choose the right vector database for your RAG app