LangChain vs LlamaIndex: 2025 Comparison

Overview

LangChain

General-purpose LLM application framework

Strong focus on chains, agents, and tools

Large ecosystem and community

More verbose but flexible

LlamaIndex

Specialized for data ingestion and RAG

Better for document-heavy applications

More opinionated, easier to get started with RAG

Superior indexing and retrieval

When to Use LangChain

Building multi-step agent workflows

Integrating many external tools/APIs

Need flexibility in chain design

Complex conversation management

python
from langchain.agents import create_react_agent, AgentExecutor
from langchain.tools import DuckDuckGoSearchRun, PythonREPLTool
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="gpt-4o")
tools = [DuckDuckGoSearchRun(), PythonREPLTool()]
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
result = executor.invoke({"input": "Research and analyze Tesla stock performance"})

When to Use LlamaIndex

Document Q&A systems

Enterprise knowledge bases

Complex RAG pipelines

Structured data retrieval

python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter
Load and index documents
documents = SimpleDirectoryReader("./docs").load_data()
parser = SentenceSplitter(chunk_size=1024, chunk_overlap=20)
nodes = parser.get_nodes_from_documents(documents)
Create index and query
index = VectorStoreIndex(nodes)
query_engine = index.as_query_engine(
    similarity_top_k=5,
    response_mode="tree_summarize"
)
response = query_engine.query("Summarize the key findings")

Performance Comparison

AspectLangChainLlamaIndex

RAG QualityGoodExcellent Agent CapabilityExcellentGood Learning CurveMediumLow (for RAG) EcosystemLargeGrowing CustomizationHighMedium

Can You Use Both?

Yes! LlamaIndex for retrieval, LangChain for the agent orchestration layer.

python
LlamaIndex index + LangChain agent
from llama_index.core import VectorStoreIndex
from langchain.tools import Tool
index = VectorStoreIndex(nodes)
query_engine = index.as_query_engine()
knowledge_tool = Tool(
    name="knowledge_base",
    func=query_engine.query,
    description="Query internal knowledge base"
)

2025 Recommendation

Start with LlamaIndex for RAG-centric applications

Use LangChain for complex agent workflows

Consider LangGraph for stateful multi-agent systems