LangChain vs LlamaIndex — Which Framework to Choose in 2025

Introduction#

Why This Matters#

LangChain and LlamaIndex are the two most popular Python frameworks for building LLM applications. Both provide abstractions for loading data, generating embeddings, querying vector stores, and calling language models — but they optimize for different use cases. Choosing the wrong one adds friction throughout your entire development cycle.

LangChain is a general-purpose orchestration framework: it excels at multi-step workflows, agent systems, and switching between providers. LlamaIndex is purpose-built for data indexing and retrieval: it shines when your primary use case is document Q&A or knowledge base search. Many production teams use both, combining LlamaIndex's retrieval quality with LangChain's orchestration power.

Understanding the architectural philosophy of each helps you avoid over-engineering simple RAG pipelines or under-building agent systems that need more flexibility.

LangChain: General-Purpose Orchestration#

LangChain treats LLM applications as composable pipelines. Its LCEL (LangChain Expression Language) lets you chain prompts, models, parsers, and tools using a declarative pipe operator.

Core strengths:

• 100+ integrations spanning models, vector stores, tools, and loaders
• First-class agent framework with tool calling support
• Flexible memory management for multi-turn conversations
• Multi-model support across OpenAI, Anthropic, Cohere, Hugging Face, Ollama
• LangSmith for production observability and tracing

Weaknesses:

• Steeper learning curve due to breadth of abstractions
• Can feel over-engineered for simple document Q&A workflows
• Frequent breaking changes between major versions

Best for: Agents, multi-step workflows, applications needing tool use, multi-provider flexibility.

LlamaIndex: Purpose-Built for RAG#

LlamaIndex (formerly GPT Index) specializes in data connectors, indexing strategies, and retrieval optimization. It provides higher-level abstractions for RAG that require less boilerplate than LangChain.

Core strengths:

• Purpose-built for RAG — cleaner API for document indexing and query engines
• Advanced indexing: vector, keyword, hierarchical, knowledge graph, and hybrid
• Router query engines for intelligently routing queries across multiple indexes
• Sub-question decomposition for complex multi-hop questions
• Strong observability with Arize Phoenix integration

Weaknesses:

• Limited agent capability compared to LangChain
• Fewer integrations overall (though growing rapidly)
• Less suitable for workflows that go far beyond retrieval

Best for: Document Q&A, enterprise knowledge bases, advanced RAG with multi-index routing.

Side-by-Side Feature Comparison#

RAG Example: LangChain vs LlamaIndex#

LangChain RAG#

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
 
loader = PyPDFLoader("research_paper.pdf")
docs = loader.load()
 
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
chunks = splitter.split_documents(docs)
 
vectorstore = Chroma.from_documents(chunks, OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
 
prompt = ChatPromptTemplate.from_template(
    "Context: {context}\n\nQuestion: {question}\n\nAnswer:"
)
 
chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | ChatOpenAI(model="gpt-4o")
    | StrOutputParser()
)
 
answer = chain.invoke("What are the key findings?")

LlamaIndex RAG#

from llama_index.core import SimpleDirectoryReader, VectorStoreIndex, Settings
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
 
# Configure globally
Settings.llm = OpenAI(model="gpt-4o")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
 
# Load and index
documents = SimpleDirectoryReader("./papers").load_data()
index = VectorStoreIndex.from_documents(documents)
 
# Query
query_engine = index.as_query_engine(similarity_top_k=4)
response = query_engine.query("What are the key findings?")
print(response)

LlamaIndex achieves the same result in roughly half the lines of code for standard RAG workflows.

Advanced RAG: Where LlamaIndex Leads#

LlamaIndex's RouterQueryEngine can intelligently route queries across multiple indexes:

from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import LLMSingleSelector
from llama_index.core.tools import QueryEngineTool
 
finance_engine = finance_index.as_query_engine()
legal_engine = legal_index.as_query_engine()
 
router = RouterQueryEngine(
    selector=LLMSingleSelector.from_defaults(),
    query_engine_tools=[
        QueryEngineTool.from_defaults(finance_engine, description="Financial documents and reports"),
        QueryEngineTool.from_defaults(legal_engine, description="Legal contracts and compliance"),
    ]
)
 
response = router.query("What is our revenue growth rate?")
# Automatically routes to finance_engine

Implementing equivalent routing in LangChain requires significantly more custom code.

Agent Example: Where LangChain Leads#

LangChain's agent framework is more mature for complex autonomous systems:

from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 
@tool
def run_sql_query(query: str) -> str:
    """Execute a read-only SQL query against the analytics database."""
    return "Query result: 1,542 users registered last week"
 
@tool
def send_slack_notification(message: str, channel: str) -> str:
    """Send a Slack message to the specified channel."""
    return f"Sent to #{channel}"
 
tools = [run_sql_query, send_slack_notification]
llm = ChatOpenAI(model="gpt-4o")
 
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are an analytics assistant that can query data and report results."),
    ("human", "{input}"),
    MessagesPlaceholder("agent_scratchpad"),
])
 
agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True, max_iterations=5)
 
result = executor.invoke({
    "input": "How many users signed up last week? Post it to the #metrics channel."
})

Hybrid Architecture#

Many production teams combine both frameworks — LlamaIndex for retrieval, LangChain for orchestration:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
 
# LlamaIndex handles retrieval
documents = SimpleDirectoryReader("./knowledge_base").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=5)
 
def retrieve_context(question: str) -> str:
    return str(query_engine.query(question))
 
# LangChain handles orchestration
llm = ChatOpenAI(model="gpt-4o")
prompt = ChatPromptTemplate.from_template(
    "Based on the following context, answer the question.\n\nContext: {context}\n\nQuestion: {question}"
)
 
chain = prompt | llm | StrOutputParser()
 
question = "What is our refund policy for enterprise customers?"
context = retrieve_context(question)
answer = chain.invoke({"context": context, "question": question})

Common Mistakes / Pitfalls#

• Choosing LlamaIndex for an agent-heavy system that needs dynamic tool selection — LangChain agents are more mature
• Choosing LangChain when you only need document Q&A — LlamaIndex is simpler and faster to set up
• Mixing both frameworks without clear boundaries — define which handles retrieval and which handles orchestration
• Not benchmarking retrieval quality on your specific dataset — default chunking settings often underperform
• Locking into one framework too early before validating your use case

Best Practices#

• For new projects: start with LlamaIndex if retrieval is the core use case; start with LangChain if you need agents or multi-provider support
• Profile retrieval quality with RAGAS or DeepEval before going to production
• Use async APIs in both frameworks for production throughput (.aquery(), .ainvoke())
• Abstract the retrieval interface so you can swap frameworks without touching business logic
• Monitor token costs — LlamaIndex defaults can generate expensive prompts for complex queries

Key Takeaways#

• LangChain is a general-purpose LLM orchestration framework optimized for agents, multi-step workflows, and provider flexibility
• LlamaIndex is purpose-built for RAG — it has simpler APIs, advanced indexing strategies, and better retrieval primitives
• LangChain requires more code for basic RAG but provides more control for complex orchestration scenarios
• LlamaIndex's RouterQueryEngine can automatically route queries to the right index — LangChain requires custom routing logic
• Both frameworks support OpenAI, Anthropic, Cohere, and local models via Hugging Face or Ollama
• Many production teams use both: LlamaIndex for retrieval, LangChain for orchestration and agent logic
• RAGAS is the standard evaluation framework for measuring RAG quality regardless of which framework you use
• The choice between them should be driven by your primary use case, not community popularity or star count