LangChain vs LlamaIndex:
Which Should You Use in 2025?

What Are These Frameworks, Really?

If you have spent any time building LLM-powered applications in Python, you have almost certainly landed on the same crossroads: LangChain or LlamaIndex? Both are open-source, both are actively maintained, both have enormous GitHub star counts — and both can feel overwhelming when you are just trying to get a prototype off the ground.

The honest answer to "langchain vs llamaindex" is that they were designed with different primary goals, and understanding those goals makes the choice straightforward. LangChain began as a general-purpose framework for composing LLM calls into chains and later evolved to cover agents, tool use, and retrieval. LlamaIndex (formerly GPT Index) was built from the start around one core problem: how do you efficiently index and retrieve information from your own data for use with an LLM?

In practice, teams end up using one as a primary framework and reaching for the other when it fills a gap. This guide will help you figure out which one should be your default — and when to combine them.

Feature Comparison Table

The table below scores each framework across the dimensions that matter most in production AI systems. Ratings reflect the state of each library as of mid-2025.

RAG: How They Compare

Retrieval-augmented generation (RAG) is the most common use case for both frameworks. The idea is simple: instead of relying on an LLM's training data, you pull relevant documents from your own knowledge base and include them in the prompt context at query time.

LlamaIndex RAG Strengths

LlamaIndex was built for this. Its default path — load documents, build a vector store index, query it — requires remarkably little code and makes smart decisions by default. Beyond basic vector search, it ships with:

• HyDE (Hypothetical Document Embeddings) — generates a hypothetical answer first, then uses that to retrieve more relevant chunks
• Sub-question decomposition — breaks complex queries into sub-questions, retrieves separately, then synthesizes
• Knowledge Graph indexes — extracts entities and relationships for structured retrieval
• Recursive retrieval and node postprocessors — re-rank, filter, or expand results before synthesis
• Ingestion pipelines — deduplicate and cache chunks to avoid reprocessing on updates

LangChain RAG Strengths

LangChain's retrieval story is solid, particularly if you are already using it for other pipeline steps. Its advantages are composability and integration breadth:

• Native integration with 50+ vector stores (Pinecone, Weaviate, pgvector, Chroma, etc.)
• Ensemble retrievers for combining dense and sparse (BM25) retrieval
• Contextual compression retrievers for post-retrieval filtering
• Seamless chaining of retrieval into broader pipelines via LCEL
• Multi-query retrieval to generate multiple query variations automatically

Agents and Orchestration

The agent landscape shifted significantly in 2024-2025. LangChain responded by creating LangGraph — a separate but tightly integrated library that models agent workflows as directed graphs. This gives developers explicit control over state, branching, cycles, and human-in-the-loop interrupts that the earlier ReAct-style agents lacked.

LangGraph (LangChain's Agent Layer)

LangGraph treats your agent as a state machine. Nodes are functions or LLM calls; edges define transitions between them. You can define conditional edges (route based on LLM output), add persistence via checkpointers, and insert human approval steps between any two nodes. This makes it the leading choice for production agentic systems where predictability and debuggability matter.

LlamaIndex Agents

LlamaIndex agents have matured considerably. The framework supports ReAct agents, OpenAI function-calling agents, and its own Agentic RAG patterns where the agent decides which indexes or tools to query. For document-centric tasks — an agent that can search multiple indexes, summarize documents, and answer follow-up questions — LlamaIndex agents are expressive and easy to configure. For complex multi-agent coordination, LangGraph still leads.

Code Examples: The Same Task in Both Frameworks

Nothing clarifies a framework comparison faster than side-by-side code. Below we implement the same task in both libraries: build a Q&A system over a set of PDF documents using a vector store.

LlamaIndex: Document Q&A

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

# Configure global settings (LlamaIndex 0.10+ pattern)
Settings.llm = OpenAI(model="gpt-4o", temperature=0.1)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

# Load documents from a local directory
documents = SimpleDirectoryReader("./docs").load_data()

# Build vector index — chunks, embeds, and stores automatically
index = VectorStoreIndex.from_documents(documents)

# Create a query engine with default synthesis
query_engine = index.as_query_engine(similarity_top_k=5)

# Ask a question
response = query_engine.query(
    "What are the main risks outlined in the compliance documents?"
)

print(response.response)

# Inspect source nodes for citations
for node in response.source_nodes:
    print(f"Source: {node.metadata.get('file_name')} — score: {node.score:.3f}")

LangChain: Document Q&A

from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

# Load and split documents
loader = PyPDFDirectoryLoader("./docs")
docs = loader.load()

splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000, chunk_overlap=200
)
chunks = splitter.split_documents(docs)

# Embed and store in Chroma
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = Chroma.from_documents(chunks, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Define prompt template
prompt = ChatPromptTemplate.from_template("""
Answer the question based only on the context below.

Context: {context}

Question: {question}
""")

# Build LCEL chain
llm = ChatOpenAI(model="gpt-4o", temperature=0.1)

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# Ask a question
response = chain.invoke(
    "What are the main risks outlined in the compliance documents?"
)
print(response)

Both examples produce similar results. The LlamaIndex version is more concise for the happy path — document loading, chunking, embedding, and indexing are all handled inside from_documents(). The LangChain version requires a bit more wiring but makes each step explicit and easy to swap out (change the splitter, swap the vector store, add a different retriever) without restructuring the whole pipeline.

LangGraph: A Simple Agentic RAG Loop

from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool
from typing import TypedDict, Annotated
import operator

# Define state schema
class AgentState(TypedDict):
    messages: Annotated[list, operator.add]

# Define a retrieval tool
@tool
def search_docs(query: str) -> str:
    """Search internal documents for relevant information."""
    # retriever.invoke(query) would go here
    return f"Relevant docs for: {query}"

tools = [search_docs]
llm = ChatOpenAI(model="gpt-4o").bind_tools(tools)

# Define nodes
def agent(state):
    return {"messages": [llm.invoke(state["messages"])]}

def should_continue(state):
    last = state["messages"][-1]
    return "tools" if last.tool_calls else END

# Build the graph
graph = StateGraph(AgentState)
graph.add_node("agent", agent)
graph.add_node("tools", ToolNode(tools))
graph.set_entry_point("agent")
graph.add_conditional_edges("agent", should_continue)
graph.add_edge("tools", "agent")

app = graph.compile()

# Run the agent
result = app.invoke({"messages": [("user", "What are our Q3 revenue risks?")]})

When to Use Which

Choose LlamaIndex when:

• Your core feature is RAG over private documents, databases, or APIs
• You need to ingest and index data from many heterogeneous sources (PDFs, Notion, Slack, SQL, etc.)
• You want advanced retrieval strategies (HyDE, sub-question, re-ranking) with minimal boilerplate
• You are building an enterprise knowledge base or internal search tool
• Your team is smaller and you want to move fast on the data layer without writing glue code
• You want a managed ingestion pipeline via LlamaCloud

Choose LangChain / LangGraph when:

• You are building complex, multi-step pipelines with conditional routing and branching logic
• Your application involves multiple AI agents coordinating with each other
• You need human-in-the-loop approvals or interrupts in your agent workflow
• You require rich observability and evaluation via LangSmith
• You want a standardized interface across many different LLM providers and tools
• Your app goes beyond retrieval: classification, extraction, code generation, multi-turn planning

The Hybrid Approach: Using Both Together

Many production teams do not choose one framework exclusively — they let each library do what it does best and compose them at the seam. This is increasingly common as both frameworks have stabilized their APIs and made interoperability straightforward.

The most common hybrid architecture looks like this:

1. LlamaIndex handles data ingestion and indexing. Documents are loaded, parsed, chunked, embedded, and stored using LlamaIndex's ingestion pipeline and query engine. This gives you access to advanced retrieval modes without writing them from scratch.
2. LlamaIndex query engines are wrapped as LangChain tools. A LlamaIndex query engine can be exposed as a callable function and decorated with @tool, making it a first-class citizen in any LangChain or LangGraph workflow.
3. LangGraph orchestrates the overall agent. The higher-level agent loop — deciding when to retrieve, when to call external APIs, when to ask the user for clarification — is modeled as a LangGraph state graph, giving you explicit control and observability.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from langchain_core.tools import tool

# --- LlamaIndex side: build the retrieval engine ---
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=6)

# --- Wrap as a LangChain tool ---
@tool
def internal_knowledge_search(query: str) -> str:
    """Search the internal knowledge base for relevant information.
    Use this when the user asks about company policies, products, or documents."""
    response = query_engine.query(query)
    return str(response)

# --- Now use this tool inside any LangChain/LangGraph agent ---
# tools = [internal_knowledge_search, web_search, calculator, ...]
# graph = build_langgraph_agent(tools)

This pattern is production-proven and sidesteps the "pick one" dilemma entirely. The overhead is minimal — the two frameworks share common abstractions like OpenAI clients and vector store adapters, so there is no meaningful performance cost to using both.

Cost and Operational Considerations

Both frameworks are open-source and free to use. The costs in production come from the underlying infrastructure: LLM API calls, embedding model calls, vector store hosting, and compute for your application server. Neither LangChain nor LlamaIndex adds meaningful overhead to these costs.

Where costs differ between frameworks

Embedding calls: LlamaIndex's default chunking and indexing tends to produce more granular chunks (smaller chunk sizes, higher overlap) than LangChain's defaults. This means slightly more embedding API calls during ingestion. You can tune both to match. At scale, batching and caching embeddings (both frameworks support this) is more impactful than framework choice.

LLM calls per query: LlamaIndex's advanced retrieval modes (sub-question decomposition, HyDE) make additional LLM calls per user query. This increases accuracy but also cost. LangChain's multi-query retriever does the same. If token cost is a constraint, stick to standard vector retrieval in either framework.

Managed tiers: LangSmith (LangChain's observability platform) has a free tier for up to 5,000 traces/month, then paid. LlamaCloud (LlamaIndex's managed ingestion) is priced by data volume. Both are optional — you can run either framework entirely on your own infrastructure with no managed services.

Developer time cost

This is often the largest cost differential. LlamaIndex's tighter focus on RAG means less code and fewer decisions for document Q&A applications. LangChain's broader API means more time reading documentation and assembling pieces. Choose the framework that reduces cognitive overhead for your specific use case, not the one with more stars.

Frequently Asked Questions