LangChain vs LlamaIndex

LangChain and LlamaIndex are the two most-cited frameworks for building applications on top of LLMs. They are often mentioned in the same breath, but they grew from different starting points and still have different strengths. Knowing which is which saves a lot of architectural backtracking.

Origins shape philosophy

LangChain started as a toolkit for composing LLM calls into chains and agents. Its mental model is graph-like: nodes that transform inputs into outputs, connected by edges. Over time it grew RAG support, integrations with dozens of vector stores, and the LangGraph extension for stateful agent workflows.

LlamaIndex (originally GPT Index) started narrower: it was a data framework for connecting LLMs to your data. Its mental model is index-first: ingest documents, build an index, query the index with an LLM in the loop. It has since expanded to support agents and workflows, but retrieval is still its center of gravity.

Both can build a RAG pipeline. Both can run agents. But their defaults reflect their roots: LangChain’s defaults assume you are composing many components; LlamaIndex’s defaults assume you are answering questions over your data.

RAG: who is more ergonomic

For a basic RAG flow, LlamaIndex is the shorter path. It bakes in document loaders, parsers, indices, retrievers, and response synthesizers, and it ties them together with sensible defaults.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

docs = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(docs)
qe = index.as_query_engine()
print(qe.query("Summarize the support escalation policy."))

LangChain’s equivalent is more explicit. You construct a loader, a splitter, an embedding model, a vector store, a retriever, and a chain. That verbosity is useful when you want to swap any piece, but it is more code to start with.

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

docs = DirectoryLoader("./data").load()
chunks = RecursiveCharacterTextSplitter(chunk_size=800).split_documents(docs)
store = FAISS.from_documents(chunks, OpenAIEmbeddings())
qa = RetrievalQA.from_chain_type(llm=ChatOpenAI(), retriever=store.as_retriever())
print(qa.invoke("Summarize the support escalation policy."))

If your project is mostly “answer questions over documents”, start with LlamaIndex. If RAG is one piece of a larger pipeline with non-RAG steps, LangChain’s explicitness pays off.

Agents and tool use

Both frameworks support agents that pick tools, call them, and reason about results. LangChain pushes you toward LangGraph for anything serious: a stateful graph with explicit nodes, edges, and a typed state object. That structure pays off as agents grow, because the alternative, a single tangled loop, becomes unmaintainable fast.

LlamaIndex offers its own workflows abstraction with a similar flavor. The patterns are converging: explicit graphs with checkpoints, retries, and human-in-the-loop steps. The choice between them at this layer comes down to which API your team finds clearer.

Integrations and ecosystem

LangChain has the larger integration catalog by far. Almost every model, vector store, loader, and tool has a LangChain wrapper. This is genuinely useful for prototyping. It is also a liability: wrappers move quickly, sometimes break, and add an indirection that can be harder to debug than calling the underlying SDK directly.

LlamaIndex has a smaller but more curated catalog focused on data sources and retrievers. Quality is generally high, but you may need to drop down to direct SDK calls for niche integrations.

Observability and production realities

Both frameworks integrate with tracing tools. LangSmith (from the LangChain team) is the most polished tracing experience but is a paid service. Open source alternatives like Phoenix and Langfuse work with both frameworks and are worth setting up early. Without traces, every multi-step LLM pipeline becomes guesswork.

In production, the most common pain is version churn. Both libraries iterate quickly, sometimes with breaking changes. Pin versions, keep the framework-touching code in a thin layer, and write integration tests for the parts you cannot afford to break.

When to skip both

If your application is one or two LLM calls, a vector search, and a tool call, you do not need a framework. The underlying SDKs (OpenAI, Anthropic, your vector database client) are small and stable. A few hundred lines of your own code is easier to maintain than a dependency that ships breaking changes every month.

Frameworks earn their place when you have many integrations, complex agent loops, or want to share patterns across a team. For a single focused product, hand-rolled often wins.

A simple heuristic

If your application is fundamentally “ask questions over our data” or “build a smart document workflow”, LlamaIndex gives you more out of the box. If you are composing diverse LLM steps, juggling several tools, or building agentic workflows, LangChain plus LangGraph is the broader toolkit. If you are doing one focused thing, write it yourself with the raw SDKs and revisit later. None of these answers are wrong; the wrong answer is picking a framework without thinking about which problem you actually have.