LangChain orchestrates agents and integrations, LlamaIndex owns retrieval and RAG. Here's where they overlap, where they don't, and which to reach for.
LangChain vs LlamaIndex — Which LLM Framework to Use
Every team building on top of an LLM eventually hits the same fork: do you pull in LangChain, LlamaIndex, both, or neither? The two frameworks get pitched as rivals, but they started from opposite corners of the problem. Understanding where each began explains most of the differences you'll run into.
LangChain started as glue. It's an orchestration framework: chains, agents, tool calling, prompt templates, and a very broad set of integrations for models, vector stores, and APIs. If your app needs to call a model, decide what to do with the answer, call a tool, then loop, LangChain gives you the plumbing. Its newer sibling, LangGraph, is where the interesting work happens now: it models agents as explicit state machines, which makes multi-step, stateful, human-in-the-loop flows far more debuggable than the old opaque agent executor.
LlamaIndex started from the data side. Its core job is retrieval: loading documents, chunking them, indexing them, and getting the right context in front of the model at query time. If your problem is "I have 40,000 PDFs and I need the model to answer questions about them accurately," LlamaIndex is purpose-built for that. It ships mature abstractions for node parsing, retrievers, rerankers, and query engines that would take real effort to assemble by hand.
Rough rule: LangChain: orchestration and agents. LlamaIndex: retrieval and RAG.
The clean split breaks down because both frameworks grew toward the middle. LangChain has retrieval abstractions and vector store integrations. LlamaIndex has agents, tool calling, and multi-step workflows. So you can build a RAG app in either, and you can build an agent in either.
In practice the quality differs by heritage. LlamaIndex's retrieval is deeper: better default chunking, more retriever strategies, first-class reranking, and query transformations like sub-question decomposition that LangChain makes you wire up yourself. LangChain's agent and integration surface is broader: more tools, more model providers, and LangGraph, which has no real equivalent in LlamaIndex for complex control flow.
Here's the same job in both. First, LlamaIndex, where retrieval is the happy path:
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
docs = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(docs)
query_engine = index.as_query_engine(similarity_top_k=4)
response = query_engine.query("What is our refund window for enterprise plans?")
print(response)
Loading, chunking, embedding, indexing, and querying are five lines because that's the framework's whole reason to exist. Now the same idea in LangChain, using its Expression Language to compose a retrieval chain:
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
store = FAISS.from_texts(texts, OpenAIEmbeddings())
retriever = store.as_retriever(search_kwargs={"k": 4})
prompt = ChatPromptTemplate.from_template(
"Answer using only this context:\n{context}\n\nQuestion: {question}"
)
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| ChatOpenAI(model="gpt-4o-mini")
)
print(chain.invoke("What is our refund window for enterprise plans?").content)
LangChain makes the wiring explicit. That's more code, but you can see every stage and swap any of them. That trade-off is the whole argument between these frameworks.
Both frameworks earned a reputation for hiding too much. A default LangChain agent used to do a dozen prompt manipulations you never saw, so when it misbehaved you were debugging someone else's hidden prompt. LlamaIndex has the same risk at the retrieval layer: default chunk sizes, default prompts, and default synthesis strategies that quietly shape your answers.
The frameworks have responded. LangChain Expression Language and LangGraph both push toward explicit composition, and LlamaIndex now exposes its lower-level modules so you can bypass the one-liners. The lesson holds either way: use the high-level helpers to get running, then drop to the lower-level APIs before you go to production, so you actually control the prompts and parameters that affect output.
Both frameworks now offer a way to model multi-step processes. LangGraph uses a graph of nodes and edges with shared state; it's built for cycles, branching, checkpointing, and interrupting for human approval. It's the stronger choice when your agent loops, retries, and needs durable state you can inspect mid-run.
LlamaIndex Workflows use an event-driven model: steps emit and consume events. It's lighter and reads naturally for pipelines, and it fits well when the orchestration mostly serves a retrieval-heavy flow. If your hard problem is control flow, LangGraph is more mature. If your hard problem is data and the orchestration is secondary, Workflows keep you in one framework.
Observability: LangChain's biggest production advantage is LangSmith, its tracing and eval platform. Being able to see every step, token, and latency number of a chain in production is worth a lot, and it works even if you use LangChain lightly. LlamaIndex leans on integrations with tools like Arize Phoenix and OpenTelemetry rather than a first-party platform.
Debugging: LangGraph's explicit state makes failures reproducible; you can replay from a checkpoint. Opaque agent loops in either framework are the hardest thing to debug, which is another reason to prefer explicit graphs.
Lock-in: Both frameworks wrap your prompts, your model calls, and your vector store access in their own abstractions. Migrating off later is real work. Keep your prompts, business logic, and data-access code separable from framework glue so a future rewrite touches the edges, not the core.
Plenty of apps don't need either framework. If you're making a single model call, or a straight-line prompt with a tool call or two, the provider SDK plus a few functions is less code and less to debug than a framework. Frameworks pay off when you have real orchestration complexity or real retrieval complexity. Below that threshold they add abstraction you'll spend time fighting. If you want to compare the raw providers underneath, see our guide to the best LLM APIs and AI infrastructure.
Ask what your hard problem is.
- Retrieval quality over lots of documents is the core challenge: start with LlamaIndex.
- Multi-step agents, tool orchestration, or broad integrations are the core challenge: start with LangChain and LangGraph.
- Both matter: use LlamaIndex as a retrieval component inside a LangGraph agent. They compose; LlamaIndex query engines can be exposed as LangChain tools.
- Neither is complex yet: call the provider SDK directly and add a framework when you actually feel the pain.
If retrieval is the whole game, it's also worth surveying the broader field in our roundup of the best RAG frameworks before committing.
For a RAG-first product where answer quality against your own data is the thing that matters, we'd reach for LlamaIndex and stay close to its lower-level retrieval APIs. For an agentic product with tools, branching, and human approval steps, we'd reach for LangGraph and lean hard on LangSmith for observability. For anything simpler than that, we'd skip both and write the twenty lines ourselves. The frameworks are tools for specific kinds of complexity, not a default starting point, and the teams that treat them that way ship faster and debug less.
