LangChain vs LlamaIndex: Choosing a RAG and Agent Framework in 2026

Introduction

The "LangChain vs LlamaIndex" question usually arrives at a specific moment. A prototype that answered questions over a folder of PDFs now has to cite its sources, survive a schema change in the document store, call two internal APIs, and be debuggable by someone who did not write it. The prototype was built in an afternoon with whichever framework the first tutorial used, and the team is now deciding whether to keep it or rewrite it.

Both projects started in late 2022 and have since converged in features, which makes the choice harder than the marketing suggests. Each ships loaders, splitters, vector store integrations, retrievers, agents, and tracing hooks. What differs is the center of gravity. LlamaIndex grew out of the problem of getting data into a shape a model can query, so its abstractions are documents, nodes, indexes, and query engines. LangChain grew out of the problem of composing model calls and tools, so its abstractions are runnables, prompts, tool bindings, and, through LangGraph, stateful graphs.

The pain points developers report in 2026 are less about missing features and more about surface area. Package splits (langchain-core, langchain-community, provider packages, llama-index-core, hundreds of integration packages) mean version pinning matters. Abstractions that hid prompts in early releases were replaced by ones that expose them, and old tutorials no longer run. And teams that adopt either framework for a three-step pipeline sometimes discover they have imported a large dependency tree to avoid writing about eighty lines of code.

This article compares the two on the dimensions that decide real projects: data ingestion, retrieval quality controls, agent orchestration, observability, and maintenance cost. It includes one runnable recipe that builds the same local retrieval pipeline in both frameworks against a local Ollama model, so you can compare them on your own documents. No benchmark numbers appear here because retrieval quality depends on your corpus; the evaluation matrix below is a set of design observations, not measurements.

Architectural Breakdown & Core Mechanics

LlamaIndex: data first

LlamaIndex models a knowledge base as a pipeline with named stages. A reader turns files or API responses into Document objects. A node parser splits them into Node objects that keep metadata and relationships to their parent document and neighbors. An index (vector, keyword, summary, property graph, or a composition of these) stores the nodes. A retriever pulls candidates, optional node postprocessors rerank or filter them, and a response synthesizer builds the final answer from the retrieved context. The QueryEngine is the object that wires those stages together, and each stage can be swapped independently.

This structure pays off when the hard part of the project is the data. Hierarchical chunking, sentence-window retrieval, metadata filters, document-level summaries used as a routing layer, and structured extraction all have first-class support because the framework treats the node graph as the primary data structure. The LlamaIndex repository also carries parsing and workflow tooling for messy inputs such as scanned documents and tables.

LangChain: composition first

LangChain's core abstraction is the Runnable: anything with invoke, stream, and batch methods that can be chained with the | operator in LangChain Expression Language. A prompt template, a chat model, an output parser, and a retriever are all runnables, so a RAG pipeline is a chain of runnables whose inputs and outputs you can inspect at each boundary. Tool calling is modeled as binding function schemas to a chat model, and the agent loop is where LangGraph comes in: a graph of nodes that read and write a shared state object, with conditional edges, checkpointing, and human-in-the-loop interrupts.

That design pays off when the hard part is control flow. A support agent that retrieves, decides whether the answer is grounded, rewrites the query, retries once, and escalates to a human is a graph, and LangGraph expresses it directly. The LangChain project and its catalog tool entry cover the ecosystem; the retrieval pieces are present but intentionally thinner than LlamaIndex's data tooling.

Where they overlap

Both frameworks can use the same embedding models, the same vector stores, and the same local inference servers. An embedding model such as BGE-M3 works in either, and a cross-encoder such as the BGE reranker v2 m3 can be attached as a reranking step in either. Because both accept an OpenAI-compatible endpoint, a local server removes provider lock-in. Locally, FastEmbed is a lightweight alternative to full PyTorch embedding stacks when you want quantized ONNX embeddings without a GPU.

Comparative Benchmarks & Evaluation Matrix

The table below compares design properties, not speed or accuracy. Where a cell makes a judgment, it reflects the architecture described above and the public documentation of each project; treat it as a starting hypothesis to test on your data.

Two practical readings of the matrix. First, if your failure analysis keeps ending at "the wrong chunk was retrieved," LlamaIndex's node-level controls address that layer directly. Second, if it keeps ending at "the agent took a step it should not have," LangGraph's explicit state and interrupts address that layer directly. Neither framework fixes a bad chunking strategy or an unlabeled evaluation set, and both are optional: a plain Python function that embeds, searches, and calls an API is a legitimate choice for a small pipeline.

Who should not adopt either: a single-prompt classification endpoint, or a service whose only retrieval step is a keyword query against a database you already operate. The extra dependency tree buys nothing there.

Step-by-Step Implementation Recipe

The script below builds the same local pipeline in both frameworks: load text files from ./docs, embed them with a small BGE model on CPU, retrieve four chunks, and answer with a local Ollama model. Run ollama pull qwen3:8b first, install the packages listed in the header comment, and adjust package versions to what your environment resolves; the integration packages change names and import paths more often than the cores do.

# pip install llama-index llama-index-llms-ollama llama-index-embeddings-huggingface \

#             langchain langchain-ollama langchain-huggingface langchain-text-splitters \

#             langchain-community sentence-transformers

import sys

from pathlib import Path

QUESTION = sys.argv[1] if len(sys.argv) > 1 else "What is the refund policy?"

DOCS_DIR = Path("./docs")

EMBED_MODEL = "BAAI/bge-small-en-v1.5"

LLM_MODEL = "qwen3:8b"

def run_llamaindex(question: str) -> str:

from llama_index.core import SimpleDirectoryReader, Settings, VectorStoreIndex

from llama_index.embeddings.huggingface import HuggingFaceEmbedding

from llama_index.llms.ollama import Ollama

Settings.llm = Ollama(model=LLM_MODEL, request_timeout=120.0)

Settings.embed_model = HuggingFaceEmbedding(model_name=EMBED_MODEL)

documents = SimpleDirectoryReader(str(DOCS_DIR)).load_data()

index = VectorStoreIndex.from_documents(documents)

engine = index.as_query_engine(similarity_top_k=4)

response = engine.query(question)

sources = {n.node.metadata.get("file_name", "?") for n in response.source_nodes}

return f"{response}\n\nSources: {sorted(sources)}"

def run_langchain(question: str) -> str:

from langchain_community.document_loaders import DirectoryLoader, TextLoader

from langchain_core.output_parsers import StrOutputParser

from langchain_core.prompts import ChatPromptTemplate

from langchain_core.vectorstores import InMemoryVectorStore

from langchain_huggingface import HuggingFaceEmbeddings

from langchain_ollama import ChatOllama

from langchain_text_splitters import RecursiveCharacterTextSplitter

loader = DirectoryLoader(str(DOCS_DIR), glob="**/*.txt", loader_cls=TextLoader)

chunks = RecursiveCharacterTextSplitter(chunk_size=800, chunk_overlap=100).split_documents(

loader.load()

)

store = InMemoryVectorStore.from_documents(

chunks, HuggingFaceEmbeddings(model_name=EMBED_MODEL)

)

retriever = store.as_retriever(search_kwargs={"k": 4})

prompt = ChatPromptTemplate.from_template(

"Answer using only the context. If the context is insufficient, say so.\n\n"

"Context:\n{context}\n\nQuestion: {question}"

)

chain = prompt | ChatOllama(model=LLM_MODEL) | StrOutputParser()

hits = retriever.invoke(question)

context = "\n---\n".join(h.page_content for h in hits)

answer = chain.invoke({"context": context, "question": question})

sources = {h.metadata.get("source", "?") for h in hits}

return f"{answer}\n\nSources: {sorted(sources)}"

if __name__ == "__main__":

if not DOCS_DIR.exists():

sys.exit("Create ./docs and add a few .txt files first.")

print("=== LlamaIndex ===")

print(run_llamaindex(QUESTION))

print("\n=== LangChain ===")

print(run_langchain(QUESTION))

Run it with three or four questions whose answers you already know, and compare the two outputs and their source lists. Differences at this scale come from chunking defaults rather than from either framework's design, which is the point: set the same chunk size and overlap in both before drawing a conclusion, then add a reranker and measure again. Keep a small labeled set of questions with expected source passages and check retrieval hits separately from answer quality, because a fluent answer can hide a retrieval miss.

When the pipeline needs branching, the usual next step is LangGraph with the retriever as one node. A rule worth enforcing early is a hard recursion limit and a human confirmation step before any tool call that writes data, which the LangChain and CrewAI agentic rules file encodes for Cursor.

Strategic Catalog Integrations

Use these catalog entries to extend the recipe, grouped by the layer they affect:

• Frameworks and repos: compare the LangChain repository with the LlamaIndex repository, and look at CrewAI if your agent design is role-based rather than graph-based.
• Local inference: run the generation step on Ollama (the Ollama project page covers install and model management) or move to vLLM when concurrency grows.
• Embeddings and reranking: try BGE-M3 for multilingual corpora and the BGE reranker v2 m3 as the second-stage scorer.
• Agent safety: adopt the LangChain and CrewAI agentic rules so tool-calling code gets recursion limits and structured outputs by default, and review the resilient tool-use agent prompt for retry and failure-handling wording.
• Learning paths: the LangChain for LLM Application Development course covers chains and agents, and the Full Stack LLM Bootcamp covers evaluation and deployment concerns that apply to either framework.

Frequently Asked Questions (FAQ)

Is LlamaIndex better than LangChain for RAG?

For retrieval-centered applications, LlamaIndex gives you more built-in control over parsing, node relationships, routing, and postprocessing, so teams with complex documents often reach a good baseline faster. LangChain can build the same pipeline and is the stronger choice when retrieval is one step inside a larger agent. The deciding factor is where your failures occur, not which project has more integrations.

Can I use LangChain and LlamaIndex together?

Yes. A common pattern uses LlamaIndex for ingestion and retrieval, then exposes the query engine as a tool inside a LangGraph agent. The cost is two dependency trees and two tracing models, so adopt the combination only when each framework is doing work the other would do poorly.

Do I need either framework to build RAG?

No. A pipeline that embeds chunks, stores them in a vector database or Postgres, retrieves the top results, and sends them to a model is a small amount of plain code. Frameworks earn their place when you need many connectors, swappable components, tracing, or graph-style control flow. If you only need one of those, a single focused library may be enough.

How do I run LangChain or LlamaIndex fully offline?

Point both at a local OpenAI-compatible or native Ollama endpoint and use a locally cached embedding model. The recipe above does this: embeddings run through sentence-transformers on CPU and generation runs on an Ollama model. Download the model weights once while online, then disable network access and confirm nothing in your pipeline calls a hosted API.