LlamaIndex vs. LangChain: Which should you use in 2026?

For retrieval-augmented generation (RAG), LlamaIndex is usually the stronger starting point when retrieval over private data is the hard part. LangChain is usually better when a large language model (LLM) application needs multistep agents, tools, and durable orchestration. They can work together, and either framework still depends on the quality of the documents entering the pipeline.

LlamaIndex vs. LangChain at a glance

The table below summarizes each framework’s primary strengths. Their capabilities have converged over time: LlamaIndex added agents and workflows, while LangChain added retrieval, as the frameworks’ official comparison(opens in a new tab) explains. The details were verified against the LangChain 1.x documentation(opens in a new tab), LlamaIndex releases(opens in a new tab), and LangGraph 1.x in mid-2026; check the current releases before choosing a version. LlamaIndex staying on a 0.x version is a versioning convention, not a maturity signal.

What LlamaIndex is for

LlamaIndex describes itself as a data framework for LLM apps, built around loading, indexing, storing, and querying private data — the classic RAG pipeline. Its strengths are retrieval and indexing: indices, query engines, and a large connector marketplace (LlamaHub) for getting data in. It has since expanded into agents via LlamaIndex Workflows (an event-driven, step-based execution model) and now positions itself around document agents, but retrieval over private data is still its core.

What LangChain is for

LangChain is a general framework for LLM applications, with a broad integration surface and a current emphasis on agents. Its documentation now leads with a configurable agent harness built on LangGraph. LangGraph is a separate, low-level runtime for building stateful, multi-actor agents, with durable execution and human-in-the-loop support. LangChain also offers LangSmith for tracing and evaluation. It supports retrieval and RAG too, but its primary focus remains orchestration rather than retrieval.

The same RAG pipeline in each framework

The clearest way to see the difference is a minimal RAG pipeline in each framework. LlamaIndex reaches a working query engine in a few lines, with sensible RAG defaults baked in:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Uses OpenAI by default — set OPENAI_API_KEY.
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
print(query_engine.query("What changed in Q2 revenue?"))

LangChain expects each step to be assembled by hand — load, split, embed, retrieve, and wire the chain. That means more surface area, but more control over each stage. On LangChain 1.x, the legacy retrieval-chain helpers are maintained in the separate langchain-classic package, so install it along with the integration packages used below:

from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_core.prompts import ChatPromptTemplate
from langchain_classic.chains import create_retrieval_chain
from langchain_classic.chains.combine_documents import create_stuff_documents_chain

# Load > split > embed > retrieve > generate (set OPENAI_API_KEY).
docs = TextLoader("data.txt").load()
splits = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200).split_documents(docs)
vectorstore = InMemoryVectorStore.from_documents(splits, OpenAIEmbeddings())
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

prompt = ChatPromptTemplate.from_template(
    "Answer using only the context:\n\n{context}\n\nQuestion: {input}"
)
chain = create_retrieval_chain(retriever, create_stuff_documents_chain(ChatOpenAI(), prompt))
print(chain.invoke({"input": "What changed in Q2 revenue?"})["answer"])

LlamaIndex supplies more RAG defaults, while LangChain exposes the pipeline stages for broader composition.

Retrieval, agents, and interoperability

The familiar shorthand — “LlamaIndex for retrieval, LangChain for orchestration” — still describes their main strengths, although both now cover more of the pipeline. LangChain also ships a LlamaIndexRetriever, which lets a LlamaIndex index serve as a retriever inside a LangChain chain. A pipeline can therefore use LlamaIndex for ingestion, indexing, and retrieval, and LangChain for orchestration and generation.

Which one to use (is LlamaIndex better than LangChain?)

Neither framework is better for every RAG system. Start with LlamaIndex when retrieval over private documents is the main challenge, and start with LangChain when the pipeline needs broad orchestration across tools and providers. Because they interoperate, a pipeline can also use both. Match the initial choice to the requirements below:

Most teams can start with one framework and add the other when a specific requirement appears. Their interoperability makes that change less disruptive than replacing the pipeline.

Both core frameworks are free, but their defaults affect LLM token costs. Chunk size, retrieval depth, agent retries, and multistep chains determine how much context each query sends to the model. LlamaIndex handles more of these choices through retrieval defaults, while LangChain exposes them in the pipeline configuration. Measure token use per query before traffic grows.

Before tuning either framework, check the input. A parser that loses content or structure limits the retrieval quality of both.

How document extraction affects both frameworks

A RAG pipeline can retrieve only what its parser preserves. LlamaIndex’s own team calls parsing “the silent bottleneck,”(opens in a new tab) noting that “if your parser destroys table structure, loses reading order, or flattens charts into garbage text, the rest of the stack does not matter.” Both frameworks support a range of document loaders(opens in a new tab) and parsing integrations. A basic loader such as LangChain’s PyPDFLoader isn’t designed to preserve every scanned page or complex layout, so high-fidelity tables and scans can require a dedicated parser such as LlamaParse, Unstructured, or a commercial extraction API. The Extract document elements guide shows how typed elements, bounding boxes, and reading order can be preserved across PDFs, images, and scans.

Here’s what that looks like. A basic loader on a financial table often returns a flat run of numbers, with the row and column relationships gone:

Revenue Q1 Q2 Q3 4.2 4.9 5.1 Growth 12% 17% 4%

A structure-preserving extractor keeps the table intact, so a retrieved chunk still answers “Q2 growth”:

| Metric  | Q1  | Q2  | Q3  |
| ------- | --- | --- | --- |
| Revenue | 4.2 | 4.9 | 5.1 |
| Growth  | 12% | 17% | 4%  |

No embedding model or retriever can recover the association the parser threw away.

Why structure matters more than one score

An aggregate accuracy score can hide structural failures. If a parser loses row-to-column associations, retrieval tuning can’t reconstruct them. An evaluation should therefore check whether the system returns the correct value from the correct table location, not just whether an answer appears plausible.

No independent benchmark compares LangChain’s PyPDFLoader, LlamaParse, and Nutrient in one controlled test. However, a 2026 study of PDF parsing in RAG(opens in a new tab) evaluated PyPDFLoader alongside PDF-to-Markdown pipelines and found that preprocessing and structure-aware chunking materially affected downstream answer accuracy. For how extraction quality can be measured reproducibly, our benchmark methodology documents the corpus and harness in full.

Evaluate the ingestion paths on the documents the application will process:

1. Pull 50–100 documents representative of the real distribution — the multicolumn, scanned, and table-heavy cases, not just the clean PDFs.
2. Run each ingestion path and inspect the parsed output directly before embedding.
3. Check whether table rows keep their column associations and whether reading order survives on multicolumn pages.
4. Only then measure end-to-end answer quality. A retrieval problem and an extraction problem look identical from the answer alone.

Where Nutrient fits

Nutrient isn’t a RAG framework and doesn’t replace LangChain or LlamaIndex; it sits earlier in the pipeline. The Nutrient document extraction API and AI Document Processing turn tables, key-value pairs, multicolumn layouts, and scans into structured, source-grounded output, with optical character recognition (OCR) built in.

Each extracted value includes a bounding box, confidence score, and interpretable match label, so teams can audit the input before it reaches the index. The platform can also be self-hosted. Its deterministic OCR and intelligent character recognition (ICR) modes run without an external model dependency. Some AI-augmented modes use a hosted LLM.

Nutrient is SOC 2 Type 2 audited. The platform serves more than 3,000 organizations, including 15 percent of Global 500 companies, and processes more than 1 billion document interactions annually. The Data Extraction API takes a few minutes to set up. For a direct comparison with LlamaIndex’s parser, see the Nutrient vs. LlamaIndex breakdown.

Try it on a document

A free API key returns structured elements — each with a bounding box and confidence score — in a few lines:

import requests

response = requests.post(
    "https://api.nutrient.io/extraction/parse",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"file": open("document.pdf", "rb")},
    data={"instructions": '{"mode":"understand","output":{"format":"spatial"}}'},
)
print(response.json())

Get a free API key(opens in a new tab) and follow the RAG ingestion guide to wire Nutrient into an existing pipeline.

Conclusion

LlamaIndex is a strong starting point for retrieval-heavy RAG, while LangChain suits pipelines that need broad orchestration. They can also run together. In either case, evaluate parsing before tuning retrieval because lost table structure, reading order, or scanned content can’t be recovered downstream. Start with Nutrient’s free tier(opens in a new tab) to test the extraction layer on representative documents.

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