LangChain vs LlamaIndex 2026: Which Framework for RAG and Agents

Short answer: pick LlamaIndex when the hard part of your RAG app is the documents, such as messy PDFs, tables and scans, and you want retrieval working with little code. Pick LangChain when the hard part is the agent around retrieval: tool calls, many model providers, approvals and a TypeScript codebase. Many production teams use both, with LlamaIndex retrieving and LangChain or LangGraph orchestrating. Both are MIT-licensed and free, checked 30 October 2026.

The two have grown toward each other. LlamaIndex now ships agents and event-driven Workflows, and LangChain 1.x builds its agents on LangGraph. But their companies now point in different directions: LangChain's commercial product is LangSmith, for tracing and deploying agents, while LlamaIndex's is LlamaParse, a document parsing platform. That split tells you where each framework will keep getting better.

LangChain vs LlamaIndex at a glance

Read on each project's GitHub repository, PyPI, LangSmith pricing and LlamaParse pricing on 30 October 2026.

What is the real difference between LangChain and LlamaIndex?

LlamaIndex starts from your data; LangChain starts from the model loop. LlamaIndex's docs describe it as a Python toolkit for building agents over your data, built from data connectors, indexes and vector stores, and query and chat engines. A few lines take you from a folder of documents to answers with citations, and the lower-level API lets you swap any piece.

LangChain's docs open with create_agent, a minimal agent harness: you pick a model, give it tools and a prompt, and shape its behavior with middleware for guardrails, retries and routing. Retrieval is one tool among many. Its strength is the standard model interface, which lets you switch between OpenAI, Anthropic, Google and others with small code changes.

For a pure question-answering app over company documents, LlamaIndex's defaults get you further with less code. For an assistant that searches documents, then files a ticket, then waits for a manager's approval, LangChain's agent model fits better.

Verdict: LlamaIndex for retrieval-first apps, LangChain for agent-first apps.

Which is better for RAG on messy documents?

LlamaIndex, and the reason is its parsing stack. Its own docs are blunt about it: the built-in readers are fine for clean text, but for scanned PDFs, forms, spreadsheets and slide decks, the quality of what goes into the index decides the quality of every answer. LlamaParse handles that with OCR and table and chart extraction, and LiteParse is a free, open-source parser that runs locally with no API key.

LangChain connects to many document loaders and vector stores, but its commercial focus is LangSmith, not parsing. A LangChain team with complex PDFs usually ends up adding a separate parsing service, which is one more vendor to manage. If your documents are clean Markdown, HTML or plain text, this difference mostly disappears.

Verdict: LlamaIndex.

Which is better for agents and multi-step workflows?

LangChain, by a margin that has grown in 2026. Its agents run on LangGraph, which gives them durable execution, so an agent can survive a failure and resume, plus human-in-the-loop steps. LangChain also points teams that want a ready-made harness to Deep Agents, with subagents and context compression built in.

LlamaIndex's Workflows are a real answer: event-driven, multi-step processes with branching, retries and human review that can be deployed as services. For an agent whose main job is reading documents, they are enough. For long-running agents that call many outside systems, LangGraph has the deeper tooling.

Verdict: LangChain.

Python or TypeScript?

This is where the choice can be made for you. LangChain maintains both a Python library and a JavaScript/TypeScript library, langchainjs, which is under active development. LlamaIndex's TypeScript library is deprecated: its GitHub repository is archived with a notice that it is no longer maintained, the last commit dates from March 2026, and the last npm release from December 2025.

LlamaIndex still offers TypeScript SDKs for its paid LlamaParse API, so a Node app can call LlamaParse for parsing. But the open-source framework itself is now Python only.

Verdict: LangChain, and it is not close for TypeScript teams.

How much do LangChain and LlamaIndex cost?

Both frameworks are free under the MIT licence. You pay only for the hosted products, and for the model and vector database you run underneath.

Prices from LangSmith pricing and LlamaParse pricing, US view, read on 30 October 2026. LlamaParse credits cost $1.25 per 1,000. Pro currently adds a one-time bonus of 800K credits for business email domains, which LlamaIndex labels limited-time.

The two products are not substitutes. LangSmith is observability, evaluation and deployment for agents; LlamaParse is document parsing, extraction and managed indexing. A team that uses both frameworks may end up paying for both.

What parsing 10,000 PDF pages costs on LlamaParse

LlamaParse charges credits per page by tier, per its pricing docs: 1 credit on Fast, 3 on Cost-effective, 10 on Agentic and 45 on Agentic Plus.

Our arithmetic: pages × credits per page × $1.25 per 1,000 credits, before indexing, retrieval and storage credits.

Dupple data: the tier you choose changes the parsing bill 45-fold for the same 10,000 pages, from $12.50 on Fast to $562.50 on Agentic Plus (our arithmetic from LlamaIndex's published credit rates, October 2026). The Fast run fits inside the Free plan's monthly credits; the Agentic run needs Starter plus about 60K pay-as-you-go credits.

On the LangChain side, a 3-developer team on LangSmith Plus pays $117 a month for seats, with 10k base traces included (our arithmetic). Re-parsing the same file within 48 hours is free on LlamaParse because results are cached, which matters when you are tuning settings.

Can you use LangChain and LlamaIndex together?

Yes, and it is a sensible default for serious RAG agents. Let LlamaIndex own ingestion and retrieval: parsing, chunking, indexing and the query engine. Then expose that query engine as a tool and let a LangChain or LangGraph agent decide when to call it, alongside its other tools. A LangChain tool is a Python function, so wrapping a LlamaIndex query engine takes a few lines.

The cost of this setup is two sets of abstractions to learn and two dependency trees to keep updated. It is worth it when both halves are hard: messy documents and a multi-step agent. When only one half is hard, pick the framework built for that half and keep the stack small.

Who should pick which?

• Pick LlamaIndex if you are building document Q&A, search over contracts or reports, or anything where answer quality depends on parsing PDFs and tables well, and your team writes Python.
• Pick LangChain if you are building an agent that uses retrieval as one tool among several, switches between model providers, needs human approvals, or lives in a TypeScript codebase.
• Use both if you need production-grade retrieval over messy documents inside a long-running agent.

For the wider field, see the best RAG tools, the best vector databases for the storage layer, and the best AI agent frameworks for orchestration options beyond these two. If you are starting from zero, how to build an AI agent walks through the pieces, LLM observability tools covers tracing once it runs, and how much it costs to build an AI agent puts a budget on the whole project. Toolradar also keeps a side-by-side tool profile: LangChain vs LlamaIndex on Toolradar.

How we compared them

We read LangChain's overview docs and pricing page, LlamaIndex's framework docs, pricing page and LlamaParse pricing docs, and the LangChain, LangChain.js, LlamaIndex and LlamaIndex.TS repositories on 30 October 2026. Versions come from PyPI. Dupple runs no test lab, so the verdicts follow from each framework's documented design, not from benchmarks; totals are our arithmetic from list prices.

FAQ

Bottom line

LlamaIndex is the better retrieval engine, and LangChain is the better agent runtime. Decide which of the two is your real bottleneck, start there, and add the other only when the second half of the problem gets hard too.

Cite this: Dupple, "LangChain vs LlamaIndex 2026: Which Framework for RAG and Agents", November 2026.

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