# LangChain vs LlamaIndex

> Choose LlamaIndex when the hard part is your data, meaning parsing messy documents, indexing them well and getting accurate retrieval for a RAG assistant. Choose LangChain when the hard part is the agent, meaning tool calling, middleware, multi-step control and broad provider integrations, in Python or TypeScript. Many production systems use LlamaIndex for retrieval and LangChain or LangGraph for the agent around it.

|  | LangChain | LlamaIndex |
| --- | --- | --- |
| Primary focus | Building agents and LLM applications; create_agent is the main entry point | Agents over your data, with deep ingestion, indexing and retrieval |
| Agent runtime | Agents run on LangGraph, with durable execution, persistence and human-in-the-loop | Agents plus event-driven Workflows with branching, retries and human review |
| Customizing behavior | Middleware for guardrails, retries, routing and tool policies | Swap any component through the lower-level API |
| Retrieval depth | Solid retrievers, vector store and document loader integrations | Many index types, query engines, rerankers and retrieval strategies built in |
| Document parsing | Relies on loaders and third-party parsers | LlamaParse, a hosted parser for scans, forms, tables and slides, plus a local open-source parser |
| Languages | Python and TypeScript, both actively maintained | Python framework; the TypeScript framework is deprecated |
| Observability | LangSmith tracing and evaluation | Integrations with third-party tracing and evaluation tools |
| License | Open source (MIT) | Open source (MIT); LlamaParse is a paid hosted service |

## Choose LangChain if

- You are building an agent that calls many tools and APIs, not just a search over documents
- Your team writes TypeScript, or needs Python and TypeScript to match
- You want middleware for guardrails, retries and tool policies
- You plan to grow into LangGraph for long-running, stateful agents

## Choose LlamaIndex if

- The core job is accurate answers from a large or messy document set
- Your documents include scans, tables, forms and slide decks that need good parsing
- You want to experiment with several index and retrieval strategies quickly
- Your team works in Python

## The short version

LangChain and LlamaIndex overlap, but their centers of gravity differ. LangChain is an agent framework: you compose a model, tools, a prompt and middleware, and its agents run on LangGraph. LlamaIndex is a data framework for agents: it is strongest at turning messy documents into something a model can search and cite accurately.

Ask where your project is hard. If it is retrieval quality, start with LlamaIndex. If it is tool orchestration and control, start with LangChain.

## Where LangChain wins

**Agents and tools.** LangChain's create_agent gives you a configurable agent loop with tool calling, structured output and model choice. Because it runs on LangGraph, you get durable execution, persistence and human-in-the-loop when you need them.

**Middleware.** Guardrails, retries, model routing and custom tool policies plug in as middleware instead of being written into every agent. That keeps production behavior consistent.

**TypeScript.** LangChain maintains both Python and TypeScript libraries. For a team building in Node.js or Next.js, this is often decisive.

**Tracing and evaluation.** LangSmith gives step-by-step traces and evaluation datasets, which is how you catch regressions when prompts or models change.

## Where LlamaIndex wins

**Retrieval quality.** LlamaIndex ships many index types, query engines, rerankers and retrieval patterns. Trying hybrid search, hierarchical retrieval or sub-question decomposition is a configuration change, not a rewrite.

**Document parsing.** LlamaParse, LlamaIndex's hosted document platform, handles scans, complex tables, forms and slide decks that break basic PDF loaders. Bad parsing is the most common reason RAG assistants give wrong answers, so this matters.

**Workflows over data.** Event-driven Workflows combine agents, connectors and tools with branching, retries and human review, which suits document-heavy processes like [AI document data extraction](/guides/ai-document-data-extraction/).

**Fast experiments.** The high-level API gets a working query engine running in a few lines, and the lower-level API lets you replace any component later.

## What we recommend

For a [RAG knowledge assistant](/guides/build-rag-knowledge-assistant/) over complex documents, we usually reach for LlamaIndex for ingestion and retrieval. For agents that act across business systems, we use LangChain or LangGraph, often calling a LlamaIndex retriever as one tool. For simple assistants, we sometimes use neither and call the model SDK directly.

Before choosing a framework, decide whether retrieval or fine-tuning fits the problem ([RAG vs fine-tuning](/compare/rag-vs-fine-tuning/)) and budget with [the cost of a RAG knowledge assistant](/cost/cost-of-rag-knowledge-assistant/).

## Frequently asked questions

### Is LlamaIndex only for RAG?

No. LlamaIndex also has agents and event-driven Workflows. Its strength, though, is data, meaning ingestion, parsing, indexing and retrieval, so it shines most when an agent's main job is to work over documents.

### Can I use LangChain and LlamaIndex together?

Yes. A common pattern wraps a LlamaIndex query engine or retriever as a tool, and a LangChain or LangGraph agent calls that tool alongside CRM, database and email tools. Each framework does what it is best at.

### Is there a TypeScript version of LlamaIndex?

The LlamaIndex TypeScript framework is marked deprecated and no longer maintained. TypeScript teams usually choose LangChain.js, call LlamaParse through its API for parsing, or keep retrieval in a Python service.

### Do I need a framework at all for a RAG assistant?

Not always. A simple assistant over a few hundred documents can be built with a model provider's SDK, an embeddings call and pgvector. Frameworks pay off when you need many data sources, careful retrieval tuning or agent behavior. Start simple and add a framework when it removes real code.

### Which works better with Claude?

Both support Anthropic's Claude models and OpenAI's GPT models, as well as many others. Model choice should follow your quality, latency and cost tests, not the framework.

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Source: https://www.lumeor.studio/compare/langchain-vs-llamaindex/
Published by Lumeor Studio, Newtown, PA. hello@lumeor.studio. Get a written scope: https://www.lumeor.studio/start/