LangChain vs LlamaIndex
General agent orchestration vs. retrieval-first indexing. LangChain for broad tool chains, LlamaIndex when RAG is the whole job.
LangChain
The most popular framework for building LLM applications
View full detailsLangChain and LlamaIndex are the two best-known Python frameworks for building LLM applications, and they overlap more every year: both now do agents, both do retrieval-augmented generation (RAG). The difference is center of gravity. LangChain started as a general toolkit for chaining LLM calls and tools together, and has grown a large ecosystem: LangGraph for agent orchestration and LangSmith for observability. LlamaIndex started specifically as the framework for connecting LLMs to your data (ingestion, indexing, and retrieval), and that data layer remains its strongest, most polished part.
For a pure 'chat with my documents' product, LlamaIndex usually gets you to good retrieval quality with less code. For applications where the agent logic, tool use, and orchestration are the hard part, LangChain's ecosystem is the more common default. Many production teams use pieces of both.
| LangChain | LlamaIndex | |
|---|---|---|
| Best for | General agents, orchestration, tool use | Data ingestion, indexing, RAG quality |
| Origin focus | Chaining LLM calls and tools | Connecting LLMs to your data |
| RAG support | Capable, more assembly required | Core strength, batteries included |
| Agent support | Strong, especially via LangGraph | Present, less central |
| Ecosystem | Largest: LangGraph, LangSmith, huge integrations | Strong data connectors, LlamaCloud services |
| Abstraction style | Many layers, sometimes criticised as heavy | Focused, fewer concepts for RAG |
| Learning curve | Steeper: large API surface | Gentler for the RAG use case |
Editor rating is our overall editorial judgment of the tool, out of 5. It is not a test score and not a user-review average. Ratings have not been re-scored since the tool was listed here. Each tool page says why.
Identify your hard problem: data or orchestration
If your challenge is getting good answers out of messy documents (PDFs, wikis, databases), that is a retrieval problem, LlamaIndex's specialty. If your challenge is agents calling tools, branching, and coordinating steps, that is orchestration, where LangChain's ecosystem is deeper. Name your bottleneck first.
Prototype the same RAG pipeline in both
A weekend each is enough. Index the same document set, ask the same twenty questions, and compare answer quality and how much code you wrote. Frameworks reveal their personality fast in a prototype, and retrieval quality on your data matters more than any benchmark.
Evaluate the ecosystem you will grow into
Production LLM apps need observability, evaluation, and deployment. LangChain pairs naturally with LangSmith and LangGraph; LlamaIndex offers its own cloud services and integrates with third-party tooling. Check which ecosystem fits your team's existing stack and how much vendor coupling you accept.
Remember you can mix them
These are libraries, not religions. A common pattern uses LlamaIndex for ingestion and retrieval inside a LangChain or LangGraph application that handles the agent logic. If both prototypes had strengths, combining the data layer of one with the orchestration of the other is legitimate.
When to pick LangChain
Pick LangChain if your application is mostly about agents, tool use, and orchestration, and you want the largest ecosystem of integrations and supporting tools.
When to pick LlamaIndex
Pick LlamaIndex if your application is mostly about getting high-quality answers from your own documents and data, and you want the shortest path to solid RAG.
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Frequently asked questions
How are these comparisons built?
From the same editorial data we maintain for every tool: category, pricing, strengths and best-fit users. When an editor has written a verdict for a pair, it appears here too; otherwise the table is generated directly from the tool data.
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Yes. Pick any two from the selector above. The side-by-side view is generated from our tool catalog on the spot.
How current is the pricing?
Pricing reflects our last editorial review of each tool. Vendors change plans often, so confirm on the vendor's site before you buy.