LangChain
The most widely used framework for chaining models, tools and memory.
open source
Pricing The framework is open source and free; LangSmith tracing is a separate paid product. source ↗
LangChain ↗Head to head
LangChain vs LlamaIndex
LlamaIndex if the problem is getting your own documents into a model; LangChain if the problem is orchestrating tools and agents. The two have converged and either can do both, so the honest question is which shape your project actually is. And worth saying plainly: for a single-step application, calling the provider SDK directly is often simpler than either, and easier to debug when it misbehaves.
Checked 2026-07-29 · we re-check quarterly · how we compare
LlamaIndex
A data framework focused on getting your own documents into a model.
open source
Pricing Open source and free; LlamaCloud parsing and hosting are billed separately by usage. source ↗
LlamaIndex ↗Side by side
| LangChain | LlamaIndex | |
|---|---|---|
| Built around | Chains, tools and agents. | Indexing and retrieval over your data. |
| Retrieval | Capable, one concern among many. | The core focus, with more strategies out of the box. |
| Agents | The larger, more developed surface. | Present, less central. |
| Abstraction | Heavier — powerful, and more to learn. | Lighter for retrieval-shaped work. |
| Ecosystem | The largest, with the most integrations. | Smaller but focused. |
| Observability | LangSmith, sold separately. | Integrates with third-party tracing. |
Pick LangChain if…
- You are orchestrating multiple tools, steps or agents.
- You want the largest ecosystem of ready-made integrations.
- You expect to need tracing and evaluation tooling alongside it.
- Your application's complexity is in the control flow, not the data.
Pick LlamaIndex if…
- Your problem is retrieval over your own documents.
- You want the shortest path from a folder of files to working answers.
- You prefer lighter abstractions you can read through quickly.
- Document parsing and chunking quality is your bottleneck.
Pick neither if…
- Your app makes one model call with a prompt — the provider SDK is less code and far easier to debug.
- You want type-safe, minimal abstractions: PydanticAI or Mastra are worth a look.
- You are still working out whether the idea works at all; frameworks add indirection exactly when you need to see what is happening.
Every comparison here says when the honest answer is "not this one". A page that can only recommend is an advertisement.
Questions people actually ask
Should I use LangChain or LlamaIndex?
LlamaIndex if you are building retrieval over your own documents; LangChain if you are orchestrating tools and agents. They overlap heavily now, so the deciding factor is which shape dominates your project.
Do I need a framework at all?
Often not. For a single model call with a prompt, the provider's SDK is less code and easier to debug. Frameworks earn their keep once you have retrieval, tools and multiple steps to coordinate.
Can I use both together?
Yes — a common pattern is LlamaIndex for the retrieval layer inside a LangChain application. It does mean two sets of abstractions to keep in your head.
Which is better for RAG?
LlamaIndex was built for it and gives you more retrieval strategies out of the box. LangChain does RAG perfectly well, particularly if retrieval is one step in a larger flow.
Related
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