Buying Guide
LangChain vs. LlamaIndex
Both are open-source frameworks for building on top of LLMs, but they emerged with different centers of gravity — LangChain broader, LlamaIndex more retrieval-focused.
Choose LangChain — Your project needs multi-step agents and tool orchestration beyond pure document retrieval.
Choose LlamaIndex — Your project is fundamentally 'answer questions from our data' and nothing broader.
| Scope | LangChain covers a broader range of patterns (chains, agents, tool use, retrieval); LlamaIndex concentrates specifically on connecting LLMs to your data. |
| When retrieval is the whole problem | For a project that's essentially 'answer questions from our documents,' LlamaIndex's narrower focus can mean less incidental complexity. |
| When you need broader agent tooling | LangChain's wider toolkit is more useful once the project needs multi-step agents and tool orchestration beyond pure retrieval. |
Cost implications
Both are open-source frameworks with no license cost; the real cost is engineering time — a mismatched framework choice adds development time working around the wrong abstraction, not a bill.
Hidden tradeoffs
- — Broader frameworks like LangChain carry more abstraction layers, which can mean more surface area to debug when something goes wrong.
- — A narrowly-focused tool like LlamaIndex is simpler until the project's scope grows past what it was built for.
Common mistakes
- — Reaching for LangChain's full agent toolkit for a project that's purely retrieval, adding complexity the project doesn't need.
- — Starting with LlamaIndex and hitting a wall when the project grows into multi-step agent behavior it wasn't designed for.
When to choose neither
For a very simple single-document Q&A tool, calling the model API directly with the document in context can be simpler than either framework.
Decision checklist
- □Is retrieval the entire problem, or one piece of a larger multi-step system?
- □How likely is the project to grow into agent/tool-orchestration territory later?
- □Does your team already have depth in one framework that outweighs a marginal fit difference?
Bottom line
Both are legitimate — we pick based on whether the project is fundamentally a retrieval problem (LlamaIndex) or a broader agentic system (LangChain), not brand preference.
Have a project in mind?
Tell us what you're trying to automate or build — we'll reply with next steps, not a sales pitch.