Both frameworks connect LLMs to your data, but they were built with different centers of gravity — LlamaIndex around data indexing and retrieval, LangChain around chaining steps and building agents. The right choice depends on which problem is harder in your project.
For pure document Q&A, LlamaIndex alone is often enough and simpler to reason about. For agents that need to act — calling tools, making decisions, orchestrating multiple steps — LangChain (often paired with LlamaIndex for the retrieval layer) is the stronger foundation. Many production systems use both.
It's not strictly necessary — agents can be built with direct API calls and custom orchestration code. LangChain saves time on common patterns (tool calling, memory, chaining) but adds abstraction overhead that isn't always worth it for simple use cases.
The abstraction layers that make prototyping fast can make debugging and performance tuning harder in production. Teams often end up understanding the underlying API calls anyway, which raises the question of how much the framework is actually saving.
If retrieval quality is your main challenge, LlamaIndex's data-indexing focus usually gets you there with less code. If you also need orchestration, tool use, or multi-step agent behavior, LangChain fills that gap — the two are often combined.
We start with the actual bottleneck: retrieval quality points toward LlamaIndex, orchestration complexity points toward LangChain, and a narrow, well-understood pipeline is often faster to build custom than to force into either framework.
Tell us what you're building and we'll recommend a framework — or no framework — in a 30-minute call.