A junior engineer asked me: "Should I learn LangChain or LlamaIndex?"

My answer: "What are you building?"

They didn't have an answer. They wanted to learn a framework for the sake of having it on their resume.

This is backwards.

Frameworks are tools. You choose a tool based on the job, not the other way around. Learning LangChain without a project is like learning a hammer without anything to build.

Here's my honest, opinionated guide:

LangChain: good for building complex agent workflows, multi-step chains, and when you need lots of integrations. Can be over-engineered for simple tasks.

LlamaIndex: best for data-centric applications — RAG, knowledge bases, document QA. More focused and simpler for these use cases.

LangGraph: when you need stateful, graph-based agent workflows with complex branching and memory.

Haystack: if you want an opinionated, production-focused pipeline approach.

Or... just use the APIs directly. For many applications, calling the LLM API with a well-structured prompt and parsing the response is simpler and more maintainable than adding a framework.

The best framework is the one that matches your problem. Start with the problem. The framework will follow.