Practical uses
- Build a RAG chatbot for company docs.
- Index massive PDFs/Word by chunks.
- Agent querying multiple sources.
Pros and cons
What LlamaIndex does especially well — and the limits to anticipate.
Pros
- Mature, stable OSS framework.
- 100+ data connectors built-in.
- Clear RAG abstractions.
- Free for prototyping.
Cons
- Learning curve Python/Node.
- LlamaCloud costs for production.
- Vast ecosystem → choice paralysis.
LlamaIndex: when it makes sense.
LlamaIndex is standard for RAG dev; for simple search, basic tools suffice.
Keep if
You're building a RAG app.. You index multi-source (connectors).
Challenge if
You want a no-dev solution.. Your use case is simple (1 doc).
