Use LlamaIndex if your application is fundamentally about your documents, ingesting them, parsing them well, and retrieving accurately. It is the strongest open option for that slice. If your problem is agent orchestration or general workflow logic rather than data, a general agent framework is the better center of gravity, possibly with LlamaIndex handling just the retrieval layer.
What is LlamaIndex?
LlamaIndex is an open-source Python and TypeScript framework focused on connecting LLMs to your own data, covering document loading, parsing, indexing, retrieval, and agentic workflows over that data. It is the go-to library when the hard part of your problem is the documents rather than the agent logic. The company also runs LlamaCloud, a paid managed service whose LlamaParse product handles messy PDFs, tables, and scanned documents better than most open-source parsers.
Best for
Developers building RAG and document-heavy applications where ingestion, parsing, and retrieval quality matter most.
Not for
Teams whose core problem is agent orchestration rather than document data, or anyone who needs a no-code solution.
Strengths
- Best-in-class focus on the data side: loaders, parsing, chunking, and indexing
- Hundreds of data connectors through LlamaHub
- LlamaParse handles complex PDFs, tables, and scanned documents unusually well
- Supports agentic workflows and multi-step retrieval, not just basic RAG
- Available in Python and TypeScript
- Works alongside other frameworks, so you can use it just for ingestion and retrieval
Limitations
- Less oriented toward general agent orchestration than LangGraph or the agent-first frameworks
- The best document parsing lives in the paid LlamaCloud service, and credit costs vary a lot with document complexity
- Layers of abstraction around indexes and query engines can obscure what retrieval is actually doing
- Fast-moving API with significant reorganizations between major versions
- You still build and host the application, LlamaCloud only covers parsing and indexing
LlamaIndex pricing
Free open-source framework; LlamaCloud is credit-based, where cost per page rises steeply with document complexity and the parsing mode you choose.
Pricing reflects public plans as of July 2, 2026 and can change. Check LlamaIndex for the latest.
LlamaIndex FAQ
LlamaIndex vs LangChain, which should I use?
LlamaIndex is deeper on the data side, document parsing, indexing, and retrieval quality, while LangChain is broader, with more integrations and general LLM app tooling. Many teams use LlamaIndex for ingestion and retrieval inside a LangChain or custom application. If your app is mostly RAG over difficult documents, start with LlamaIndex.
Is LlamaIndex free?
The open-source framework is free, and you pay only for model APIs and hosting. LlamaCloud, including LlamaParse, is a paid credit-based service with a free monthly credit allowance and paid plans starting around $50/mo.
What is LlamaParse and do I need it?
LlamaParse is LlamaCloud's document parsing service, and it is notably good at tables, layouts, and scanned PDFs that break simpler parsers. If your documents are clean text you can skip it and use open-source loaders; if they are messy financial or legal PDFs, it often pays for itself in retrieval accuracy.
Is LlamaIndex good for agents?
It supports agentic workflows and multi-step retrieval well, especially agents that reason over documents. For complex general-purpose agent orchestration with loops, state, and human approvals, frameworks like LangGraph or Pydantic AI are more purpose-built, and can call LlamaIndex retrieval as a tool.
Looking at alternatives? LlamaIndex is a different tool for a different job: it is for engineers building document intelligence into a product. Autonoly handles the surrounding business workflows, moving the extracted answers into CRMs, spreadsheets, and follow-up actions, which is where operators without a Python codebase actually live. See the Autonoly review.