LlamaIndex
What is LlamaIndex?
Data framework for LLM applications. Connects custom data to LLMs for RAG, Q&A over documents, and knowledge base chatbots.
LlamaIndex is a data framework for LLM applications. It connects your own data to models for RAG, question answering over documents and knowledge bases, and the framework itself is free.
What are LlamaIndex's key features?
Data connectors
Hundreds of pre-built loaders for pulling in PDFs, databases, APIs, and other sources as context for an LLM.
Indexing strategies
Multiple ways to structure retrieved data (vector, tree, keyword) depending on the shape of the query workload.
Query engines
Pre-built patterns for turning a natural-language question into a retrieval query over your indexed data.
Agents
Build agents that can call tools and reason over retrieved data, not just answer single-shot questions.
LlamaParse
A document-parsing service specifically tuned for extracting clean text from complex PDFs and tables.
Framework-agnostic integration
Works alongside LangChain or standalone, so it can slot into an existing LLM application stack.
What are LlamaIndex's pros and cons?
Pros
- Free, open-source core with strong RAG focus
- Excellent document parsing, especially for complex PDFs
- Wide range of data connectors out of the box
- LlamaCloud simplifies production RAG infrastructure
Cons
- More narrowly focused on data/RAG than general-purpose LangChain
- Managed LlamaCloud costs scale with document volume
- Requires Python/JS familiarity to use effectively
What can you use LlamaIndex for?
How much does LlamaIndex cost?
LlamaIndex the open-source framework is free; LlamaCloud, its managed data/parsing platform, offers a free tier with paid plans for higher usage.
Framework (open-source)
- Unlimited use
- MIT license
- Data connectors for 100+ sources
LlamaCloud Free
- Limited monthly parsing credits
- Managed indexing
- Hosted retrieval API
LlamaCloud Pro
- Higher parsing/document limits
- Priority processing
- Team features
How to use LlamaIndex
- Install LlamaIndex for Python or TypeScript via pip or npm.
- Load your documents using a data connector suited to the source (PDF, database, API, etc.).
- Build an index over the loaded data using the indexing strategy that fits your query patterns.
- Create a query engine or agent that retrieves relevant context and passes it to an LLM.
- Iterate on retrieval quality, using LlamaParse for cleaner extraction from complex documents if needed.
Best LlamaIndex alternatives
Is LlamaIndex worth it?
LlamaIndex is the most focused, well-documented tool specifically for retrieval-augmented generation over your own data. For broader agent orchestration beyond retrieval, LangChain's wider toolset fits better -- and neither is usable at all without real coding experience.
How we write this page: details come from the tool's official site, documentation and pricing page, plus published reviews where noted. We have not tested every feature hands-on, and prices and limits change, so confirm them on the official site before you buy.
Frequently Asked Questions
Is LlamaIndex free?
Yes, the core framework is free and open-source. LlamaParse and LlamaCloud have usage-based pricing for their hosted document-parsing services.
How is LlamaIndex different from LangChain?
LlamaIndex is more narrowly focused on data indexing and retrieval (RAG); LangChain is a broader framework for chains, agents, and general LLM app orchestration. Many teams use both together.
Do I need to know how to code?
Yes, it is a Python/TypeScript library, not a no-code tool.
What is LlamaParse for?
It is a specialized parsing service for extracting clean, structured text from complex documents like PDFs with tables, improving retrieval quality over naive text extraction.
Can LlamaIndex handle very large document sets?
Yes, that is its core use case, though performance depends on the vector store and indexing strategy chosen for the workload.
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