What is LlamaIndex?
LlamaIndex is an open-source data framework for building applications powered by large language models. While other frameworks focus on chains and agents, LlamaIndex starts with your data. It provides tools to ingest, structure, index, and query your documents so LLMs can work with them effectively.
The framework started as "GPT Index" in late 2022 and has grown into the go-to solution for retrieval-augmented generation (RAG). If you're building anything that needs to answer questions about your own documents, LlamaIndex should be on your shortlist.
Key Features
Data Connectors (LlamaHub)
LlamaIndex supports 150+ data connectors through LlamaHub. PDFs, Notion pages, Slack messages, SQL databases, Google Drive, Confluence, web pages. If your data lives somewhere, there's probably a connector for it. This matters because the hardest part of RAG isn't the retrieval algorithm. It's getting your data into a format the system can work with.
Index Types
Not all data is the same, and LlamaIndex reflects that. You can build vector indexes for semantic search, keyword indexes for exact matching, tree indexes for hierarchical summarization, and knowledge graph indexes for relationship-heavy data. Each index type optimizes for different query patterns. Most projects start with a vector index and add others as needs evolve.
Query Engines and Pipelines
LlamaIndex's query engine layer sits between your index and the LLM. You can configure retrieval strategies (top-k, hybrid, recursive), add re-ranking, filter by metadata, and compose multiple indexes into a single query pipeline. This is where LlamaIndex pulls ahead of building RAG from scratch. The pipeline abstraction handles the messy details of retrieval that trip up DIY implementations.
LlamaParse
Document parsing sounds boring until you try to extract tables from a PDF. LlamaParse is LlamaIndex's document parsing service, and it handles complex layouts, tables, images, and multi-column formats that break simpler parsers. It's part of LlamaCloud and uses a credit-based pricing model (1,000 credits = $1).
LlamaCloud
LlamaCloud is the managed service layer. It provides hosted parsing (LlamaParse), managed indexes, and retrieval APIs so you don't have to run your own vector database infrastructure. The free tier includes 1,000 credits per month. Paid tiers use a credit system where pricing varies by operation complexity.
LlamaIndex vs LangChain
This is the comparison that comes up constantly. Here's the short version: LlamaIndex is better for data ingestion and RAG. LangChain is better for agents and complex workflows. They're not mutually exclusive.
LlamaIndex gives you more control over how your data is indexed and retrieved. LangChain gives you more flexibility in how your LLM interacts with tools and makes decisions. Many production systems use LlamaIndex for the data pipeline and LangChain (or LangGraph) for the agent logic. See our full LangChain vs LlamaIndex comparison for a deeper breakdown.
Pricing
The core LlamaIndex library is free and MIT licensed. You can build and deploy RAG applications without paying anything beyond your LLM and hosting costs. LlamaCloud adds managed services with a credit-based model. The free tier gives you 1,000 credits per month, which is enough for experimenting. Production usage will require a paid plan, with pricing depending on your parsing and indexing volume.
Getting Started
Install with pip install llama-index. The quickstart builds a basic RAG pipeline in about 10 lines of code: load documents, create an index, and query it. From there, you can swap in different embedding models, add metadata filtering, try different index types, and build multi-step query pipelines. The documentation is solid, and the Discord community is active and helpful.
✓ Pros
- Best-in-class RAG pipeline with multiple index types out of the box
- 150+ data connectors for ingesting documents, APIs, databases, and more
- LlamaParse handles complex PDFs, tables, and images better than most alternatives
- Clean, intuitive API that's simpler than LangChain for data-focused tasks
- Active development with frequent releases and strong community
✗ Cons
- Less flexible than LangChain for complex agent workflows
- LlamaCloud credit-based pricing can be confusing at first
- TypeScript version lags behind Python in features
- Smaller ecosystem of third-party tutorials compared to LangChain
Who Should Use LlamaIndex?
Ideal For:
- RAG application developers who need production-grade retrieval pipelines without reinventing the wheel
- Teams with messy document collections where LlamaParse and data connectors save weeks of preprocessing work
- Python developers building search or Q&A systems who want a framework that prioritizes data quality over abstraction depth
- Prompt engineers focused on retrieval quality where index tuning and query pipelines matter more than agent orchestration
Maybe Not For:
- Complex multi-agent workflows where LangChain's LangGraph is a better fit
- Teams that need JavaScript-first support since the TypeScript version isn't at feature parity yet
- Simple chatbot projects where calling an LLM API directly is easier than setting up an index
Our Verdict
LlamaIndex is the best framework for building RAG applications in 2026. That's not a controversial take. While LangChain tries to be everything for everyone, LlamaIndex focuses on the data pipeline and does it exceptionally well. The data connectors, index types, and query engines are all designed around one question: how do you get the right context to your LLM?
LlamaCloud and LlamaParse add a managed layer that's worth considering if you're processing lots of documents. The credit-based pricing takes some getting used to, but it's fair for what you get. The main limitation is scope. If you need complex agent logic, tool use, or state machines, you'll want LangChain or CrewAI alongside LlamaIndex. Many production teams use both, and that's probably the right call.