LlamaIndex
by LlamaIndex
The data framework for LLM apps. RAG, ingestion, structured extraction, agents over your data.
OSS
LlamaIndex
Added 17 May 2026
Overview
LlamaIndex is the most-used framework for connecting LLMs to your data. Ingestion pipelines, vector stores, query engines, structured extraction, and an agent layer that reasons over the data layer. Python and TypeScript both first-class. Pairs with most vector databases and providers.
Best for
Best for
Teams whose agent value comes from their own data, not just the model
Use cases
- Production RAG over a large corpus of internal docs
- Structured extraction from messy PDFs and emails
- Agents that query your data layer as a tool
- Hybrid keyword + vector retrieval pipelines
Notes
Why it matters
Most agent value comes from the user’s own data, not from the model. A data framework that handles ingestion, retrieval, and structured extraction is the difference between a demo and a production agent.
How teams use it in production
Start with a single corpus and a single query engine. Add structured extraction next. Only add the agent layer once the data layer earns its keep.
What to watch
The convergence between data frameworks (LlamaIndex), orchestration (LangGraph), and SDKs (Vercel AI) is the live question for how agent stacks ship in 2026.
Pros
- Most-complete RAG framework in 2026
- Strong structured extraction patterns
- Provider-agnostic across vector stores and LLMs
- Python and TypeScript both production-ready
Cons
- Heavy framework, learning curve is real
- API surface is wide, easy to over-engineer
- Some abstractions are leaky on edge cases
Pairs with
Other entries in the index that connect to this one. Click through to see the chain.