LlamaIndex 2026: Document Agents and RAG Pipelines Explained (51k Stars)

You have documents everywhere - PDFs, Notion, databases - and you want one interface that answers questions across all of them. LlamaIndex is built exactly for this.

## LlamaIndex: The Data Framework for LLM Applications LlamaIndex (51,277 GitHub stars, MIT license) is the data framework for LLM apps - the piece that connects your documents to your model. It popularized the term RAG in the developer world and has grown into a full platform for building document agents: ingest from 30+ sources, index into vector stores, query with advanced retrieval, and orchestrate agentic loops over your data. ## The Core Concepts **Data connectors (LlamaHub).** Read from PDFs, Notion, Confluence, Slack, databases, and web pages with one-line loaders. The hub has hundreds of community connectors. **Indexes.** Convert documents into searchable structures: VectorStoreIndex (semantic search), SummaryIndex (whole-doc queries), and KnowledgeGraphIndex (relationships). **Query engines.** The retrieval + synthesis loop that answers questions. You can compose them: route a question to the vector index or the summary index depending on intent. **Agents.** The layer on top: an LLM with tools (query engines, document loaders, arbitrary functions) that decides what to call. A document agent can look at a PDF, find the relevant section, check another doc, and synthesize - multi-step reasoning over your data. ## A Minimal Example ```python from llama_index.core import VectorStoreIndex, SimpleDirectoryReader docs = SimpleDirectoryReader("docs/").load_data() index = VectorStoreIndex.from_documents(docs) query_engine = index.as_query_engine() print(query_engine.query("What is our refund policy?")) ``` That is a working RAG app in six lines - load, index, query. ## When to Reach for Agents Query engines answer single questions; agents handle workflows - compare across documents, summarize a whole folder, then draft an email. The pattern in 2026: start with a query engine, add agentic tools only when questions become multi-step. Agents cost more tokens and add failure modes; the simpler tool that solves the problem is the better one. ## FAQ **Is LlamaIndex free?** Yes - MIT licensed open source; LlamaCloud is the paid hosted version. **LlamaIndex vs LangChain?** Overlapping but different centers of gravity: LlamaIndex is data/index-first; LangChain is chain/agent-first. Many projects use both. **Does it work with local models?** Yes - Ollama, vLLM, and any OpenAI-compatible endpoint. **Can it query SQL and vector stores together?** Yes - SQL + vector + document indexes can be composed in one query pipeline.