LlamaIndex
The leading data framework for LLM applications. LlamaIndex specializes in connecting LLMs to your data sources — PDFs, databases, APIs — with best-in-class RAG pipelines.
MIT
TypeScript / Python
Why LlamaIndex?
Building RAG over documents, PDFs, or databases
You want a data-centric approach vs LangChain's agent-centric
Complex ingestion pipelines with multiple data sources
Signal Breakdown
What drives the Trust Score
Download Trend
Last 12 months
Tradeoffs & Caveats
Know before you commitYou need agent orchestration (use LangChain/CrewAI)
Simple single-document Q&A (overkill)
Your team is already deep in LangChain ecosystem
Pricing
Free tier & paid plans
100% free, open-source (MIT)
Free & open-source
LlamaCloud managed service available for production
Alternative Tools
Other options worth considering
The original LLM orchestration framework with a huge pre-built ecosystem of chains, agents, memory, and tool integrations. Very high adoption but community sentiment has shifted — frequent breaking changes are a known pain point.
Often Used Together
Complementary tools that pair well with LlamaIndex
Learning Resources
Docs, videos, tutorials, and courses
Get Started
Repository and installation options
View on GitHub
github.com/run-llama/LlamaIndexTS
npm install llamaindexpip install llama-indexQuick Start
Copy and adapt to get going fast
import { VectorStoreIndex, SimpleDirectoryReader } from 'llamaindex';
const documents = await new SimpleDirectoryReader().loadData({ directoryPath: './data' });
const index = await VectorStoreIndex.fromDocuments(documents);
const queryEngine = index.asQueryEngine();
const response = await queryEngine.query({ query: 'What is the main topic?' });
console.log(response.toString());Code Examples
Common usage patterns
RAG over PDFs
Build a Q&A system over PDF documents
from llama_index.core import VectorStoreIndex
from llama_index.readers.file import PDFReader
documents = PDFReader().load_data("report.pdf")
index = VectorStoreIndex.from_documents(documents)
engine = index.as_query_engine(similarity_top_k=3)
response = engine.query("Summarize the key findings")Chat with memory
Build a chat engine with conversation history
const chatEngine = index.asChatEngine({ chatHistory: [] });
const response1 = await chatEngine.chat({ message: 'What is this document about?' });
const response2 = await chatEngine.chat({ message: 'Can you elaborate on the first point?' });Community Notes
Real experiences from developers who've used this tool