---

name: llamaindex description: Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications. version: 1.0.0 author: Orchestra Research license: MIT tags: [Agents, LlamaIndex, RAG, Document Ingestion, Vector Indices, Query Engines, Knowledge Retrieval, Data Framework, Multimodal, Private Data, Connectors] dependencies: [llama-index, openai, anthropic]

LlamaIndex - Data Framework for LLM Applications

The leading framework for connecting LLMs with your data.

When to use LlamaIndex

Use LlamaIndex when:

• Building RAG (retrieval-augmented generation) applications
• Need document question-answering over private data
• Ingesting data from multiple sources (300+ connectors)
• Creating knowledge bases for LLMs
• Building chatbots with enterprise data
• Need structured data extraction from documents

Metrics:

• 45,100+ GitHub stars
• 23,000+ repositories use LlamaIndex
• 300+ data connectors (LlamaHub)
• 1,715+ contributors
• v0.14.7 (stable)

Use alternatives instead:

• LangChain: More general-purpose, better for agents
• Haystack: Production search pipelines
• txtai: Lightweight semantic search
• Chroma: Just need vector storage

Quick start

Installation

# Starter package (recommended)
pip install llama-index

# Or minimal core + specific integrations
pip install llama-index-core
pip install llama-index-llms-openai
pip install llama-index-embeddings-openai

5-line RAG example

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

# Load documents
documents = SimpleDirectoryReader("data").load_data()

# Create index
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query