LlamaIndex is an open-source data framework for building retrieval-augmented generation (RAG) applications and document agents. It provides the infrastructure to connect large language models (LLMs) with private and external data sources, enabling developers to build production-grade AI applications that can search, summarize, extract, and reason over documents. The platform includes both a free open-source Python library (50,000+ GitHub stars, MIT license) and a cloud-based enterprise platform called LlamaParse.
How It Works
LlamaIndex solves the core problem of augmenting LLMs with private data. It offers data connectors to ingest information from over 130 file formats and data sources (PDFs, Office documents, spreadsheets, images, APIs, SQL databases), structures that data into retrievable indices and knowledge graphs, and provides a query interface that feeds relevant context into any LLM prompt. The framework uses a modular architecture where developers can swap in different LLMs, embedding models, vector stores, and retrieval strategies without rewriting application logic.
Key Capabilities
The platform offers several core products. LlamaParse is an enterprise-grade agentic OCR and document parsing engine that handles complex layouts, handwritten text, tables, charts, and embedded images using VLM-powered document understanding agents with auto-correction loops. LlamaExtract turns unstructured content into structured data using schema-based extraction agents. Index provides enterprise chunking and embedding pipelines for high-precision RAG retrieval. Split and Classify enable document segmentation and categorization using natural language rules. Workflows allows building multi-step document agents with orchestration capabilities.
Integrations
The framework integrates with over 300 LLM, embedding, and vector store providers through LlamaHub, including OpenAI, Anthropic, Hugging Face, Ollama, Cohere, Pinecone, Weaviate, Chroma, and Qdrant. LiteParse is a fully open-source, local-first document parser that runs without cloud dependencies or LLM token costs. The platform supports 80+ languages for document processing.
Intended Users
LlamaIndex is designed for engineering and R&D teams, data scientists, and AI developers building document-intensive applications. It serves industries including finance (research, due diligence, invoice processing), insurance (claims automation, underwriting), manufacturing (specs, manuals, inspection reports), and healthcare (medical records, clinical research). Enterprise customers include Carlyle, NttData, Boeing (via Jeppesen), and Delphi.
Security & Compliance
LlamaIndex offers SOC 2 Type II, GDPR, and HIPAA compliance. The enterprise platform provides 99.9% uptime SLAs, flexible deployment options (SaaS cloud or private VPC across any cloud provider), granular access controls, SSO/MFA, and dedicated support. Data cached on the platform is retained only for 48 hours before permanent deletion, with the option to disable caching entirely.
Pricing
LlamaParse operates on a credit-based system (1,000 credits ≈ $1.25). The Free plan includes 10,000 credits per month (approximately 1,000 pages) with 1 user and basic support. Starter plan is $50/month with 40,000 included credits and pay-as-you-go up to 400,000 credits for 5 users. Pro plan is $500/month with 400,000 included credits, pay-as-you-go up to 4,000,000 credits for 10 users, and Slack support. Enterprise plan offers custom pricing with volume discounts, higher rate limits, VPC deployment, and dedicated account management. The open-source LlamaIndex library remains completely free under MIT license.
