Description
LlamaIndex is an AI framework whose homepage now leads with LlamaParse, its agentic document processing product, rather than a general retrieval-augmented generation (RAG) pitch. The framework's roots are still there: it gives developers building blocks to ingest, structure and retrieve private or domain-specific data so an LLM can answer questions and reason over content it was never trained on, but the flagship product today is document parsing and extraction at what LlamaIndex describes as the frontier of cost and accuracy.
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LlamaParse parses more than 50 unstructured file types, including PDFs, Office documents and images, and handles embedded images, complex layouts, multi-page tables and handwritten notes. Task-specific agents route different content, such as text, charts and tables, to specialized parsing experts, with auto-correction loops that detect and fix errors on messy scans. An Extract feature turns unstructured content into structured data against a defined schema without training a custom model, a Split feature segments a document into logical sections from a natural-language description, a Classify feature auto-categorizes documents by natural-language rules, and an Index feature runs an enterprise-grade chunking and embedding pipeline for RAG retrieval.
For local or offline use, LiteParse is an open-source, VLM-free parser from the LlamaParse team that runs fully on a user's own machine, with no cloud calls and no LLM tokens spent, installable via 'npm install @llamaindex/liteparse'.
LlamaIndex positions this document intelligence work around specific industries: insurance (claims and underwriting automation), finance (financial research and due diligence), manufacturing (extracting insights from specs and inspection reports) and healthcare (medical records and handwritten doctor's notes). It reports more than 1 billion documents processed, more than 25 million package downloads a month, and more than 300,000 LlamaParse users, alongside a claimed 99.9% uptime and HIPAA, GDPR and SOC2 compliance for enterprise deployments.
LlamaParse is priced on a credit system (1,000 credits = $1.25). A Free plan includes 10,000 credits a month (roughly 1,000 pages), basic support and up to 100 users. Starter is $50/month with 40,000 included credits and pay-as-you-go up to 400,000 credits. Pro is $500/month with 400,000 included credits, pay-as-you-go up to $5,000/month, and priority Slack support. Enterprise is custom-priced with volume discounts, 5x higher rate limits, enterprise SSO and a dedicated account manager. LlamaIndex is built for developers and technical teams working with document-heavy pipelines rather than end users looking for a ready-made chat product.
- CategoryAI Document & PDF Tools
- Pricing ModelFreemium
- DeploymentCloud, VPC
- PlatformsWeb
LlamaIndex's Core Features
LlamaParse: agentic parsing across 50+ unstructured file types, including images and handwriting
Task-specific parsing agents for text, charts and tables, with auto-correction loops
Extract: schema-based structured data extraction without training a custom model
Split: segmenting a document into logical sections from a natural-language description
Classify: auto-categorizing documents using natural-language rules
Index: enterprise-grade chunking and embedding pipeline for RAG retrieval
LiteParse: open-source, local, VLM-free document parsing with no cloud calls or token cost
Industry-specific workflows for insurance, finance, manufacturing and healthcare
Getting Started with LlamaIndex
Install: run 'npm install @llamaindex/liteparse' for local parsing, or sign up for the hosted LlamaParse API
Upload a document: send a PDF, Office file or scanned image to be parsed
Choose a parsing tier: pick a cost-effective, agentic or agentic-plus tier depending on accuracy needs
Extract structured data: define a schema for the Extract agents to fill from the document
Build downstream: feed the parsed or indexed output into a RAG or agent pipeline
Track usage: monitor credits consumed (1,000 credits = $1.25) from the dashboard
LlamaIndex's Use Cases
- Parsing complex documents
- Structured data extraction
- Building RAG applications
- Insurance claims automation
