LlamaIndex is the platform behind what they call "agentic OCR" - AI agents that parse, extract, and process documents across 50+ file types. It started as an open-source RAG framework and has evolved into a full document intelligence platform for enterprises.
What it does
The core idea is task-specific agents that break down content (text, charts, tables, handwritten notes) and route each piece to the right extraction model. Instead of a single OCR pass, multiple agents collaborate on a document.
The platform has four pillars:
- LlamaParse - the parsing engine. Handles complex layouts, embedded images, multi-page tables, handwritten notes. Supports 50+ unstructured file types.
- LlamaAgents - one-click deployment of document agents. Pre-built templates for common document workflows.
- Extract - structured data extraction from parsed documents.
- Index - ingestion and RAG pipeline for search and retrieval.
Agent Workflows
LlamaIndex introduced Agent Workflows with ACP (Agent Communication Protocol) integration. This means filesystem tools, MCP servers, persistent memory, and multi-step orchestration. You can chain agents together for end-to-end document automation.
They also ship pre-built document agent templates for instant deployment, plus a "vibe-coding" extraction mode where you describe what you want in natural language.
Recent launches
- LlamaAgents for one-click agent deployment
- LlamaParse v2 with improved accuracy
- LlamaSheets for messy spreadsheet parsing
- LlamaSplit for document separation
- Parsebench on Kaggle - the first document OCR benchmark for AI agents
Why it matters
This is where RAG meets document processing. Instead of treating OCR as a pre-processing step, LlamaIndex bakes it into an agentic loop. Good fit for enterprise document automation where layouts are complex and accuracy matters.
