# LlamaParse

> Hosted document-parsing API from LlamaIndex that turns complex PDFs — tables, charts, figures, handwriting — into clean, LLM-ready Markdown for RAG.

LlamaParse is LlamaIndex's hosted parsing API that converts messy documents — PDFs with tables, charts, figures, and handwriting — into clean, structured Markdown for RAG. It is layout-aware, supports 90+ formats and 100+ languages, offers multiple cost-vs-accuracy modes up to agentic multimodal parsing, and plugs straight into LlamaIndex ingestion.

Website: https://www.llamaindex.ai/llamaparse

LlamaParse is the document-ingestion service from **LlamaIndex**, built to solve the unglamorous but decisive first step of RAG: getting clean, structured text out of messy source files. It is a **hosted API** (part of LlamaCloud) — you send a PDF, PowerPoint, Word doc, spreadsheet, or image and get back **LLM-ready Markdown** that preserves headings, reading order, and (crucially) tables.

The differentiator is **complex-PDF and table accuracy**. Ordinary text extraction scrambles multi-column pages and collapses tables into unaligned tokens that wreck retrieval downstream. LlamaParse is layout-aware and offers a range of parsing modes that trade cost against accuracy — from fast text-only extraction up to **agentic, multimodal parsing** that screenshots each page and uses a vision model to reconstruct its structure. It supports 90+ file types and 100+ languages.

Because it is made by LlamaIndex, it drops directly into the broader **LlamaIndex ingestion pipeline** — parse, then chunk, embed, and index — but the Markdown it returns is framework-agnostic and works just as well feeding LangChain, a raw vector store, or any other stack. The parsing service itself is proprietary and hosted; the official SDK clients are open-source.

Pricing is **freemium**: new accounts get a monthly free credit allowance, then usage is metered in credits, with cost per page scaling by the mode you choose. Pair it with a deliberate [chunking strategy](/skills/data/chunking-strategy-optimizer) and read [how RAG works](/guides/concepts/how-rag-works) to see where parsing fits in the pipeline.

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_Source: https://agentscamp.com/tools/llamaparse — Tool on AgentsCamp._