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.
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 and read how RAG works to see where parsing fits in the pipeline.
Frequently asked questions
- What is LlamaParse?
- LlamaParse is a hosted document-parsing API from LlamaIndex (part of LlamaCloud). You upload a file via the web UI, REST API, or Python/TypeScript SDK and it returns clean Markdown or structured text — preserving tables, headings, and reading order — so the output is ready to chunk, embed, and retrieve in a RAG pipeline.
- Is LlamaParse free?
- It is freemium. New accounts get a monthly free credit allowance (enough for thousands of pages), after which parsing is metered in credits. Cost per page depends on the mode you pick — fast text extraction is cheapest, while agentic multimodal parsing costs more per page but handles the hardest layouts.
- How is LlamaParse different from plain PDF text extraction?
- Naive extractors flatten complex layouts — they scramble multi-column text and turn tables into unaligned token soup. LlamaParse is layout-aware and offers multimodal modes that screenshot each page and reconstruct its structure with a vision model, so embedded tables, charts, and figures survive as usable Markdown instead of garbage.
Related
- How RAG Actually Works: Ingestion, Chunking, Retrieval & RerankingA clear, practical walkthrough of the retrieval-augmented generation pipeline — what each stage does, where it fails, and how the pieces fit together.
- Best RAG Frameworks in 2026A roundup of the top RAG frameworks in 2026 — LlamaIndex, LangChain, Haystack, and DSPy — and which one fits your retrieval stack.
- Multimodal RAG over PDFs, Scans & Charts: Two Approaches That Actually WorkRAG over visual documents — PDFs, scans, charts — where text-only extraction loses tables and layout. Parse-then-text vs embed-the-page-image, with trade-offs.
- Chunking Strategy OptimizerFind the chunking strategy and size that maximizes retrieval quality for a specific corpus, by sweeping configurations against a fixed eval set instead of guessing. Use when RAG answers miss obvious content, when standing up a new corpus, or when picking chunk size/overlap.
- Multimodal Document ExtractorExtract structured data from documents and images with a vision-language model — define the target schema, prompt the VLM to fill it from the page (invoices, forms, receipts, statements, IDs), and verify critical fields against the source. Use when you need reliable structured output from messy, varied, or scanned documents that defeat template-based OCR.