Handles many input formats (PDF, images, DOCX, PPTX, XLSX, HTML, EPUB) and outputs Markdown, JSON, HTML, or chunks sized for RAG
Preserves structure: tables, equations as LaTeX, code blocks, reading order, and image extraction, with headers/footers removed
Selective OCR keeps it fast by only invoking the vision model on bad or scanned pages, with a CPU-only no-OCR path available
Optional --use_llm hybrid mode works with Gemini, Claude, OpenAI-compatible, Azure, Vertex, OpenRouter, or local Ollama models
Apache-2.0 code with CLI, Python API, batch processing, and a simple API server
Open-source frameworks are free (MIT)
Strong focus on retrieval and data quality
LlamaParse excels at complex document parsing
Managed LlamaCloud for production data workflows
Python and TypeScript support
Cons
Model weights are not freely commercial: a modified OpenRAIL-M license caps free use at under $5M funding/revenue and requires paid licensing above that
Best accuracy needs a GPU plus a local inference server (vLLM via Docker, or llama.cpp on CPU/Apple Silicon), adding setup
Very complex layouts with nested tables and forms may still convert poorly
The built-in API server is explicitly basic and not intended for large-scale production use
Overlaps with LangChain, adding decision fatigue
LlamaCloud credit costs scale with document complexity
Framework surface area has grown large
Fast-moving APIs can change between versions
Advanced agent features less mature than dedicated agent frameworks
Comparison generated from each tool's listing. Add or remove tools above to change it.
