Choosing a Document Parser

Use this guide to pick the right tool in the scientific-agent-skills repo (or LlamaParse for cloud escalation).

flowchart TD
  start[User has a document task]
  start --> q1{Need PDF merge split forms or encryption utilities?}
  q1 -->|yes| pdfSkill[pdf skill]
  q1 -->|no| q2{Need Markdown audio video EPUB or Azure table extraction?}
  q2 -->|yes| markitdown[markitdown skill]
  q2 -->|no| q3{Need bounding boxes fast local parse or page PNGs for agents?}
  q3 -->|yes| liteparse[liteparse skill]
  q3 -->|no| q4{Complex tables handwriting or production cloud pipeline?}
  q4 -->|yes| llamaparse[LlamaParse cloud]
  q4 -->|no| liteparse

Comparison table

Criterion	LiteParse	MarkItDown	pdf skill	LlamaParse
Primary output	Layout text + JSON with bboxes	Markdown	PDF bytes / extracted text	Structured markdown / JSON (cloud)
Runs locally	Yes	Yes	Yes	No (cloud API)
Bounding boxes	Yes	No	Limited	Yes (cloud)
OCR	Tesseract + optional HTTP OCR	Yes (images/PDF)	Via external tools	Advanced
Page screenshots	Yes (PNG)	No	Image extract only	Varies
Office → text	Via LibreOffice convert	Native converters	N/A	Yes
Audio / video / EPUB	No	Yes	No	Some formats
PDF merge / split / forms	No	No	Yes	No
Best for	RAG grounding, agent vision, batch PDF corpus	LLM-friendly Markdown pipelines	PDF manipulation	Hard documents at scale

Decision rules

Choose LiteParse when

You need coordinates for citations, highlighting, or layout-aware chunking.
You want fast local parsing without API keys.
You are building multimodal workflows (parse JSON + page screenshots).
You are batch-processing folders of PDFs for a literature review pipeline.
Scanned PDFs need OCR with optional custom HTTP OCR backends.

Choose MarkItDown when

The downstream step expects Markdown (RAG, summarization, notebook ingestion).
Inputs include HTML, EPUB, audio, YouTube, or you want Azure Document Intelligence for tables.
You do not need per-span bounding boxes.

Choose the pdf skill when

The task is PDF file operations: merge, split, rotate, watermark, fill forms, encrypt/decrypt.
You only need simple text extraction without spatial layout or OCR orchestration.

Choose LlamaParse when

Documents have dense tables, multi-column layouts, charts, or handwriting beyond what local parsers handle well.
You are building a production document pipeline and accept cloud dependency and signup.

Link: https://docs.cloud.llamaindex.ai/llamaparse/overview

Combining tools

Common pipelines:

LiteParse → chunk + embed — JSON/text for vector store; bboxes for UI highlights.
LiteParse screenshots + vision model — figures and tables; text JSON for search.
LiteParse text → MarkItDown-style post-processing — only if you must have Markdown; otherwise use LiteParse text directly.
pdf skill merge → LiteParse parse — assemble supplementary PDFs, then extract.

Avoid running LiteParse and MarkItDown on the same file unless you have distinct consumers (coordinates vs Markdown).