liteparse README

The project's README from github.com/run-llama/liteparse. As one markdown file: README.md.

LiteParse

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Looking for LiteParse V1? Follow this link to the old code

LiteParse is a standalone OSS PDF parsing tool focused exclusively on fast and light parsing. It provides high-quality spatial text parsing with bounding boxes, without proprietary LLM features or cloud dependencies. Everything runs locally on your machine.

Hitting the limits of local parsing? For complex documents (dense tables, multi-column layouts, charts, handwritten text, or scanned PDFs), you'll get significantly better results with LlamaParse, our cloud-based document parser built for production document pipelines. LlamaParse handles the hard stuff so your models see clean, structured data and markdown.

Sign up for LlamaParse free

Overview

flowchart LR
      subgraph Input["Input Formats"]
          direction TB
          PDF["PDF"]
          DOCX["DOCX"]
          XLSX["XLSX"]
          PPTX["PPTX"]
          IMG["Images"]
      end

      subgraph Core["Rust Core"]
          direction TB
          CONV["Format Conversion\nLibreOffice / Rust image + resvg + usvg crates"]
          EXTRACT["Text Extraction\nPDFium C library"]
          OCR["Selective OCR\nTesseract / HTTP / Custom"]
          MERGE["OCR Merge\nNative text + OCR results"]
          PROJ["Grid Projection\nSpatial layout reconstruction"]
          CONV --> EXTRACT
          EXTRACT --> OCR --> MERGE --> PROJ
          EXTRACT --> MERGE
      end

      subgraph Output[" Output "]
          direction TB
          JSON["Structured JSON\ntext + bounding boxes"]
          TEXT["Plain Text\nlayout-preserved"]
          SCREEN["Screenshots\nPNG rendering"]
      end

      subgraph Bindings["Language Bindings"]
          direction TB
          NAPI["Node.js / TypeScript\nnapi-rs"]
          PYO3["Python\nPyO3"]
          WASM["Browser / WASM\nwasm-bindgen"]
          CLI["CLI\ncargo / npm / pip"]
          NAPI ~~~ PYO3 ~~~ WASM ~~~ CLI
      end

      PDF --> EXTRACT
      DOCX & XLSX & PPTX & IMG --> CONV
      PROJ --> JSON & TEXT & SCREEN
      JSON & TEXT & SCREEN --> Bindings

      style Input fill:#F5F5F5,color:#000000,stroke:#37D7FA,stroke-width:2px
      style Core fill:#F5F5F5,color:#000000,stroke:#3E18F9,stroke-width:2px
      style Output fill:#F5F5F5,color:#000000,stroke:#FF8705,stroke-width:2px
      style Bindings fill:#F5F5F5,color:#000000,stroke:#FF8DF2,stroke-width:2px

      style PDF fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
      style DOCX fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
      style XLSX fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
      style PPTX fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px
      style IMG fill:#96E7F9,color:#000000,stroke:#37D7FA,stroke-width:1px

      style CONV fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
      style EXTRACT fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
      style OCR fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
      style MERGE fill:#92AEFF,color:#000000,stroke:#4B72FE,stroke-width:1px
      style PROJ fill:#4B72FE,color:#FFFFFF,stroke:#3E18F9,stroke-width:2px

      style JSON fill:#FFBD74,color:#000000,stroke:#FF8705,stroke-width:1px
      style TEXT fill:#FFBD74,color:#000000,stroke:#FF8705,stroke-width:1px
      style SCREEN fill:#FFBD74,color:#000000,stroke:#FF8705,stroke-width:1px

      style NAPI fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
      style PYO3 fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
      style WASM fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px
      style CLI fill:#FFBFF8,color:#000000,stroke:#FF8DF2,stroke-width:1px

Benchmarks

LiteParse is measured on multiple public Doc→Markdown benchmarks. All numbers below were produced on this machine from one command (see Reproducing), with every tool at its latest release as of 2026-09-09. LiteParse and the "model-free" competitors use no ML model at all (no LLM, no layout model, no GPU). All tested methods have permissive licenses and can be run locally with minimal dependencies.

Benchmark Metric LiteParse + Tesseract OCR + PaddleOCR Best other model-free tool
ParseBench (2,049 docs) Overall (mean of 5 categories) 0.364 0.380 0.389 pdf-inspector 0.283
opendataloader-bench (200 docs) Overall (NID + TEDS + MHS) 0.886 0.896 0.901 opendataloader 0.842
olmOCR-bench (1,403 pages) % tests passed 39.6 41.1 42.2 pdf-inspector 33.7
ParseBench

Rule-based scoring, no LLM judge. Each column is ParseBench's canonical per-category metric (Tables = GriTS/TRM composite; the others are rule pass-rates). Overall is the mean of the five, as on the ParseBench leaderboard.

Pipeline Overall Tables Charts Content Faithfulness Semantic Formatting Visual Grounding
LiteParse + PaddleOCR 0.389 0.430 0.013 0.787 0.402 0.314
LiteParse + Tesseract 0.380 0.428 0.012 0.751 0.399 0.307
LiteParse (no OCR) 0.364 0.424 0.013 0.700 0.385 0.297
pdf-inspector 1.19 0.283 0.277 0.017 0.598 0.426 0.099
opendataloader 2.5.7 0.277 0.349 0.006 0.663 0.258 0.110
markitdown 0.1.7 0.185 0.158 0.020 0.652 0.001 0.110

Notes:

  • Visual Grounding scores layout blocks with bounding boxes (lit parse --extract-blocks).
  • Charts is near zero for every tool here — none reconstruct chart data.
opendataloader-bench

NID = reading-order similarity, TEDS = table structure, MHS = heading hierarchy. Overall is the harness's own mean.

Engine Overall NID TEDS MHS
LiteParse + PaddleOCR 0.901 0.932 0.832 0.840
LiteParse + Tesseract 0.896 0.928 0.829 0.828
LiteParse (no OCR) 0.886 0.917 0.818 0.821
nutrient (commercial) 0.885 0.925 0.708 0.819
opendataloader 2.5.7 0.842 0.912 0.483 0.757
markitdown 0.1.7 0.589 0.844 0.273 0.000

Notes:

  • nutrient has no runnable parser in this harness (commercial)
  • The corpus is native-text PDFs, so OCR gains come from embedded figures and a handful of scanned pages.
olmOCR-bench

Score = average of per-category pass rates. The two math categories require LaTeX output and are 0% for every tool here; they still count in the average.

Engine Overall baseline headers_footers multi_column table_tests long_tiny_text old_scans arxiv_math old_scans_math
LiteParse + PaddleOCR 42.2 99.9 48.7 69.1 54.0 46.4 19.4 0.0 0.0
LiteParse + Tesseract 41.1 99.9 52.1 66.2 54.1 42.5 13.9 0.0 0.0
LiteParse (no OCR) 39.6 99.9 55.8 66.3 52.5 29.2 13.3 0.0 0.0
pdf-inspector 1.19 33.7 82.9 62.1 49.7 43.6 17.6 13.3 0.0 0.0
opendataloader 2.5.7 32.5 86.9 36.6 63.7 24.9 34.8 13.3 0.0 0.0
markitdown 0.1.7 28.7 86.8 38.8 39.3 19.9 31.2 13.3 0.0 0.0

Notes:

  • headers_footers expects letterhead and footer text to be absent. OCR recovers that text from logo and address images on single-page documents, where the repeated-header filter cannot fire, so the OCR rows score lower there by design. We chose not to drop text by page position. pdf-inspector 1.19 refuses scanned and image-based pages outright (192 of the 1,403), which is why it tops this category while scoring nothing on those pages elsewhere.
  • old_scans is largely cursive handwriting; Tesseract cannot read it, PaddleOCR partially can.
How the runs are configured
  • Same scorer for every tool. Each benchmark's own evaluator, unmodified. The ground truth in all three corpora is plain text, so every tool including LiteParse runs with hyperlink syntax off (--no-links); everything else is the default lit parse --format markdown.
  • OCR modes. No OCR is --no-ocr. Tesseract is the built-in engine with no setup. PaddleOCR is the PP-OCRv5 mobile detector and English recognizer served over the OCR HTTP API by ocr/rapidocr, which runs the PaddleOCR models on ONNX Runtime — the same models as ocr/paddleocr, just 20–40× faster per page on CPU-only machines.
  • Competitors are the free converters we could run locally, each at its latest release on 2026-09-09: markitdown 0.1.7, opendataloader-pdf 2.5.7 (its non-hybrid mode), pdf-inspector 1.19.0. Each benchmark's own runner for the tool is used as-is. Numbers from public leaderboards or LLM-assisted modes are not mixed in.
  • Machine: Apple M2 Max, 12 cores, 32 GB. LiteParse rows: v2.14.4 (2026-09-09).

Reproducing the benchmarks

./run_benchmarks.sh --liteparse-only                # all three benches, no OCR
./run_benchmarks.sh --liteparse-only --ocr=tesseract
( cd ocr/rapidocr && uv run server.py ) &           # then:
./run_benchmarks.sh --liteparse-only --ocr=paddle
./run_benchmarks.sh --competitors-only              # re-run the free competitors only
./run_benchmarks.sh                                 # everything

Results and a SUMMARY.md land in bench_results/latest/.

Installation

Install via your preferred package manager. All versions (except WASM) ship with the same lit CLI.

Language Install Library Docs
Node.js / TypeScript npm i -g @llamaindex/liteparse Node.js README
Python pip install liteparse Python README
Rust cargo install liteparse (CLI) / cargo add liteparse (lib) Rust README (crates.io)
Browser (WASM) npm i @llamaindex/liteparse-wasm WASM README

Agent Skill

You can use liteparse as an agent skill, downloading it with the skills CLI tool:

npx skills add run-llama/llamaparse-agent-skills --skill liteparse

Or copy-pasting the SKILL.md file to your own skills setup.

See the Agent Skill guide for requirements and usage patterns.

CLI Usage

The CLI is the same across all installations (npm, pip, cargo install).

Parse Files

# Basic parsing
lit parse document.pdf

# Parse to Markdown — headings, tables, lists, images, links
lit parse document.pdf --format markdown -o output.md

# Parse with specific format
lit parse document.pdf --format json -o output.json

# Parse specific pages
lit parse document.pdf --target-pages "1-5,10,15-20"

# Parse without OCR
lit parse document.pdf --no-ocr

# Include page-scoped vector path data in JSON
lit parse document.pdf --format json --extract-vector-graphics

# Include rich per-item PDF text metadata
lit parse document.pdf --format json --extract-text-metadata

# Include page annotations in structured JSON
lit parse document.pdf --format json --extract-annotations

# Include AcroForm widget fields and values (repairs orphaned widgets in memory)
lit parse document.pdf --format json --extract-form-fields

# Parse a remote PDF
curl -sL https://example.com/report.pdf | lit parse -

Markdown Output

LiteParse can render documents directly to Markdown. This means reconstructing headings, tables, lists, images, and links from the spatial layout. This is ideal for feeding documents to LLMs and RAG pipelines. This mode is purely heuristics and rule-based, so complex documents may not render perfectly, but it will be fast.

# Render to Markdown
lit parse document.pdf --format markdown -o output.md

# Strip images instead of emitting placeholders
lit parse document.pdf --format markdown --image-mode off

# Extract embedded images to disk and reference them from the markdown
lit parse document.pdf --format markdown --image-mode embed --extract-images --image-output-dir ./images

# Extract image bytes and metadata without changing Markdown image handling
lit parse document.pdf --format json --extract-images

# Emit link text as plain text (no [text](url) syntax)
lit parse document.pdf --format markdown --no-links

# Include tagged-PDF logical structure in JSON
lit parse document.pdf --format json --extract-structure-tree

# Include the classified layout blocks (with bounding boxes) in JSON
lit parse document.pdf --format json --extract-blocks

Image handling is controlled by --image-mode:

Mode Behavior
placeholder (default) Emits ![](img_pN_K.png) references in reading order
off Strips images entirely
embed Emits the same image references as placeholder

--extract-images is the only option that enables embedded-image extraction. --image-output-dir requires it and writes the extracted bytes to disk. JSON output contains each image's name, path, page bbox, intrinsic pixel dimensions, rotation, format, and duplicate relationship; pixel bytes are never embedded in JSON. Identical image resources reuse the same output file.

Library callers can opt in with extract_images: true (Rust), extractImages: true (Node/WASM), or extract_images=True (Python). It defaults to false. Markdown image mode controls presentation only; placeholder refs are still discovered without bytes.

Markdown reconstruction quality varies with document complexity. For the hardest documents (dense tables, multi-column layouts, scans), LlamaParse remains the most accurate option.

Vector Graphics

Vector path output is opt-in because path-heavy PDFs can produce large payloads. Enable it with --extract-vector-graphics, Rust/Python extract_vector_graphics = true, or JavaScript/WASM extractVectorGraphics: true. Each page then includes vector_graphics (vectorGraphics in JavaScript) with:

The representation follows LlamaParse PDFium path extraction; LiteParse calls the shape rectangle bbox rather than PDFium's coords, and uses width / height rather than w / h. The field is absent (or None/undefined) by default. Diagonal and curved segments are represented by their parent shape but are not emitted as lines.

Tagged PDF structure tree

Enable --extract-structure-tree (Rust/Python extract_structure_tree, JavaScript/WASM extractStructureTree) to add a page-scoped structure_tree. It preserves every root and recursively exposes element type, ID, actual/alternate text, title, typed scalar attributes, marked-content IDs, children, and referenced link annotations. The field is absent by default; enabled untagged pages contain roots: [].

Layout blocks

The Markdown renderer works by classifying each page into blocks — headings, paragraphs, list items, tables, code, rules, figures — and then rendering them. Enable --extract-blocks (Rust/Python extract_blocks, JavaScript/WASM extractBlocks) to get that decomposition as data instead of only as rendered text, with the coordinates the classifier used.

Each page gains a blocks array in reading order — the same order, and the same blocks, the Markdown output is built from. Every block carries:

Table cells are objects, not bare strings — each has text and its own bbox, so a cell can be mapped back to the region of the page it was read from. For ruled tables that box is the drawn grid cell; for borderless tables it is the extent of the spans the cell was built from. Cells that exist only to square off a ragged grid carry no bbox, since they have no ink behind them.

{
  "kind": "table",
  "bbox": { "x": 72.0, "y": 310.5, "width": 468.0, "height": 96.0 },
  "header": [
    { "text": "Territory Code", "bbox": { "x": 72.0, "y": 310.5, "width": 120.0, "height": 24.0 } },
    { "text": "Factor",         "bbox": { "x": 192.0, "y": 310.5, "width": 96.0, "height": 24.0 } }
  ],
  "rows": [
    [
      { "text": "001", "

liteparse (run-llama/liteparse)

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Liteparse
Что это такое?LiteParse — это отдельный open-source парсер PDF, сделанный с упором на быструю и лёгкую работу: он извлекает текст с координатами (bounding box) и выдаёт результат в Markdown, JSON или обычном тексте. Всё работает локально, без облака и обращений к LLM.
Это утилита командной строки или библиотека?И то, и другое. При любой установке (npm, pip, cargo) ставится один и тот же CLI 'lit', а ещё её можно использовать как библиотеку из Rust, Node.js/TypeScript, Python или в браузере через WASM.
Нужно ли доустанавливать что-то ещё, например OCR-движок?Нет. Tesseract OCR встроен и работает сразу, без настройки. Дополнительно по желанию можно подключить другие OCR-серверы (EasyOCR, PaddleOCR, собственный) через стандартный HTTP API.
Есть ли документы, с которыми он справляется плохо?Да. Восстановление Markdown в нём основано на правилах, поэтому плотные таблицы, многоколоночная вёрстка и диаграммы могут получаться неидеально. Для самых сложных документов README отсылает к облачному LlamaParse от тех же авторов.
На каких платформах и языках он работает?Linux, macOS (Intel/ARM) и Windows, а также браузер через WASM. В Python и Node.js есть режим пула воркеров для настоящей параллельной обработки и жёсткие таймауты на каждый разбор, так что проблемный PDF не сможет заблокировать весь конвейер.
Как он сравнивается с другими локальными конвертерами?На публичных бенчмарках Doc-to-Markdown, запущенных локально без ML-моделей, LiteParse лидирует среди решений без моделей: 0.364 на ParseBench (2 049 документов) против 0.283 у pdf-inspector и 0.185 у markitdown, 0.886 на opendataloader-bench и 39.6% на olmOCR-bench. С подключённым PaddleOCR результаты растут до 0.389, 0.901 и 42.2.
Могу ли я использовать его из AI-агента для кодинга?Да — он поставляется как agent skill. Скачайте его через skills CLI или скопируйте файл SKILL.md в свою систему навыков; в руководстве по Agent Skill описаны требования и варианты использования.
Что он умеет извлекать, кроме простого текста?Блоки макета (--extract-blocks) с типом каждого блока и координатами bbox, дерево структуры тегированных PDF, опционально векторную графику, а также встроенные изображения (--extract-images записывает байты на диск, а в JSON сохраняются имена, пути, размеры и поворот).
Как он связан с LlamaParse?Оба продукта созданы командой LlamaIndex (run-llama). LiteParse — это быстрый локальный парсер без моделей, а LlamaParse — облачный сервис той же команды для production-конвейеров обработки документов; его рекомендуют, когда локальный разбор упирается в предел на плотных таблицах, диаграммах или сканах.
What are similar projects?pdf-inspector, opendataloader-pdf, MinerU, pdfcraft, xerj, anydoc, marked, zerox
Which topics is it in?text-extraction, pdf-parser, benchmarking, document, parsing, parser, ocr, document-processing, wasm, json, pdf, rag
Creator
run-llama
Size
15 MB
Stars
12.9K
Updated
CommitOct 12Releasedocker-v2.16.0Oct 12
Age
8mo
Commits · 90 Days