＋ Add to collection

Install (CLI)

npx skillmds add k-dense-ai/liteparse

Or Copy and Paste in Your Agent chat

Please install the Agent Skill "k-dense-ai/liteparse" for me from skillmd.com. A SkillMD skill is a SKILL.md file — plain markdown plus a few companion files — that you save into my skills directory. Use whichever fetch route fits your setup: (1) if you have a terminal, run npx -y skillmds add k-dense-ai/liteparse; (2) otherwise fetch the file manifest at https://api.skillmd.com/api/skills/k-dense-ai/liteparse/bundle?format=manifest and download each listed file from its "url" into liteparse/ in your skills directory; (3) if the SkillMD MCP tools are connected, call skillmd_download with slug "k-dense-ai/liteparse" and save the returned files under liteparse/. Show me the SKILL.md before saving if you'd like me to review it. If your skills directory is read-only (e.g. the Claude desktop app), save the files to a working folder instead and tell me — I'll add them through my app's skills settings.

README badge

Show this skill's registry status in your repo:

[![SkillMD](https://skillmd.com/badge/k-dense-ai/liteparse.svg)](https://skillmd.com/skills/k-dense-ai/liteparse)

liteparse

Name: liteparse
Rating: 4.2 (5 reviews)
Author: K-Dense AI

k-dense-ai/liteparse · Agent Skill (multi-file)

by K-Dense AI · bundle

Published Jul 5, 2026 · Last updated Jul 6, 2026

Parse PDFs, Office files, and images locally with layout-preserved text, bounding boxes, OCR, and page screenshots for RAG and multimodal agents.

SKILL.md

LiteParse — Local Document Parsing

Overview

LiteParse is a fast, open-source document parser (Rust core, Python/Node bindings) focused on local, layout-aware text extraction with bounding boxes. It does not produce Markdown and does not call cloud LLMs. Outputs are plain text (layout-preserved) or structured JSON with per-page text_items (position, font metadata, optional confidence).

Version note: Examples target liteparse 2.0.0 (PyPI, May 2026). The upstream V1 branch is legacy; this skill documents V2 / main only.

For parser selection vs MarkItDown, the pdf skill, or LlamaParse, see references/choosing_a_parser.md.

When to Use This Skill

Use LiteParse when you need:

Fast local parsing of PDFs or converted Office/image files without cloud dependencies
Spatial text with bounding boxes for layout-aware RAG, citation grounding, or figure/table region logic
OCR on scanned PDFs or images (bundled Tesseract, or a user-run HTTP OCR server)
Page screenshots (PNG) for multimodal agents that must see charts, figures, or handwriting
Batch ingestion of literature folders, supplementary PDFs, or protocol libraries
Page subsets or password-protected PDFs

When Not to Use

Task	Use instead
Markdown for LLM ingestion (EPUB, audio, YouTube, HTML)	`markitdown` skill
Merge/split PDFs, forms, watermarks, rotation	`pdf` skill
Dense tables, handwriting, production cloud pipelines	LlamaParse (cloud; sign up separately)

Installation

uv pip install "liteparse==2.0.0"

This installs the Python bindings and the lit CLI. Verify:

lit --help
python -c "import liteparse; print(liteparse.__version__)"

Optional system tools (for non-PDF inputs):

LibreOffice — Word, Excel, PowerPoint, OpenDocument, CSV/TSV
ImageMagick — PNG, JPEG, TIFF, WebP, SVG, etc.

Install commands are in references/ocr_and_formats.md.

Node.js / TypeScript (optional): npm i @llamaindex/liteparse — see references/api_reference.md.

Quick Start

Python

from liteparse import LiteParse

parser = LiteParse(quiet=True)
result = parser.parse("paper.pdf")
print(result.text)

for page in result.pages:
    print(f"Page {page.page_num}: {len(page.text_items)} items")

CLI

# Layout-preserved text (default)
lit parse paper.pdf

# Structured JSON with bounding boxes
lit parse paper.pdf --format json -o paper.json

# Disable OCR on text-native PDFs (faster)
lit parse paper.pdf --no-ocr

Core Workflows

1. Parse to layout-preserved text

Best for quick full-document text or feeding chunkers that do not need coordinates.

parser = LiteParse(ocr_enabled=True, quiet=True)
result = parser.parse("document.pdf")
full_text = result.text

lit parse document.pdf -o output.txt

2. Parse to structured JSON (bounding boxes)

Use when building layout-aware RAG, highlighting source regions, or joining text with screenshots.

import json
from liteparse import LiteParse

parser = LiteParse(output_format="json", quiet=True)
result = parser.parse("document.pdf")

# Programmatic access
for page in result.pages:
    for item in page.text_items:
        bbox = (item.x, item.y, item.width, item.height)
        # item.text, item.confidence, item.font_name, item.font_size

lit parse document.pdf --format json -o document.json

JSON field layout: references/output_formats.md.

3. Parse specific pages

parser = LiteParse(target_pages="1-5,10,15-20", quiet=True)
result = parser.parse("long_paper.pdf")

lit parse long_paper.pdf --target-pages "1-5,10"

4. Parse from bytes or stdin

Useful for uploads, S3 downloads, or piping remote PDFs.

with open("document.pdf", "rb") as f:
    result = parser.parse(f.read())

curl -sL https://example.com/report.pdf | lit parse -

5. Page screenshots for multimodal agents

Screenshots capture visual content that text extraction alone misses (figures, complex tables, handwriting).

from pathlib import Path

parser = LiteParse(dpi=150, quiet=True)
shots = parser.screenshot("document.pdf", page_numbers=[1, 2, 3])
out = Path("screenshots")
out.mkdir(exist_ok=True)
for s in shots:
    (out / f"page_{s.page_num}.png").write_bytes(s.image_bytes)

lit screenshot document.pdf --target-pages "1,3,5" -o ./screenshots
lit screenshot document.pdf --dpi 300 -o ./screenshots

Combine JSON parse + screenshots when an agent needs both coordinates and pixels for the same pages.

6. Batch-parse a directory

For large corpora, prefer the CLI (parallel OCR workers) or the bundled script.

lit batch-parse ./papers ./parsed --format json --recursive
lit batch-parse ./papers ./parsed --extension .pdf --no-ocr

python scripts/batch_parse_dir.py ./papers ./parsed --format json --recursive

See scripts/batch_parse_dir.py for a Python batch wrapper without network calls.

7. OCR configuration

OCR is on by default. Tesseract is bundled; no extra install for basic English OCR.

parser = LiteParse(
    ocr_enabled=True,
    ocr_language="eng",       # Tesseract codes: fra, deu, etc.
    num_workers=4,            # parallel OCR (default: CPU cores - 1)
    dpi=150,                  # higher DPI → better OCR, slower
)

lit parse scan.pdf --ocr-language fra
lit parse scan.pdf --no-ocr
lit parse scan.pdf --ocr-server-url http://localhost:8080/ocr

Offline / air-gapped: set TESSDATA_PREFIX to a directory of .traineddata files, or pass --tessdata-path. Details: references/ocr_and_formats.md.

8. Encrypted PDFs

parser = LiteParse(password="secret", quiet=True)
result = parser.parse("protected.pdf")

lit parse protected.pdf --password secret

9. Search text items by phrase

Merge adjacent items and return combined bounding boxes for a phrase (e.g. section titles).

from liteparse import search_items

page = result.get_page(1)
matches = search_items(page.text_items, "Materials and Methods", case_sensitive=False)

Multi-Format Inputs

Category	Extensions (examples)	Requirement
PDF	`.pdf`	Native
Office	`.docx`, `.xlsx`, `.pptx`, `.doc`, `.odt`, …	LibreOffice
Images	`.png`, `.jpg`, `.tiff`, `.webp`, `.svg`, …	ImageMagick

Files are converted to PDF internally, then parsed. If conversion tools are missing, parsing fails with an actionable error — install the dependency and retry.

Performance Tips

--no-ocr on born-digital PDFs — largest speedup
target_pages — parse only methods/supplement sections
num_workers — scale OCR across CPU cores
max_pages — cap very large files (default 1000)
lit batch-parse — directory-scale jobs with --recursive and --extension
Lower dpi (e.g. 100) when OCR quality is already sufficient

Reference Files

File	Read when
`references/choosing_a_parser.md`	Unsure whether to use LiteParse, MarkItDown, pdf, or LlamaParse
`references/api_reference.md`	Python/TypeScript API, types, `search_items`
`references/cli_reference.md`	Full `lit` command flags
`references/output_formats.md`	JSON schema, bboxes, confidence scores
`references/ocr_and_formats.md`	Tesseract, HTTP OCR, LibreOffice, ImageMagick

Troubleshooting

Issue	Fix
Office file fails	Install LibreOffice; ensure `soffice` is on PATH (Windows: add LibreOffice `program` dir)
Image fails	Install ImageMagick; verify `convert` or `magick` works
OCR poor quality	Increase `--dpi`; try `--ocr-language`; or HTTP OCR server
OCR slow	`--no-ocr` if not needed; reduce pages; increase `num_workers`
Air-gapped OCR	`export TESSDATA_PREFIX=/path/to/tessdata` or `--tessdata-path`
`ParseError` on bytes	Ensure input is valid PDF bytes (Office bytes need a file path + conversion)

Resources

GitHub: https://github.com/run-llama/liteparse
Docs: https://developers.llamaindex.ai/liteparse/
PyPI: https://pypi.org/project/liteparse/2.0.0/
npm: https://www.npmjs.com/package/@llamaindex/liteparse
OCR API spec: https://github.com/run-llama/liteparse/blob/main/OCR_API_SPEC.md

---
name: liteparse
description: Parse PDFs, Office files, and images locally with layout-preserved text, bounding boxes, OCR, and page screenshots for RAG and multimodal agents.
license: Apache-2.0
---

# LiteParse — Local Document Parsing

## Overview

LiteParse is a fast, open-source document parser (Rust core, Python/Node bindings) focused on **local, layout-aware text extraction** with bounding boxes. It does not produce Markdown and does not call cloud LLMs. Outputs are **plain text** (layout-preserved) or **structured JSON** with per-page `text_items` (position, font metadata, optional confidence).

**Version note:** Examples target **liteparse 2.0.0** (PyPI, May 2026). The upstream V1 branch is legacy; this skill documents **V2 / main** only.

For parser selection vs MarkItDown, the `pdf` skill, or LlamaParse, see `references/choosing_a_parser.md`.

## When to Use This Skill

Use LiteParse when you need:

- **Fast local parsing** of PDFs or converted Office/image files without cloud dependencies
- **Spatial text** with bounding boxes for layout-aware RAG, citation grounding, or figure/table region logic
- **OCR** on scanned PDFs or images (bundled Tesseract, or a user-run HTTP OCR server)
- **Page screenshots** (PNG) for multimodal agents that must see charts, figures, or handwriting
- **Batch ingestion** of literature folders, supplementary PDFs, or protocol libraries
- **Page subsets** or **password-protected** PDFs

## When Not to Use

| Task | Use instead |
|------|-------------|
| Markdown for LLM ingestion (EPUB, audio, YouTube, HTML) | `markitdown` skill |
| Merge/split PDFs, forms, watermarks, rotation | `pdf` skill |
| Dense tables, handwriting, production cloud pipelines | [LlamaParse](https://docs.cloud.llamaindex.ai/llamaparse/overview) (cloud; sign up separately) |

## Installation

```bash
uv pip install "liteparse==2.0.0"
```

This installs the Python bindings and the **`lit`** CLI. Verify:

```bash
lit --help
python -c "import liteparse; print(liteparse.__version__)"
```

**Optional system tools** (for non-PDF inputs):

- **LibreOffice** — Word, Excel, PowerPoint, OpenDocument, CSV/TSV
- **ImageMagick** — PNG, JPEG, TIFF, WebP, SVG, etc.

Install commands are in `references/ocr_and_formats.md`.

**Node.js / TypeScript** (optional): `npm i @llamaindex/liteparse` — see `references/api_reference.md`.

---

## Quick Start

### Python

```python
from liteparse import LiteParse

parser = LiteParse(quiet=True)
result = parser.parse("paper.pdf")
print(result.text)

for page in result.pages:
    print(f"Page {page.page_num}: {len(page.text_items)} items")
```

### CLI

```bash
# Layout-preserved text (default)
lit parse paper.pdf

# Structured JSON with bounding boxes
lit parse paper.pdf --format json -o paper.json

# Disable OCR on text-native PDFs (faster)
lit parse paper.pdf --no-ocr
```

---

## Core Workflows

### 1. Parse to layout-preserved text

Best for quick full-document text or feeding chunkers that do not need coordinates.

```python
parser = LiteParse(ocr_enabled=True, quiet=True)
result = parser.parse("document.pdf")
full_text = result.text
```

```bash
lit parse document.pdf -o output.txt
```

### 2. Parse to structured JSON (bounding boxes)

Use when building layout-aware RAG, highlighting source regions, or joining text with screenshots.

```python
import json
from liteparse import LiteParse

parser = LiteParse(output_format="json", quiet=True)
result = parser.parse("document.pdf")

# Programmatic access
for page in result.pages:
    for item in page.text_items:
        bbox = (item.x, item.y, item.width, item.height)
        # item.text, item.confidence, item.font_name, item.font_size
```

```bash
lit parse document.pdf --format json -o document.json
```

JSON field layout: `references/output_formats.md`.

### 3. Parse specific pages

```python
parser = LiteParse(target_pages="1-5,10,15-20", quiet=True)
result = parser.parse("long_paper.pdf")
```

```bash
lit parse long_paper.pdf --target-pages "1-5,10"
```

### 4. Parse from bytes or stdin

Useful for uploads, S3 downloads, or piping remote PDFs.

```python
with open("document.pdf", "rb") as f:
    result = parser.parse(f.read())
```

```bash
curl -sL https://example.com/report.pdf | lit parse -
```

### 5. Page screenshots for multimodal agents

Screenshots capture visual content that text extraction alone misses (figures, complex tables, handwriting).

```python
from pathlib import Path

parser = LiteParse(dpi=150, quiet=True)
shots = parser.screenshot("document.pdf", page_numbers=[1, 2, 3])
out = Path("screenshots")
out.mkdir(exist_ok=True)
for s in shots:
    (out / f"page_{s.page_num}.png").write_bytes(s.image_bytes)
```

```bash
lit screenshot document.pdf --target-pages "1,3,5" -o ./screenshots
lit screenshot document.pdf --dpi 300 -o ./screenshots
```

Combine **JSON parse + screenshots** when an agent needs both coordinates and pixels for the same pages.

### 6. Batch-parse a directory

For large corpora, prefer the CLI (parallel OCR workers) or the bundled script.

```bash
lit batch-parse ./papers ./parsed --format json --recursive
lit batch-parse ./papers ./parsed --extension .pdf --no-ocr
```

```bash
python scripts/batch_parse_dir.py ./papers ./parsed --format json --recursive
```

See `scripts/batch_parse_dir.py` for a Python batch wrapper without network calls.

### 7. OCR configuration

OCR is **on by default**. Tesseract is bundled; no extra install for basic English OCR.

```python
parser = LiteParse(
    ocr_enabled=True,
    ocr_language="eng",       # Tesseract codes: fra, deu, etc.
    num_workers=4,            # parallel OCR (default: CPU cores - 1)
    dpi=150,                  # higher DPI → better OCR, slower
)
```

```bash
lit parse scan.pdf --ocr-language fra
lit parse scan.pdf --no-ocr
lit parse scan.pdf --ocr-server-url http://localhost:8080/ocr
```

**Offline / air-gapped:** set `TESSDATA_PREFIX` to a directory of `.traineddata` files, or pass `--tessdata-path`. Details: `references/ocr_and_formats.md`.

### 8. Encrypted PDFs

```python
parser = LiteParse(password="secret", quiet=True)
result = parser.parse("protected.pdf")
```

```bash
lit parse protected.pdf --password secret
```

### 9. Search text items by phrase

Merge adjacent items and return combined bounding boxes for a phrase (e.g. section titles).

```python
from liteparse import search_items

page = result.get_page(1)
matches = search_items(page.text_items, "Materials and Methods", case_sensitive=False)
```

---

## Multi-Format Inputs

| Category | Extensions (examples) | Requirement |
|----------|----------------------|-------------|
| PDF | `.pdf` | Native |
| Office | `.docx`, `.xlsx`, `.pptx`, `.doc`, `.odt`, … | LibreOffice |
| Images | `.png`, `.jpg`, `.tiff`, `.webp`, `.svg`, … | ImageMagick |

Files are converted to PDF internally, then parsed. If conversion tools are missing, parsing fails with an actionable error — install the dependency and retry.

---

## Performance Tips

- **`--no-ocr`** on born-digital PDFs — largest speedup
- **`target_pages`** — parse only methods/supplement sections
- **`num_workers`** — scale OCR across CPU cores
- **`max_pages`** — cap very large files (default 1000)
- **`lit batch-parse`** — directory-scale jobs with `--recursive` and `--extension`
- Lower **`dpi`** (e.g. 100) when OCR quality is already sufficient

---

## Reference Files

| File | Read when |
|------|-----------|
| `references/choosing_a_parser.md` | Unsure whether to use LiteParse, MarkItDown, pdf, or LlamaParse |
| `references/api_reference.md` | Python/TypeScript API, types, `search_items` |
| `references/cli_reference.md` | Full `lit` command flags |
| `references/output_formats.md` | JSON schema, bboxes, confidence scores |
| `references/ocr_and_formats.md` | Tesseract, HTTP OCR, LibreOffice, ImageMagick |

---

## Troubleshooting

| Issue | Fix |
|-------|-----|
| Office file fails | Install LibreOffice; ensure `soffice` is on PATH (Windows: add LibreOffice `program` dir) |
| Image fails | Install ImageMagick; verify `convert` or `magick` works |
| OCR poor quality | Increase `--dpi`; try `--ocr-language`; or HTTP OCR server |
| OCR slow | `--no-ocr` if not needed; reduce pages; increase `num_workers` |
| Air-gapped OCR | `export TESSDATA_PREFIX=/path/to/tessdata` or `--tessdata-path` |
| `ParseError` on bytes | Ensure input is valid PDF bytes (Office bytes need a file path + conversion) |

---

## Resources

- **GitHub**: https://github.com/run-llama/liteparse
- **Docs**: https://developers.llamaindex.ai/liteparse/
- **PyPI**: https://pypi.org/project/liteparse/2.0.0/
- **npm**: https://www.npmjs.com/package/@llamaindex/liteparse
- **OCR API spec**: https://github.com/run-llama/liteparse/blob/main/OCR_API_SPEC.md

Files

This skill is a package of 7 files. Install with the command above, or download the folder.

📄SKILL.md entry
📁references
📄api_reference.md 4.3 KB
📄choosing_a_parser.md 3.3 KB
📄cli_reference.md 3.2 KB
📄ocr_and_formats.md 3.1 KB
📄output_formats.md 3.8 KB
📁scripts
⚙️batch_parse_dir.py 4.2 KB

K-Dense-AI/scientific-agent-skills/tree/main/skills/liteparse commit · 26fd7a8451

▌
rag-architect · alirezarezvani bundle
Design, tune, and evaluate production RAG pipelines with deterministic tools for chunking, pipeline design, and retrieval evaluation.

20.4k
repo stars
▌
llamaindex · orchestra-research bundle
Connects LLMs with user data for RAG applications, document Q&A, and knowledge retrieval using 300+ data connectors and vector indices.

10.4k
repo stars
▌
azure-ai-contentunderstanding-py · microsoft
Extract semantic content from documents, images, audio, and video using Azure AI Content Understanding SDK for Python.

2.7k
repo stars
▌
azure-ai-document-intelligence-ts · microsoft
Extract text, tables, and structured data from documents using Azure Document Intelligence. Process invoices, receipts, IDs, forms, or build custom document models.

2.7k
repo stars
▌
mini-context-graph · github bundle
Build a persistent, compounding knowledge base that combines a wiki, knowledge graph, and raw source storage for structured retrieval with provenance.

36.2k
repo stars
▌
chroma · orchestra-research bundle
Store and query embeddings with metadata filtering, vector search, and full-text search using an open-source database that scales from notebooks to production.

10.4k
repo stars

Frequently asked questions

How do I install the liteparse skill?

Run npx skillmds add k-dense-ai/liteparse in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the liteparse skill do?

Parse PDFs, Office files, and images locally with layout-preserved text, bounding boxes, OCR, and page screenshots for RAG and multimodal agents. It is listed under AI & ML, Data & Analytics, Data Analysis, RAG & Embeddings on SkillMD.

Is liteparse safe to use?

SkillMD's automated safety review verdict for this skill is CAUTION. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, makes network calls, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with liteparse?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is liteparse free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under Apache-2.

Who published liteparse?

K-Dense AI (@k-dense-ai) published this skill. Their other Agent Skills are listed on their SkillMD profile.