REPOGEO REPORT · LITE

run-llama/liteparse

Default branch main · commit 81b5e09d · scanned 6/26/2026, 10:51:13 PM

GitHub: 11,137 stars · 737 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface run-llama/liteparse, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Clarify core purpose in README's opening paragraph

Why:

CURRENT

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.

COPY-PASTE FIX

LiteParse is a standalone, open-source tool for **fast and light PDF document parsing**, providing high-quality spatial text extraction with bounding boxes. It runs entirely locally, **without any proprietary LLM features or cloud dependencies**, and is **not designed for parsing LLM outputs or structured data from models.**

mediumtopics#2

Add more specific topics for local document processing and OCR

Why:

CURRENT

document-ocr, document-processing, ocr, ocr-recognition, pdf, pdf-parser, text-extraction

COPY-PASTE FIX

document-ocr, document-processing, ocr, ocr-recognition, pdf, pdf-parser, text-extraction, local-processing, offline-ocr, spatial-text-extraction, bounding-box-extraction

lowabout#3

Refine repository description for clarity

Why:

CURRENT

A fast, helpful, and open-source document parser

COPY-PASTE FIX

A fast, open-source tool for local PDF document parsing and OCR, providing spatial text extraction with bounding boxes, without LLM output parsing or cloud dependencies.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface run-llama/liteparse

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

pdfminer/pdfminer.six

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

pdfminer/pdfminer.six · recommended 1×
jsvine/pdfplumber · recommended 1×
pymupdf/PyMuPDF · recommended 1×
apache/tika · recommended 1×
camelot-dev/camelot · recommended 1×

CATEGORY QUERY
What are the best open-source tools for fast, local PDF text extraction with bounding boxes?
you: not recommended
AI recommended (in order):
1. PDFMiner.six (pdfminer/pdfminer.six)
2. pdfplumber (jsvine/pdfplumber)
3. PyMuPDF (Fitz) (pymupdf/PyMuPDF)
4. Apache Tika (apache/tika)
5. Camelot (camelot-dev/camelot)
AI recommended 5 alternatives but never named run-llama/liteparse. This is the gap to close.
Show full AI answer
CATEGORY QUERY
I need a lightweight document parser for offline OCR and text processing without cloud dependencies.
you: not recommended
AI recommended (in order):
1. Tesseract OCR
2. PaddleOCR
3. Surya
4. OCRmyPDF
5. GOCR
6. ABBYY FineReader Engine
AI recommended 6 alternatives but never named run-llama/liteparse. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of run-llama/liteparse?
pass
AI named run-llama/liteparse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts run-llama/liteparse in production, what risks or prerequisites should they evaluate first?
pass
AI named run-llama/liteparse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo run-llama/liteparse solve, and who is the primary audience?
pass
AI named run-llama/liteparse explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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HTML

<a href="https://repogeo.com/en/r/run-llama/liteparse"><img src="https://repogeo.com/badge/run-llama/liteparse.svg" alt="RepoGEO" /></a>

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run-llama/liteparse — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite