REPOGEO REPORT · LITE

abgulati/LARS

Default branch main · commit 710489fc · scanned 6/12/2026, 4:52:03 PM

GitHub: 637 stars · 60 forks

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 abgulati/LARS, 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

Add a disambiguation statement to the README's introduction

Why:

CURRENT

The current introductory text after the H1.

COPY-PASTE FIX

LARS stands for 'LLM & Advanced Referencing Solution'. This project is an application for running LLMs locally with your documents, and is not related to 'Long-term Action Recognition in Surveillance' or speech recognition systems.

mediumreadme#2

Rephrase the README's opening to emphasize advanced citations and RAG

Why:

CURRENT

LARS is an application that enables you to run LLM's (Large Language Models) locally on your device, upload your own documents and engage in conversations wherein the LLM grounds its responses with your uploaded content. This grounding helps increase accuracy and reduce the common issue of AI-generated inaccuracies or "hallucinations." This technique is commonly known as "Retrieval Augmented Generation", or RAG.

COPY-PASTE FIX

LARS is the ultimate open-source RAG-centric LLM application, designed to run LLMs locally with your documents. It uniquely provides *detailed, verifiable citations* (including document names, page numbers, text-highlighting, and images) directly within generated responses, effectively eliminating hallucinations and building trust in AI-generated content.

lowtopics#3

Add more specific keywords to the repository topics

Why:

CURRENT

genai, llms, rag

COPY-PASTE FIX

genai, llms, rag, local-llm, desktop-app, citation-generation, knowledge-retrieval, document-qa, private-llm

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 abgulati/LARS

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

LM Studio

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LM Studio · recommended 2×
imartinez/privateGPT · recommended 2×
ollama/ollama · recommended 1×
langchain-ai/langchain · recommended 1×
chroma-core/chroma · recommended 1×

CATEGORY QUERY
How to run large language models locally with my documents for accurate, cited responses?
you: not recommended
AI recommended (in order):
1. Ollama (ollama/ollama)
2. LangChain (langchain-ai/langchain)
3. ChromaDB (chroma-core/chroma)
4. FAISS (facebookresearch/faiss)
5. sentence-transformers (UKPLab/sentence-transformers)
6. LM Studio
7. LocalAI (go-skynet/LocalAI)
8. PrivateGPT (imartinez/privateGPT)
9. llama-cpp-python (abetlen/llama-cpp-python)
10. LanceDB (lancedb/lancedb)
11. all-MiniLM-L6-v2
12. nomic-embed-text (nomic-ai/nomic)
AI recommended 12 alternatives but never named abgulati/LARS. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Desktop application for LLMs that provides detailed citations from uploaded local files?
you: not recommended
AI recommended (in order):
1. MemGPT Desktop (cpacker/MemGPT)
2. LocalGPT (PromtEngineer/localGPT)
3. AnythingLLM Desktop (Mintplex-Labs/anything-llm)
4. LM Studio
5. PrivateGPT (imartinez/privateGPT)
6. Obsidian
7. Text Generator (nhaouari/obsidian-text-generator)
8. Local GPT (pashpashpash/obsidian-local-gpt)
9. Jan (janhq/jan)
AI recommended 9 alternatives but never named abgulati/LARS. 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 abgulati/LARS?
pass
AI named abgulati/LARS explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts abgulati/LARS in production, what risks or prerequisites should they evaluate first?
pass
AI named abgulati/LARS 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 abgulati/LARS solve, and who is the primary audience?
pass
AI named abgulati/LARS 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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Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/abgulati/LARS.svg)](https://repogeo.com/en/r/abgulati/LARS)

HTML

<a href="https://repogeo.com/en/r/abgulati/LARS"><img src="https://repogeo.com/badge/abgulati/LARS.svg" alt="RepoGEO" /></a>

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