RRepoGEO

REPOGEO REPORT · LITE

Libr-AI/OpenFactVerification

Default branch main · commit 6e1ee9e5 · scanned 6/18/2026, 7:52:01 PM

GitHub: 1,149 stars · 65 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)

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

AI VISIBILITY SCORE
33 /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
2 / 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 Libr-AI/OpenFactVerification, 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
    Reposition README's opening to emphasize end-to-end pipeline for automated fact verification

    Why:

    CURRENT
    # Loki: An Open-source Tool for Fact Verification

## Overview
Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims...
    COPY-PASTE FIX
    # Loki: An Open-source End-to-End Pipeline for Automated Fact Verification

## Overview
Loki is a comprehensive open-source solution designed to automate the entire process of verifying factuality in extensive textual content. It provides an end-to-end pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is especially useful for journalists, researchers, and anyone building systems to reduce AI model hallucinations.
  • hightopics#2
    Add more specific topics to clarify the repo's function

    Why:

    CURRENT
    ai, factuality, hallucination
    COPY-PASTE FIX
    ai, factuality, hallucination, fact-checking, claim-verification, nlp-pipeline, llm-hallucination-reduction, open-source-ai
  • mediumreadme#3
    Add a dedicated section explaining Loki's unique differentiation

    Why:

    COPY-PASTE FIX
    Add the following section to the README, ideally after the 'Overview':

## Why Loki? Our Differentiators

Unlike generic NLP services, static knowledge bases like DBpedia/Wikidata, or simple claim detection tools, Loki provides a complete, modular, and transparent end-to-end pipeline for automated fact verification. It is specifically engineered to handle complex, long-form textual content and to directly address the challenge of AI model hallucinations by offering a verifiable, evidence-based audit trail for every claim.

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 Libr-AI/OpenFactVerification
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DBpedia
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DBpedia · recommended 2×
  2. Wikidata · recommended 2×
  3. Google Cloud Natural Language API · recommended 1×
  4. Azure AI Language · recommended 1×
  5. Amazon Comprehend · recommended 1×
  • CATEGORY QUERY
    How to automate fact-checking and claim verification for extensive textual content?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Natural Language API
    2. Azure AI Language
    3. Amazon Comprehend
    4. OpenFactCheck
    5. ClaimBuster
    6. Google Search API
    7. Bing Search API
    8. Scrapy
    9. DBpedia
    10. Wikidata
    11. Google Knowledge Graph API
    12. spaCy
    13. Hugging Face Transformers
    14. PyTorch
    15. TensorFlow
    16. Factmata

    AI recommended 16 alternatives but never named Libr-AI/OpenFactVerification. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for open-source tools to verify factual accuracy and reduce AI model hallucinations.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Wikidata
    4. DBpedia
    5. Ragas
    6. Arize Phoenix
    7. Sentence-Transformers
    8. FAISS

    AI recommended 8 alternatives but never named Libr-AI/OpenFactVerification. 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 Libr-AI/OpenFactVerification?
    pass
    AI did not name Libr-AI/OpenFactVerification — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Libr-AI/OpenFactVerification in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Libr-AI/OpenFactVerification 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 Libr-AI/OpenFactVerification solve, and who is the primary audience?
    pass
    AI named Libr-AI/OpenFactVerification explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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Libr-AI/OpenFactVerification — 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