RRepoGEO

REPOGEO REPORT · LITE

meta-llama/PurpleLlama

Default branch main · commit e7051068 · scanned 5/8/2026, 5:07:29 PM

GitHub: 4,168 stars · 731 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 meta-llama/PurpleLlama, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-security, llm-safety, red-teaming, blue-teaming, generative-ai, ai-security, machine-learning-security, llm-evaluation, ai-trust-safety
  • highhomepage#2
    Add the official project homepage

    Why:

    COPY-PASTE FIX
    https://ai.meta.com/llama/purple-llama
  • mediumreadme#3
    Clarify the project's license(s) in the README

    Why:

    CURRENT
    Components within the Purple Llama project
    COPY-PASTE FIX
    Components within the Purple Llama project are licensed under [SPECIFIC LICENSE(S) HERE, e.g., Apache 2.0 for some parts, MIT for others]. Please refer to the LICENSE file for full details on each component's licensing.

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 meta-llama/PurpleLlama
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
leondf/garak
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. leondf/garak · recommended 1×
  2. laiyer-ai/llm-guard · recommended 1×
  3. OWASP LLM Top 10 · recommended 1×
  4. Garak-LLM/PromptInject · recommended 1×
  5. microsoft/responsible-ai-toolbox · recommended 1×
  • CATEGORY QUERY
    What open-source tools are available to assess and improve large language model security?
    you: not recommended
    AI recommended (in order):
    1. Garak (leondf/garak)
    2. LLM Guard (laiyer-ai/llm-guard)
    3. OWASP LLM Top 10
    4. PromptInject (Garak-LLM/PromptInject)
    5. Red Teaming Toolkit (from Microsoft) (microsoft/responsible-ai-toolbox)
    6. SecLLM (SecLLM/SecLLM)

    AI recommended 6 alternatives but never named meta-llama/PurpleLlama. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking solutions for red teaming and blue teaming generative AI models to enhance safety.
    you: not recommended
    AI recommended (in order):
    1. OWASP Top 10 for Large Language Models (LLMs)
    2. Garak (llm-red-team/garak)
    3. Microsoft Guidance (microsoft/guidance)
    4. PromptInject
    5. Robust Intelligence (RI) AI Firewall
    6. Adversarial Robustness Toolbox (ART) (Trusted-AI/adversarial-robustness-toolbox)
    7. Hugging Face Evaluate (huggingface/evaluate)

    AI recommended 7 alternatives but never named meta-llama/PurpleLlama. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 meta-llama/PurpleLlama?
    pass
    AI named meta-llama/PurpleLlama explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of meta-llama/PurpleLlama. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/meta-llama/PurpleLlama.svg)](https://repogeo.com/en/r/meta-llama/PurpleLlama)
HTML
<a href="https://repogeo.com/en/r/meta-llama/PurpleLlama"><img src="https://repogeo.com/badge/meta-llama/PurpleLlama.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

meta-llama/PurpleLlama — 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