RRepoGEO

REPOGEO REPORT · LITE

ogx-ai/llama-stack-apps

Default branch main · commit 10eff824 · scanned 5/13/2026, 10:46:52 AM

GitHub: 4,298 stars · 645 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 ogx-ai/llama-stack-apps, 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 for categorization

    Why:

    COPY-PASTE FIX
    llama-stack, agentic-ai, generative-ai, llm-applications, multi-step-reasoning, tool-use, llama-3, llama-guard, ai-examples
  • highreadme#2
    Clarify README's opening sentence to position as production-ready examples for Llama Stack

    Why:

    CURRENT
    This repo shows examples of applications built on top of Llama Stack.
    COPY-PASTE FIX
    This repository provides curated, production-ready example applications built on the Llama Stack, demonstrating how to leverage its agentic capabilities for multi-step reasoning, tool use, and safety features.
  • mediumabout#3
    Refine repository description for clarity

    Why:

    CURRENT
    Agentic components of the Llama Stack APIs
    COPY-PASTE FIX
    Example agentic applications demonstrating the Llama Stack for building production-ready LLM solutions.

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 ogx-ai/llama-stack-apps
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. Microsoft Semantic Kernel · recommended 1×
  4. OpenAI Assistants API · recommended 1×
  5. Haystack · recommended 1×
  • CATEGORY QUERY
    How can I build AI applications that perform complex tasks with multiple steps?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Microsoft Semantic Kernel
    4. OpenAI Assistants API
    5. Haystack

    AI recommended 5 alternatives but never named ogx-ai/llama-stack-apps. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help integrate external tools and safety features into generative AI models?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Guardrails AI (guardrails-ai/guardrails)
    4. NeMo Guardrails (NVIDIA/NeMo-Guardrails)
    5. OpenAI Evals (openai/evals)
    6. Hugging Face Transformers (huggingface/transformers)
    7. MLflow (mlflow/mlflow)

    AI recommended 7 alternatives but never named ogx-ai/llama-stack-apps. 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 ogx-ai/llama-stack-apps?
    pass
    AI named ogx-ai/llama-stack-apps explicitly

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

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

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

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ogx-ai/llama-stack-apps — 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