REPOGEO REPORT · LITE

ogx-ai/llama-stack-apps

Default branch main · commit 10eff824 · scanned 6/23/2026, 9:52:04 PM

GitHub: 4,304 stars · 640 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

22 /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

1 / 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

highreadme#1

Reposition README's opening paragraph to clarify purpose

Why:

CURRENT

This repo shows examples of applications built on top of Llama Stack. Starting Llama 3.1 you can build agentic applications capable of: - breaking a task down and performing multi-step reasoning. - using tools to perform some actions - built-in: the model has built-in knowledge of tools like search or code interpreter - zero-shot: the model can learn to call tools using previously unseen, in-context tool definitions - providing system level safety protections using models like Llama Guard.

COPY-PASTE FIX

This repository provides **reference implementations and templates** for building agentic applications using the Llama Stack. These applications demonstrate how to leverage Llama 3.1 for multi-step reasoning, tool usage (built-in and zero-shot), and system-level safety with Llama Guard. They serve as practical guides for developers looking to integrate Llama Stack components into their own agentic AI projects.

hightopics#2

Add specific topics to improve categorization

Why:

COPY-PASTE FIX

agentic-ai, llm-agents, llama-stack, multi-step-reasoning, tool-use, llama-guard, ai-applications, reference-implementations, generative-ai

mediumhomepage#3
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://discord.gg/llama-stack
```

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

Of all named tools, what % are you?

Top rival

LangChain

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

LangChain · recommended 2×
LlamaIndex · recommended 2×
Microsoft Semantic Kernel · recommended 2×
Haystack · recommended 2×
OpenAI Assistants API · recommended 2×

CATEGORY QUERY
How to build AI applications that can perform complex multi-step tasks?
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Microsoft Semantic Kernel
4. Haystack
5. AutoGPT
6. BabyAGI
7. OpenAI Assistants API
8. Prefect
9. Apache Airflow
10. Dagster
AI recommended 10 alternatives but never named ogx-ai/llama-stack-apps. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a framework to integrate custom tools and safety checks into AI agents.
you: not recommended
AI recommended (in order):
1. LangChain
2. LlamaIndex
3. Haystack
4. Microsoft Semantic Kernel
5. AgentVerse
6. OpenAI Assistants API
7. Auto-GPT
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 did not name ogx-ai/llama-stack-apps — 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 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 did not name ogx-ai/llama-stack-apps — 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?

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MARKDOWN (README)

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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