RRepoGEO

REPOGEO REPORT · LITE

a16z-infra/llm-app-stack

Default branch main · commit 865ec83f · scanned 5/9/2026, 7:32:51 PM

GitHub: 1,304 stars · 147 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 a16z-infra/llm-app-stack, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise description to the repository's About section

    Why:

    COPY-PASTE FIX
    A comprehensive, curated list of tools, projects, and vendors across the entire LLM application stack, from data pipelines to app hosting.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root, for example, an MIT License, to clarify usage rights for contributors and users.

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 a16z-infra/llm-app-stack
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. langchain-ai/langchain · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. OpenAI API · recommended 1×
  5. Azure OpenAI Service · recommended 1×
  • CATEGORY QUERY
    What are the essential components and tools for building a robust LLM application?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. OpenAI API
    4. Azure OpenAI Service
    5. Anthropic Claude API
    6. Google Gemini API
    7. Hugging Face Transformers (huggingface/transformers)
    8. Hugging Face Hub
    9. Pinecone
    10. Weaviate (weaviate/weaviate)
    11. Qdrant (qdrant/qdrant)
    12. Chroma (chroma-core/chroma)
    13. Streamlit (streamlit/streamlit)
    14. Gradio (gradio-app/gradio)
    15. MLflow (mlflow/mlflow)
    16. Weights & Biases (wandb/wandb)

    AI recommended 16 alternatives but never named a16z-infra/llm-app-stack. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How do I choose the right vector database or orchestration framework for my AI project?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Qdrant
    4. Milvus
    5. Zilliz
    6. Chroma
    7. LangChain
    8. LlamaIndex
    9. Haystack
    10. OpenAI Functions / Tools
    11. Microsoft Semantic Kernel

    AI recommended 11 alternatives but never named a16z-infra/llm-app-stack. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 a16z-infra/llm-app-stack?
    pass
    AI did not name a16z-infra/llm-app-stack — 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 a16z-infra/llm-app-stack in production, what risks or prerequisites should they evaluate first?
    pass
    AI named a16z-infra/llm-app-stack 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 a16z-infra/llm-app-stack solve, and who is the primary audience?
    pass
    AI named a16z-infra/llm-app-stack 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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a16z-infra/llm-app-stack — 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