RRepoGEO

REPOGEO REPORT · LITE

a16z-infra/llm-app-stack

Default branch main · commit 865ec83f · scanned 6/19/2026, 4:59:06 PM

GitHub: 1,310 stars · 148 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
30 /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
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 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise description and relevant topics to the repository

    Why:

    CURRENT
    Description: (none)
Topics: (none)
    COPY-PASTE FIX
    Description: A comprehensive, curated list of tools, projects, and vendors across the entire LLM application stack, from data pipelines to app hosting.
Topics: llm, generative-ai, ai-stack, llm-tools, vector-databases, orchestration, ai-architecture, awesome-list, curated-list, llm-ecosystem
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    License: (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, or GPL-3.0) that aligns with the project's intent.
  • mediumreadme#3
    Clarify the README's main heading to emphasize its role as a curated list

    Why:

    CURRENT
    # LLM App Stack
    COPY-PASTE FIX
    # LLM App Stack: A Curated Guide to Tools and Services

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
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. Pinecone · recommended 2×
  3. Docker · recommended 2×
  4. LangChain · recommended 1×
  5. Weaviate · recommended 1×
  • CATEGORY QUERY
    What tools and services are essential for building a robust large language model application?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. LangChain
    3. Pinecone
    4. Weaviate
    5. Chroma
    6. Hugging Face Transformers
    7. Hugging Face Hub
    8. FastAPI
    9. Flask
    10. Docker
    11. Kubernetes
    12. AWS EKS
    13. Google GKE
    14. Azure AKS
    15. Weights & Biases
    16. MLflow

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

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a complete list of components for developing a generative AI solution?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. TensorFlow (tensorflow/tensorflow)
    3. JAX (google/jax)
    4. Hugging Face Transformers (huggingface/transformers)
    5. OpenAI API
    6. Stability AI (Stability-AI/StableDiffusion)
    7. Google AI Studio
    8. Vertex AI
    9. Hugging Face Datasets (huggingface/datasets)
    10. Pandas (pandas-dev/pandas)
    11. NumPy (numpy/numpy)
    12. PIL (Pillow) (python-pillow/Pillow)
    13. FFmpeg
    14. Google Cloud Platform (GCP)
    15. Amazon Web Services (AWS)
    16. Microsoft Azure
    17. RunPod
    18. Vast.ai
    19. Hugging Face Inference Endpoints
    20. Hugging Face Spaces
    21. Gradio (gradio-app/gradio)
    22. Streamlit (streamlit/streamlit)
    23. Docker
    24. Kubernetes (kubernetes/kubernetes)
    25. ONNX Runtime (microsoft/onnxruntime)
    26. TensorRT (NVIDIA/TensorRT)
    27. MLflow (mlflow/mlflow)
    28. Weights & Biases (W&B) (wandb/wandb)
    29. Prometheus (prometheus/prometheus)
    30. Grafana (grafana/grafana)
    31. Pinecone
    32. Weaviate (weaviate/weaviate)
    33. Chroma (chroma-core/chroma)

    AI recommended 33 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 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?

  • 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?

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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