RRepoGEO

REPOGEO REPORT · LITE

r2d4/react-llm

Default branch main · commit fbf85dde · scanned 6/7/2026, 9:56:40 PM

GitHub: 700 stars · 31 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 r2d4/react-llm, 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 H1 and opening paragraph to clarify client-side inference

    Why:

    CURRENT
    # @react-llm/headless

Easy-to-use headless React Hooks to run LLMs in the browser with WebGPU. As simple as `useLLM()`.
    COPY-PASTE FIX
    # @react-llm/headless: Run LLMs in the Browser with React Hooks (Client-Side, WebGPU-Accelerated)

Easy-to-use headless React Hooks to run LLMs entirely in the browser with WebGPU. As simple as `useLLM()`, this library enables private, on-device LLM inference directly within your React applications, differentiating it from cloud-based LLM APIs.
  • mediumtopics#2
    Add more specific topics to highlight client-side and browser-based LLM inference

    Why:

    CURRENT
    headless, llm, react, webgpu
    COPY-PASTE FIX
    headless, llm, react, webgpu, client-side, browser-llm, on-device-ai, llm-inference
  • mediumreadme#3
    Add a 'Why choose react-llm?' section to differentiate from direct competitors

    Why:

    COPY-PASTE FIX
    ## Why choose @react-llm/headless?

While libraries like WebLLM or Transformers.js provide powerful client-side inference, `@react-llm/headless` focuses specifically on providing idiomatic React hooks for seamless integration into your UI. It prioritizes developer experience for React applications, offering a 'Bring your own UI' approach with features like persistent conversation storage and model caching, all while leveraging WebGPU for acceleration.

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 r2d4/react-llm
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. Google AI Studio / Gemini API · recommended 1×
  3. Hugging Face Inference API · recommended 1×
  4. Anthropic Claude API · recommended 1×
  5. Azure OpenAI Service · recommended 1×
  • CATEGORY QUERY
    How to integrate large language models directly into a React web application?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Google AI Studio / Gemini API
    3. Hugging Face Inference API
    4. Anthropic Claude API
    5. Azure OpenAI Service
    6. Node.js with Express
    7. Next.js API Routes
    8. AWS Lambda
    9. Google Cloud Functions

    AI recommended 9 alternatives but never named r2d4/react-llm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a headless React library to run LLMs client-side with WebGPU acceleration.
    you: not recommended
    AI recommended (in order):
    1. Transformers.js (huggingface/transformers.js)
    2. WebLLM (mlc-ai/web-llm)
    3. ONNX Runtime Web (microsoft/onnxruntime)
    4. TensorFlow.js (tensorflow/tfjs)
    5. PyTorch Live (pytorch/live)

    AI recommended 5 alternatives but never named r2d4/react-llm. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 r2d4/react-llm?
    pass
    AI named r2d4/react-llm explicitly

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

  • If a team adopts r2d4/react-llm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named r2d4/react-llm 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 r2d4/react-llm solve, and who is the primary audience?
    pass
    AI named r2d4/react-llm explicitly

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

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