RRepoGEO

REPOGEO REPORT · LITE

eastriverlee/LLM.swift

Default branch main · commit 427f3b89 · scanned 6/9/2026, 6:47:01 AM

GitHub: 863 stars · 120 forks

AI VISIBILITY SCORE
28 /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
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 eastriverlee/LLM.swift, 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 opening to highlight Swift-native, idiomatic API for Apple devices

    Why:

    CURRENT
    # LLM.swift

`LLM.swift` is a simple and readable library that allows you to interact with large language models locally with ease for macOS, iOS, watchOS, tvOS, and visionOS.
    COPY-PASTE FIX
    # LLM.swift

`LLM.swift` is a Swift-native, idiomatic library for local Large Language Model (LLM) inference directly within your Apple applications (macOS, iOS, watchOS, tvOS, visionOS). Built upon the highly optimized `llama.cpp` backend, it provides a simple and readable API to interact with GGUF models on-device.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://swiftpackageindex.com/eastriverlee/LLM.swift
  • lowreadme#3
    Add a 'Why LLM.swift?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why LLM.swift?

While other solutions exist, LLM.swift stands out by offering a truly Swift-native and idiomatic API for integrating local LLMs into your Apple apps. It leverages the performance of `llama.cpp` under the hood, but provides a clean, Swift-first interface, making on-device GGUF model inference straightforward for macOS, iOS, and other Apple platforms.

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 eastriverlee/LLM.swift
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Core ML
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 2×
  2. MLX · recommended 2×
  3. llama.cpp · recommended 2×
  4. Transformers.swift · recommended 2×
  5. ONNX Runtime · recommended 1×
  • CATEGORY QUERY
    How can I run large language models directly on my iOS or macOS app?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. MLX
    3. llama.cpp
    4. Transformers.swift
    5. ONNX Runtime
    6. TensorFlow Lite
    7. PyTorch Mobile

    AI recommended 7 alternatives but never named eastriverlee/LLM.swift. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Swift libraries enable local GGUF model inference for Apple platform development?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. Swiftgger
    3. LLaMACC
    4. MLX
    5. Core ML
    6. ml-ane-transformers
    7. coremltools
    8. Transformers.swift

    AI recommended 8 alternatives but never named eastriverlee/LLM.swift. 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 eastriverlee/LLM.swift?
    pass
    AI did not name eastriverlee/LLM.swift — 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 eastriverlee/LLM.swift in production, what risks or prerequisites should they evaluate first?
    pass
    AI named eastriverlee/LLM.swift 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 eastriverlee/LLM.swift solve, and who is the primary audience?
    pass
    AI named eastriverlee/LLM.swift explicitly

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

Embed your GEO score

Drop this badge into the README of eastriverlee/LLM.swift. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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eastriverlee/LLM.swift — 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