RRepoGEO

REPOGEO REPORT · LITE

pepperoni21/ollama-rs

Default branch master · commit 6b480212 · scanned 5/18/2026, 7:51:57 AM

GitHub: 1,031 stars · 158 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
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 pepperoni21/ollama-rs, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    rust, ollama, llm, api-client, large-language-models, machine-learning, ai
  • highreadme#2
    Refine the README's main heading to be more descriptive

    Why:

    CURRENT
    # Ollama-rs
    COPY-PASTE FIX
    # Ollama-rs: An Idiomatic Rust Client for the Ollama API
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://docs.rs/ollama-rs

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 pepperoni21/ollama-rs
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
rust-llm/llm
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. rust-llm/llm · recommended 2×
  2. huggingface/candle · recommended 2×
  3. huggingface/rust-bert · recommended 2×
  4. intel/openvino-rs · recommended 1×
  5. onnxruntime/onnxruntime-rs · recommended 1×
  • CATEGORY QUERY
    How can I integrate local large language models into my Rust application?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (rust-llm/llm)
    2. candle (huggingface/candle)
    3. rust-bert (huggingface/rust-bert)
    4. OpenVINO (intel/openvino-rs)
    5. ONNX Runtime (onnxruntime/onnxruntime-rs)
    6. TensorFlow Lite (rust-tflite/tflite)

    AI recommended 6 alternatives but never named pepperoni21/ollama-rs. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Rust client library to manage and interact with local AI models.
    you: not recommended
    AI recommended (in order):
    1. llm (rust-llm/llm)
    2. candle (huggingface/candle)
    3. tract (sonos/tract)
    4. rust-bert (huggingface/rust-bert)
    5. tch-rs (LaurentMazare/tch-rs)
    6. onnxruntime-rs (microsoft/onnxruntime-rs)

    AI recommended 6 alternatives but never named pepperoni21/ollama-rs. 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 pepperoni21/ollama-rs?
    pass
    AI did not name pepperoni21/ollama-rs — 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 pepperoni21/ollama-rs in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pepperoni21/ollama-rs 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 pepperoni21/ollama-rs solve, and who is the primary audience?
    pass
    AI named pepperoni21/ollama-rs explicitly

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

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pepperoni21/ollama-rs — 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