RRepoGEO

REPOGEO REPORT · LITE

erikbern/ann-benchmarks

Default branch main · commit f402b2cc · scanned 6/30/2026, 10:48:24 AM

GitHub: 5,695 stars · 901 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
73 /100
Needs work
Category recall
1 / 2
Avg rank #1.0 when recommended
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 erikbern/ann-benchmarks, 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's opening to emphasize 'benchmarking framework'

    Why:

    CURRENT
    The current README starts with 'Benchmarking nearest neighbors' and then lists libraries.
    COPY-PASTE FIX
    Modify the very first sentence or H1 of the README to clearly state: 'ANN-Benchmarks: A Comprehensive Benchmarking Framework for Approximate Nearest Neighbor Algorithms.'
  • mediumtopics#2
    Expand GitHub topics to include 'benchmarking-framework' and 'algorithm-comparison'

    Why:

    CURRENT
    benchmark, docker, nearest-neighbors
    COPY-PASTE FIX
    benchmark, docker, nearest-neighbors, benchmarking-framework, performance-evaluation, algorithm-comparison
  • lowreadme#3
    Add a 'Why ANN-Benchmarks?' section to highlight its unique value

    Why:

    COPY-PASTE FIX
    Add a new section to the README, perhaps titled 'Why ANN-Benchmarks?', with content like: 'Unlike individual Approximate Nearest Neighbor (ANN) libraries, ANN-Benchmarks provides a neutral, standardized, and reproducible platform for objective performance comparison across a wide range of algorithms and datasets. It helps researchers and practitioners select the most suitable ANN solution for their specific needs.'

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
1 / 2
50% of queries surface erikbern/ann-benchmarks
Avg rank
#1.0
Lower is better. #1 = top recommendation.
Share of voice
5%
Of all named tools, what % are you?
Top rival
Faiss
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Faiss · recommended 2×
  2. NMSLIB · recommended 2×
  3. Annoy · recommended 2×
  4. ScaNN · recommended 2×
  5. SIFT1M/SIFT1B · recommended 1×
  • CATEGORY QUERY
    How to objectively compare performance of various approximate nearest neighbor algorithms?
    you: #1
    AI recommended (in order):
    1. Ann-Benchmarks ← you
    2. Faiss
    3. NMSLIB
    4. SIFT1M/SIFT1B
    5. Glove-100/Glove-200
    6. Deep1B
    7. MS-MARCO
    8. HNSW
    9. IVF
    10. LSH
    11. Annoy
    12. ScaNN
    13. DiskANN
    14. Product Quantization
    Show full AI answer
  • CATEGORY QUERY
    Need a tool to evaluate different nearest neighbor search implementations for high-dimensional data.
    you: not recommended
    AI recommended (in order):
    1. Annoy
    2. Faiss
    3. ScaNN
    4. NMSLIB
    5. Hnswlib
    6. FLANN
    7. SciPy's `spatial.KDTree`
    8. SciPy's `spatial.cKDTree`

    AI recommended 8 alternatives but never named erikbern/ann-benchmarks. 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 erikbern/ann-benchmarks?
    pass
    AI named erikbern/ann-benchmarks explicitly

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

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

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

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erikbern/ann-benchmarks — 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