RRepoGEO

REPOGEO REPORT · LITE

metaopt/torchopt

Default branch main · commit 960fb0aa · scanned 5/29/2026, 3:27:18 AM

GitHub: 631 stars · 44 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 metaopt/torchopt, 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 the README's opening statement to emphasize unique differentiators

    Why:

    CURRENT
    TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
    COPY-PASTE FIX
    TorchOpt is an efficient library for **functional and stateless differentiable optimization** built upon PyTorch, specifically designed for **higher-order gradient computation** in meta-learning, bilevel optimization, and other advanced gradient-based techniques.
  • mediumreadme#2
    Add a dedicated comparison section in the README

    Why:

    COPY-PASTE FIX
    Add a new section, e.g., "### Why TorchOpt? (vs. torch.optim)" or "### TorchOpt vs. torch.optim", detailing how TorchOpt provides functional and stateless optimizers suitable for higher-order gradients, contrasting with `torch.optim`'s imperative and stateful approach.
  • mediumreadme#3
    Enhance the "Flexible" feature description in the README

    Why:

    CURRENT
    Flexible**: TorchOpt provides both functional and objective-oriented API for users' different preferences. Users can implement differentiable optimization in JAX-like or PyTorch-like style.
    COPY-PASTE FIX
    Flexible**: TorchOpt provides both functional and objective-oriented APIs. Users can implement **differentiable optimization with higher-order gradients** in a **JAX-like functional style** or a PyTorch-like imperative style, catering to different preferences for meta-learning and bilevel optimization.

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 metaopt/torchopt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 2×
  2. Lightning-AI/lightning · recommended 1×
  3. optuna/optuna · recommended 1×
  4. ray-project/ray · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    How to perform efficient differentiable optimization within a PyTorch deep learning workflow?
    you: not recommended
    AI recommended (in order):
    1. PyTorch built-in optimizers (pytorch/pytorch)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. Optuna (optuna/optuna)
    4. Ray Tune (ray-project/ray)
    5. DeepSpeed (microsoft/DeepSpeed)
    6. torch.optim.LBFGS (pytorch/pytorch)

    AI recommended 6 alternatives but never named metaopt/torchopt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a PyTorch library for meta-learning with support for implicit differentiation techniques.
    you: not recommended
    AI recommended (in order):
    1. higher
    2. Learn2Learn
    3. Meta-Learning with Implicit Differentiation (MLID)
    4. PyTorch-Lightning
    5. torch-meta

    AI recommended 5 alternatives but never named metaopt/torchopt. 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 metaopt/torchopt?
    pass
    AI named metaopt/torchopt explicitly

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

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

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

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metaopt/torchopt — 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