RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/HyperAgents

Default branch main · commit 59a68f67 · scanned 6/22/2026, 6:28:18 AM

GitHub: 2,591 stars · 340 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 facebookresearch/HyperAgents, 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

2 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 clarify its unique agent paradigm

    Why:

    CURRENT
    <p>Self-referential self-improving agents that can optimize for any computable task</p>
    COPY-PASTE FIX
    <p>HyperAgents is a novel framework for building self-referential, self-improving AI agents designed to optimize for *any* computable task, moving beyond traditional reinforcement learning paradigms.</p>
  • mediumlicense#2
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under the terms specified in the [LICENSE.md](LICENSE.md) file. Please review the file for full details on usage and distribution.

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 facebookresearch/HyperAgents
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. OpenAI Gym · recommended 1×
  3. Farama Foundation Gymnasium · recommended 1×
  4. Ray RLLib · recommended 1×
  5. DeepMind Acme · recommended 1×
  • CATEGORY QUERY
    How to build autonomous AI agents that can self-optimize for diverse computational problems?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym
    2. Farama Foundation Gymnasium
    3. Ray RLLib
    4. DeepMind Acme
    5. Google Cloud AutoML
    6. H2O.ai
    7. AutoKeras
    8. PyTorch
    9. TensorFlow
    10. higher
    11. learn2learn
    12. Mesa
    13. OpenAI Universe
    14. Procgen Benchmark

    AI recommended 14 alternatives but never named facebookresearch/HyperAgents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Tools for developing AI systems capable of continuous self-improvement and task adaptation?
    you: not recommended
    AI recommended (in order):
    1. Ray RLlib (ray-project/ray)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. higher (facebookresearch/higher)
    4. learn2learn (learn2learn/learn2learn)
    5. Avalanche (ContinualAI/avalanche)
    6. CL-PyTorch (ContinualAI/cl-pytorch)
    7. Ray (ray-project/ray)
    8. Apache Spark (apache/spark)
    9. Pyro (pyro-ppl/pyro)
    10. TensorFlow Probability (tensorflow/probability)

    AI recommended 10 alternatives but never named facebookresearch/HyperAgents. 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 facebookresearch/HyperAgents?
    pass
    AI did not name facebookresearch/HyperAgents — 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 facebookresearch/HyperAgents in production, what risks or prerequisites should they evaluate first?
    pass
    AI named facebookresearch/HyperAgents 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 facebookresearch/HyperAgents solve, and who is the primary audience?
    pass
    AI named facebookresearch/HyperAgents explicitly

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

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facebookresearch/HyperAgents — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite