RRepoGEO

REPOGEO REPORT · LITE

facebookresearch/HyperAgents

Default branch main · commit 59a68f67 · scanned 5/12/2026, 12:17:56 AM

GitHub: 2,465 stars · 315 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 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
    Expand README opening to clarify unique positioning

    Why:

    CURRENT
    <p>Self-referential self-improving agents that can optimize for any computable task</p>
    COPY-PASTE FIX
    HyperAgents is a research framework for developing and evaluating self-referential, self-improving AI agents. It enables agents to optimize their own performance for any computable task, offering a unique approach beyond conventional reinforcement learning libraries.
  • mediumlicense#2
    Clarify license terms in the README

    Why:

    COPY-PASTE FIX
    This project is licensed under the terms specified in the [LICENSE.md](LICENSE.md) file. Please refer to that 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
Farama-Foundation/Gymnasium
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Farama-Foundation/Gymnasium · recommended 1×
  2. DLR-RM/stable-baselines3 · recommended 1×
  3. ray-project/ray · recommended 1×
  4. deepmind/acme · recommended 1×
  5. pytorch/rl · recommended 1×
  • CATEGORY QUERY
    How to create AI agents that self-improve and optimize performance across diverse tasks?
    you: not recommended
    AI recommended (in order):
    1. Gymnasium (Farama-Foundation/Gymnasium)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. Ray RLlib (ray-project/ray)
    4. DeepMind Acme (deepmind/acme)
    5. TorchRL (pytorch/rl)
    6. LangChain (langchain-ai/langchain)
    7. OpenAI GPT-4
    8. Anthropic Claude
    9. Microsoft AutoGen (microsoft/autogen)
    10. LlamaIndex (run-llama/llama_index)
    11. TensorFlow Agents (tensorflow/agents)

    AI recommended 11 alternatives but never named facebookresearch/HyperAgents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to develop adaptive AI agents for general task optimization and self-improvement.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym/Farama Foundation Gymnasium
    2. RLlib
    3. DeepMind Acme
    4. Meta AI TorchRL
    5. Stable Baselines3
    6. TF-Agents

    AI recommended 6 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 named facebookresearch/HyperAgents explicitly

    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 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?

Embed your GEO score

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facebookresearch/HyperAgents — 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