RRepoGEO

REPOGEO REPORT · LITE

HKUDS/OpenSpace

Default branch main · commit d1e367d0 · scanned 5/8/2026, 11:13:01 AM

GitHub: 6,073 stars · 752 forks

AI VISIBILITY SCORE
35 /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
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 HKUDS/OpenSpace, 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
    Add a concise, declarative definition immediately after the H1

    Why:

    COPY-PASTE FIX
    (Insert this line right after the H1, before the table)
"OpenSpace is an open-source framework for building, evolving, and managing autonomous AI agents that learn and share skills, designed to be smarter, low-cost, and self-evolving."
  • mediumhomepage#2
    Add the community URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://open-space.cloud/

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 HKUDS/OpenSpace
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ray · recommended 2×
  2. MLflow · recommended 2×
  3. OpenAI API · recommended 1×
  4. LangChain · recommended 1×
  5. LlamaIndex · recommended 1×
  • CATEGORY QUERY
    How can I build AI agents that learn and improve over time while reducing operational costs?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. LangChain
    3. LlamaIndex
    4. Ray
    5. MLflow
    6. Hugging Face Transformers
    7. Hugging Face Datasets

    AI recommended 7 alternatives but never named HKUDS/OpenSpace. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help manage and share evolving skills across a fleet of autonomous AI agents?
    you: not recommended
    AI recommended (in order):
    1. OpenCog Hyperon
    2. ROS 2
    3. Behavior Trees
    4. BehaviorTree.CPP
    5. Py_Trees
    6. Kubernetes
    7. Kubernetes Operators
    8. Operator SDK
    9. Kopf
    10. Ray
    11. Apache Kafka
    12. Neo4j
    13. Amazon Neptune
    14. MLflow

    AI recommended 14 alternatives but never named HKUDS/OpenSpace. 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 HKUDS/OpenSpace?
    pass
    AI named HKUDS/OpenSpace explicitly

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

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

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

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