RRepoGEO

REPOGEO REPORT · LITE

SafeRL-Lab/cheetahclaws

Default branch main · commit e33dd499 · scanned 6/12/2026, 11:27:02 AM

GitHub: 721 stars · 258 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 SafeRL-Lab/cheetahclaws, 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
    Add a clear positioning statement in the README's opening

    Why:

    CURRENT
    The current README H2 is 'CheetahClaws: A Fast and Easy-to-Use Agent Harness Infrastructure for Long-Horizon, Multi-Model, and Tool-Using AI Systems'.
    COPY-PASTE FIX
    Add a sentence immediately after the H2 (or as the first paragraph) like: 'CheetahClaws is designed as a robust alternative to frameworks like LangChain, LlamaIndex, and Semantic Kernel, specifically for building and managing complex, long-horizon AI agents that integrate multiple models and external tools.'
  • mediumtopics#2
    Expand topics to include agent orchestration and tool-use frameworks

    Why:

    CURRENT
    agentic-ai, claude, claude-code, memory, openclaw, python, skills
    COPY-PASTE FIX
    agentic-ai, claude, claude-code, memory, openclaw, python, skills, agent-framework, agent-orchestration, tool-use, multi-modal-ai, llm-agents
  • lowcomparison#3
    Add a 'Comparison to Alternatives' section in the README

    Why:

    COPY-PASTE FIX
    Add a new section in the README, perhaps titled 'CheetahClaws vs. Other Agent Frameworks' or 'Why CheetahClaws?', that briefly outlines its unique advantages compared to LangChain, LlamaIndex, and similar tools. This section should highlight its strengths in long-horizon, multi-model, and tool-using AI systems.

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 SafeRL-Lab/cheetahclaws
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Microsoft Semantic Kernel · recommended 1×
  4. OpenAI Assistants API · recommended 1×
  5. Haystack · recommended 1×
  • CATEGORY QUERY
    How to build robust multi-model AI agents that use external tools effectively?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Microsoft Semantic Kernel
    4. OpenAI Assistants API
    5. Haystack
    6. AutoGPT
    7. BabyAGI
    8. CrewAI

    AI recommended 8 alternatives but never named SafeRL-Lab/cheetahclaws. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What infrastructure helps manage state and memory for complex long-running AI agent workflows?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Redis
    4. PostgreSQL
    5. Faiss
    6. Apache Kafka
    7. Durable Functions
    8. Step Functions
    9. Workflows

    AI recommended 9 alternatives but never named SafeRL-Lab/cheetahclaws. 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 SafeRL-Lab/cheetahclaws?
    pass
    AI named SafeRL-Lab/cheetahclaws explicitly

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

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

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

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SafeRL-Lab/cheetahclaws — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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