RRepoGEO

REPOGEO REPORT · LITE

camel-ai/loong

Default branch main · commit 10c218e7 · scanned 6/9/2026, 8:27:45 PM

GitHub: 504 stars · 42 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
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 camel-ai/loong, 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
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm, multi-agent-systems, chain-of-thought, synthetic-data, llm-agents, ai-agents, reinforcement-learning, data-generation, llm-verification, large-language-models
  • highreadme#2
    Reposition the README's opening statement to clarify core problem and solution

    Why:

    CURRENT
    🐉 Loong Project is a collaborative effort to explore whether reasoning-capable models can bootstrap themselves from small, high-quality **seed datasets** by generating synthetic data and verifying LLM agent responses.
    COPY-PASTE FIX
    Loong is a framework for synthesizing high-quality, long Chain of Thought (CoT) reasoning at scale, enabling LLM agents to bootstrap from small seed datasets through iterative generation and robust verification. It helps researchers and developers scale LLM agent self-improvement and generate synthetic data for training.
  • mediumhomepage#3
    Add the project homepage URL to repository metadata

    Why:

    COPY-PASTE FIX
    https://www.camel-ai.org/

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 camel-ai/loong
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GPT-4
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GPT-4 · recommended 1×
  2. Claude 3 · recommended 1×
  3. Llama 3 · recommended 1×
  4. nlpaug · recommended 1×
  5. TextAttack · recommended 1×
  • CATEGORY QUERY
    How to generate high-quality synthetic datasets for large language model training and verification?
    you: not recommended
    AI recommended (in order):
    1. GPT-4
    2. Claude 3
    3. Llama 3
    4. nlpaug
    5. TextAttack
    6. custom Python scripts
    7. Jinja2
    8. Faker
    9. Synthea
    10. CodeBERT
    11. CodeGen

    AI recommended 11 alternatives but never named camel-ai/loong. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help scale complex Chain of Thought reasoning and verify LLM agent outputs?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. BabyAGI
    5. OpenAI Function Calling / Tool Use
    6. Weights & Biases (W&B) Prompts
    7. Guardrails AI
    8. DeepMind's AlphaCode

    AI recommended 8 alternatives but never named camel-ai/loong. 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 camel-ai/loong?
    pass
    AI named camel-ai/loong explicitly

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

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

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

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camel-ai/loong — 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