RRepoGEO

REPOGEO REPORT · LITE

mll-lab-nu/RAGEN

Default branch main · commit 20daedc4 · scanned 5/29/2026, 10:53:51 AM

GitHub: 2,674 stars · 225 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 mll-lab-nu/RAGEN, 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 explicit clarification in README to differentiate RAGEN from RAG

    Why:

    COPY-PASTE FIX
    Note: RAGEN (Reasoning AGENT) is a framework for Reinforcement Learning with LLM agents, and is distinct from Retrieval Augmented Generation (RAG) systems.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    reinforcement-learning, llm-agents, reasoning, machine-learning, deep-learning, ai-agents, diagnostics, training-stability, large-language-models
  • mediumreadme#3
    Refine the README's initial descriptive paragraph for clarity and impact

    Why:

    CURRENT
    <p align="center"> <strong>RAGEN</strong> (<b>R</b>easoning <b>AGEN</b>T) is a flexible RL framework for training reasoning agents. </p> <p align="center"> We develop <strong>diagnostics to understand <i>how</i> agent RL training works </strong>, and how to fix hidden issues. </p>
    COPY-PASTE FIX
    <p align="center"> <strong>RAGEN</strong> (<b>R</b>easoning <b>AGEN</b>T) is a flexible Reinforcement Learning (RL) framework specifically designed for training and diagnosing Large Language Model (LLM) reasoning agents. It provides powerful diagnostics to understand <i>how</i> agent RL training works in interactive, stochastic environments, and offers lightweight interventions to fix hidden issues like reasoning collapse for stable training. </p>

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 mll-lab-nu/RAGEN
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. meta-llama/llama3 · recommended 1×
  3. mistralai/mistral-src · recommended 1×
  4. GPT-2/3/4 · recommended 1×
  5. thu-ml/tianshou · recommended 1×
  • CATEGORY QUERY
    How to train large language model agents using reinforcement learning in dynamic environments?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Llama 3 (meta-llama/llama3)
    3. Mistral (mistralai/mistral-src)
    4. GPT-2/3/4
    5. Tianshou (thu-ml/tianshou)
    6. Stable Baselines3 (DLR-RM/stable-baselines3)
    7. RLlib (ray-project/ray)
    8. Unity ML-Agents (Unity-Technologies/ml-agents)
    9. OpenAI Gym (openai/gym)
    10. PyTorch (pytorch/pytorch)
    11. TensorFlow (tensorflow/tensorflow)
    12. OpenAI API
    13. Anthropic Claude
    14. Google Gemini API
    15. LangChain (langchain-ai/langchain)
    16. LlamaIndex (run-llama/llama_index)
    17. DreamerV3 (danijar/dreamer)

    AI recommended 17 alternatives but never named mll-lab-nu/RAGEN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Framework for diagnosing and improving reinforcement learning training stability for reasoning agents?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B)
    2. TensorBoard
    3. RLlib
    4. DeepMind's Acme
    5. Matplotlib
    6. Seaborn
    7. PyTorch Lightning
    8. TensorFlow Keras Callbacks

    AI recommended 8 alternatives but never named mll-lab-nu/RAGEN. 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 mll-lab-nu/RAGEN?
    pass
    AI named mll-lab-nu/RAGEN explicitly

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

  • If a team adopts mll-lab-nu/RAGEN in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mll-lab-nu/RAGEN 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 mll-lab-nu/RAGEN solve, and who is the primary audience?
    pass
    AI named mll-lab-nu/RAGEN 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
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