RRepoGEO

REPOGEO REPORT · LITE

hyp1231/awesome-llm-powered-agent

Default branch main · commit c5a68187 · scanned 5/15/2026, 8:47:58 PM

GitHub: 2,232 stars · 216 forks

AI VISIBILITY SCORE
22 /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
1 / 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 hyp1231/awesome-llm-powered-agent, 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
    Reposition and clarify the README's opening paragraph to state it's a historical awesome list

    Why:

    CURRENT
    Thanks to the impressive planning, reasoning, and tool-calling capabilities of Large Language Models (LLMs), people are actively studying and developing **LLM-powered agents.** These agents are possible to autonomously (and collaboratively) solve complex tasks, or simulate human interactions. Our goal with this project is to build an exhaustive collection of awesome resources relevant to LLM-powered agents encompassing papers, repositories, and more. We strive to keep these updated regularly and continuously. **We greatly appreciate any contributions via PRs, issues, emails, or other methods.Note that this repository is not under active maintenance. It mainly contains papers that appear before Oct. 2023, with several further papers. If you would like to have your paper included, please feel free to initiate a pull request.
    COPY-PASTE FIX
    This **awesome list** provides a comprehensive, largely historical collection of resources on LLM-powered agents, primarily curating papers and repositories published before October 2023. While not under active maintenance, it serves as a valuable archive for foundational developments, and contributions are welcome.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/hyp1231/awesome-llm-powered-agent
  • lowtopics#3
    Add more specific 'awesome list' related topics

    Why:

    CURRENT
    awesome-list, chatgpt, embodied-agent, embodied-ai, foundation-model, foundation-models, generative-agents, generative-ai, generative-model, generative-models, gpt-4, large-language-model, large-language-models, llm, llm-powered-agents, llms
    COPY-PASTE FIX
    awesome-list, llm-resources, llm-papers, llm-repositories, chatgpt, embodied-agent, embodied-ai, foundation-model, foundation-models, generative-agents, generative-ai, generative-model, generative-models, gpt-4, large-language-model, large-language-models, llm, llm-powered-agents, llms

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 hyp1231/awesome-llm-powered-agent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. Hugging Face Transformers · recommended 1×
  4. OpenAI API · recommended 1×
  5. Microsoft AutoGen · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources on building and understanding LLM-powered agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. OpenAI API
    5. Microsoft AutoGen
    6. DeepLearning.AI
    7. Awesome-LLM-Agents

    AI recommended 7 alternatives but never named hyp1231/awesome-llm-powered-agent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best research papers and repositories for developing autonomous AI agents?
    you: not recommended
    AI recommended (in order):
    1. RLlib (https://github.com/ray-project/ray/tree/master/rllib)
    2. Stable Baselines3 (https://github.com/DLR-RM/stable-baselines3)
    3. OpenAI Baselines
    4. MADDPG
    5. QMIX
    6. COMA
    7. MARLlib
    8. PettingZoo
    9. PlaNet
    10. DreamerV2
    11. DreamerV3

    AI recommended 11 alternatives but never named hyp1231/awesome-llm-powered-agent. 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 hyp1231/awesome-llm-powered-agent?
    pass
    AI did not name hyp1231/awesome-llm-powered-agent — 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?

  • If a team adopts hyp1231/awesome-llm-powered-agent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hyp1231/awesome-llm-powered-agent 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 hyp1231/awesome-llm-powered-agent solve, and who is the primary audience?
    pass
    AI did not name hyp1231/awesome-llm-powered-agent — 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?

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  • Brand-free category queries5 vs 2 in Lite
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