RRepoGEO

REPOGEO REPORT · LITE

huangjia2019/ai-agents

Default branch main · commit de997471 · scanned 6/16/2026, 8:14:07 AM

GitHub: 502 stars · 129 forks

AI VISIBILITY SCORE
28 /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
2 / 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 huangjia2019/ai-agents, 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 the README's opening to clarify the repo's purpose

    Why:

    CURRENT
    # 大模型应用开发 动手做AI Agent

支持佳哥：购书链接

支持佳哥：购书链接

## GPT图解
    COPY-PASTE FIX
    # 大模型应用开发 动手做AI Agent

This repository provides introductory examples for building LLM-based AI agents, serving as companion code for the book 《大模型应用开发 动手做AI Agent》. These are simple, beginner-friendly examples designed to guide newcomers, rather than a comprehensive framework. For more advanced examples, please refer to resources like OpenAI Cookbook or LangChain Examples.

支持佳哥：购书链接
  • highlicense#2
    Add a LICENSE file or state the license clearly in README

    Why:

    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0 if applicable) in the repository root, or add a clear statement to the README like: 'This repository's code is released under the [Specify License Name] license.'
  • mediumtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    agent, ai, llm, nlp
    COPY-PASTE FIX
    ai-agents, llm-examples, book-companion, beginner-friendly, generative-ai, python-examples

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 huangjia2019/ai-agents
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. CrewAI · recommended 2×
  4. AutoGen · recommended 1×
  5. Haystack · recommended 1×
  • CATEGORY QUERY
    How can I get started with building LLM-powered AI agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGen
    4. Haystack
    5. CrewAI
    6. Transformers Agents (Hugging Face)

    AI recommended 6 alternatives but never named huangjia2019/ai-agents. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are some simple, hands-on examples for developing AI agents with large language models?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT Plugins / Custom Instructions
    2. LangChain
    3. LlamaIndex
    4. AutoGPT
    5. BabyAGI
    6. CrewAI
    7. Guidance by Microsoft

    AI recommended 7 alternatives but never named huangjia2019/ai-agents. 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 huangjia2019/ai-agents?
    pass
    AI named huangjia2019/ai-agents explicitly

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

  • If a team adopts huangjia2019/ai-agents in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huangjia2019/ai-agents 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 huangjia2019/ai-agents solve, and who is the primary audience?
    pass
    AI did not name huangjia2019/ai-agents — 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?

Embed your GEO score

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huangjia2019/ai-agents — 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