RRepoGEO

REPOGEO REPORT · LITE

HKUDS/AutoAgent

Default branch main · commit 16c12b05 · scanned 5/24/2026, 8:48:14 PM

GitHub: 9,339 stars · 1,314 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 HKUDS/AutoAgent, 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 more specific topics to improve categorization

    Why:

    CURRENT
    agent, llms
    COPY-PASTE FIX
    llm-agents, agent-framework, zero-code, natural-language-processing, automated-ai, multi-agent-systems, self-developing-ai, agent-orchestration
  • mediumreadme#2
    Refine README opening to highlight core differentiator

    Why:

    CURRENT
    Welcome to AutoAgent! AutoAgent is a **Fully-Automated** and highly **Self-Developing** framework that enables users to create and deploy LLM agents through **Natural Language Alone**.
    COPY-PASTE FIX
    Welcome to AutoAgent! AutoAgent is the leading **fully-automated, zero-code framework** for the **automatic generation and self-organization of multi-agent systems** based on natural language, a key differentiator from alternatives requiring manual agent definition.
  • lowcomparison#3
    Add a comparison section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'AutoAgent vs. Other Frameworks' or 'Why AutoAgent?' that briefly compares its fully-automated, zero-code, natural language-driven approach to popular alternatives like LangChain, CrewAI, and AutoGPT, emphasizing its unique automatic multi-agent system generation.

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 HKUDS/AutoAgent
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. LangServe · recommended 1×
  4. LangSmith · recommended 1×
  5. OpenAI Assistants API · recommended 1×
  • CATEGORY QUERY
    How can I build and deploy LLM agents using only natural language?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangServe
    3. LangSmith
    4. OpenAI Assistants API
    5. Microsoft Copilot Studio
    6. Google Dialogflow CX
    7. Voiceflow
    8. LlamaIndex

    AI recommended 8 alternatives but never named HKUDS/AutoAgent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a fully automated framework for developing and orchestrating collaborative LLM agent systems.
    you: not recommended
    AI recommended (in order):
    1. AutoGPT
    2. MetaGPT
    3. CrewAI
    4. LangChain
    5. LlamaIndex
    6. Open Interpreter
    7. BabyAGI

    AI recommended 7 alternatives but never named HKUDS/AutoAgent. 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 HKUDS/AutoAgent?
    pass
    AI named HKUDS/AutoAgent explicitly

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

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

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

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HKUDS/AutoAgent — 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