RRepoGEO

REPOGEO REPORT · LITE

kevinrgu/autoagent

Default branch main · commit eb3f185d · scanned 5/13/2026, 3:57:56 PM

GitHub: 4,414 stars · 499 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 kevinrgu/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 specific topics to improve category visibility

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai-agents, llm-agents, autonomous-agents, agent-engineering, prompt-optimization, tool-configuration, harness-engineering, meta-agents
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT License) in the repository root.
  • mediumabout#3
    Expand the 'About' description to include key functionality

    Why:

    CURRENT
    autonomous harness engineering
    COPY-PASTE FIX
    Autonomous harness engineering for AI agents: self-configuring, self-optimizing LLM agents that iterate on prompts, tools, and orchestration.

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 kevinrgu/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. Ray Tune · recommended 2×
  4. Optuna · recommended 1×
  5. Weights & Biases · recommended 1×
  • CATEGORY QUERY
    How can I autonomously optimize AI agent prompts and tool configurations?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Optuna
    4. Weights & Biases
    5. Ray Tune
    6. PromptPerfect
    7. LiteLLM

    AI recommended 7 alternatives but never named kevinrgu/autoagent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for tools to build self-configuring AI agents that iterate on their own design.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. BabyAGI
    5. OpenAI API
    6. GPT-4
    7. Ray
    8. Ray RLlib
    9. Ray Tune
    10. Pydantic
    11. MLflow

    AI recommended 11 alternatives but never named kevinrgu/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
    fail

    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 kevinrgu/autoagent?
    pass
    AI named kevinrgu/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 kevinrgu/autoagent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kevinrgu/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 kevinrgu/autoagent solve, and who is the primary audience?
    pass
    AI named kevinrgu/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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kevinrgu/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