RRepoGEO

REPOGEO REPORT · LITE

kevinrgu/autoagent

Default branch main · commit eb3f185d · scanned 6/24/2026, 2:57:29 AM

GitHub: 4,505 stars · 503 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 for AI agent optimization

    Why:

    COPY-PASTE FIX
    ["ai-agents", "llm-agents", "agent-optimization", "prompt-engineering", "autonomous-agents", "agent-harness", "meta-learning", "llm-ops"]
  • highreadme#2
    Strengthen README's opening to clarify core function

    Why:

    CURRENT
    > Like autoresearch but for agent engineering. Give an AI agent a task, let it build and iterate on an agent harness autonomously overnight. It modifies the system prompt, tools, agent configuration, and orchestration, runs the benchmark, checks the score, keeps or discards the change, and repeats.
    COPY-PASTE FIX
    > AutoAgent is an autonomous meta-agent that engineers and optimizes AI agent harnesses. Give it a task, and it will autonomously build, iterate, and benchmark agent configurations, system prompts, tools, and orchestration overnight to achieve optimal performance, much like autoresearch but specifically for agent engineering.
  • mediumlicense#3
    Add a LICENSE file or clarify licensing in README

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) or add a clear statement about the project's license(s) directly in the README.

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
Weights & Biases
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Weights & Biases · recommended 1×
  2. MLflow · recommended 1×
  3. LangChain · recommended 1×
  4. Promptfoo · recommended 1×
  5. Ray Tune · recommended 1×
  • CATEGORY QUERY
    How to autonomously build and iterate on AI agent harnesses for optimal performance?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases
    2. MLflow
    3. LangChain
    4. Promptfoo
    5. Ray Tune
    6. ZenML
    7. OpenAI Evals

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

    Show full AI answer
  • CATEGORY QUERY
    Tools for automatically optimizing AI agent prompts and configurations through iterative benchmarking?
    you: not recommended
    AI recommended (in order):
    1. Weights & Biases (W&B) Prompts (wandb/wandb)
    2. LangChain (langchain-ai/langchain)
    3. OpenAI Evals (openai/evals)
    4. Humanloop
    5. PromptLayer (Magniv/promptlayer)
    6. MLflow (mlflow/mlflow)
    7. Guidance (microsoft/guidance)

    AI recommended 7 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