RRepoGEO

REPOGEO REPORT · LITE

evo-hq/evo

Default branch main · commit e66a5aa5 · scanned 6/1/2026, 10:22:22 PM

GitHub: 858 stars · 66 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 evo-hq/evo, 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 README's opening sentence to clarify core purpose

    Why:

    CURRENT
    A plugin for your agentic framework that optimizes code through experiments
    COPY-PASTE FIX
    Evo turns your codebase into an autoresearch loop for autonomous code optimization. It discovers what to measure, instruments the benchmark, then runs tree search with parallel subagents.
  • mediumtopics#2
    Add more specific topics for code optimization and search

    Why:

    CURRENT
    agent-skills, autonomous-agents, autoresearch, claude-code, code-optimization, codex, evolutionary-algorithms, llm-agents
    COPY-PASTE FIX
    agent-skills, autonomous-agents, autoresearch, claude-code, code-optimization, codex, evolutionary-algorithms, llm-agents, tree-search, program-synthesis, automated-refactoring, performance-tuning
  • lowreadme#3
    Add a comparison section to differentiate from general agent/ML tools

    Why:

    COPY-PASTE FIX
    Add a new section to the README, for example, under a heading like 'Why Evo is Unique': 'Evo stands apart from general LLM agent frameworks (like LangChain or AutoGPT) by specializing in autonomous code optimization. Unlike broader ML experiment management tools (e.g., MLflow, Ray Tune), Evo leverages tree search with parallel LLM subagents to systematically discover and implement code improvements.'

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 evo-hq/evo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. Farama-Foundation/Gymnasium · recommended 1×
  3. DLR-RM/stable-baselines3 · recommended 1×
  4. mlflow/mlflow · recommended 1×
  5. DeepMind AlphaDev · recommended 1×
  • CATEGORY QUERY
    How to automatically improve code performance by running experiments with AI agents?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Gym / Farama Foundation Gymnasium (Farama-Foundation/Gymnasium)
    2. Stable Baselines3 (DLR-RM/stable-baselines3)
    3. Ray RLlib (ray-project/ray)
    4. Ray Tune (ray-project/ray)
    5. MLflow (mlflow/mlflow)
    6. DeepMind AlphaDev
    7. GitHub Copilot / OpenAI Codex
    8. GPT-4

    AI recommended 8 alternatives but never named evo-hq/evo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable LLM agents to conduct autonomous code optimization through tree search?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. BabyAGI
    5. OpenAI Assistants API
    6. Haystack
    7. ast
    8. tree-sitter
    9. openai
    10. transformers

    AI recommended 10 alternatives but never named evo-hq/evo. 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 evo-hq/evo?
    pass
    AI named evo-hq/evo explicitly

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

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

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

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evo-hq/evo — 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