RRepoGEO

REPOGEO REPORT · LITE

shyamsaktawat/OpenAlpha_Evolve

Default branch main · commit 0389aea0 · scanned 5/20/2026, 11:32:58 PM

GitHub: 1,017 stars · 150 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
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 shyamsaktawat/OpenAlpha_Evolve, 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 explicitly clarify the project's domain

    Why:

    CURRENT
    OpenAlpha_Evolve is an open-source Python framework inspired by the groundbreaking research on autonomous coding agents like DeepMind's AlphaEvolve. It's a **regeneration** of the core idea: an intelligent system that iteratively writes, tests, and improves code using Large Language Models (LLMs) via LiteLLM, guided by the principles of evolution.
    COPY-PASTE FIX
    OpenAlpha_Evolve is an open-source Python framework for **AI-driven algorithmic innovation and automated code generation**, inspired by DeepMind's AlphaEvolve. It's an intelligent system that iteratively writes, tests, and improves code using Large Language Models (LLMs) via LiteLLM, guided by evolutionary principles. **This project focuses on autonomous coding agents and code optimization, not financial algorithmic trading.**
  • mediumtopics#2
    Add more specific topics related to autonomous code generation and LLM agents

    Why:

    CURRENT
    alphacode, alphafold, coding-agent, discovery, distributed-evolutionary-algorithms, evolution-computing, evolutionary-algorithm, evolutionary-algorithms, genetic-algorithm, google, iterative-methods, iterative-refinement, llm-engineering, llm-ensemble, llm-inference, openevolve, optimize
    COPY-PASTE FIX
    alphacode, alphafold, coding-agent, discovery, distributed-evolutionary-algorithms, evolution-computing, evolutionary-algorithm, evolutionary-algorithms, genetic-algorithm, google, iterative-methods, iterative-refinement, llm-engineering, llm-ensemble, llm-inference, openevolve, optimize, code-generation, code-refinement, llm-agents, autonomous-ai, ai-code-optimizer
  • lowhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/shyamsaktawat/OpenAlpha_Evolve

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 shyamsaktawat/OpenAlpha_Evolve
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. Docker · recommended 1×
  5. GitHub Copilot · recommended 1×
  • CATEGORY QUERY
    How to build an AI system that autonomously writes and refines code using LLMs?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. LangChain (langchain-ai/langchain)
    3. LlamaIndex (run-llama/llama_index)
    4. Docker
    5. GitHub Copilot
    6. Git
    7. GitHub APIs
    8. GitLab APIs
    9. pytest (pytest-dev/pytest)
    10. JUnit (junit-team/junit5)
    11. NUnit (nunit/nunit)

    AI recommended 11 alternatives but never named shyamsaktawat/OpenAlpha_Evolve. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Python framework to apply evolutionary algorithms for automated code optimization.
    you: not recommended
    AI recommended (in order):
    1. DEAP
    2. PyGAD
    3. TPOT
    4. ECJ

    AI recommended 4 alternatives but never named shyamsaktawat/OpenAlpha_Evolve. 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 shyamsaktawat/OpenAlpha_Evolve?
    pass
    AI named shyamsaktawat/OpenAlpha_Evolve explicitly

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

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

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shyamsaktawat/OpenAlpha_Evolve — 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
shyamsaktawat/OpenAlpha_Evolve — RepoGEO report