RRepoGEO

REPOGEO REPORT · LITE

gepa-ai/gepa

Default branch main · commit 92dadfff · scanned 6/30/2026, 10:53:47 PM

GitHub: 5,449 stars · 443 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
35 /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
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 gepa-ai/gepa, 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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-optimization, prompt-engineering, evolutionary-algorithms, ai-agents, code-optimization, text-evolution, pareto-optimization, generative-ai, machine-learning, reflection
  • highreadme#2
    Reposition README opening to emphasize unique combination

    Why:

    CURRENT
    <p align="center">
  
</p>

<p align="center">
  <strong>Optimize any text parameter — prompts, code, agent architectures, configurations — using LLM-based reflection and Pareto-efficient evolutionary search.</strong>
</p>
    COPY-PASTE FIX
    GEPA is a Python framework for optimizing LLM prompts, code, and AI agent configurations using a novel combination of LLM-based reflection and Pareto-efficient evolutionary search.

<p align="center">
  
</p>

<p align="center">
  <strong>Optimize any text parameter — prompts, code, agent architectures, configurations — using LLM-based reflection and Pareto-efficient evolutionary search.</strong>
</p>
  • mediumabout#3
    Expand the 'About' description to include evolutionary search

    Why:

    CURRENT
    Optimize prompts, code, and more with AI-powered Reflective Text Evolution
    COPY-PASTE FIX
    Optimize LLM prompts, code, and AI agent configurations using AI-powered Reflective Text Evolution and Pareto-efficient evolutionary search.

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 gepa-ai/gepa
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 1×
  2. LlamaIndex · recommended 1×
  3. Auto-GPT · recommended 1×
  4. Microsoft's Guidance · recommended 1×
  5. Ragas · recommended 1×
  • CATEGORY QUERY
    How can I automatically improve my large language model prompts for better performance?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Auto-GPT
    4. Microsoft's Guidance
    5. Ragas
    6. Arize AI Phoenix
    7. Optuna
    8. Weights & Biases Sweeps
    9. OpenAI's GPT-4
    10. ReAct
    11. CoT

    AI recommended 11 alternatives but never named gepa-ai/gepa. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a tool to optimize AI agent configurations or code through evolutionary algorithms.
    you: not recommended
    AI recommended (in order):
    1. DEAP
    2. PyGAD
    3. NEAT-Python
    4. OpenAI Gym
    5. ECJ
    6. Nevergrad

    AI recommended 6 alternatives but never named gepa-ai/gepa. 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 gepa-ai/gepa?
    pass
    AI named gepa-ai/gepa explicitly

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

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

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

Embed your GEO score

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gepa-ai/gepa — 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