RRepoGEO

REPOGEO REPORT · LITE

gepa-ai/gepa

Default branch main · commit 5910c641 · scanned 5/19/2026, 1:22:28 PM

GitHub: 4,512 stars · 375 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's primary value proposition

    Why:

    CURRENT
    <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
    <p align="center"> <strong>GEPA is a Python framework for optimizing textual parameters in AI systems. It leverages LLM-based reflection and Pareto-efficient evolutionary search to automatically improve prompts, code, and agent architectures.</strong> </p>
  • mediumabout#2
    Refine the repository description for clarity and specificity

    Why:

    CURRENT
    Optimize prompts, code, and more with AI-powered Reflective Text Evolution
    COPY-PASTE FIX
    GEPA: A Python framework for optimizing prompts, code, and AI agent architectures using LLM-based reflection and 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. OpenAI's Function Calling · recommended 1×
  4. Microsoft's Guidance · recommended 1×
  5. Weights & Biases (W&B Prompts) · recommended 1×
  • CATEGORY QUERY
    How to automatically optimize large language model prompts using AI reflection techniques?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. OpenAI's Function Calling
    4. Microsoft's Guidance
    5. Weights & Biases (W&B Prompts)
    6. Argilla

    AI recommended 6 alternatives but never named gepa-ai/gepa. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks exist for evolving and optimizing textual parameters in AI systems?
    you: not recommended
    AI recommended (in order):
    1. Optuna
    2. Ray Tune
    3. Weights & Biases (W&B) Sweeps
    4. Ax (Adaptive Experimentation Platform)
    5. Hyperopt
    6. Promptfoo
    7. OpenAI Evals

    AI recommended 7 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?

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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