RRepoGEO

REPOGEO REPORT · LITE

google-deepmind/opro

Default branch main · commit a76bdce2 · scanned 6/9/2026, 5:42:43 PM

GitHub: 754 stars · 93 forks

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 google-deepmind/opro, 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-as-optimizer, prompt-optimization, large-language-models, generative-ai, machine-learning, deep-learning, ai-optimization, meta-learning, prompt-engineering
  • highreadme#2
    Reposition the README's opening paragraph to highlight OPRO's unique value

    Why:

    CURRENT
    # Large Language Models as Optimizers
    
    This repository contains the code for the paper
    
    > Large Language Models as Optimizers
    > Chengrun Yang*, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen* [* Equal Contribution]
    > _arXiv: 2309.03409_
    COPY-PASTE FIX
    # OPRO: Large Language Models as Optimizers
    
    This repository provides the official code for **OPRO (Optimizing Prompts for Reasoning)**, a novel framework that leverages Large Language Models *as optimizers* to iteratively generate, evaluate, and refine prompts for other LLMs. OPRO automates the discovery of effective prompts, offering a powerful alternative to manual prompt engineering for improving LLM performance on tasks like mathematical and combinatorial optimization.
  • mediumabout#3
    Update the repository description to be more explicit about its function

    Why:

    CURRENT
    official code for "Large Language Models as Optimizers"
    COPY-PASTE FIX
    Official code for OPRO: a framework that uses Large Language Models as optimizers to automatically generate and refine prompts for other LLMs, improving performance on various tasks.

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 google-deepmind/opro
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PromptPerfect
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PromptPerfect · recommended 2×
  2. LangChain · recommended 2×
  3. Weights & Biases Prompts · recommended 1×
  4. OpenAI Evals · recommended 1×
  5. Humanloop · recommended 1×
  • CATEGORY QUERY
    How can I automatically improve the performance of my large language model prompts?
    you: not recommended
    AI recommended (in order):
    1. PromptPerfect
    2. Weights & Biases Prompts
    3. LangChain
    4. OpenAI Evals
    5. Humanloop
    6. Guardrails AI
    7. Microsoft Guidance

    AI recommended 7 alternatives but never named google-deepmind/opro. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help use language models to refine other language model instructions?
    you: not recommended
    AI recommended (in order):
    1. Guidance
    2. LangChain
    3. LlamaIndex
    4. OpenAI API
    5. PromptPerfect

    AI recommended 5 alternatives but never named google-deepmind/opro. 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 google-deepmind/opro?
    pass
    AI named google-deepmind/opro explicitly

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

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

Embed your GEO score

Drop this badge into the README of google-deepmind/opro. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/google-deepmind/opro.svg)](https://repogeo.com/en/r/google-deepmind/opro)
HTML
<a href="https://repogeo.com/en/r/google-deepmind/opro"><img src="https://repogeo.com/badge/google-deepmind/opro.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

google-deepmind/opro — 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