RRepoGEO

REPOGEO REPORT · LITE

MaestroError/LarAgent

Default branch main · commit d1947214 · scanned 6/14/2026, 3:36:32 PM

GitHub: 639 stars · 58 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 MaestroError/LarAgent, 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
    Clarify LarAgent's role as an AI agent development framework in the README

    Why:

    CURRENT
    LarAgent is an open-source framework that lets you create powerful AI agents in Laravel with minimal configuration and maximum extensibility.
    COPY-PASTE FIX
    LarAgent is an open-source framework that lets you create powerful AI agents in Laravel with minimal configuration and maximum extensibility. It provides a complete structure for orchestrating complex agent behaviors, going beyond simple API wrappers to offer a full development environment.
  • mediumtopics#2
    Add more specific topics to emphasize 'agent framework' and 'orchestration'

    Why:

    CURRENT
    ai, ai-agents, framework, laravel, laravel-package
    COPY-PASTE FIX
    ai, ai-agents, framework, laravel, laravel-package, ai-agent-framework, agent-orchestration, laravel-ai
  • mediumcomparison#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## LarAgent vs. Other AI Tools

LarAgent stands apart from simple OpenAI API clients or general-purpose AI libraries like Laravel LangChain by offering a dedicated framework for building and orchestrating AI agents directly within your Laravel application. While clients provide access to AI models, LarAgent focuses on the full lifecycle of agent development, including state management, tool integration, and complex workflow automation, all with Laravel's familiar productivity.

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 MaestroError/LarAgent
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI PHP Client
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI PHP Client · recommended 2×
  2. Laravel LangChain · recommended 1×
  3. Guzzle HTTP Client · recommended 1×
  4. Laravel Octane · recommended 1×
  5. Laravel Queues · recommended 1×
  • CATEGORY QUERY
    How can I easily integrate and manage AI agents within my existing Laravel application?
    you: not recommended
    AI recommended (in order):
    1. Laravel LangChain
    2. OpenAI PHP Client
    3. Guzzle HTTP Client
    4. Laravel Octane
    5. Laravel Queues
    6. Laravel Vapor

    AI recommended 6 alternatives but never named MaestroError/LarAgent. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for building and deploying AI agents in a PHP backend?
    you: not recommended
    AI recommended (in order):
    1. Laravel
    2. FastAPI
    3. Flask
    4. Symfony
    5. PHP-ML
    6. OpenAI PHP Client
    7. Google Cloud AI Platform
    8. AWS AI Services
    9. RoadRunner
    10. Swoole
    11. Llama.cpp PHP Bindings

    AI recommended 11 alternatives but never named MaestroError/LarAgent. 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 MaestroError/LarAgent?
    pass
    AI named MaestroError/LarAgent explicitly

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

  • If a team adopts MaestroError/LarAgent in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MaestroError/LarAgent 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 MaestroError/LarAgent solve, and who is the primary audience?
    pass
    AI named MaestroError/LarAgent 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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MARKDOWN (README)
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HTML
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MaestroError/LarAgent — 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