RRepoGEO

REPOGEO REPORT · LITE

yomorun/yomo

Default branch main · commit 9f659a29 · scanned 5/22/2026, 5:27:53 PM

GitHub: 1,903 stars · 143 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 yomorun/yomo, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. Choose a standard open-source license (e.g., Apache-2.0, MIT) and include its full text.
  • highreadme#2
    Reposition the README's opening paragraph to clarify YoMo's role as a framework distinct from platforms or inference runtimes

    Why:

    CURRENT
    YoMo is an open-source LLM Function Calling Framework for building scalable and ultra-fast AI Agents.
    COPY-PASTE FIX
    YoMo is an open-source **framework** for building, deploying, and orchestrating scalable and ultra-fast AI Agents with LLM Function Calling. It provides the geo-distributed edge infrastructure and real-time stream processing capabilities for your AI applications, offering a distinct solution from cloud platforms or inference-only toolkits.
  • mediumabout#3
    Enhance the 'About' description with more specific keywords from the target queries

    Why:

    CURRENT
    🦖 Serverless AI Agent Framework with Geo-distributed Edge AI Infra.
    COPY-PASTE FIX
    🦖 Serverless framework for building and deploying real-time LLM Function Calling AI Agents on geo-distributed edge infrastructure, ensuring ultra-low latency stream processing.

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 yomorun/yomo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Platform
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Platform · recommended 1×
  2. Vertex AI · recommended 1×
  3. Cloud Functions · recommended 1×
  4. Cloud Spanner · recommended 1×
  5. HTTP(S) Load Balancing · recommended 1×
  • CATEGORY QUERY
    How can I build scalable, low-latency AI agents with a serverless, geo-distributed architecture?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Platform
    2. Vertex AI
    3. Cloud Functions
    4. Cloud Spanner
    5. HTTP(S) Load Balancing
    6. Cloud Pub/Sub
    7. Amazon Web Services
    8. AWS Lambda
    9. Amazon SageMaker
    10. Amazon DynamoDB Global Tables
    11. Amazon API Gateway
    12. Amazon SQS
    13. Amazon SNS
    14. Microsoft Azure
    15. Azure Functions
    16. Azure Machine Learning
    17. Azure Cosmos DB
    18. Azure API Management
    19. Azure Service Bus
    20. Azure Event Grid
    21. Cloudflare Workers AI
    22. Cloudflare Workers
    23. Durable Objects
    24. Cloudflare R2
    25. Vercel
    26. Next.js
    27. Upstash
    28. OpenAI API
    29. Anthropic API
    30. Hugging Face Inference API

    AI recommended 30 alternatives but never named yomorun/yomo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework helps deploy real-time LLM function calling tools for stream processing at the edge?
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. TensorFlow Lite
    3. ONNX Runtime
    4. PyTorch Mobile / LibTorch
    5. NVIDIA JetPack SDK (with TensorRT)
    6. AWS IoT Greengrass (with SageMaker Neo)

    AI recommended 6 alternatives but never named yomorun/yomo. 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 yomorun/yomo?
    pass
    AI named yomorun/yomo explicitly

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

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

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

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yomorun/yomo — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite