RRepoGEO

REPOGEO REPORT · LITE

flagos-ai/FlagScale

Default branch main · commit 30eafd0d · scanned 6/3/2026, 7:57:07 PM

GitHub: 517 stars · 154 forks

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 flagos-ai/FlagScale, 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
    large-language-models, llm-toolkit, ai-system-software, distributed-ml, model-scaling, deep-learning, machine-learning, flagos
  • highreadme#2
    Elevate the core value proposition in the README's opening

    Why:

    CURRENT
    The current README starts with badges and an update block before introducing FlagScale's core purpose in an 'Overview' section.
    COPY-PASTE FIX
    FlagScale is a core component of FlagOS — a unified, open-source AI system software stack that fosters an open technology ecosystem by seamlessly integrating various models, systems, and chips. Following the principle of "develop once, migrate across various chips", FlagOS aims to unlock the full computational potential of hardware, break down barriers between different chip
  • mediumhomepage#3
    Add the project homepage URL

    Why:

    COPY-PASTE FIX
    https://flagos.io/

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 flagos-ai/FlagScale
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 4×
  2. vLLM · recommended 1×
  3. TGI (Text Generation Inference) · recommended 1×
  4. DeepSpeed-MII (Model Inference Interface) · recommended 1×
  5. OpenVINO · recommended 1×
  • CATEGORY QUERY
    What are the best open-source toolkits for deploying and managing large language models efficiently?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI (Text Generation Inference)
    3. DeepSpeed-MII (Model Inference Interface)
    4. OpenVINO
    5. TensorRT-LLM
    6. Ray Serve
    7. ONNX Runtime

    AI recommended 7 alternatives but never named flagos-ai/FlagScale. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an open-source AI system software stack to build and scale large models.
    you: not recommended
    AI recommended (in order):
    1. Transformers (huggingface/transformers)
    2. Accelerate (huggingface/accelerate)
    3. Diffusers (huggingface/diffusers)
    4. Text Generation Inference (TGI) (huggingface/text-generation-inference)
    5. Optimum (huggingface/optimum)
    6. PEFT (huggingface/peft)
    7. Datasets (huggingface/datasets)
    8. Tokenizers (huggingface/tokenizers)
    9. PyTorch (pytorch/pytorch)
    10. PyTorch Lightning (Lightning-AI/lightning)
    11. DeepSpeed (microsoft/DeepSpeed)
    12. Fully Sharded Data Parallel (FSDP)
    13. JAX (google/jax)
    14. Flax (google/flax)
    15. Haiku (deepmind/dm-haiku)
    16. Orbax (google/orbax)
    17. Pallas
    18. TensorFlow (tensorflow/tensorflow)
    19. Keras (keras-team/keras)
    20. TensorFlow Distributed
    21. TensorFlow Serving (tensorflow/serving)
    22. Ray Core (ray-project/ray)
    23. Ray Train (ray-project/ray)
    24. Ray Data (ray-project/ray)
    25. Ray Serve (ray-project/ray)
    26. NeMo (NVIDIA/NeMo)
    27. Triton (openai/triton)

    AI recommended 27 alternatives but never named flagos-ai/FlagScale. 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 flagos-ai/FlagScale?
    pass
    AI named flagos-ai/FlagScale explicitly

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

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

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

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flagos-ai/FlagScale — 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