RRepoGEO

REPOGEO REPORT · LITE

Tencent/KsanaLLM

Default branch main · commit b313eafb · scanned 5/29/2026, 8:03:15 PM

GitHub: 541 stars · 45 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 Tencent/KsanaLLM, 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
  • highabout#1
    Add a concise GitHub repository description

    Why:

    COPY-PASTE FIX
    High-performance and easy-to-use engine for LLM inference and serving, supporting various models and hardware.
  • mediumreadme#2
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    Add a 'License' section to the README with: 'This project is licensed under [insert specific license name(s) here]. Please see the LICENSE file for full details.'

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 Tencent/KsanaLLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
vllm-project/vllm
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. vllm-project/vllm · recommended 1×
  2. triton-inference-server/server · recommended 1×
  3. NVIDIA/FasterTransformer · recommended 1×
  4. microsoft/DeepSpeed · recommended 1×
  5. NVIDIA/TensorRT-LLM · recommended 1×
  • CATEGORY QUERY
    How can I achieve high-throughput and low-latency LLM inference for serving large models?
    you: not recommended
    AI recommended (in order):
    1. vLLM (vllm-project/vllm)
    2. Triton Inference Server (triton-inference-server/server)
    3. FasterTransformer (NVIDIA/FasterTransformer)
    4. DeepSpeed-MII (microsoft/DeepSpeed)
    5. TensorRT-LLM (NVIDIA/TensorRT-LLM)
    6. OpenVINO (openvinotoolkit/openvino)
    7. llama.cpp (ggerganov/llama.cpp)

    AI recommended 7 alternatives but never named Tencent/KsanaLLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a high-performance LLM serving engine compatible with Hugging Face models and OpenAI API.
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. TGI (Text Generation Inference)
    3. LiteLLM
    4. OpenLLM
    5. Ray Serve

    AI recommended 5 alternatives but never named Tencent/KsanaLLM. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 Tencent/KsanaLLM?
    pass
    AI named Tencent/KsanaLLM explicitly

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

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