RRepoGEO

REPOGEO REPORT · LITE

jd-opensource/xllm

Default branch main · commit ad5057b1 · scanned 5/25/2026, 6:11:41 PM

GitHub: 1,300 stars · 209 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 jd-opensource/xllm, 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
    Strengthen README's opening statement to reflect full model and accelerator support

    Why:

    CURRENT
    xLLM is an efficient LLM inference framework, specifica
    COPY-PASTE FIX
    xLLM is a high-performance inference engine designed for a wide range of AI models, including LLM, VLM, DiT, and REC, with optimizations for diverse AI accelerators.
  • mediumtopics#2
    Expand GitHub Topics with broader inference and accelerator terms

    Why:

    CURRENT
    deepseek, glm, inference, inference-engine, large-language-models, llm-inference, qwen
    COPY-PASTE FIX
    deepseek, glm, inference, inference-engine, large-language-models, llm-inference, qwen, deep-learning-inference, ai-accelerators, gpu-inference, tensorrt, onnx, pytorch, vlm-inference, dit-inference
  • lowreadme#3
    Ensure 'Project Overview' is the first substantive section in README

    Why:

    COPY-PASTE FIX
    Reorder the README so that the 'Project Overview' section, containing the core description of xLLM, appears immediately after the language links and any essential badges, before the 'News' section.

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 jd-opensource/xllm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA/TensorRT-LLM
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA/TensorRT-LLM · recommended 1×
  2. vllm-project/vllm · recommended 1×
  3. microsoft/DeepSpeed · recommended 1×
  4. openvinotoolkit/openvino · recommended 1×
  5. microsoft/onnxruntime · recommended 1×
  • CATEGORY QUERY
    What are the best inference engines for accelerating large language model deployments?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT-LLM (NVIDIA/TensorRT-LLM)
    2. vLLM (vllm-project/vllm)
    3. DeepSpeed-MII (microsoft/DeepSpeed)
    4. OpenVINO (openvinotoolkit/openvino)
    5. ONNX Runtime (microsoft/onnxruntime)
    6. Triton Inference Server (triton-inference-server/server)
    7. llama.cpp (ggerganov/llama.cpp)

    AI recommended 7 alternatives but never named jd-opensource/xllm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an optimized inference solution for various AI models across different accelerators.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO
    3. ONNX Runtime
    4. TVM
    5. TorchScript
    6. TensorFlow Lite
    7. TensorFlow Serving
    8. DeepSparse

    AI recommended 8 alternatives but never named jd-opensource/xllm. 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 jd-opensource/xllm?
    pass
    AI named jd-opensource/xllm explicitly

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

  • If a team adopts jd-opensource/xllm in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jd-opensource/xllm 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 jd-opensource/xllm solve, and who is the primary audience?
    pass
    AI named jd-opensource/xllm 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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite