RRepoGEO

REPOGEO REPORT · LITE

kvcache-ai/Mooncake

Default branch main · commit 0fceaee2 · scanned 5/26/2026, 1:27:16 PM

GitHub: 5,425 stars · 787 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 kvcache-ai/Mooncake, 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
    Reposition the README's first descriptive sentence to highlight its solution-oriented value

    Why:

    CURRENT
    Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI.
    COPY-PASTE FIX
    Mooncake is a high-performance serving platform for Large Language Model (LLM) inference, featuring a KVCache-centric disaggregated architecture that leverages RDMA for optimal performance and memory efficiency.
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Comparison with Existing Solutions' or 'Why Mooncake?' that explicitly compares Mooncake's disaggregated KVCache and RDMA approach against popular LLM serving frameworks like vLLM, PagedAttention, and NVIDIA Triton Inference Server, highlighting its advantages for specific use cases.
  • lowtopics#3
    Add 'serving' and 'performance' to the repository topics

    Why:

    CURRENT
    disaggregation, inference, kvcache, llm, rdma, sglang, vllm
    COPY-PASTE FIX
    disaggregation, inference, kvcache, llm, rdma, sglang, vllm, serving, performance

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 kvcache-ai/Mooncake
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. PagedAttention · recommended 1×
  3. ray-project/ray · recommended 1×
  4. Plasma · recommended 1×
  5. openvswitch/ovs · recommended 1×
  • CATEGORY QUERY
    How to optimize large language model inference with a disaggregated KVCache architecture?
    you: not recommended
    AI recommended (in order):
    1. vLLM (vllm-project/vllm)
    2. PagedAttention
    3. Ray (ray-project/ray)
    4. Plasma
    5. Open vSwitch (OVS) (openvswitch/ovs)
    6. DPDK (Data Plane Development Kit) (DPDK/dpdk)
    7. Redis (redis/redis)
    8. Apache Ignite (apache/ignite)
    9. UCX (Unified Communication X) (openucx/ucx)

    AI recommended 9 alternatives but never named kvcache-ai/Mooncake. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a high-performance serving solution for LLMs using RDMA and disaggregated memory.
    you: not recommended
    AI recommended (in order):
    1. LightSpeed Inference
    2. NVIDIA Triton Inference Server
    3. Open MPI
    4. Ray
    5. DeepSpeed-MII

    AI recommended 5 alternatives but never named kvcache-ai/Mooncake. 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 kvcache-ai/Mooncake?
    pass
    AI named kvcache-ai/Mooncake explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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