RRepoGEO

REPOGEO REPORT · LITE

codefuse-ai/ModelCache

Default branch main · commit b7e03ecf · scanned 6/3/2026, 6:41:45 PM

GitHub: 947 stars · 60 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 codefuse-ai/ModelCache, 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 opening to clearly state ModelCache's purpose as an LLM optimization system.

    Why:

    CURRENT
    The README starts with <h1>ModelCache</h1> followed by language links and <h2>Contents</h2>, lacking an immediate, clear positioning statement.
    COPY-PASTE FIX
    Add the following sentence directly after the main <h1>ModelCache</h1> title: "ModelCache is an open-source semantic caching system designed to optimize Large Language Model (LLM) applications by reducing latency and API costs through intelligent query-result caching."
  • mediumtopics#2
    Expand GitHub topics to emphasize system nature and benefits.

    Why:

    CURRENT
    llm, semantic-cache
    COPY-PASTE FIX
    llm, semantic-cache, llm-optimization, cost-reduction, performance, caching-system, ai-infrastructure
  • mediumlicense#3
    Clarify the project's license in the README.

    Why:

    COPY-PASTE FIX
    Add a section to the README, for example: "## License
This project is licensed under a custom license. Please refer to the [LICENSE](LICENSE) file for complete 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 codefuse-ai/ModelCache
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI's GPT-3.5 Turbo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI's GPT-3.5 Turbo · recommended 1×
  2. GPT-4 · recommended 1×
  3. Anthropic's Claude 3 Haiku · recommended 1×
  4. Google's Gemini 1.5 Flash · recommended 1×
  5. Mistral AI's Mistral 7B · recommended 1×
  • CATEGORY QUERY
    How can I reduce latency and API costs for my large language model applications?
    you: not recommended
    AI recommended (in order):
    1. OpenAI's GPT-3.5 Turbo
    2. GPT-4
    3. Anthropic's Claude 3 Haiku
    4. Google's Gemini 1.5 Flash
    5. Mistral AI's Mistral 7B
    6. Mixtral 8x7B
    7. Redis
    8. Memcached
    9. Vercel AI SDK
    10. LangChain
    11. SQLite
    12. OpenAI API
    13. Anthropic API
    14. Google API
    15. Ollama
    16. vLLM
    17. Hugging Face Transformers library
    18. NVIDIA TensorRT-LLM
    19. Llama.cpp
    20. Hugging Face Optimum

    AI recommended 20 alternatives but never named codefuse-ai/ModelCache. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for implementing semantic caching to optimize LLM interactions?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (https://github.com/weaviate/weaviate)
    3. Qdrant (https://github.com/qdrant/qdrant)
    4. Milvus (https://github.com/milvus-io/milvus)
    5. Redis (https://github.com/redis/redis)
    6. RediSearch (https://github.com/RediSearch/RediSearch)
    7. Guava Cache (https://github.com/google/guava)
    8. functools.lru_cache
    9. LiteLLM (https://github.com/BerriAI/litellm)
    10. LangChain (https://github.com/langchain-ai/langchain)

    AI recommended 10 alternatives but never named codefuse-ai/ModelCache. 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 codefuse-ai/ModelCache?
    pass
    AI named codefuse-ai/ModelCache explicitly

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

  • If a team adopts codefuse-ai/ModelCache in production, what risks or prerequisites should they evaluate first?
    pass
    AI named codefuse-ai/ModelCache 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 codefuse-ai/ModelCache solve, and who is the primary audience?
    pass
    AI named codefuse-ai/ModelCache 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
  • Prioritized action items8 vs 3 in Lite