RRepoGEO

REPOGEO REPORT · LITE

kagisearch/pyllms

Default branch main · commit 0ca11338 · scanned 6/14/2026, 4:22:52 AM

GitHub: 820 stars · 55 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 kagisearch/pyllms, 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
    Rephrase README deprecation notice to clarify current utility

    Why:

    CURRENT
    ## Note: PyLLMS is deprecated. We recommend using pydantic-ai instead.
    COPY-PASTE FIX
    ## Note: PyLLMs is deprecated for new development. While we recommend `pydantic-ai` for active projects, PyLLMs remains a valuable resource for historical reference and its built-in model performance benchmark capabilities.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    python, llm, large-language-models, llm-api, llm-benchmark, openai, anthropic, google-llm, groq, reka, together-ai, ai21, cohere, aleph-alpha, huggingface-hub
  • mediumreadme#3
    Emphasize benchmarking capabilities in README introduction

    Why:

    CURRENT
    PyLLMs is a minimal Python library to connect to various Language Models (LLMs) with a built-in model performance benchmark.
    COPY-PASTE FIX
    PyLLMs offers a minimal Python interface for connecting to a wide array of Language Models (LLMs), uniquely featuring a robust, built-in model performance benchmark. This makes it ideal for developers needing to quickly compare LLM provider performance and integrate diverse models.

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 kagisearch/pyllms
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. LiteLLM · recommended 2×
  4. OpenAI Python Library · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How can I easily integrate various large language models into my Python application?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. LiteLLM
    4. OpenAI Python Library
    5. Hugging Face Transformers
    6. Google Cloud Generative AI SDK for Python

    AI recommended 6 alternatives but never named kagisearch/pyllms. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python tools help evaluate and compare performance across different LLM providers?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Helicone
    4. OpenAI Evals
    5. LiteLLM
    6. PromptTools

    AI recommended 6 alternatives but never named kagisearch/pyllms. 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 kagisearch/pyllms?
    pass
    AI named kagisearch/pyllms explicitly

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

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

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

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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