RRepoGEO

REPOGEO REPORT · LITE

eyurtsev/kor

Default branch main · commit f6dc6554 · scanned 6/23/2026, 9:06:58 PM

GitHub: 1,684 stars · 95 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 eyurtsev/kor, 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
  • highabout#1
    Update the repository description to be more informative

    Why:

    CURRENT
    LLM(😽)
    COPY-PASTE FIX
    Extract structured data from text using LLMs, especially those without native function calling or tool APIs.
  • highreadme#2
    Reposition the README's opening statement to highlight Kor's specific niche

    Why:

    CURRENT
    # Kor
    
    This is a half-baked prototype that "helps" you extract structured data from text using LLMs 🧩.
    
    Specify the schema of what should be extracted and provide some examples.
    
    Kor will generate a prompt, send it to the specified LLM and parse out the
    output.
    
    You might even get results back.
    
    So yes – it’s just another wrapper on top of LLMs with its own flavor of abstractions. 😸
    COPY-PASTE FIX
    # Kor: Structured Data Extraction for LLMs (especially those without native tool calling)
    
    Kor is a Python library designed to help you reliably extract structured data from text using Large Language Models (LLMs). It is particularly well-suited for "old style" LLMs that do not have a chat interface or native tool calling APIs. Specify your desired output schema, provide examples, and Kor will generate prompts, interact with the LLM, and parse the output into structured data.
  • mediumtopics#3
    Enhance repository topics with more specific keywords

    Why:

    CURRENT
    information-extraction, llm, natural-language, natural-language-processing, natural-language-understanding
    COPY-PASTE FIX
    information-extraction, llm, natural-language, natural-language-processing, natural-language-understanding, structured-output, legacy-llm, no-function-calling, prompt-engineering

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 eyurtsev/kor
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI GPT-4 / GPT-3.5 Turbo
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI GPT-4 / GPT-3.5 Turbo · recommended 1×
  2. Anthropic Claude 3 · recommended 1×
  3. run-llama/llama_index · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. Mistral Large / Mixtral 8x7B · recommended 1×
  • CATEGORY QUERY
    How can I extract structured data from unstructured text using large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI GPT-4 / GPT-3.5 Turbo
    2. Anthropic Claude 3
    3. LlamaIndex (run-llama/llama_index)
    4. LangChain (langchain-ai/langchain)
    5. Mistral Large / Mixtral 8x7B
    6. Instructor (jxnl/instructor)
    7. Llama 3
    8. Falcon
    9. Zephyr

    AI recommended 9 alternatives but never named eyurtsev/kor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools provide structured output from LLMs lacking native function calling capabilities?
    you: not recommended
    AI recommended (in order):
    1. Guidance
    2. Instructor
    3. JSONFormer
    4. LMQL
    5. Outlines
    6. LiteLLM
    7. LangChain

    AI recommended 7 alternatives but never named eyurtsev/kor. 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 eyurtsev/kor?
    pass
    AI named eyurtsev/kor explicitly

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

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

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

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eyurtsev/kor — 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