RRepoGEO

REPOGEO REPORT · LITE

eyurtsev/kor

Default branch main · commit f6dc6554 · scanned 5/13/2026, 10:01:36 AM

GitHub: 1,685 stars · 96 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 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 repository description for clarity

    Why:

    CURRENT
    LLM(😽)
    COPY-PASTE FIX
    Extract structured, type-safe data from unstructured text using LLMs, especially for models without native tool-calling APIs. Uses Pydantic for schema definition.
  • highreadme#2
    Reposition README's core value proposition

    Why:

    CURRENT
    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 is a robust library for **structured, type-safe data extraction from unstructured text using LLMs**, particularly effective for models that do not support native tool-calling APIs. Define your desired output schema using Pydantic, provide examples, and Kor handles prompt generation, LLM interaction, and output parsing, including re-asking for malformed responses. Integrated with LangChain, Kor excels in prompt-based/parsing extraction.
  • mediumtopics#3
    Add more specific topics

    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, pydantic, structured-data, schema-extraction

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 API
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 1×
  2. Anthropic Claude · recommended 1×
  3. llamaindex/llamaindex · recommended 1×
  4. langchain-ai/langchain · recommended 1×
  5. huggingface/transformers · recommended 1×
  • CATEGORY QUERY
    How to extract structured information from unstructured text using large language models?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude
    3. LlamaIndex (llamaindex/llamaindex)
    4. LangChain (langchain-ai/langchain)
    5. Hugging Face Transformers (huggingface/transformers)
    6. Google Cloud Vertex AI
    7. Amazon Bedrock

    AI recommended 7 alternatives but never named eyurtsev/kor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good library for schema-guided information extraction with LLMs?
    you: not recommended
    AI recommended (in order):
    1. Instructor
    2. LangChain
    3. LlamaIndex
    4. Guidance
    5. Marvin
    6. OpenAI Functions/Tools
    7. jsonformer

    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