RRepoGEO

REPOGEO REPORT · LITE

google/langfun

Default branch main · commit e80093b1 · scanned 6/1/2026, 4:21:54 PM

GitHub: 911 stars · 75 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 google/langfun, 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 paragraph to highlight "Object-Oriented Prompting"

    Why:

    CURRENT
    Langfun is a PyGlove powered library that aims to make language models (LM) fun to work with. Its central principle is to enable seamless integration between natural language and programming by treating language as functions. Through the introduction of *Object-Oriented Prompting*, Langfun empowers users to prompt LLMs using objects and types, offering enhanced control and simplifying agent development.
    COPY-PASTE FIX
    Langfun is a Python framework for **Object-Oriented Prompting**, enabling seamless integration between natural language and programming by treating language as functions. It empowers developers to prompt LLMs using objects and types, offering enhanced control and simplifying agent development for robust LLM applications.
  • hightopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    framework, llms, nlp
    COPY-PASTE FIX
    framework, llms, nlp, object-oriented-llm, llm-agents, prompt-engineering, python-llm-framework
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://[YOUR_LANGFUN_DOCS_OR_PROJECT_SITE_URL_HERE]

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 google/langfun
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. Pydantic · recommended 1×
  4. Instructor · recommended 1×
  5. OpenAI Python Library · recommended 1×
  • CATEGORY QUERY
    How can I apply object-oriented principles when building applications with large language models?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Pydantic
    4. Instructor
    5. OpenAI Python Library

    AI recommended 5 alternatives but never named google/langfun. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework simplifies LLM agent development by treating natural language as functions and objects?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI Functions
    5. Marvin
    6. CrewAI

    AI recommended 6 alternatives but never named google/langfun. 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 google/langfun?
    pass
    AI named google/langfun explicitly

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

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

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

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google/langfun — 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