RRepoGEO

REPOGEO REPORT · LITE

kyrolabs/awesome-langchain

Default branch main · commit 792b2b97 · scanned 5/13/2026, 5:38:01 PM

GitHub: 9,344 stars · 842 forks

AI VISIBILITY SCORE
22 /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
1 / 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 kyrolabs/awesome-langchain, 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
    Strengthen README opening to emphasize "LLM application development"

    Why:

    CURRENT
    > Curated list of tools and projects using LangChain.
LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast.
Here is an attempt to keep track of the initiatives around LangChain.
    COPY-PASTE FIX
    > The definitive curated list of tools, projects, and resources for developers building Large Language Model (LLM) applications with the LangChain framework.
LangChain is an amazing framework for LLM development, and its ecosystem is growing fast. This list helps you quickly discover and navigate the best initiatives around LangChain for your projects.
  • mediumabout#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/kyrolabs/awesome-langchain
  • mediumtopics#3
    Add more specific LLM-related topics

    Why:

    CURRENT
    ai, awesome, awesome-list, langchain, llm
    COPY-PASTE FIX
    ai, awesome, awesome-list, langchain, llm, llm-applications, llm-development, ai-development

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 kyrolabs/awesome-langchain
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome-LLM-Apps
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome-LLM-Apps · recommended 1×
  2. LLM-Tool-Explorer · recommended 1×
  3. LangChain · recommended 1×
  4. LlamaIndex · recommended 1×
  5. deepset/haystack · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of tools for building large language model applications?
    you: not recommended
    AI recommended (in order):
    1. Awesome-LLM-Apps
    2. LLM-Tool-Explorer

    AI recommended 2 alternatives but never named kyrolabs/awesome-langchain. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source projects and templates for developing with LLM orchestration frameworks?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/haystack)
    4. OpenAI Cookbook
    5. Microsoft Guidance
    6. LiteLLM
    7. PromptFlow

    AI recommended 7 alternatives but never named kyrolabs/awesome-langchain. 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 kyrolabs/awesome-langchain?
    pass
    AI did not name kyrolabs/awesome-langchain — likely talking about a different project

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

  • If a team adopts kyrolabs/awesome-langchain in production, what risks or prerequisites should they evaluate first?
    pass
    AI named kyrolabs/awesome-langchain 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 kyrolabs/awesome-langchain solve, and who is the primary audience?
    pass
    AI did not name kyrolabs/awesome-langchain — likely talking about a different project

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

Embed your GEO score

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MARKDOWN (README)
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