RRepoGEO

REPOGEO REPORT · LITE

gensx-inc/gensx

Default branch main · commit f5429942 · scanned 6/3/2026, 1:36:43 AM

GitHub: 528 stars · 24 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 gensx-inc/gensx, 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 explicitly state its core identity and unique approach

    Why:

    CURRENT
    GenSX is a simple TypeScript framework for building complex LLM applications. It's a workflow engine designed for building agents, chatbots, and long-running workflows.
    COPY-PASTE FIX
    GenSX is a TypeScript framework for building complex LLM agents and long-running workflows using a familiar React-like component model. It offers a lightning-fast dev loop and is designed for natural composition and type-safety.
  • mediumtopics#2
    Add more specific topics to improve keyword matching

    Why:

    CURRENT
    generative-ai, llm-framework, llms, workflow
    COPY-PASTE FIX
    generative-ai, llm-framework, llms, workflow, agents, typescript, react-components, type-safe, tracing
  • lowreadme#3
    Explicitly highlight the 'React-like components' paradigm in the README's 'Why GenSX?' section

    Why:

    COPY-PASTE FIX
    Add a new bullet point under 'Why GenSX?' like: '- ⚛️ **React-like Components**: Build workflows with familiar component-based patterns, making development intuitive and composable.'

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 gensx-inc/gensx
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
run-llama/llama_index
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. run-llama/llama_index · recommended 2×
  2. langchain-ai/langchainjs · recommended 1×
  3. microsoft/TypeChat · recommended 1×
  4. temporalio/sdk-typescript · recommended 1×
  5. nestjs/nest · recommended 1×
  • CATEGORY QUERY
    How to build complex LLM agents and long-running workflows using TypeScript?
    you: not recommended
    AI recommended (in order):
    1. LangChain.js (langchain-ai/langchainjs)
    2. LlamaIndex.TS (run-llama/llama_index)
    3. TypeChat (microsoft/TypeChat)
    4. Temporal.io (temporalio/sdk-typescript)
    5. NestJS (nestjs/nest)
    6. OpenAI SDK for TypeScript (openai/openai-node)

    AI recommended 6 alternatives but never named gensx-inc/gensx. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a type-safe framework for composing generative AI components with built-in tracing.
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LangSmith
    3. LlamaIndex (run-llama/llama_index)
    4. OpenAI Functions/Tools
    5. OpenTelemetry
    6. Helicone (helicone/helicone)
    7. Arize AI Phoenix (Arize-AI/phoenix)
    8. Guardrails AI (guardrails-ai/guardrails)
    9. Magentic (jacksmith15/magentic)
    10. Haystack (deepset-ai/haystack)
    11. Instructor (jxnl/instructor)

    AI recommended 11 alternatives but never named gensx-inc/gensx. 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 gensx-inc/gensx?
    pass
    AI named gensx-inc/gensx explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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