RRepoGEO

REPOGEO REPORT · LITE

stoyan-stoyanov/llmflows

Default branch main · commit 3c9e59f6 · scanned 6/12/2026, 1:07:30 AM

GitHub: 706 stars · 35 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 stoyan-stoyanov/llmflows, 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 statement to emphasize its framework nature and differentiators

    Why:

    CURRENT
    LLMFlows is a framework for building simple, explicit, and transparent LLM(Large Language Model) applications such as chatbots, question-answering systems, and agents.
    COPY-PASTE FIX
    LLMFlows is a lightweight and modular Python framework for building simple, explicit, and transparent LLM applications, including chatbots, question-answering systems, and agents, with a focus on direct control and easy debugging.
  • mediumtopics#2
    Add more specific topics related to LLM frameworks and orchestration

    Why:

    CURRENT
    ai, chatgpt, gpt-4, llm, llm-inference, llmops, llms, machine-learning, openai, prompt-engineering, python, question-answering, vector-database
    COPY-PASTE FIX
    ai, chatgpt, gpt-4, llm, llm-inference, llmops, llms, machine-learning, openai, prompt-engineering, python, question-answering, vector-database, llm-framework, llm-orchestration, llm-agents, workflow-engine
  • lowreadme#3
    Add a dedicated section in the README highlighting key differentiators

    Why:

    COPY-PASTE FIX
    ## Why LLMFlows? (Key Differentiators)

Unlike more complex LLM frameworks, LLMFlows prioritizes a lightweight, modular design, offering developers direct, explicit control over LLM workflow definition. This focus on simplicity and transparency makes debugging, monitoring, and maintenance straightforward, avoiding hidden prompts or LLM calls.

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 stoyan-stoyanov/llmflows
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. Guidance · recommended 1×
  4. Haystack · recommended 1×
  5. Instructor · recommended 1×
  • CATEGORY QUERY
    What are good Python frameworks for building transparent and explicit LLM applications?
    you: not recommended
    AI recommended (in order):
    1. Guidance
    2. LangChain
    3. LlamaIndex
    4. Haystack
    5. Instructor
    6. LiteLLM

    AI recommended 6 alternatives but never named stoyan-stoyanov/llmflows. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I build explicit and transparent LLM applications for easier debugging and monitoring?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. LlamaIndex
    4. LlamaCloud
    5. LlamaParse
    6. OpenAI Evals
    7. Weights & Biases
    8. W&B Prompts
    9. Humanloop
    10. MLflow
    11. Guardrails AI

    AI recommended 11 alternatives but never named stoyan-stoyanov/llmflows. 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 stoyan-stoyanov/llmflows?
    pass
    AI named stoyan-stoyanov/llmflows explicitly

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

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

    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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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite