RRepoGEO

REPOGEO REPORT · LITE

SylphAI-Inc/AdalFlow

Default branch main · commit 810de99d · scanned 6/20/2026, 6:56:37 AM

GitHub: 4,166 stars · 376 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 SylphAI-Inc/AdalFlow, 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 main heading to emphasize LLM application framework and auto-optimization

    Why:

    CURRENT
    <h2><p align="center">⚡ AdalFlow is a PyTorch-like library to build and auto-optimize any LM workflows, from Chatbots, RAG, to Agents. ⚡</p></h2>
    COPY-PASTE FIX
    <h2><p align="center">⚡ AdalFlow: The Python Framework for Building & Auto-Optimizing LLM Applications (RAG, Agents, Chatbots) ⚡</p></h2>
  • mediumtopics#2
    Add more specific topics related to LLM frameworks and prompt optimization

    Why:

    CURRENT
    agent, ai, auto-prompting, bm25, chatbot, faiss, framework, generative-ai, information-retrieval, llm, machine-learning, nlp, optimizer, python, question-answering, rag, reranker, retriever, summarization, trainer
    COPY-PASTE FIX
    agent, ai, auto-prompting, bm25, chatbot, faiss, framework, generative-ai, information-retrieval, llm, machine-learning, nlp, optimizer, python, question-answering, rag, reranker, retriever, summarization, trainer, llm-framework, prompt-optimization, ai-agents, llm-ops
  • mediumreadme#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## AdalFlow vs. LangChain/LlamaIndex

AdalFlow differentiates itself by providing a unified, PyTorch-like auto-differentiative framework for both zero-shot and few-shot prompt optimization, focusing on end-to-end LLM application building and performance tuning rather than just orchestration. Our approach emphasizes lightweight, open-source agents with built-in Human-in-the-Loop and Tracing functionalities, designed for deep optimization of RAG, agents, and chatbots.

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 SylphAI-Inc/AdalFlow
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. deepset/Haystack · recommended 1×
  4. microsoft/autogen · recommended 1×
  5. DSPy · recommended 1×
  • CATEGORY QUERY
    What are good Python frameworks for building and optimizing complex LLM applications like RAG or agents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/Haystack)
    4. AutoGen (microsoft/autogen)
    5. DSPy
    6. Guidance (microsoft/guidance)

    AI recommended 6 alternatives but never named SylphAI-Inc/AdalFlow. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a library to automatically optimize prompt engineering and LLM application performance.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. PromptLayer
    4. Weights & Biases (W&B) Prompts
    5. OpenPrompt
    6. Guardrails AI
    7. LiteLLM

    AI recommended 7 alternatives but never named SylphAI-Inc/AdalFlow. 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 SylphAI-Inc/AdalFlow?
    pass
    AI named SylphAI-Inc/AdalFlow explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite