RRepoGEO

REPOGEO REPORT · LITE

viddexa/autollm

Default branch main · commit c369a039 · scanned 5/19/2026, 9:56:58 AM

GitHub: 1,004 stars · 99 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
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 viddexa/autollm, 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 README's opening to clearly state core value proposition

    Why:

    CURRENT
    The README excerpt shows a centered div, then "questions | feature requests", then badges, before the "🤔 why autollm?" section.
    COPY-PASTE FIX
    # AutoLLM: Ship RAG based LLM web apps in seconds.

AutoLLM simplifies and unifies the development of Retrieval-Augmented Generation (RAG) based Large Language Model (LLM) web applications, offering a 1-line RAG LLM Engine and 1-line FastAPI integration. It provides a unified API for 100+ LLMs and 20+ Vector Databases, making it a powerful alternative to LangChain, LlamaIndex, and LiteLLM for rapid deployment.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://pypi.org/project/autollm/
  • lowreadme#3
    Remove or relocate non-essential content from the very top of the README

    Why:

    CURRENT
    <div align="center">
  <p>
    <a align="center" href="" target="_blank">

    </a>
  </p>

<br>

questions | feature requests

<br>
    COPY-PASTE FIX
    Remove or relocate the "questions | feature requests" line and the initial empty `div` block to allow the core value proposition to be immediately visible.

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 viddexa/autollm
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Streamlit · recommended 1×
  4. Gradio · recommended 1×
  5. FastAPI · recommended 1×
  • CATEGORY QUERY
    How can I quickly build a retrieval-augmented generation LLM web application in Python?
    you: not recommended
    AI recommended (in order):
    1. Streamlit
    2. LlamaIndex
    3. LangChain
    4. Gradio
    5. FastAPI
    6. React
    7. Vue.js
    8. Panel
    9. NumPy
    10. Pandas
    11. Matplotlib
    12. Flask
    13. Django
    14. Jinja2

    AI recommended 14 alternatives but never named viddexa/autollm. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a Python framework to unify access to multiple LLMs and vector databases for RAG.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack (deepset/Haystack)
    4. LiteLLM
    5. Instructor
    6. Ragas

    AI recommended 6 alternatives but never named viddexa/autollm. 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 viddexa/autollm?
    pass
    AI named viddexa/autollm explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of viddexa/autollm. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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viddexa/autollm — 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