REPOGEO REPORT · LITE

viddexa/autollm

Default branch main · commit c369a039 · scanned 6/30/2026, 6:42:01 PM

GitHub: 1,004 stars · 98 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 highlight RAG LLM web app deployment

Why:

CURRENT

## 🤔 why autollm?

**Simplify. Unify. Amplify.**

COPY-PASTE FIX

## 🤔 why autollm?

**AutoLLM helps you ship RAG-based LLM web applications in seconds, simplifying the deployment of powerful AI tools.**

mediumtopics#2

Add specific topics for LLM cost tracking and web applications

Why:

CURRENT

anthropic, bedrock, cohere, fastapi, gradio, langchain, large-language-models, llama-index, llama2, llm, openai, palm, pypi, python, retrieval-augmented-generation, vector-database, vertex-ai

COPY-PASTE FIX

anthropic, bedrock, cohere, fastapi, gradio, langchain, large-language-models, llama-index, llama2, llm, llm-cost-tracking, llm-web-apps, openai, palm, pypi, python, retrieval-augmented-generation, vector-database, vertex-ai

mediumreadme#3

Add a dedicated section in README for LLM cost calculation

Why:

COPY-PASTE FIX

## 💰 Cost Calculation

AutoLLM provides built-in cost calculation for over 100 LLMs, allowing you to monitor and optimize your application's expenses directly.

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

Of all named tools, what % are you?

Top rival

run-llama/llama_index

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

run-llama/llama_index · recommended 2×
langchain-ai/langchain · recommended 2×
streamlit/streamlit · recommended 1×
Streamlit Cloud · recommended 1×
Google Cloud Run · recommended 1×

CATEGORY QUERY
How to quickly build and deploy retrieval-augmented generation LLM web applications?
you: not recommended
AI recommended (in order):
1. Streamlit (streamlit/streamlit)
2. LlamaIndex (run-llama/llama_index)
3. LangChain (langchain-ai/langchain)
4. Streamlit Cloud
5. Google Cloud Run
6. AWS Fargate
7. Azure Container Apps
8. Gradio (gradio-app/gradio)
9. Hugging Face Spaces
10. Anvil
11. Panel (holoviz/panel)
12. FastAPI (tiangolo/fastapi)
13. AWS Lambda
14. Azure Functions
15. Dify (dify-ai/dify)
16. Flowise (FlowiseAI/Flowise)
AI recommended 16 alternatives but never named viddexa/autollm. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a Python library for easily integrating multiple LLM providers with RAG and cost tracking.
you: not recommended
AI recommended (in order):
1. LangChain (langchain-ai/langchain)
2. LlamaIndex (run-llama/llama_index)
3. LiteLLM (BerriAI/litellm)
4. Haystack (deepset/Haystack)
5. OpenAI Python Client (openai/openai-python)
AI recommended 5 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.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/viddexa/autollm.svg)](https://repogeo.com/en/r/viddexa/autollm)

HTML

<a href="https://repogeo.com/en/r/viddexa/autollm"><img src="https://repogeo.com/badge/viddexa/autollm.svg" alt="RepoGEO" /></a>

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