REPOGEO REPORT · LITE

aurora-develop/aurora

Default branch main · commit 4b5ed155 · scanned 5/20/2026, 2:11:44 AM

GitHub: 2,195 stars · 639 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 aurora-develop/aurora, 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 H1 to clearly state the project's purpose
Why:
CURRENT
```
# AURORA
```
COPY-PASTE FIX
```
# AURORA: Self-host a free GPT-3.5 API proxy with a web UI
```
mediumtopics#2
Add more specific topics to improve category visibility
Why:
CURRENT
```
chatgpt, free, gpt
```
COPY-PASTE FIX
```
chatgpt, free, gpt, llm-api, openai-api, self-hosted, docker, ai-chat-interface
```
mediumhomepage#3
Add a homepage URL to the repository's 'About' section
Why:
COPY-PASTE FIX
```
https://github.com/aurora-develop/aurora
```

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 aurora-develop/aurora

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Ollama

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Ollama · recommended 1×
LM Studio · recommended 1×
oobabooga/text-generation-webui · recommended 1×
LocalAI · recommended 1×
llama.cpp · recommended 1×

CATEGORY QUERY
How can I self-host a free large language model API for development?
you: not recommended
AI recommended (in order):
1. Ollama
2. LM Studio
3. text-generation-webui (oobabooga/text-generation-webui)
4. LocalAI
5. llama.cpp
6. Hugging Face `transformers` library
AI recommended 6 alternatives but never named aurora-develop/aurora. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for an open-source AI chat interface deployable with Docker for local use.
you: not recommended
AI recommended (in order):
1. Chatbot UI (mckaywrigley/chatbot-ui)
2. Open WebUI (open-webui/open-webui)
3. LocalAI Web UI (go-skynet/LocalAI-webui)
4. LobeChat (lobehub/lobe-chat)
5. GPT-Next-Web (Yidadaa/ChatGPT-Next-Next-Web)
6. AnythingLLM (Mintplex-Labs/anythingllm)
AI recommended 6 alternatives but never named aurora-develop/aurora. 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 aurora-develop/aurora?
pass
AI named aurora-develop/aurora explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts aurora-develop/aurora in production, what risks or prerequisites should they evaluate first?
pass
AI named aurora-develop/aurora 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 aurora-develop/aurora solve, and who is the primary audience?
pass
AI named aurora-develop/aurora explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/aurora-develop/aurora.svg)](https://repogeo.com/en/r/aurora-develop/aurora)

HTML

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

Pro

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aurora-develop/aurora — 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