REPOGEO REPORT · LITE

areal-project/AReaL

Default branch main · commit 1fab24a2 · scanned 5/24/2026, 8:32:20 PM

GitHub: 5,210 stars · 504 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 areal-project/AReaL, 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

Explicitly clarify the 'AReaL' acronym in the README's first paragraph

Why:

CURRENT

AReaL is a reinforcement learning (RL) infrastructure designed to bridge foundation model training with modern agent-based applications.

COPY-PASTE FIX

AReaL (Asynchronous Reinforcement Learning) is a reinforcement learning (RL) infrastructure, *distinct from Augmented Reality (AR) applications*, designed to bridge foundation model training with modern agent-based applications.

mediumabout#2

Update the repository description to disambiguate 'AReaL'

Why:

CURRENT

The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.

COPY-PASTE FIX

AReaL: The Asynchronous Reinforcement Learning (RL) Bridge for LLM-based Agent Applications. *Distinct from Augmented Reality (AR) projects.* Made Simple & Flexible.

lowcomparison#3

Add a 'Comparison' section to the README

Why:

COPY-PASTE FIX

## Comparison with Existing RL Frameworks
AReaL differentiates itself from frameworks like Ray, RLlib, DeepMind's Acme, and OpenAI Baselines by focusing on a fully asynchronous RL training paradigm specifically optimized for large-scale reasoning and agentic models, bridging foundation model training with modern agent-based applications. Our emphasis is on accessibility, efficiency, and cost-effectiveness for LLM-based agent development, offering a unique blend of scalability and flexibility.

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 areal-project/AReaL

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ray-project/ray

Recommended in 3 of 2 queries

COMPETITOR LEADERBOARD

ray-project/ray · recommended 3×
Hugging Face Transformers · recommended 1×
Hugging Face Accelerate · recommended 1×
DeepMind's Acme · recommended 1×
RLlib · recommended 1×

CATEGORY QUERY
How to efficiently train large-scale LLM-based agents using reinforcement learning?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Hugging Face Accelerate
3. DeepMind's Acme
4. RLlib
5. OpenAI Baselines
6. Stable Baselines3
7. PyTorch FSDP
8. Colossal-AI
9. DeepSpeed
AI recommended 9 alternatives but never named areal-project/AReaL. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What infrastructure supports scalable asynchronous reinforcement learning for complex agentic models?
you: not recommended
AI recommended (in order):
1. Ray (ray-project/ray)
2. RLlib (ray-project/ray)
3. Ray Tune (ray-project/ray)
4. Kubernetes (kubernetes/kubernetes)
5. Kubeflow (kubeflow/kubeflow)
6. MetaFlow (Netflix/metaflow)
7. Argo Workflows (argoproj/argo-workflows)
8. Google Cloud ML Engine
9. AI Platform
10. AWS SageMaker
11. Azure ML
12. PyTorch Lightning (Lightning-AI/lightning)
13. TensorFlow (tensorflow/tensorflow)
14. OpenSpiel (deepmind/open_spiel)
AI recommended 14 alternatives but never named areal-project/AReaL. 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 areal-project/AReaL?
pass
AI named areal-project/AReaL explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts areal-project/AReaL in production, what risks or prerequisites should they evaluate first?
pass
AI named areal-project/AReaL 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 areal-project/AReaL solve, and who is the primary audience?
pass
AI named areal-project/AReaL 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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Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/areal-project/AReaL.svg)](https://repogeo.com/en/r/areal-project/AReaL)

HTML

<a href="https://repogeo.com/en/r/areal-project/AReaL"><img src="https://repogeo.com/badge/areal-project/AReaL.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