REPOGEO REPORT · LITE

OpenPipe/ART

Default branch main · commit 7a3d33be · scanned 5/27/2026, 8:07:26 AM

GitHub: 9,841 stars · 872 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 OpenPipe/ART, 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 opening to clarify it's a serverless RL service for LLM agents

Why:

CURRENT

Train multi-step agents for real-world tasks using GRPO.

COPY-PASTE FIX

OpenPipe/ART is a serverless reinforcement learning *service* designed to train multi-step AI agents, especially for large language models like Qwen, GPT-OSS, and Llama, using techniques like GRPO.

mediumcomparison#2

Add a 'Why OpenPipe/ART?' or 'Comparison' section to the README

Why:

COPY-PASTE FIX

## Why OpenPipe/ART?
Unlike general-purpose RL libraries such as Stable Baselines3 or Ray RLlib, OpenPipe/ART provides a fully managed, serverless platform specifically optimized for training multi-step LLM agents. While cloud services like AWS SageMaker offer compute, OpenPipe/ART delivers a specialized, end-to-end solution for RL with LLMs, handling infrastructure, scaling, and deployment automatically.

lowabout#3

Refine the GitHub repository description

Why:

CURRENT

Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!

COPY-PASTE FIX

OpenPipe/ART is a serverless reinforcement learning service for training multi-step AI agents, especially for large language models (Qwen, GPT-OSS, Llama). Get on-the-job training for your agents with GRPO.

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 OpenPipe/ART

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Stable Baselines3 (SB3)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Stable Baselines3 (SB3) · recommended 1×
Ray RLlib · recommended 1×
DeepMind's Acme · recommended 1×
Farama Foundation Gymnasium · recommended 1×
Unity ML-Agents · recommended 1×

CATEGORY QUERY
How can I train multi-step AI agents effectively using reinforcement learning techniques?
you: not recommended
AI recommended (in order):
1. Stable Baselines3 (SB3)
2. Ray RLlib
3. DeepMind's Acme
4. Farama Foundation Gymnasium
5. Unity ML-Agents
6. Google Dopamine
7. TensorFlow Agents (TF-Agents)
AI recommended 7 alternatives but never named OpenPipe/ART. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What tools offer serverless reinforcement learning for large language model agent training?
you: not recommended
AI recommended (in order):
1. AWS SageMaker
2. AWS Fargate
3. AWS Lambda
4. AWS Step Functions
5. Amazon SageMaker Endpoints
6. Google Cloud Vertex AI
7. GKE Autopilot
8. Google Cloud Run
9. Google Cloud Functions
10. Vertex AI Endpoints
11. Azure Machine Learning
12. Azure Container Apps
13. Azure Functions
14. Azure Kubernetes Service (AKS)
15. Azure Machine Learning Endpoints
16. Azure OpenAI Service
17. Ray
18. RLlib
19. KubeRay
20. EKS
21. GKE
22. Anyscale Platform
23. Hugging Face Accelerate
24. Stable Baselines3
25. CleanRL
26. Hugging Face Inference Endpoints
AI recommended 26 alternatives but never named OpenPipe/ART. 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 OpenPipe/ART?
pass
AI named OpenPipe/ART explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts OpenPipe/ART in production, what risks or prerequisites should they evaluate first?
pass
AI named OpenPipe/ART 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 OpenPipe/ART solve, and who is the primary audience?
pass
AI named OpenPipe/ART 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 OpenPipe/ART. 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/OpenPipe/ART.svg)](https://repogeo.com/en/r/OpenPipe/ART)

HTML

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

Pro

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OpenPipe/ART — 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