REPOGEO REPORT · LITE

sail-sg/envpool

Default branch main · commit 6e1f5b60 · scanned 5/21/2026, 10:47:03 AM

GitHub: 1,429 stars · 138 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

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 sail-sg/envpool, 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 its role as a specialized high-performance backend

Why:

CURRENT

EnvPool is a C++-based batched environment pool with pybind11 and thread pool. It has high performance (~1M raw FPS with Atari games, ~3M raw FPS with MuJoCo simulator on DGX-A100) and compatible APIs (supports Gymnasium and dm_env, both sync and async, both single and multi player environment).

COPY-PASTE FIX

EnvPool is a high-performance C++-based execution engine designed to dramatically speed up reinforcement learning training by providing a batched, vectorized environment pool. It integrates seamlessly with popular RL frameworks and environments like Gymnasium and dm_env, offering unparalleled throughput for parallel environment interaction.

mediumtopics#2
Add more specific topics to highlight vectorized environment execution and RL acceleration
Why:
COPY-PASTE FIX
```
vectorized-environments, rl-acceleration, environment-execution-engine, cxx-rl-backend, high-throughput-environments
```

mediumreadme#3

Add explicit target audience and problem statement to README introduction

Why:

COPY-PASTE FIX

It is designed for RL researchers and practitioners who need to accelerate their training workflows by efficiently managing and executing large numbers of parallel environments.

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 sail-sg/envpool

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Ray RLib

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Ray RLib · recommended 1×
Stable Baselines3 · recommended 1×
OpenAI Gym/Farama Foundation Gymnasium · recommended 1×
Acme · recommended 1×
TorchRL · recommended 1×

CATEGORY QUERY
How to speed up reinforcement learning training with high-performance parallel environment execution?
you: not recommended
AI recommended (in order):
1. Ray RLib
2. Stable Baselines3
3. OpenAI Gym/Farama Foundation Gymnasium
4. Acme
5. TorchRL
6. PettingZoo
7. Custom MPI/Distributed Python
AI recommended 7 alternatives but never named sail-sg/envpool. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a C++ backend for fast, vectorized reinforcement learning environments compatible with standard APIs.
you: not recommended
AI recommended (in order):
1. OpenSpiel
2. DeepMind Lab
3. Gymnasium
4. Pybind11
5. Boost.Python
6. Unity ML-Agents
7. Unity
8. MuJoCo
9. Isaac Gym
10. Isaac Sim
AI recommended 10 alternatives but never named sail-sg/envpool. 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 sail-sg/envpool?
pass
AI named sail-sg/envpool explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts sail-sg/envpool in production, what risks or prerequisites should they evaluate first?
pass
AI named sail-sg/envpool 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 sail-sg/envpool solve, and who is the primary audience?
pass
AI named sail-sg/envpool 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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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/sail-sg/envpool.svg)](https://repogeo.com/en/r/sail-sg/envpool)

HTML

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

Pro

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sail-sg/envpool — 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