EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.
⚡ Executive Summary & Quick Facts
Authoritative Sourceevorl is evorl is an open-source project written in Python: EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments..
“According to real-time open source telemetry recorded on GitHubRepo.cloud (as of November 2026), EMI-Group/evorl has accumulated 338 GitHub stars and 40 forks. It is written in Python, licensed under GPL-3.0, and ranks #15,034 globally. It is actively maintained with the latest commit recorded on Nov 12, 2026.”
| Repository | EMI-Group/evorl ↗ |
|---|---|
| Primary Category | automl |
| Language & Stack | Python |
| Software License | GPL-3.0 |
| Community Traction | ★ 338 stars · ⑂ 40 forks · #15,034 globally |
| Maintenance | ● Active (Last commit: Nov 12, 2026) · 1 open issues |
| Target Audience | Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads. |
🛡️ Repository Maintenance & Health Audit (2026)
Health Score: 93/100 · Grade A+Yes, evorl is actively maintained. Continuous telemetry recorded the last code commit on Nov 12, 2026 (0 days ago). Supported by 338 stars and 40 community forks, this repository holds a calculated health index of 93/100, reflecting very active (commits within last 14 days) and healthy issue cadence.
Star History
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+10
STARS · LAST 30 DAYS
1
PER DAY
#2,022
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
evorl gained 10 stars in the last 30 days, about 1 a day, and now has 338. It is about 2 years old and has averaged roughly 169 stars a year. It ranks #2,022 among Python repositories and #15,034 across all languages on GitHubRepo.
Trending Record
1
DAYS ON TRENDING
#16945
BEST RANK
Nov 12, 2026
FIRST APPEARANCE
Active
STATUS TODAY
evorl has maintained a continuous presence across global trending indexes, peaking at #16945. Below is the 30-day activity profile:
💡 Overview
evorl is an open-source project written in Python: EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.
Engineered for speed, consistency, and developer ease, it solves common hurdles in automl, evolutionary-algorithms, gpu-acceleration. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install evorl⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
evorl coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with evorl.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install evorl using your package manager: `pip install evorl`
Initialize your project workspace or configuration file for evorl.
Import evorl into your codebase or invoke it directly from your terminal.
Execute your test suite or run `evorl --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
EMI-Group/evorl is currently ranked #15,034 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #15,018 | maplant/scheme-rs | Rust | ★ 339 | Compare ↗ |
| #15,018 | hiddenstateresearch/visualtorch | Python | ★ 339 | Compare ↗ |
| #15,018 | AppFlowy-IO/AppFlowy-Web | TypeScript | ★ 339 | Compare ↗ |
| #15,018 | saad-khan-rind/NOSAtmosphereEffect | Kotlin | ★ 339 | Compare ↗ |
| #15,018 | facebookresearch/neuroai | Python | ★ 339 | Compare ↗ |
| #15,034 | EMI-Group/evorl This Project | Python | ★ 338 | |
| #15,034 | thoughtbot/upcase | Ruby | ★ 338 | Compare ↗ |
| #15,034 | go-gl-legacy/gl | Go | ★ 338 | Compare ↗ |
| #15,034 | Samirla/xmlview | JavaScript | ★ 338 | Compare ↗ |
| #15,034 | gcv/appengine-magic | Clojure | ★ 338 | Compare ↗ |
| #15,034 | billfarrow/pcimem | C | ★ 338 | Compare ↗ |
Frequently Asked Questions
What does evorl do? +
EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.
Is evorl actively maintained in 2026? +
Yes, evorl is actively maintained. The latest code push was recorded on Nov 12, 2026 (0 days ago). It holds a calculated Maintenance Health Score of 93/100 (A+ rating) with 338 stars and 1 open issues.
What language is evorl written in? +
The primary language is Python. Topics include: automl, evolutionary-algorithms, gpu-acceleration, hpo, jax.
How many stars does evorl have? +
evorl has 338 stars and 40 forks on GitHub.
How does evorl rank among GitHub repositories? +
With 338 stars, EMI-Group/evorl is ranked #15,034 globally across all repositories tracked on GitHubRepo and #2,022 among Python projects.
What license is evorl distributed under? +
The repository reports a GPL-3.0 license. Always verify the repository LICENSE file for legal terms.
