No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by areal-project · Agent Tool · ★ 5.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is AReaL safe to install? View the security audit →
AReaL: A Large-Scale Asynchronous Reinforcement Learning System WeChat (微信) Group | AReaL is a reinforcement learning (RL) infrastructure designed to bridge foundation model training with modern agent-based applications. It was originally developed by researchers and engineers from Tsinghua IIIS and the AReaL Team at Ant Group. Built on a fully asynchronous RL training paradigm, AReaL is optimized for efficiency and scalability, making it particularly well-suited for training large-scale reasoning and agentic models. AReaL’s mission is to make building AI agents accessible, efficient, and cost-effective for a broad community of developers and researchers. Like
| Stars | 5,763 |
| Forks | 603 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 65.2247495747633/100 |
| Open Issues | 133 |
| Last Updated | 2026-09-15 |
| Created | 2025-02-24 |
| Platforms | python |
| Est. Tokens | ~19k |
These tools work well together with AReaL for enhanced workflows:
Looking for a AReaL alternative? If you're comparing AReaL with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.
Your agent in your terminal, equipped with local tools: writes code, uses the terminal, browses the web. Make
🦞 Just talk to your agent — it learns and EVOLVES 🧬.
ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning & ReCall: Learning to Reason with
Platform for stateful agents: AI with advanced memory that can learn and self-improve over time.
🔥 MaxKB is an open-source platform for building enterprise-grade agents. 强大易用的开源企业级智能体平台。
Explore other popular agent tool tools:
AReaL is The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.. It is categorized as a Agent Tool with 5.8k GitHub stars.
AReaL is primarily written in Python. It covers topics such as agent, llm, llm-agent.
You can find installation instructions and usage details in the AReaL GitHub repository at github.com/areal-project/AReaL. The project has 5.8k stars and 603 forks, indicating an active community.
AReaL is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to AReaL on Agent Skills Hub include AReaL, gptme, MetaClaw. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: