by inclusionAI · Agent Tool · ★ 5.2k
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AReaL: A Large-Scale Asynchronous Reinforcement Learning System WeChat (微信) Group | AReaL is an open-source fully asynchronous reinforcement learning training system for large reasoning and agentic models, developed by members from Tsinghua IIIS and the AReaL Team at Ant Group. Built upon the open-source project ReaLHF, we are fully committed to open-source principles by providing the training details, data, and infrastructure required to reproduce our results, along with the models themselves. AReaL aims to help everyone build their own AI agents easily and affordably. Our team loves milk tea because it's delicious, customizable, and affordable—we hope you enjoy our project just as much as you'd enjoy real milk tea. Cheers! AReaL Highlights ⚡ Flexibility: Seamless customization for
| Stars | 5,153 |
| Forks | 495 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 45.582/100 |
| Open Issues | 66 |
| Last Updated | 2026-05-10 |
| Created | 2025-02-24 |
| Platforms | python |
| Est. Tokens | ~19218k |
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AReaL is The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.. It is categorized as a Agent Tool with 5.2k 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/inclusionAI/AReaL. The project has 5.2k stars and 495 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.