by areal-project · Agent Tool · ★ 5.3k
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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 | 5,321 |
| Forks | 523 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 49.022/100 |
| Open Issues | 91 |
| Last Updated | 2026-06-19 |
| Created | 2025-02-24 |
| Platforms | python |
| Est. Tokens | ~19k |
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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 5.3k 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 5.3k stars and 523 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.