CodeAct vs OpenHuman
Side-by-side comparison of two AI agent tools
Short answer
- CodeAct has had no commit in 28 months; OpenHuman is actively maintained (22,600 commits in the last 90 days).
- OpenHuman is growing faster: +3,180 GitHub stars in the last 30 days vs +11 for CodeAct.
- Pick CodeAct for: official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi. Pick OpenHuman for: openHuman is the fastest, cheapest, most efficient open-source agent harness.
From GitHub data refreshed daily.
CodeActopen-source
Official Repo for ICML 2024 paper "Executable Code Actions Elicit Better LLM Agents" by Xingyao Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, Heng Ji.
O
OpenHumanopen-source
OpenHuman is the fastest, cheapest, most efficient open-source agent harness. Written in Rust
Metrics
| CodeAct | OpenHuman | |
|---|---|---|
| Stars | 1.7k | 40.4k |
| Star velocity /mo | 10.952380952380953 | 3.2k |
| Commits (90d) | 0 | 22.6k |
| Releases (6m) | 0 | 10 |
| Overall score | 0.2079188143428804 | 0.9408550749378012 |
Pros
- +统一动作空间设计显著提升了智能体在复杂任务上的成功率,相比传统Text/JSON方法提升高达20%
- +集成Python解释器支持代码执行和动态修正,提供了强大的自我纠错和迭代改进能力
- +提供完整的开源生态系统,包括训练数据集、预训练模型和部署工具,支持研究和生产应用
Cons
- -需要Python环境和代码执行权限,在受限环境下部署存在安全性考虑
- -模型推理和代码执行的双重开销可能增加延迟和计算成本
- -对代码生成质量依赖较高,错误的代码可能导致任务失败或系统异常
Use Cases
- •自动化API集成和数据处理任务,智能体可以动态调用各种API并处理响应数据
- •复杂的多步骤问题解决,如数据分析、文件操作和系统管理任务
- •教育和研究场景中的交互式编程助手,能够执行代码并根据结果调整解决方案
FAQ
- Which is more popular, CodeAct or OpenHuman?
- OpenHuman has more GitHub stars (40,447 vs 1,705).
- Which is more actively developed, CodeAct or OpenHuman?
- OpenHuman had more commits in the last 90 days (22,600 vs 0).
- Should I use CodeAct or OpenHuman?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.