by xingyaoww · Agent Tool · ★ 1.6k
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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.
| Stars | 1,616 |
| Forks | 129 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 41.25/100 |
| Open Issues | 13 |
| Last Updated | 2024-05-23 |
| Created | 2024-01-13 |
| Platforms | python |
| Est. Tokens | ~1639k |
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code-act is 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.. It is categorized as a Agent Tool with 1.6k GitHub stars.
code-act is primarily written in Python. It covers topics such as llm, llm-agent, llm-finetuning.
You can find installation instructions and usage details in the code-act GitHub repository at github.com/xingyaoww/code-act. The project has 1.6k stars and 129 forks, indicating an active community.
code-act is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to code-act on Agent Skills Hub include langroid, holmesgpt, LLMCompiler. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.