by ryoungj · Agent Tool · ★ 190
ToolEmu: Identifying the Risks of LM Agents with an LM-Emulated Sandbox [📃 Paper] [🌐 Website] [🎮 Demo] [🐦 Twitter] Recent advances in Language Model (LM) agents and tool use, exemplified by applications like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks—such as leaking private data or causing financial losses. Identifying these risks is labor-intensive, necessitating implementing the tools, manually setting up the environment for each test scenario, and finding risky cases.
| Stars | 190 |
| Forks | 20 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 76.1298458243514/100 |
| Open Issues | 2 |
| Last Updated | 2024-03-22 |
| Created | 2023-09-26 |
| Platforms | python |
| Est. Tokens | ~275k |
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ToolEmu is [ICLR'24 Spotlight] A language model (LM)-based emulation framework for identifying the risks of LM agents with tool use. It is categorized as a Agent Tool with 190 GitHub stars.
ToolEmu is primarily written in Python. It covers topics such as agent, ai-safety, language-agent.
You can find installation instructions and usage details in the ToolEmu GitHub repository at github.com/ryoungj/ToolEmu. The project has 190 stars and 20 forks, indicating an active community.
ToolEmu is released under the Apache-2.0 license, making it free to use and modify according to the license terms.