by ryoungj · Agent Tool · ★ 190
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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. As tools and agents become more complex, the high cost of testing these agents will make it increasingly difficult to find high-stakes, long-tailed risks. ToolEmu is an LM-based emulation framework that enables identifying and assessing such risks at scale, facilitating the development of safter LM agents. This repo contains the code for: testing LM agents with specific test cases in emulation evaluating LM agents with our automatic evaluators and curated benchmark [
| Stars | 190 |
| Forks | 20 |
| Language | Python |
| Category | Agent Tool |
| License | Apache-2.0 |
| Quality Score | 46.35/100 |
| 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.
The top alternatives to ToolEmu on Agent Skills Hub include anchoring-ai, chatgpt-cli, ToolOrchestra. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.