It is an AI-powered tool called SWE-agent that takes a GitHub issue and attempts to automatically fix it using a language model like GPT-4 or another model of choice.
It is an AI-powered tool called SWE-agent that takes a GitHub issue and attempts to automatically fix it using a language model like GPT-4 or another model of choice. Additionally, it can be used for offensive cybersecurity tasks, such as capture-the-flag challenges, or competitive coding problems. SWE-agent operates by using configurable agent-computer interfaces (ACIs) to interact with isolated computer environments, enabling autonomous tool usage by the language model. It is built and maintained by researchers from Princeton University and Stanford University.
SWE-agent includes a mode called EnIGMA, specifically designed for solving offensive cybersecurity challenges, achieving state-of-the-art results on multiple benchmarks. EnIGMA introduces features like a debugger, server connection tools, and a summarizer for handling long outputs, which are available across all modes of SWE-agent. The project is an academic initiative started at Princeton University by researchers including John Yang, Carlos E. Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press.
The tool is open-source under the MIT license, and users are encouraged to cite the work if they find it helpful. For more information, users can refer to the documentation, join the Discord community, or read the associated research paper. Contact information for the primary researchers is provided for further inquiries.
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It is an AI-powered tool called SWE-agent that takes a GitHub issue and attempts to automatically fix it using a language model like GPT-4 or another model of choice.
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It is an AI-powered tool called SWE-agent that takes a GitHub issue and attempts to automatically fix it using a language model like GPT-4 or another model of choice.
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