{"name":"Agentless: An Agentless Approach to Solve Software Development Problems","description":"Agentless is an innovative open-source project that offers an agentless approach to automatically solve software development problems. It streamlines the bug-fixing process through localization, repair, and patch validation phases. This tool aims to enhance efficiency in addressing software issues, particularly demonstrated by its performance on benchmarks like SWE-bench lite.","github":"https://github.com/OpenAutoCoder/Agentless","url":"https://osrepos.com/repo/openautocoder-agentless","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/openautocoder-agentless","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/openautocoder-agentless.md","json":"https://osrepos.com/repo/openautocoder-agentless.json","topics":["agent","artificial-intelligence","llm","software-development","python","software-engineering","automated-bug-fixing"],"keywords":["agent","artificial-intelligence","llm","software-development","python","software-engineering","automated-bug-fixing"],"stars":null,"summary":"Agentless is an innovative open-source project that offers an agentless approach to automatically solve software development problems. It streamlines the bug-fixing process through localization, repair, and patch validation phases. This tool aims to enhance efficiency in addressing software issues, particularly demonstrated by its performance on benchmarks like SWE-bench lite.","content":"## Introduction\n\nAgentless is an innovative open-source project by OpenAutoCoder that introduces an agentless methodology for automatically resolving software development issues. Unlike traditional agent-based systems, Agentless streamlines the problem-solving process into three distinct phases: localization, repair, and patch validation. This structured approach aims to efficiently identify, fix, and verify solutions for software bugs.\n\nThe project has demonstrated impressive results, achieving significant solve rates on benchmarks like SWE-bench lite, making it a notable tool in automated software engineering.\n\n## Installation\n\nTo get started with Agentless, follow these steps to set up your environment:\n\nFirst, clone the repository and navigate into its directory:\n\nshell\ngit clone https://github.com/OpenAutoCoder/Agentless.git\ncd Agentless\n\n\nNext, create and activate a Conda environment, then install the required dependencies:\n\nshell\nconda create -n agentless python=3.11\nconda activate agentless\npip install -r requirements.txt\nexport PYTHONPATH=$PYTHONPATH:$(pwd)\n\n\nFinally, export your OpenAI API key to enable the tool's functionality:\n\nshell\nexport OPENAI_API_KEY={key_here}\n\n\nYou are now ready to run Agentless on problems, for example, those found in SWE-bench. For detailed instructions on reproducing SWE-bench lite experiments, refer to the [SWE-bench README](https://github.com/OpenAutoCoder/Agentless/blob/main/README_swebench.md){target=\"_blank\" rel=\"noopener noreferrer\"} file within the repository.\n\n## Examples\n\nWhile direct interactive examples are not provided in the main README, Agentless is designed to run on problems from benchmarks like SWE-bench. Users can follow the detailed instructions in the [SWE-bench README](https://github.com/OpenAutoCoder/Agentless/blob/main/README_swebench.md){target=\"_blank\" rel=\"noopener noreferrer\"} within the repository to reproduce experiments and apply Agentless to these standardized software development challenges.\n\nThe project's effectiveness is further illustrated by its comparison graph against other open-source agent-based approaches on SWE-bench lite, showcasing its competitive performance in automated bug fixing.\n\n## Why Use Agentless?\n\nAgentless stands out for its unique agentless approach, which simplifies the architecture for automated software problem-solving. Its three-phase process, encompassing localization, repair, and patch validation, provides a robust and systematic method for addressing bugs.\n\nThe project has achieved high solve rates on challenging benchmarks such as SWE-bench lite, demonstrating its capability to effectively fix real-world software issues. Furthermore, its cost-effectiveness, as highlighted by an average cost of $0.34 per issue, makes it an attractive solution for developers looking to automate parts of their debugging workflow.\n\n## Links\n\n*   **GitHub Repository**: [https://github.com/OpenAutoCoder/Agentless](https://github.com/OpenAutoCoder/Agentless){target=\"_blank\" rel=\"noopener noreferrer\"}\n*   **Arxiv Paper**: [https://arxiv.org/abs/2407.01489](https://arxiv.org/abs/2407.01489){target=\"_blank\" rel=\"noopener noreferrer\"}\n*   **Latest Release Artifacts**: [https://github.com/OpenAutoCoder/Agentless/releases/tag/v1.5.0](https://github.com/OpenAutoCoder/Agentless/releases/tag/v1.5.0){target=\"_blank\" rel=\"noopener noreferrer\"}","metrics":{"detailViews":5,"githubClicks":3},"dates":{"published":null,"modified":"2025-12-23T12:01:14.000Z"}}