Agentless: An Agentless Approach to Solve Software Development Problems

Introduction

Agentless 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.

The project has demonstrated impressive results, achieving significant solve rates on benchmarks like SWE-bench lite, making it a notable tool in automated software engineering.

Installation

To get started with Agentless, follow these steps to set up your environment:

First, clone the repository and navigate into its directory:

git clone https://github.com/OpenAutoCoder/Agentless.git
cd Agentless

Next, create and activate a Conda environment, then install the required dependencies:

conda create -n agentless python=3.11
conda activate agentless
pip install -r requirements.txt
export PYTHONPATH=$PYTHONPATH:$(pwd)

Finally, export your OpenAI API key to enable the tool's functionality:

export OPENAI_API_KEY={key_here}

You 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 file within the repository.

Examples

While 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 within the repository to reproduce experiments and apply Agentless to these standardized software development challenges.

The 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.

Why Use Agentless?

Agentless 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.

The 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.

Links

• GitHub Repository: https://github.com/OpenAutoCoder/Agentless
• Arxiv Paper: https://arxiv.org/abs/2407.01489
• Latest Release Artifacts: https://github.com/OpenAutoCoder/Agentless/releases/tag/v1.5.0