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Agentless

(★ 2,098)

Agentless🐱: an agentless approach to automatically solve software development problems

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Download Latest Version (.zip)

- - combine.py
  - FL.py
  - Index.py
  - localize.py
  - retrieve.py
- - repair.py
  - rerank.py
- - generate_reproduction_tests.py
  - run_regression_tests.py
  - run_reproduction_tests.py
  - run_tests.py
  - select_regression_tests.py
- - api_requests.py
  - compress_file.py
  - index_skeleton.py
  - model.py
  - parse_global_var.py
  - postprocess_data.py
  - preprocess_data.py
  - utils.py
- graph_classification.py
- load_filtered_benchmark.py
- README.md
- requirements_graph.txt
- swebench_lite_classifications.csv
- - cost.py
- get_patch_info.py
- get_repo_structure.py
- comparison_graph.png
.gitignore
.pre-commit-config.yaml
LICENSE
README.md
README_swebench.md
requirements.txt

# Installation Guide

1. Get the code

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

Downloads the entire project code from GitHub to your computer.

cd Agentless

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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# 😺 Agentless

<p align="center">
    <a href="https://arxiv.org/abs/2407.01489"><img src="https://img.shields.io/badge/📃-Arxiv-b31b1b?style=for-the-badge"></a>
    <a href="https://github.com/OpenAutoCoder/Agentless/blob/master/LICENSE"><img src="https://forthebadge.com/images/badges/license-mit.svg" style="height: 28px"></a>
</p>

<p align="center">
    <big><a href="#-news">😽News</a></big> |
    <big><a href="#-setup">🐈Setup</a></big> |
    <big><a href="#-comparison">🧶Comparison</a></big> | 
    <big><a href="#-artifacts">🐈‍⬛Artifacts</a></big> |
    <big><a href="#-citation">📝Citation</a></big> |
    <big><a href="#-acknowledgement">😻Acknowledgement</a></big>
</p>

## 😽 News

- *Dec 2nd, 2024*: We integrated Agentless with Claude 3.5 Sonnet to achieve 40.7% and 50.8% solve rate on SWE-bench lite and verified 
- *Oct 28th, 2024*: We just released OpenAutoCoder-Agentless 1.5! 
- *July 1st, 2024*: We just released OpenAutoCoder-Agentless 1.0! **Agentless** currently is the best open-source approach on SWE-bench lite with 82 fixes (27.3%) and costing on average $0.34 per issue.

## 😺 About

**Agentless** is an *agentless* approach to automatically solve software development problems. To solve each issue, **Agentless** follows a simple three phase process: localization, repair, and patch validation.
- 🙀 **Localization**: Agentless employs a hierarchical process to first localize the fault to specific files, then to relevant classes or functions, and finally to fine-grained edit locations
- 😼 **Repair**: Agentless takes the edit locations and samples multiple candidate patches per bug in a simple diff format
- 😸 **Patch Validation**: Agentless selects the regression tests to run and generates additional reproduction test to reproduce the original error. Using the test results, Agentless re-ranks all remaining patches to selects one to submit

## 🐈 Setup

First create the environment

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

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

<details><summary>⏬ Developer Setup</summary>
<div>

```shell
# for contribution, please install the pre-commit hook.
pre-commit install  # this allows a more standardized code style
```

</div>
</details>

Then export your OpenAI API key 
```shell
export OPENAI_API_KEY={key_here}
```

Now you are ready to run **Agentless** on the problems in SWE-bench!

> [!NOTE]
> 
> To reproduce the full SWE-bench lite experiments and follow our exact setup as described in the paper. Please see this [README](https://github.com/OpenAutoCoder/Agentless/blob/main/README_swebench.md)

## 🧶 Comparison

Below shows the comparison graph between **Agentless** and the best open-source agent-based approaches on SWE-bench lite

<p align="center">
<img src="./resources/comparison_graph.png" style="width:75%; margin-left: auto; margin-right: auto;">
</p>

## 🐈‍⬛ Artifacts

You can download the complete artifacts of **Agentless** in our [v1.5.0 release](https://github.com/OpenAutoCoder/Agentless/releases/tag/v1.5.0):
- 🐈‍⬛ agentless_swebench_lite: complete Agentless run on SWE-bench Lite
- 🐈‍⬛ agentless_swebench_verified: complete Agentless run on SWE-bench Verified
- 🐈‍⬛ swebench_repo_structure: preprocessed structure information for each SWE-Bench problem

You can also checkout `classification/` folder to obtain our manual classifications of SWE-bench-lite as well as our filtered SWE-bench-lite-*S* problems.

## 📝 Citation

```bibtex
@article{agentless,
  author    = {Xia, Chunqiu Steven and Deng, Yinlin and Dunn, Soren and Zhang, Lingming},
  title     = {Agentless: Demystifying LLM-based Software Engineering Agents},
  year      = {2024},
  journal   = {arXiv preprint},
}
```

> [!NOTE]
> 
> The first two authors contributed equally to this work, with author order determined via [*Nigiri*](https://senseis.xmp.net/?Nigiri)

## 😻 Acknowledgement

* [SWE-bench](https://www.swebench.com/)
* [Aider](https://github.com/paul-gauthier/aider)
* [SWE-bench-docker](https://github.com/aorwall/SWE-bench-docker)