Agentless

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Basic Information

Agentless is an open research and engineering project that implements an "agentless" automated approach to solving software development problems, primarily focused on program repair and debugging benchmarks. The repository encodes a three-phase pipeline used to tackle each bug: hierarchical localization to narrow faults from files to functions and edit locations, repair via sampling multiple candidate patches in diff format, and patch validation that selects regression tests and generates reproduction tests to re-rank and choose fixes. The codebase is intended to run experiments on the SWE-bench benchmark suite and reproduce results reported in the associated paper. It provides scripts, dependencies, and instructions to set up a Python environment and requires an OpenAI API key for certain model-driven components. Releases include full artifacts for SWE-bench experiments and the README links to reproducibility details and evaluations.

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App Details

Features
The project documents and implements several concrete features for automated program repair: a hierarchical fault localization pipeline that drills from file-level down to fine-grained edit locations, a repair module that generates multiple candidate patches expressed as diffs, and a validation stage that selects regression tests and creates reproduction tests to validate patches and re-rank candidates. Artifacts and preprocessed dataset outputs for SWE-bench experiments are included in releases. The repository contains setup instructions, a conda-based Python environment, a requirements file, and developer pre-commit hooks. Experimental results and comparisons against agent-based approaches are provided, and the README notes integration with external LLMs and experimental metrics such as solve rates and average cost per issue reported in the paper and news entries.
Use Cases
Agentless helps researchers and developers evaluate and reproduce an alternative approach to LLM-based program repair that avoids agent orchestration by using a structured localization-repair-validation pipeline. It provides reproducible artifacts and scripts to run on SWE-bench, enabling comparison with other agent-based systems and replication of the reported experimental results such as solve rates, verified runs, and cost statistics. The tooling reduces manual effort in generating and validating candidate fixes by automating test selection and reproduction test generation. It is useful for benchmarking, for exploring fault localization and patch-ranking strategies, and for extending or adapting the pipeline to new datasets or models. The README also supplies citation and acknowledgment information for academic use.

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