Agentless is an open-source framework that applies large language models to automatically resolve software development issues without relying on complex autonomous agent systems. The project proposes an alternative approach to AI-driven code repair that avoids the overhead of multi-agent orchestration by using a structured pipeline for identifying and fixing bugs. When solving a problem, the system first performs localization to determine which files, functions, or code segments are most likely responsible for the issue. It then generates multiple candidate patches for the identified locations using language model reasoning and diff-style edits. In the final stage, the framework validates potential patches by running regression tests and additional reproduction tests to confirm whether the fix resolves the original error. Based on these results, the system ranks the candidate patches and selects the most reliable solution to submit.

Features

  • Automated software issue resolution using large language models
  • Hierarchical localization of bugs to files, classes, and functions
  • Generation of multiple candidate patches using diff-based edits
  • Automated regression and reproduction test validation pipeline
  • Ranking system to select the most reliable patch
  • Simplified architecture that avoids multi-agent orchestration

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License

MIT License

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Additional Project Details

Programming Language

Python

Related Categories

Python Large Language Models (LLM)

Registered

2026-03-06