HyperAgent: Generalist Software Engineering Agents to Solve Coding Tasks at Scale

Nghi D. Q. Bui, Phan Nhật Huy, Phong Xuan Nguyen

Abstract:

Large Language Models (LLMs) have revolutionized software engineering (SE), demonstrating remarkable capabilities in various coding tasks. While recent efforts have produced autonomous software
agents based on LLMs for end-to-end development tasks, these systems are typically designed for
specific SE tasks. We introduce HyperAgent , a novel generalist multi-agent system designed to
address a wide spectrum of SE tasks across different programming languages by mimicking human
developers’ workflows. Comprising four specialized agents—Planner, Navigator, Code Editor, and Executor—HyperAgent manages the full lifecycle of SE tasks, from initial conception to final verification.
Through extensive evaluations, HyperAgent achieves state-of-the-art performance across diverse
SE tasks: it attains a 26.00% success rate on SWE-Bench-Lite and 33.00% on SWE-Bench-Verified for
GitHub issue resolution, surpassing existing methods. Furthermore, HyperAgent demonstrates
superior performance in code generation at repository scale (RepoExec), and in fault localization and
program repair (Defects4J), often outperforming specialized systems. This work represents a significant
advancement towards versatile, autonomous agents capable of handling complex, multi-step SE tasks
across various domains and languages, potentially transforming AI-assisted software development
practices.