ALMAS: an Autonomous LLM-based Multi-Agent Software Engineering Framework
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909921224687616 |
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| author | Tawosi, Vali Ramani, Keshav Alamir, Salwa Liu, Xiaomo |
| author_facet | Tawosi, Vali Ramani, Keshav Alamir, Salwa Liu, Xiaomo |
| contents | Multi-agent Large Language Model (LLM) systems have been leading the way in applied LLM research across a number of fields. One notable area is software development, where researchers have advanced the automation of code implementation, code testing, code maintenance, inter alia, using LLM agents. However, software development is a multifaceted environment that extends beyond just code. As such, a successful LLM system must factor in multiple stages of the software development life-cycle (SDLC). In this paper, we propose a vision for ALMAS, an Autonomous LLM-based Multi-Agent Software Engineering framework, which follows the above SDLC philosophy such that it may work within an agile software development team to perform several tasks end-to-end. ALMAS aligns its agents with agile roles, and can be used in a modular fashion to seamlessly integrate with human developers and their development environment. We showcase the progress towards ALMAS through our published works and a use case demonstrating the framework, where ALMAS is able to seamlessly generate an application and add a new feature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03463 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ALMAS: an Autonomous LLM-based Multi-Agent Software Engineering Framework Tawosi, Vali Ramani, Keshav Alamir, Salwa Liu, Xiaomo Software Engineering Artificial Intelligence Multi-agent Large Language Model (LLM) systems have been leading the way in applied LLM research across a number of fields. One notable area is software development, where researchers have advanced the automation of code implementation, code testing, code maintenance, inter alia, using LLM agents. However, software development is a multifaceted environment that extends beyond just code. As such, a successful LLM system must factor in multiple stages of the software development life-cycle (SDLC). In this paper, we propose a vision for ALMAS, an Autonomous LLM-based Multi-Agent Software Engineering framework, which follows the above SDLC philosophy such that it may work within an agile software development team to perform several tasks end-to-end. ALMAS aligns its agents with agile roles, and can be used in a modular fashion to seamlessly integrate with human developers and their development environment. We showcase the progress towards ALMAS through our published works and a use case demonstrating the framework, where ALMAS is able to seamlessly generate an application and add a new feature. |
| title | ALMAS: an Autonomous LLM-based Multi-Agent Software Engineering Framework |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2510.03463 |