OrchestraAI: A Multi-Agent Generative AI for Software Development

This paper presents OrchestraAI, a multi-agent AI assistant designed to support professional software developers by combining conversational interaction with autonomous execution of development tasks, including code generation and Git version control. Unlike typical AI chatbots, OrchestraAI actively performs actions such as creating and modifying code files within a customizable local environment, preserving privacy and offering flexibility through the integration of open-source and external language models. The paper details the system’s modular architecture, which leverages LangChain and pluggable Large Language Models (LLMs), and reports qualitative results from initial evaluations. The findings indicate a strong potential for productivity improvement, along with identified limitations and proposed future improvements.

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Publication Details

Published
2025-09-22
DOI
https://doi.org/10.5753/wbots.2025.14664
Primary Topic
Semantic Web and Ontologies
Type
article
Field-Weighted Citation Impact
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article

OrchestraAI: A Multi-Agent Generative AI for Software Development

Thiago Belarmino de Souza, João Luiz Bione da Silva Holanda
Semantic Web and Ontologies
article

OrchestraAI: A Multi-Agent Generative AI for Software Development

Thiago Belarmino de Souza, João Luiz Bione da Silva Holanda
article en

Abstract

This paper presents OrchestraAI, a multi-agent AI assistant designed to support professional software developers by combining conversational interaction with autonomous execution of development tasks, including code generation and Git version control. Unlike typical AI chatbots, OrchestraAI actively performs actions such as creating and modifying code files within a customizable local environment, preserving privacy and offering flexibility through the integration of open-source and external language models. The paper details the system’s modular architecture, which leverages LangChain and pluggable Large Language Models (LLMs), and reports qualitative results from initial evaluations. The findings indicate a strong potential for productivity improvement, along with identified limitations and proposed future improvements.

Universidade La Salle (BR)
Openalex Percentile: Top 85%
Semantic Web and Ontologies
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