From Specification to Service: Accelerating API-First Development Using Multi-Agent Systems

TL;DR

This paper introduces a multi-agent system leveraging Large Language Models to automate API-first development of RESTful microservices, including specification creation, code generation, and iterative refinement through log analysis.

Contribution

It demonstrates how LLM-based agents can automate and accelerate API-first development, integrating log analysis for improved robustness and reducing development iterations.

Findings

LLMs can generate complete functional code from small, focused OpenAPI specs.

Log analysis enables efficient detection and fixing of issues during development.

The system reduces development time and improves service robustness.

Abstract

This paper presents a system that uses Large Language Models (LLMs)-based agents to automate the API-first development of RESTful microservices. This system helps to create an OpenAPI specification, generate server code from it, and refine the code through a feedback loop that analyzes execution logs and error messages. The integration of log analysis enables the LLM to detect and address issues efficiently, reducing the number of iterations required to produce functional and robust services. This study's main goal is to advance API-first development automation for RESTful web services and test the capability of LLM-based multi-agent systems in supporting the API-first development approach. To test the proposed system's potential, we utilized the PRAB benchmark. The results indicate that if we keep the OpenAPI specification small and focused, LLMs are capable of generating complete functional code with business logic that aligns to the specification. The code for the system is publicly available at https://github.com/sirbh/code-gen