# LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents

arXiv: https://arxiv.org/abs/2503.15079 · HTML: https://mlsanity.com/paper/2503.15079/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 19 Mar 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 11 of 11)

- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [Leveraging Large Language Models to Improve REST API Testing](https://mlsanity.com/paper/2312.00894/) (2023)
- [Automated Test Generation for REST APIs: No Time to Rest Yet](https://mlsanity.com/paper/2204.08348/) (2022)
- [Morest: Model-based RESTful API Testing with Execution Feedback](https://mlsanity.com/paper/2204.12148/) (2022)
- [A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs](https://mlsanity.com/paper/2411.07098/) (2024)
- [Adaptive REST API Testing with Reinforcement Learning](https://mlsanity.com/paper/2309.04583/) (2023)
- [Open Problems in Fuzzing RESTful APIs: A Comparison of Tools](https://mlsanity.com/paper/2205.05325/) (2022)
- [REST API Fuzzing by Coverage Level Guided Blackbox Testing](https://mlsanity.com/paper/2112.15485/) (2021)

## Built on by (most cited first, 5 of 5)

- [SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows](https://mlsanity.com/paper/2508.07935/) (2025)
- [Improving Proficiency and Efficiency of Android GUI Agents via Self-Generating Tool Actions](https://mlsanity.com/paper/2609.06792/) (2026)
- [MASTEST: A LLM-Based Multi-Agent System For RESTful API Tests](https://mlsanity.com/paper/2511.18038/) (2025)
- [Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Ranking](https://mlsanity.com/paper/2604.22760/) (2026)
- [Multi-Agent LLM-based Metamorphic Testing for REST APIs](https://mlsanity.com/paper/2605.28321/) (2026)

## Abstract (arXiv)

Automated testing for REST APIs has become essential for ensuring the correctness and reliability of modern web services. While existing approaches primarily focus on detecting server crashes and error codes, they often overlook logical issues that arise due to evolving business logic and domain-specific requirements. To address this limitation, we propose LogiAgent, a novel approach for logical testing of REST systems. Built upon a large language model (LLM)-driven multi-agent framework, LogiAgent integrates a Test Scenario Generator, API Request Executor, and API Response Validator to collaboratively generate, execute, and validate API test scenarios. Unlike traditional testing methods that focus on status codes like 5xx, LogiAgent incorporates logical oracles that assess responses based on business logic, ensuring more comprehensive testing. The system is further enhanced by an Execution Memory component that stores historical API execution data for contextual consistency. We conduct extensive experiments across 12 real-world REST systems, demonstrating that LogiAgent effectively identifies 234 logical issues with an accuracy of 66.19%. Additionally, it basically excels in detecting server crashes and achieves superior test coverage compared to four state-of-the-art REST API testing tools. An ablation study confirms the significant contribution of LogiAgent's memory components to improving test coverage.