# Toolsuite for Implementing Multiagent Systems Based on Communication Protocols

arXiv: https://arxiv.org/abs/2507.10324 · HTML: https://mlsanity.com/paper/2507.10324/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 81 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Evaluating Large Language Models in Theory of Mind Tasks](https://mlsanity.com/paper/2302.02083/) (2023)
- [xLAM: A Family of Large Action Models to Empower AI Agent Systems](https://mlsanity.com/paper/2409.03215/) (2024)
- [When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming](https://mlsanity.com/paper/2306.04930/) (2023)
- [On the Brittle Foundations of ReAct Prompting for Agentic Large Language Models](https://mlsanity.com/paper/2405.13966/) (2024)
- [Socially Intelligent Genetic Agents for the Emergence of Explicit Norms](https://mlsanity.com/paper/2208.03789/) (2022)

## Abstract (arXiv)

Interaction-Oriented Programming (IOP) is an approach to building a multiagent system by modeling the interactions between its roles via a flexible interaction protocol and implementing agents to realize the interactions of the roles they play in the protocol. In recent years, we have developed an extensive suite of software that enables multiagent system developers to apply IOP. These include tools for efficiently verifying protocols for properties such as liveness and safety and middleware that simplifies the implementation of agents. This paper presents some of that software suite.