Introduction How can agents be endowed with the ability to comply with social norms and allow social norms to spontaneously emerge in AI societies? Recently, a team led by Professor Wang Zhen from Northwestern Polytechnical University and researcher Hu Shuyue from the Shanghai Artificial Intelligence Laboratory proposed the first normative framework for multi-agent systems based … Read more
1. Introduction With the rapid evolution of generative artificial intelligence, multimodal large models increasingly demonstrate their advantages in multimodal content understanding and generation. Multimodal large models (hereinafter referred to as “large models”) refer to artificial intelligence models capable of processing and understanding various modalities of data inputs, including text, images, and audio-visual content. Artificial intelligence … Read more
With the latest advancements in large language models (LLMs), agentic artificial intelligence (Agentic AI) has made significant progress in real-world applications, moving towards intelligent agents based on multiple large language models that achieve perception, learning, reasoning, and collaborative actions. These multi-agent systems (MASs) based on large language models enable a group of agents to collaborate … Read more
Swarm is an innovative open-source framework designed to explore the orchestration and coordination of multi-agent systems. Developed and maintained by the OpenAI Solutions team, it provides developers with a lightweight, ergonomic, and educational environment for learning and experimenting with agent-based systems. The core design goal of Swarm is to facilitate interaction among autonomous agents (i.e., … Read more
Introduction This article will introduce a research-oriented topic regarding the observability of Multi-Agent Systems. Currently, our work is primarily based on the Swarm project released by OpenAI last month, where we analyzed the source code of the Swarm project and customized modifications to achieve better observability of multi-agent systems. Today’s discussion will revolve around three … Read more
Source: DeepHub IMBA This article is approximately 3000 words long and is recommended to be read in 6 minutes. This article introduces you to building multi-agent RAG using Llama index. Retrieval-Augmented Generation (RAG) has become a powerful technique for enhancing the capabilities of Large Language Models (LLMs). By retrieving relevant information from knowledge sources and … Read more
In the first part of this series, we introduced the overall architectural pattern of Agent systems. We discussed the three logical layers of Agent systems: the Tool Layer, Action Layer, and Reasoning Layer. We also examined mechanisms such as function calls that allow large language models (LLMs) to interact with the external world through the … Read more
This is an article published in the “Heroic Journey” column by the Hero. 2025 Issue No. 15 Total Issue No. 98 This article has a total of 6191 words and takes about 10 minutes to read. Contact the Hero to join the group. Stay updated with the latest insights and cutting-edge news from the global … Read more
With the rapid development of artificial intelligence technology, multi-agent systems have gradually become a new favorite for research and application. OpenAI’s Swarm tool undoubtedly injects strong momentum into this field. Today, I will share my insights on using Swarm to develop multi-agent applications, helping you easily master this revolutionary tool. 1. Introduction to OpenAI Swarm … Read more
Recently, Microsoft Research released a paper discussing an innovative multi-agent collaboration framework: Magentic-One. It is a general-purpose agent system designed to efficiently handle complex workflows by coordinating multiple dedicated AI agents. The system architecture of Magnetic-One is as follows, where the red part represents the brain of the entire framework, the Orchestrator, responsible for task … Read more