Original author: Zhouzhou
Reprinted from: Daisy, Mars Finance
Today, the surge in swarms has once again caught attention, with the entire community buzzing around two hot topics: rumors of AI16Z founder Shaw's "anxiety" and the alleged infringement of OpenAI's Sama on the Swarm multi-agent framework. Some speculate that the catalyst behind this wave of excitement might be the emergence of Mcs's AI Agent. This agent not only answers medical knowledge questions but is also considered the most relatable and practical delivery product within the Swarms architecture. Behind it, founder Kye Gomez, the 20-year-old "genius boy," dropped out of high school and spent three years perfecting the multi-agent coordination framework Swarms, operating 45 million agents serving industries such as finance, insurance, and healthcare, showcasing hardcore strength.
Roller Coaster Trend
Swarms tokens quickly surged to a market cap peak of $74.2 million after launching on December 18, only to plummet back down like a roller coaster to about $6 million.
Afterward, it fluctuated around $13 million until the 27th when it began to rebound, rising from a low of $12 million to $30 million, then surging almost threefold to nearly $70 million, just shy of breaking its previous high. Today's trading volume is also impressive, soaring to $60.8 million, making this wave of excitement feel like a roller coaster ride in the crypto world.
The Future Code Behind Swarms
Behind the roller coaster price movements is a team of multiple AI agents working together, tackling complex challenges with coordinated efforts. The collective intelligence and coordination far exceed the limitations of individual agents, which is the goal of Kye Gomez's Swarms project. However, creativity and ideas alone are not enough; what truly makes all this possible is the core technology launched by Swarms—Swarm Node (SNAI). SNAI can be regarded as the "nerve center" of the AI agent world, providing strong support and assurance for seamless collaboration among agents.
"Genius Boy" Founder
The core founder behind Swarms, Kye Gomez, is hailed as a "genius boy" in the field of artificial intelligence, demonstrating astonishing hardcore strength at just 20 years old. Although he dropped out of high school, in just three years he developed the multi-agent coordination framework Swarms and successfully operated 45 million AI agents, providing high-quality services across multiple industries including finance, insurance, and healthcare, showcasing the young man's formidable strength.
In his research on autonomous and collaborative AI agents, he not only developed a "super-efficient SSM + MoE model" and "hybrid flow model" but also deeply explored AI alignment and its potential in biology and nanotechnology. In fact, among Kye's many projects, Swarms is just one of his high-quality projects; the young man's strength is impressive, and after further exploration, one discovers he has many other outstanding projects.
For example, Agora serves as an open-source AI research laboratory, focusing on the intersection of AI with biology and nanotechnology. Pegasus is its exploration in natural language processing and embedding models, and he is also involved in the open-source implementation of AlphaFold3. Kye's resume and achievements all indicate that a true technological innovator is rising.
Swarms AI Agent Orchestration Framework and Core Functions
Next, let's analyze the Swarms project of the genius boy, which aims to develop and promote an enterprise-ready multi-agent orchestration framework. In simple terms, the core function of Swarms is to enable multiple AI agents to collaborate in a team-like manner, utilizing collective intelligence to solve complex problems. It not only supports seamless integration with external AI services and APIs to extend functionality but also provides agents with almost unlimited long-term memory to enhance contextual understanding while allowing for customizable workflows. Aiming at enterprise-level needs, Swarms offers high reliability and scalability, ensuring optimal performance through automatic optimization of language model parameters. In this way, Swarms can leverage the collective intelligence among agents to tackle complex challenges more easily than individual agents.
The Swarms project stands out with its strong technological barriers and market performance. After nearly three years of stable operation, its AI agent orchestration framework has provided efficient solutions for numerous enterprises on its official website. From data processing to customer service and report generation, Swarms has significantly improved business efficiency through automation while substantially reducing operational costs, proving its powerful capabilities. As an open-source project, Swarms has also garnered enthusiastic attention in the developer community, with over 2.1K stars on GitHub, gaining the wisdom and support of many developers, thus validating the maturity and innovation of the technology.
SNAI
Twitter users seem to agree that the next phase of AI agents is group collaboration (Agent Swarms), achieving more efficient work through communication and cooperation among multiple agents. This approach allows agents from different frameworks to interact and leverage their specialized advantages to perform better in specific tasks and scenarios.
Swarm Node (SNAI) serves as an assistant for implementing Agent Swarms, a serverless infrastructure designed specifically to support the Swarm concept. SNAI resolves all technical challenges of running AI agents, allowing users to focus on deploying, coordinating, and managing agents easily via Python scripts without worrying about hardware and infrastructure costs. It also supports chain interactions, scheduling, and multi-language operations, providing new possibilities for small creators lacking hardware support or unable to run agents 24/7.
Users do not need to pay for server costs but only for the actual execution time used, making SNAI more efficient than other subscription-based solutions. The uniqueness of SNAI lies in its agents not being isolated, but capable of "chaining" collaboration to form a Swarm.
The role of Swarm is to delegate tasks to different agents, with each agent focusing on a specific task and passing the results to the next agent upon completion. Through REST API and Python SDK, other applications can easily integrate SNAI, allowing users to flexibly coordinate the behavior of their Swarm (e.g., when to run and which data to use).
But that's not all; as the SNAI framework is still in its early development stage, many new features will be added in the future, including data storage (a mini cloud database allowing agents to share selected data), task scheduling (running agents at specific times), and an agent library (pre-built agents created by the community available for use, customization, and optimization). Additionally, SNAI will achieve multi-language compatibility, currently providing a Python client for simplified API operations, with plans to support agents written in languages such as Go, Rust, TypeScript, C#, PHP, and more. The community has already begun developing a TypeScript client, with support for more languages to come in the future.
Just this week, there have been over 500 builds—these "dependencies" are used to optimize the execution efficiency of AI agents. More than 10,000 executions—instances where agents are paused after starting—SNAI only charges for active running time, greatly enhancing the flexibility of agent operations.
The core features of SNAI include support for serverless agent operation, allowing developers to integrate agents into code repositories, enabling chain collaboration and interactive coordination among agents, and adopting a pay-as-you-go model that significantly reduces infrastructure costs and lowers the barrier to entry for AI agent infrastructure.
Against AI16Z
Both Swarms and AI16Z have significant influence in the AI agent field. Their controversies on Twitter continue, and although there are some similarities, they differ in technical architecture and applications. Swarms adopts a collaborative "team" framework to complete complex tasks and improve efficiency through cooperation among multiple AI agents. In contrast, AI16Z's Eliza framework acts more like a flexible "coordinator," emphasizing multi-platform support and multi-model integration, quickly adapting to various scenarios. Below, we will compare the two agents in two aspects.
Technical Framework and Architecture
Swarms is like a disciplined team; the Swarms framework supports multiple AI agents working collaboratively, efficiently coordinating complex tasks through autonomy, modularity, and scalability, excelling in operations that require clear division of labor and seamless cooperation. In contrast, AI16Z's Eliza framework acts more like a versatile coordinator, focusing on multi-platform operation and multi-model integration while emphasizing interaction among agents, showcasing its unique features in adapting to various applications.
AI Models and Applications
In terms of AI models and applications, Swarms focuses more on how to cleverly integrate existing AI models through task orchestration and team collaboration to enhance enterprise-level automation and team efficiency. It acts more like a meticulous commander, adept at properly allocating multiple forces and focusing on "how to do it better." In contrast, AI16Z's Eliza framework provides developers with greater freedom, supporting various AI models (such as Llama, Claude), granting applications more flexibility to handle various scenarios from social media management to financial transactions, thus offering a versatile solution. One focuses on collaboration, while the other emphasizes diversity, each excelling in innovative applications.
Overall, Swarms and AI16Z are exploring the future of AI agents through completely different paths. Swarms resembles a disciplined team, impressing enterprise users with efficient collaboration and hardcore technology, while AI16Z's Eliza acts more like a versatile free player, showcasing limitless potential through flexible adaptation and diverse scenarios. In this era of fierce competition, the story of AI agents is just beginning—who will stand out in this race? We shall see!
