The Rise of Swarms: How do young geniuses disrupt the AI world?

Original author: Zhouzhou

Reprint: Daisy, Mars Finance

Today, the rise of Swarms has once again caught people's attention, and the entire community is buzzing around two topics: rumors of anxiety from Shaw, the founder of AI16Z, and the suspicion that Sama of OpenAI may have infringed on Swarm's multi-agent framework. Some speculate that the behind-the-scenes promoter of this exciting rally may be Mcs' AI Agent, which not only can answer medical knowledge questions, but is also known as the most popular and practical delivery product in the Swarms architecture. Behind it is the founder Kye Gomez, a 20-year-old 'genius boy' who dropped out of high school and spent three years to complete the multi-agent coordination framework Swarms, running 45 million agents and serving finance, insurance, medical and other fields, which can be called a hardcore strength faction.

Roller Coaster Trends

After the Swarms token was launched on December 18, it quickly rose to the highest market value of 74.2 million US dollars on the 21st, but unfortunately the good times did not last. The market value fell like a roller coaster to a low of only about 6 million US dollars.

Next, it has been hovering around 13 million US dollars, until the 27th, when it started to counterattack, rising from a low point of 12 million US dollars to 30 million US dollars, then surged nearly three times to nearly 70 million US dollars, almost breaking the previous high. The trading volume today is also considerable, directly soaring to 60.8 million US dollars. This exciting market has made netizens feel like they are experiencing a roller coaster ride in the currency circle.

The Future Code Behind Swarms

Behind the roller coaster-like price trends is a team of AI agents working closely together, dividing tasks and collaborating to tackle complex challenges. The collective wisdom and coordination abilities of the team far exceed those of individual agents, which is the goal pursued by Kye Gomez's Swarms project. However, creativity and ideas alone are not enough. What truly makes all of this possible is the core technology launched by Swarms - Swarm Node (SNAI). It can be said that SNAI is the 'neural center' of the AI agent world, providing strong support and guarantee for seamless collaboration among agents.

"Genius Youth" Founder

Kye Gomez, the core founder behind Swarms, is known as a "genius teenager" in the field of artificial intelligence. At the age of only 20, he has demonstrated astonishing hardcore strength. Although he dropped out of high school, he developed Swarms, a multi-agent coordination framework, in just three years. It has successfully run 45 million AI agents, providing high-quality services to multiple industries such as finance, insurance, and healthcare, which shows the strength of this young man.

In his research on autonomous and collaborative AI agents, he has not only developed the 'super-efficient SSM + MoE model' and the 'hybrid flow model,' but also delved into the potential of AI alignment in the fields of biology and nanotechnology. In fact, in Kye's many projects, Swarms is just one of his high-quality projects. His strength as a young man is deeply hidden, and further exploration reveals that he has many other excellent projects.

For example, Agora is an open-source AI research laboratory that focuses on the integration of AI with biology and nanotechnology. Pegasus is its exploration in the field of natural language processing and embedding models, and it also participates in the open-source implementation of AlphaFold3. Kye's resume and achievements all indicate the rise of a true technological innovator.

Swarms AI Agent Layout Framework and Core Features

Next, let's start analyzing the Swarms project of the talented young genius. The project 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 work together like a team, using 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 virtually unlimited long-term memory to enhance context understanding, while allowing for custom workflows. For enterprise-level requirements, Swarms has high reliability and scalability, and ensures optimal performance by automatically optimizing language model parameters. In this way, Swarms can leverage the collective intelligence of agents to tackle complex challenges more easily than individual agents.

The Swarms project stands out with its powerful technological barriers and market performance. Its AI agent orchestration framework has been operating steadily for nearly three years and has provided efficient solutions to many enterprises on its official website. From data processing to customer service and report generation, Swarms has significantly improved business efficiency and reduced operating costs through automation, demonstrating its strength. As an open-source project, Swarms has also attracted enthusiastic attention in the developer community, with over 2.1K stars on GitHub, gaining the wisdom and support of many developers. Therefore, all this accumulation of Swarms confirms the maturity and innovation of the technology.

SNAI

Netizens on Twitter seem to agree that the next stage of AI agents is Agent Swarms, which achieve higher efficiency through communication and collaboration among multiple agents. This approach allows agents from different frameworks to communicate with each other and leverage their respective specialized advantages to perform better in specific tasks and scenarios.

Swarm Node (SNAI) is an auxiliary tool for implementing Agent Swarms, a serverless infrastructure designed to support the concept of Swarm. SNAI solves all the technical challenges of running AI agents, allowing users to deploy, coordinate, and manage agents easily through Python scripts without worrying about hardware and infrastructure costs. It also supports chain interaction, scheduling, and multi-language operation, providing new possibilities for small creators who cannot run agents around the clock or lack hardware support.

Users do not need to pay for server fees, only for the actual execution time used, which makes SNAI more efficient than other subscription-based solutions. What makes SNAI unique is that its agents are not isolated, but can collaborate in a "chain-like" manner to form a Swarm.

The role of Swarm is to assign tasks to different agents, each focusing on a specific task and passing the results to the next agent. Through REST API and Python SDK, other applications can easily integrate SNAI, and users can also flexibly coordinate the behavior of their Swarm (for example, when to run and which data to use).

But that's not all. As the SNAI framework is still in the early stages of development, there will be additional features in the future, including data storage (a mini cloud database that allows agents to share selected data), task scheduling (running agents at specific times), and agent library (ready-made agents created by the community for running, customizing, and optimizing). In addition, SNAI will also achieve multilingual compatibility. Currently, a Python client that simplifies API operations is provided, and there are plans to support agent deployment written in languages such as Go, Rust, TypeScript, C#, and PHP. The community has started developing a TypeScript client, and more languages will be supported in the future.

Only this week, there have been over 500 builds—these 'dependencies' are used to optimize the efficiency of AI agents' execution. With over 10,000 executions—instances where the agent is started and then paused, SNAI only charges for active running time, significantly enhancing the flexibility of agent operations.

The core features of SNAI include support for agentless serverless operation, allowing developers to integrate agents into code libraries, achieve agent chain collaboration and interaction coordination, while adopting a pay-as-you-go model, greatly reducing infrastructure costs and lowering the threshold for entering AI agent infrastructure.

Against AI16Z

Swarms and AI16Z both have significant influence in the field of AI agents, and the controversy between the two continues on Twitter. Although they have some similarities, they differ in technical architecture and applications. Swarms adopts a collaborative 'team' framework, completing complex tasks and improving efficiency through the cooperation of multiple AI agents. In contrast, AI16Z's Eliza framework is more like a flexible 'coordinator', emphasizing multi-platform support and multi-model integration, and can quickly adapt to multiple scenarios. The following provides a comparison of the two agents from two aspects.

Technical framework and architecture

Swarms is like a disciplined team. The Swarms framework supports multiple AI agents to work together, with autonomy, modularity, and scalability, enabling efficient collaboration and adeptness in decomposing complex tasks, achieving 'clear division of labor and seamless cooperation.' On the other hand, AI16Z's Eliza framework is more like a versatile coordinator, focusing on multi-platform operation and multi-model integration, while emphasizing interaction between agents, and has its own characteristics in flexibly adapting to various scenarios.

AI models and applications

In terms of AI models and applications, Swarms focuses more on how to cleverly integrate existing AI models, improve enterprise automation, and team efficiency through task orchestration and team collaboration. It is more like a fine commander, good at deploying multiple forces properly, and focuses on 'how to do better'. The Eliza framework of AI16Z provides developers with greater freedom, supporting multiple AI models (such as Llama, Claude), giving applications more flexibility, and able to deal with various scenarios from social media management to financial transactions, thus bringing a versatile solution. One focuses on collaboration, the other emphasizes diversity, both are equally innovative in their applications, each with its own strengths.

Overall, Swarms and AI16Z are exploring the future of AI agents in completely different ways. Swarms is more like a disciplined team, impressing enterprise users with efficient collaboration and hardcore technology, while AI16Z's Eliza is more like a versatile free player, demonstrating unlimited potential through flexible adaptation and diverse scenarios. In fact, both have their own merits. In this competitive era, the story of AI agents is just beginning. Who will stand out in this competition? Let's wait and see!