Today, the surge in Swarms has once again caught people's attention, as the entire community buzzes around two topics: the 'anxiety' rumors surrounding AI16Z founder Shaw and the allegations that OpenAI's Sama may have infringed on the Swarm multi-agent framework. Some speculate that the stimulus driving this surge may stem from the emergence of the AI Agent based on Mcs. This agent can not only answer questions about medical knowledge but is also regarded as the most accessible and practical delivery product within the Swarms architecture. Behind it is founder Kye Gomez, a 20-year-old 'genius boy' who dropped out of high school and spent three years developing the multi-agent coordination framework Swarms, operating 45 million agents and serving sectors like finance, insurance, and healthcare, showcasing formidable capabilities.
Rollercoaster trend
After the Swarms token was issued on December 18, it quickly surged to a market cap peak of $74.2 million on the 21st. Unfortunately, the good times didn't last long, as the market cap plummeted like a rollercoaster to a low of around $6 million.
After that, it fluctuated around $13 million until the 27th when it began to rebound, rising from a low of $12 million to $30 million, and then surging nearly threefold to close to $70 million, almost breaking the previous high. Today's trading volume is also impressive, skyrocketing to $60.8 million. This wave of stimulation feels like a rollercoaster experience in the crypto market to many netizens.
The future code of Swarms
Behind the rollercoaster price trends is a tight-knit team of multiple AI agents collaborating and working together to tackle complex challenges. The collective intelligence and coordination far exceed the limitations of a single agent, which is precisely the goal pursued by 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). It can be said that SNAI is the 'nerve center' of the AI agent world, providing strong support and assurance for seamless collaboration among agents.
Founder of 'Genius Boy'
The core founder behind Swarms, Kye Gomez, is hailed as a 'genius boy' in the field of artificial intelligence, showcasing remarkable hardcore strength at just 20 years old. Although he dropped out of high school, he developed the multi-agent coordination framework Swarms in just three years and successfully operated 45 million AI agents, providing high-quality services across various industries such as finance, insurance, and healthcare, demonstrating the impressive strength of this young talent.
In his research on autonomous and collaborative AI agents, he not only developed the 'super-efficient SSM + MoE model' and 'hybrid flow model' but also delved into AI alignment and its potential in the fields of biology and nanotechnology. In fact, among Kye's many projects, Swarms is just one of his quality projects. The talents of this young man are deeply hidden, and upon deeper understanding, it is found that he has many other excellent projects.
For example, Agora serves as an open-source AI research laboratory, focusing on the integration of AI with biology and nanotechnology. Pegasus is its exploration in the fields of natural language processing and embedding models, and it also participated in the open-source implementation of AlphaFold3. Kye's resume and achievements all signify the rise of a true technological innovator.
Swarms AI agent orchestration framework and core functionalities
Next, we will begin to analyze the Swarms project of Genius Boy, which aims to develop and promote an enterprise-grade production-ready multi-agent orchestration framework. In simple terms, the core function of Swarms is to allow multiple AI agents to collaborate like a team, leveraging 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 nearly unlimited long-term memory to enhance contextual understanding, while allowing for customizable workflows. For enterprise-level needs, Swarms offers high reliability and scalability, ensuring optimal performance through the automatic optimization of language model parameters. In this way, Swarms can leverage the collective intelligence among agents to tackle complex challenges more easily than a single agent.
The Swarms project stands out with its strong technical barriers and market performance. After nearly three years of stable operation, its AI agent orchestration framework has already provided numerous enterprises with efficient solutions on its official website. From data processing to customer service to report generation, Swarms has significantly enhanced business efficiency through automation while drastically reducing operational costs, proving its strength. As an open-source project, Swarms has also sparked heated interest in the developer community, with GitHub stars surpassing 2.1K, garnering the wisdom and support of many developers. Thus, everything accumulated by Swarms validates the maturity and innovation of its technology.
SNAI
Twitter users seem to agree that the next stage for 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 communicate and utilize their specialized advantages to perform better in specific tasks and scenarios.
Swarm Node (SNAI) serves as an assistant to realize Agent Swarms, a serverless infrastructure designed to support the Swarm concept. SNAI addresses all 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 operations, providing new possibilities for small creators who cannot run agents around the clock or lack hardware support.
Users do not need to pay server fees, only for the actual execution time used, making SNAI more efficient than other subscription-based solutions. The uniqueness of SNAI lies in the fact that its agents are not isolated, but can collaborate in a 'chain', forming a Swarm.
The role of Swarm is to distribute tasks among 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, and users can 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 its early development stages, multiple 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 (ready-to-use agents created by the community, available for running, customization, and optimization). Additionally, SNAI will achieve multi-language compatibility, currently providing a Python client that simplifies API operations, with plans to support agent deployments written in languages like Go, Rust, TypeScript, C#, and PHP. The community has already begun developing a TypeScript client, and more languages will be supported in the future.
In just this week, there have already been over 500 builds—these 'dependencies' are used to optimize the execution efficiency of AI agents. Over 10,000 executions—instances where agents paused after being launched, SNAI only charges for active running time, significantly enhancing the flexibility of agent operations.
The core features of SNAI include support for serverless operation of agents, allowing developers to integrate agents into codebases, implementing agent chain collaboration and interactive coordination, while adopting a pay-as-you-go model, significantly reducing infrastructure costs and lowering the entry barrier to AI agent infrastructure.
Facing AI16Z
Both Swarms and AI16Z have significant influence in the AI agent field, with ongoing controversies on Twitter. Despite some similarities, they differ in technical architecture and applications. Swarms adopts a collaborative 'team' framework, completing complex tasks and improving efficiency through cooperation among multiple AI agents. In contrast, AI16Z's Eliza framework resembles a flexible 'coordinator', emphasizing multi-platform support and multi-model integration, capable of quickly adapting to various scenarios. Below, we will compare the two agents from two perspectives.
Technical frameworks and architecture
Swarms operates like a disciplined team, with the Swarms framework supporting multiple AI agents to work collaboratively, efficiently breaking down complex tasks and completing operations marked by 'clear division of labor and seamless cooperation'. In contrast, AI16Z's Eliza framework resembles a versatile coordinator, focusing on multi-platform operation and multi-model integration, while emphasizing interaction between agents, showcasing its unique features in flexibly adapting to multi-scenario 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 is more like a meticulous commander, adept at appropriately deploying multiple forces, focusing on 'how to do better'. In contrast, AI16Z's Eliza framework offers developers greater freedom, supporting various AI models (such as Llama, Claude), providing applications with more flexibility to handle a range of scenarios from social media management to financial transactions, thus delivering a versatile solution. One focuses on collaboration, while the other emphasizes diversity; both are equally outstanding in innovative applications.
Overall, Swarms and AI16Z are exploring the future of AI agents through entirely different paths. Swarms resembles a disciplined team, impressing enterprise users with efficient collaboration and technical strength, while AI16Z's Eliza acts more like a versatile free player, showcasing infinite potential through flexible adaptation and scenario diversity. In fact, both have their strengths; in this era of fierce competition, the story of AI agents is just beginning. Who will stand out in this race? We shall see!