The counterattack surged 3 times. Why can Swarms withstand the FUD of ai16z?

Original text edited by: zhouzhou, BlockBeats

Today, the increase in swarms is eye-catching again. The whole community is excited about two topics: the rumor of anxiety of Shaw, the founder of AI16Z, and the melon that OpenAIs Sama is suspected of infringing the Swarm multi-agent framework. Some people speculate that the driving force behind this wave of stimulus pull may be the emergence of an AI Agent based on Mcs. This agent can not only answer medical common sense questions, but is also known as the most popular and practical delivery product in the Swarms architecture. Its founder Kye Gomez, a genius boy who dropped out of high school at the age of 20, spent three years to complete the multi-agent coordination framework Swarms and ran 45 million agents to serve the fields of finance, insurance, and medical care. He is a hard-core powerhouse.

Roller coaster trend

After the Swarms token was issued on December 18, its market value quickly reached a peak of 74.2 million US dollars on the 21st. Unfortunately, the good times did not last long, and the market value fell to the bottom like a roller coaster, with only about 6 million US dollars left.

Then, it fluctuated around 13 million US dollars until the 27th, when it began to counterattack, from the low of 12 million US dollars to 30 million US dollars, and then nearly tripled to nearly 70 million US dollars, almost breaking the previous high. Todays trading volume is also comparable, soaring directly to 60.8 million US dollars. This exciting market, netizens feel like a roller coaster experience package in the currency circle.

The future code behind Swarms

Behind the roller-coaster price trend, it is the multiple AI agents that work together like a closely coordinated team to jointly cope with complex challenges. Collective wisdom and coordination capabilities far exceed the limitations of a single agent, which is exactly what Kye Gomezs Swarms project pursues. However, creativity and ideas alone are not enough. What really 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, which provides strong support and guarantee for seamless collaboration between agents.

Founder of Genius Boy

The core founder behind Swarms, Kye Gomez, is known as a genius boy in the field of artificial intelligence. He has shown amazing hardcore strength at the age of 20. Although he dropped out of high school, he developed the multi-agent coordination framework Swarms in just three years and successfully ran 45 million AI agents, providing high-quality services for multiple industries such as finance, insurance and medical care. This shows how strong the young man is.

In his research on autonomous and collaborative AI agents, he not only developed the super efficient SSM + MoE model and mixed flow model, but also explored AI alignment and its potential in biology and nanotechnology. In fact, among Kyes many projects, Swarms is just one of his high-quality projects. The young mans strength is hidden. After a deeper understanding, it is found that he has many other excellent projects.

For example, Agora is an open source AI research laboratory focusing on the integration of AI with biology and nanotechnology. Pegasus is its exploration in the field of natural language processing and embedding models. He also participated in the open source implementation of AlphaFold 3. Kyes resume and achievements all indicate the rise of a true technology innovator.

Swarms AI agent orchestration framework and core features

Next, we will analyze the Swarms project of the genius boy, which hopes to develop and promote an enterprise-level production-ready multi-agent orchestration framework. Simply put, the core function of warms is to allow multiple AI agents to work together like a team and use collective wisdom to solve complex problems. It not only supports seamless integration with external AI services and APIs to expand functionality, but also provides agents with almost unlimited long-term memory to enhance contextual understanding, while allowing customized workflows. In response to enterprise-level needs, Swarms is highly reliable and scalable, and ensures optimal performance by automatically optimizing language model parameters. In this way, Swarms can leverage the collective wisdom between 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 provided efficient solutions to many companies on its official website. From data processing to customer service to report generation, Swarms has greatly improved business efficiency and significantly reduced operating costs through automation, and its strength is obvious to all. As an open source project, Swarms has attracted enthusiastic attention in the developer community. The number of stars on GitHub has exceeded 2.1K, and it has gained the wisdom and support of many developers. All that Swarms has accumulated proves the maturity and innovation of technology.

SNAI

Netizens on Twitter seem to agree that the next stage of AI agents is group collaboration (Agent Swarms), which achieves more efficient work through communication and collaboration between multiple agents. This method allows agents from different frameworks to communicate with each other and use their respective specialized advantages to perform better in specific tasks and scenarios.

Swarm Node (SNAI) is a serverless infrastructure designed to support the concept of Swarm as an aid to the implementation of Agent Swarms. SNAI solves all the technical challenges of running AI agents, allowing users to easily deploy, coordinate and manage agents through Python scripts without worrying about hardware and infrastructure costs. It also supports chained interactions, scheduling, and multi-language operations, providing new possibilities for small creators who cannot run agents 24/7 or lack hardware support.

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

The role of Swarm is to divide tasks among different agents, each of which focuses on a specific task and passes the results to the next agent after completion. Through the 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 what data to use).

But that’s not all. As the SNAI framework is still in the early stages of development, several new features will be added 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 libraries (ready-made agents created by the community that can be run, customized, and optimized). In addition, SNAI will also achieve multi-language compatibility. Currently, a Python client that simplifies API operations is provided, and it is planned to support agent deployment written in languages such as Go, Rust, TypeScript, C#, and PHP. The community has begun to develop a TypeScript client, and more languages will be supported in the future.

This week alone, there have been more than 500 builds — these “dependencies” are used to optimize the execution efficiency of AI agents. More than 10,000 executions — instances where the agent is started and then paused. SNAI only charges for active running time, greatly improving the flexibility of agent operations.

The core features of SNAI include supporting serverless operation of agents, allowing developers to integrate agents into the code base, and realizing agent chain collaboration and interactive coordination. At the same time, it adopts a pay-per-use model to significantly reduce infrastructure costs and lower the threshold for entering AI agent infrastructure.

Against AI16Z

Both Swarms and AI16Z have significant influence in the field of AI agents. The two have been controversial on Twitter. Although they have some similarities, they are different in technical architecture and application. Swarms adopts a collaborative team framework to complete complex tasks and improve efficiency through the cooperation of multiple AI agents. In contrast, AI16Zs 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 is 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, AI agents can collaborate efficiently and are good at breaking down complex tasks to complete operations with clear division of labor and seamless cooperation. AI16Zs Eliza framework is more like an all-round coordinator, focusing on multi-platform operation and multi-model integration, while emphasizing the interaction between agents, and has its own characteristics 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, and improve enterprise-level automation and team efficiency through task orchestration and team collaboration. It is more like a sophisticated commander, good at properly deploying multiple forces and focusing on how to do better. AI16Zs Eliza framework provides developers with greater freedom. It supports a variety of AI models (such as Llama, Claude), giving applications more flexibility and being able to cope with various scenarios from social media management to financial transactions, thus bringing an all-around solution. One focuses on collaboration, and the other emphasizes diversity. The two are equally matched in innovative applications, and each has its own merits.

In general, 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 technical hardcore, while Eliza of AI16Z is more like a versatile free player, showing unlimited potential with flexible adaptation and scene diversity. In fact, both have their own advantages. In this era of fierce competition, the story of AI agents has just begun. Who will stand out in this competition? Lets wait and see!

References:

https://fraxcesco.substack.com/p/introducing-swarm-node-serverless?utm_source=post-email-titlepublication_id=1419537post_id=153678118utm_campaign=email-post-titleisFreemail=truer=2i6286triedRedirect=trueutm_medium=email

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