Original author: TechFlow
Reprinted by: Luke, Mars Finance
While Bitcoin has fallen and altcoins are in the red, the memes on the level-one chain market have temporarily become a 'safe haven' for the market. Although some high-market-cap memes already listed are inevitably affected by the market downturn, the panic market sentiment for new hot assets (or rather, fast-paced projects) can at most cause some superficial damage.
Last Friday, the enterprise-level multi-agent collaboration framework Swarms announced on Twitter to 'claim' the token $swarms issued through Pump.Fun. It was termed 'claim' because $swarms did not exist on the day of the official announcement, but rather had been around two days earlier. At that time, the $swarms without official endorsement might have been viewed by the market as an ordinary 'scam project', with its market value plunging to a low of $6,000, going unnoticed.
With $arc as a precedent, the narrative of the $swarms framework has made the market buy in without hesitation. On the 'Black Friday' when altcoins plummeted, $swarms merely consolidated a bit during the worst hours of market panic and then broke through a market cap of $70 million.
By synthesizing information provided by Swarms' official website and technical documents, we have a preliminary understanding of what the Swarms framework does.
Is it another entry of tech enthusiasts?
Besides the prominent token address on the Swarms official homepage, the developer of the Swarms framework @KyeGomezB also continued to discuss token-related news that day.
According to Kye Gomes' GitHub page, the Swarms framework has already gained over 2,000 stars (the rig framework of $arc currently has 1,300 stars).
With GitHub evidence, at least the hardcore technical identity of the $swarms token is solidified.
Enterprise-level Multi-Agent Collaboration Framework
The Swarms framework was originally not exclusively for Web3 service but Crypto Native. Its core positioning is as the original meaning of the term 'Swarms'—a swarm, an enterprise-level multi-agent collaboration framework. It is not just a simple AI development tool but a complete solution focused on addressing the practical problems enterprises face in implementing AI.
In practical applications, Swarms provides a complete toolchain that enables enterprises to easily build and manage collaboration among multiple AI agents. These AI agents can be different language models, specialized tools, or custom intelligent agents, and they can seamlessly collaborate under the scheduling of Swarms to accomplish complex business tasks.
From a technical architecture perspective, the Swarms framework includes the following core components:
· Task Scheduling System: Responsible for breaking down complex tasks and assigning them to suitable AI agents
· Agent Management Module: Manages the lifecycle and status of each AI agent
· Communication Middleware: Ensures accurate and efficient information transfer between agents
· Monitoring and Logging System: Real-time tracking of the entire system's operational status
At the enterprise application level, Swarms provides:
· High Availability Assurance: Automatic fault tolerance and recovery mechanisms
· Complete Monitoring System: Real-time tracking of AI agents' performance and status
· Flexible Scalability: Easily add new AI capabilities and business logic
· Security Considerations: Comprehensive permission management and data protection mechanisms
To understand how Swarms works, we can use an orchestra as an analogy:
Imagine a large orchestra performing a symphony. Traditional AI solutions are like a jack-of-all-trades trying to play all instruments at once. In contrast, Swarms allows each 'musician' (AI agent) to focus on its expertise, collaborating under the 'conductor' (Swarms framework). The score is the standardized task flow of the entire system, while rehearsals are the ongoing optimization process of the system.
For example, in an e-commerce scenario, when a user needs personalized shopping recommendations, the system automatically coordinates multiple specialized agents. The User Profile Analysis Agent deeply understands user needs, the Product Recommendation Agent filters the most suitable products accordingly, and the Feedback Analysis Agent organizes user feedback. Finally, the Conversational Assistant Agent integrates this information into friendly suggestions for the user. These agents each perform their roles while seamlessly collaborating to ultimately provide precise services to the user.
How does it differ from other projects in the same track?
As products in the same AI framework track, both $ai16z & $ELIZA with the ELIZA framework and $arc based on the RIG framework demonstrate market recognition of the underlying infrastructure concept.
So, is the Swarms framework in competition with the other two projects? Or can they each leverage their strengths for mutual collaboration?
Twitter user @tmel0211 summarized the possible connections among the three frameworks:
1. From ELIZA to RIG (ARC), and then to the evolutionary logic of Swarms' standards and frameworks, there is no issue. ELIZA focuses on lightweight, rapid deployment to create AI agents, ARC aims to enhance resource optimization and performance for AI agent systems using Rust, while Swarms aims to construct a complex task decomposition and coordination framework for multi-AI agent collaboration, with its mixed orchestration mechanism of multiple agents, flexible combination of serial and parallel mechanisms, and multi-layer memory processing architecture. Just looking at the necessity and direction of its technological evolution logic makes a lot of sense.
2. Theoretically, Swarms can integrate ARC, and ARC can optimize ELIZA. All three frameworks share a modular design philosophy, and their technical visions are becoming increasingly grand. This could lead to an overly 'conceptualized' concern. It's still the same point: the current standard frameworks are far from the time to judge quality. We should observe the completeness of the framework's codebase and the landing situation of monolithic AI applications based on its framework. If the early technical advantages are not accurately gauged, we should focus on the implementation of applications; technology can float in the air, but user interaction experiences will definitely land.
Clearly, whether it is ELIZA, RIG, or Swarms, their feasibility and expansion opportunities are still in the early stages. Different language frameworks focus on solving different problems in the process of large-scale AI adoption, and 'mutual collaboration' is also an unavoidable theme among various frameworks in the future.
The founder faced scrutiny, and the token price fluctuated.
Although the market initially recognized the narrative of $swarms, things did not run smoothly.
On the day $swarms token exploded in popularity, $ai16z's founder Shaw @shawmakesmagic publicly criticized the developer of the Swarms framework @KyeGomezB on Twitter, stating, 'I really don't like to publicly point out other people's problems. Doing so poses a huge risk to our project, can make many people feel anxious, and I don't want to discourage those hardworking developers. But some people will steal others' work and try to claim credit for it.' He also referenced a Reddit post from 2023 to argue that Kye's behavior constituted plagiarism. The article pointed out that a GitHub repo might show evidence of stealing others' results, and that repo belongs to Swarms developer Kye.
Shaw's FUD also brought the price of $swarms close to a halving. However, in the face of this FUD, Swarms' founder Kye was also unyielding. While countering on Twitter, he launched a new token $mcs for an application called Medicalswarm based on the Swarms framework, intending to prove that his framework was not useless, but indeed 'has substance.'
Perhaps Kye is not yet familiar with the gameplay of AI memes. When the consensus around $swarms had not yet solidified and the token price was still on a downward trend, the launch of a new token was directly interpreted by many confused players as the $swarms developers abandoning the project, concluding that $swarms was no longer worth investing in. Therefore, the launch of $mcs did not initially save $swarms but rather deepened its fall, with its market cap plummeting from a high of $74 million to $6 million, along with a crash of the new token $mcs.
However, inexperienced Kye realized that his handling might be somewhat off upon seeing the situation. He quickly went live to announce that he was indeed serious about building and locked up a year's worth of $swarms tokens he held. Perhaps the founder wanted to prove himself amidst various FUD, or perhaps there were wise advisors behind him, creating such operations to gather tokens during the unstable consensus. In any case, this wave of publicity and reassurance indeed washed away many early entrants, and the market, coming back to its senses, began to buy $swarms again, with its market cap gradually recovering and stabilizing around $30 million.
Summary
As of writing, the price trend of $swarms has gradually stabilized, with a market cap still around $40 million.
The 'Fast-Paced Script' of $swarms is somewhat similar to $arc. As a token with a technical background, it saw a frenzy of buying once it fermented, reaching a market cap in the millions. However, as profit-taking occurred and market understanding and community consensus needed some time to coalesce, such tokens are bound to experience volatility in the early stages.
Whether this project truly has substance as claimed by the founder, the market will make its own corresponding choices.