Original author: TechFlow

Reprinted from: Luke, Mars Finance

As Bitcoin fell and altcoins turned red, memes on the primary chain market temporarily became a 'safe haven' for the market. Although some high market-cap memes have inevitably continued to decline due to market impacts, for new hot assets (or more aptly termed 'fast-track coins'), the panic in the market could at most only cause some superficial wounds.

Last Friday, the enterprise-level multi-agent collaboration framework Swarms announced on Twitter that it had 'claimed' the token $swarms issued through Pump.Fun. It was termed 'claimed' because $swarms was not issued on the official announcement day, but had existed two days prior. At that time, the $swarms without official endorsement may have been viewed by the market as an ordinary 'scam,' with a market cap once languishing at an unnoticed low of $6000.

With $arc as a precedent, the narrative of the $swarms framework prompted the market to buy in without hesitation. On 'Black Friday,' when altcoins were plummeting, $swarms merely consolidated for a brief period during the market's most panicked hours before directly breaking through a market cap of $70 million.

By synthesizing information from Swarms' official website and technical documentation, we have initially understood what the Swarms framework is designed to do.

Is it another round of tech enthusiasts entering the scene?

In addition to the clear token address on the Swarms official homepage, Swarms framework developer @KyeGomezB also continuously discussed token-related news that day.

According to Kye Gomes' Github page, the Swarms framework has already received over 2000 stars (the $arc's rig framework currently has 1300 stars).

With the proof from Github, at least the hardcore technical identity of the $swarms token has been solidified.

Enterprise-level Multi-Agent Collaboration Framework

The Swarms framework was originally not specifically designed for Crypto Native services in Web3; its core positioning is as the original meaning of 'Swarms'—a swarm, which is 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 issues faced by enterprises during the implementation of AI.

In practical applications, Swarms provides a complete toolchain that allows enterprises to easily build and manage collaboration between multiple AI agents. These AI agents can be different language models, specialized tools, or custom intelligent agents, working seamlessly under the scheduling of Swarms to complete 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 transmission between agents

· Monitoring and Logging System: Real-time tracking of the entire system's operating status

At the enterprise application level, Swarms provides:

· High Availability Guarantee: Automatic fault tolerance and recovery mechanisms

· Complete Monitoring System: Real-time tracking of AI agent performance and status

· Flexible Expansion Capability: 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. Swarms, on the other hand, allows each 'musician' (AI agent) to focus on their expertise, collaborating under the direction of a 'conductor' (Swarms framework). The sheet music represents the standardized task processes of the 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 will automatically coordinate multiple specialized agents. The user profile analysis agent will deeply understand user needs, the product recommendation agent will filter the most suitable products accordingly, the feedback analysis agent will organize user feedback, and finally, the dialogue assistant agent will integrate this information into friendly suggestions presented to the user. These agents each perform their roles while seamlessly cooperating to ultimately provide accurate services to the user.

What distinguishes it from other projects in the same track?

As products in the same AI framework track, whether it's $ai16z & $ELIZA with the ELIZA framework or $arc based on the rig framework, their prices reflect the market's recognition of foundational infrastructure concepts.

So, is the Swarms framework in competition with the other two projects? Or can they each leverage their strengths and collaborate?

Twitter user @tmel0211 summarized the possible connections between the three frameworks:

1. The evolution of standards and frameworks from ELIZA to RIG (ARC), and then to Swarms makes sense. ELIZA focuses on lightweight, rapid deployment of AI agents, ARC aims to enhance resource optimization and performance during AI agent operations using Rust, while Swarms seeks to build a complex task decomposition and coordination framework for multi-AI agent collaboration, with its multi-agent hybrid orchestration mechanism, flexible combination of serial and parallel mechanisms, and multi-layer memory processing architecture. Just looking at the necessity and development direction of its technical evolution logic, it makes a lot of sense.

2. In theory, Swarms could integrate ARC, and ARC could optimize ELIZA. All three frameworks share a modular design philosophy, and their technical visions are becoming increasingly grand. This could be a worryingly 'conceptual' point; as I mentioned earlier, it is not yet time to judge the merits of the current standard frameworks. We should observe the completeness of the framework's codebase and the implementation of AI applications based on the framework. If we cannot accurately assess the early technical advantages, we should focus on the implementation of applications; technology may float in the air, but interactive application experience must land on the ground.

Clearly, whether ELIZA, RIG, or Swarms, their feasibility and expansion potential are still in the early stages. Different language frameworks address different issues in the large-scale adoption of AI, and 'mutual cooperation' is also an unavoidable theme among the various frameworks in the future.

The founder faced doubts, and the token price fluctuated.

Although the market initially recognized the narrative of $swarms, things have not always run smoothly.

On the day $swarms token exploded in popularity, $ai16z founder Shaw @shawmakesmagic publicly criticized Swarms framework developer @KyeGomezB on Twitter, stating, 'I really don’t like publicly pointing out others’ problems. Doing so poses a huge risk to our project, makes many people nervous, and I don’t want to discourage those hardworking developers. But some people will steal others' work and try to take credit for it.' He referenced a 2023 Reddit post to argue Kye's plagiarism. The article pointed out that a Github repo may show signs of stealing others' work, and that repo belongs to Swarms developer Kye.

Shaw's FUD also brought $swarms' token price close to halving. However, in response to this FUD, Swarms' founder Kye was also unyielding, launching a new token $mcs based on the Swarms framework application Medicalswarm on Twitter to prove that his framework is not useless but really 'has something to it.'

Perhaps Kye is still unfamiliar with the play of AI memes. At the time when the consensus on $swarms had not yet solidified and the token price was still on a downward trend, the issuance of new coins was directly interpreted by many confused players as the $swarms Dev no longer wanting this project, making $swarms deemed unworthy of further engagement. Thus, the launch of $mcs did not initially serve to rescue $swarms, but rather led to a deeper decline, with its market cap plummeting from a high of $74 million to $6 million, dragging the new coin $mcs down with it.

However, inexperienced Kye understood that his handling might be a bit off upon seeing the situation, and later urgently started a live stream to announce that he was indeed serious about building and locked up his $swarms tokens for a year. Perhaps the founder really wanted to prove himself in the face of various FUD, or perhaps there were wise people guiding him, using such an operation to gather tokens when the consensus was unstable. In any case, this wave of actions did wash away many early players, and the market that came to its senses began to buy back $swarms, gradually stabilizing its market cap around $30 million.

Summary

As of the writing, the price trend of $swarms has gradually stabilized, with a market cap still around $40 million.

$swarms' 'fast-track script' is somewhat similar to $arc. As a technically backed token, it saw the market frenzy buy in, achieving a market cap of millions. However, as profit-taking exits and market understanding and community consensus take time to solidify, such coins will undoubtedly experience volatility in the early stages.

Whether this project is truly as substantial as the founder claims, the market will naturally make the appropriate choice.