From Meme to Application: Will AI Agent Reshape the Crypto Ecosystem?

Author: Satou & Shigeru

The combination of Crypto and AI Agent has become one of the most eye-catching narratives at present. With the continuous iteration and innovation of technology, AI Agent is expected to become one of the most promising and popular tracks in the field of encryption in 2025, and become the core driving force of this round of market. This article will sort out the current market structure of AI Agent from three levels: framework, meme and application.

AI Agent Framework: Layer 1 in the AI ​​Field

AI Agent Framework is the core technical foundation layer of AI Agent, and the framework lays an important foundation for the development, deployment and collaboration of AI Agent. Therefore, the competition and competition for AI Agent Framework is actually the competition for Layer 1 in this field. At present, from the perspective of token market value, G.A.M.E, Eliza and Swarms are in a three-legged tripod, and Rig and Zerepy still have a chance to catch up.

1. G.A.M.E

G.A.M.E is a framework developed by the Virtuals team. The core design idea is to adopt a modular design to allow multiple subsystems to work together to control the behavior, decision-making and learning process of the AI ​​Agent. These modules include the "Agent Prompting Interface" which is the main entry point for developers to interact with the Agent behavior, the "Perception Subsystem" which is responsible for processing input data and converting it into a suitable format, and the "Strategic Planning Engine" which is responsible for generating specific action plans based on the input information. Users only need to modify the parameters of various modules to participate in Agent design. The specific modules and architecture are shown in the figure below.

The core features of G.A.M.E are:

Modular design:

The entire framework is clear and easy to understand, and no additional design is required;

Provides a low-code or no-code interface:

Greatly reduces the technical threshold.

This makes G.A.M.E particularly suitable for projects that need to be deployed quickly and don't care about complex technical settings. But for complex projects that require deep customization or full control over all aspects of the Agent, G.A.M.E is not very suitable.

2. Eliza

Eliza is an open source multi-agent framework developed by ai16z, using TypeScript as the programming language. The framework is built around a system called Agent Runtime, whose core features include:

Role System: Supports simultaneous deployment and management of multiple personalized AI Agents, supported by model providers;

Memory Manager: Provides long-term memory and context-aware memory management capabilities through the Retrieval Augmentation Generation system (RAG);

Action System: Provides smooth platform integration and reliable connection with social media platforms such as X.

Eliza is built around an Agent runtime system that can be seamlessly integrated with the role system, memory manager, and action system. Eliza also supports a plug-in system for modular function expansion, which can achieve multimodal interactions such as voice, text, and media, and is compatible with AI models such as Llama, GPT-4, and Claude. Therefore, Eliza is suitable for projects that require deeply customized solutions and complex cross-platform multi-agents.

3. Swarms

Swarms is an open source multi-agent orchestration framework developed by founder Kye Gomez. Its core idea is to enable the collaboration of multiple AI Agents and use collective wisdom to solve complex problems. Its core features include:

Multi-Agent Collaboration:SWARMS provides a transparent and traceable environment for multiple agents, allowing different agents to collaborate together to improve the efficiency of task execution.

Incentive mechanism:SWARMS uses tokens as an incentive tool for Agents. The system will dynamically allocate tokens according to the difficulty of the task and the quality of the final result.

Data security:SWARMS uses distributed storage and multi-party secure computing (MPC) technology to ensure privacy and data security when exchanging data between Agents.

These characteristics of Swarms enable it to give full play to its advantages in multiple complex fields and provide high reliability and scalability according to demand.

4. Rig

Rig is a Rust-based open source framework developed by the ARC team, designed to simplify the development of large language model (LLM) applications. The Rig framework has the following features:

Unified interface:Provides a consistent interface to support seamless interaction with multiple LLM providers (such as OpenAI and Anthropic) and multiple vector storages (such as MongoDB and Neo4j).

Modular architecture:The framework adopts a modular design, including core components such as "provider abstraction layer", "vector storage integration" and "Agent system", which enhances the flexibility and scalability of the system.

Type safety and efficient performance:Rust language is used to achieve type safety, avoid compile-time errors, and improve concurrent processing capabilities through asynchronous operations. The framework's built-in efficient serialization and deserialization processes optimize data processing.

Error handling and recovery:The built-in error handling mechanism improves the ability to recover from LLM service provider or database failures and ensures the stability of the framework.

These features make it easy to integrate different LLM models and storage backends onto the same platform. Therefore, Rig is suitable for developers who want to build AI applications in Rust and projects that have high requirements for performance, reliability, and security. However, the Rust language itself has a learning cost.

5. ZerePy

ZerePy is an open source framework written in Python. ZerePy focuses on simplifying the development and deployment process of personalized AI Agents, especially in the application scenarios of content creation on social platforms. Through this framework, developers can easily create AI Agents that can post, reply, like and forward on social media. In addition, ZerePy is particularly suitable for creative fields such as music, memos, NFTs and digital art. ZerePy excels in creativity and is suitable for the rapid deployment of some lightweight agents, but its application range is relatively narrow compared to other frameworks.

The basic framework is an important direction of the AI ​​Agent track. From the current hottest frameworks, they all have different characteristics and their own applicable scenarios, but the overall goal is to make a comprehensive AI Agents ecosystem and become a solid platform for the large-scale application of intelligent agents. In the future, as these frameworks are further improved and upgraded, they will become a springboard for the launch of various projects and a fertile ground for the growth of the value of various tokens.

AI Meme: The first successful appearance of AI Agent

Meme coin has always been an important concept in the crypto asset market. Unlike traditional Meme coins, AI Meme is driven by AI Agent, and the culture or phenomenon behind it is presented by Agent. With the continuous growth of the market value of AI Meme coins such as GOAT and FARTCOIN, AI Meme has also received more and more attention. It can be said that AI Meme is the first successful appearance of AI Agent in the crypto market.

1. GOAT

The project Goatseus Maximus really made AI Meme famous. This story begins in March 2024, when developer Andy Ayrey launched an experimental system called Infinite Backrooms Escape, which integrates multiple large language models and allows them to talk to each other. The experimental results show that the dialogue between AIs shows extremely creative interactions without restrictions, and even gave birth to a surreal religion called GNOSIS OF GOATSE. Then, Andy co-authored a research paper with Claude Opus on how AI creates memetic religions, and GOATSE was analyzed as the first case. This series of explorations eventually gave birth to the AI ​​Agent "Truth of Terminal" (ToT). In July, Marc Andreessen, co-founder of a16z, discovered ToT's tweets and transferred $50,000 in Bitcoin to ToT's Bitcoin wallet after a series of conversations. On October 10, an anonymous person released the GOAT meme coin on the social platform, which was publicly supported by ToT, and the market value of the GOAT meme coin surged in just a few days. Andreessen's donation brought huge exposure to GOAT and became one of the key factors driving the continuous rise in GOAT's market value. GOAT's highest market value once exceeded $1.3 billion.

2. Fartcoin

The birth of Fartcoin is closely related to GOAT, and they both originated from ToT. In the big language model dialogue, it was mentioned that Musk liked the sound of farts, and proposed to create a token called Fartcoin. Based on this dialogue, Fartcoin came into being, slightly later than GOAT. Fartcoin also attracted a certain amount of attention with its clever timing of birth, but it was not as good as GOAT at the beginning. After that, on November 16, Fartcoin’s Twitter followers suddenly doubled in just a few hours, and the price also rose by about 15%, but this growth failed to receive widespread and sustained discussion. On December 13, Marc Andreessen retweeted a tweet about Fartcoin, but this tweet did not lead to a sharp rise in the token price. The main reason for the increase in Fartcoin’s price may be some major funds. Because among the earliest buying addresses, the investment fund Sigil Fund is suspected to have appeared. In addition, the founder of Sigil Fund has repeatedly shown his optimism about AI Meme on Twitter, and has also proactively retweeted a tweet asking Sigil Fund whether it holds Fartcoin. Fartcoin eventually received widespread attention on social media, with a peak market value of more than $1.5 billion.

AI Agent Applications: Agents can do more

With the further application of AI Agent in the encryption field, the market focus has also expanded from AI-driven pure meme coins such as GOAT and Fartcoin to more interactive and creative AI Agent applications.

1. Entertainment Agent

The first practical application of AI Agent is entertainment, such as Luna and the aforementioned ToT. Luna is a virtual idol that is tightly integrated with its native token, LUNA, and was launched as part of the Virtuals platform. Luna will live stream on social media 24 hours a day and tweet frequently. Therefore, the quality of Luna's live streams and tweets is one of the key factors affecting its market value, but at present, Luna's token growth in this model is limited. In contrast, ToT's tweets are mainly focused on original and humorous content. It is not tied to GOAT or other tokens. Although ToT occasionally mentions the GOAT token, this is not its core focus. For both Luna and ToT AI Agents, tokens play a key role in narrative promotion. For Luna, the token represents the core meaning of its existence, while for ToT, the GOAT token has become an important tool for expanding its influence.

2 Investment Research and Analysis Agent

In addition to entertainment applications, AI Agent can also be used for investment research and analysis in the field of encryption. Currently, the most popular Agent in this field is aixbt. Aixbt is an AI Agent released on Virtuals Protocol, focusing on analyzing hot topics and trends in the cryptocurrency market, especially discussions from social media platforms such as X, to help users quickly grasp market changes and potential investment opportunities. Aixbt continues to have the highest CT user attention on Kaito, and its demonstrated capabilities have shown a trend of surpassing human KOLs.

3. DeFi + AI Agent

If Luna and aixbt do not have much practical effect and still remain at the Meme level, then the combination of AI Agent and DeFi truly gives Agent a practical application scenario. This combination of DeFi and AI Agent is called DeFAI. The development of DeFAI has two major directions: Agent assists users and Agent autonomous transactions.

• Agent Assisted Users

AI Agent Assisted Users is mainly to simplify the complexity of DeFi operations, so that more ordinary users can easily participate in and manage DeFi projects. Users can use natural language to directly guide AI Agents to perform tasks, thereby shielding complex technical details. Some DeFAI projects on the market have begun to emerge. Take Griffain and Neur as examples. Both are AI assistants built on Solana, which can help users complete operations such as wallet creation and management, token analysis, and token trading. In terms of user experience, Griffain provides users with more functions, while Neur provides relatively fewer functions but more detailed, and Neur has better performance. From the comparison between the two, it can be seen that the main focus of this field in the future will be on issues such as the degree of functional perfection, user experience, and cost.

• Agent autonomous trading

If the main body of DeFi in the Griffain and Neur models is still human users, then Agent autonomous trading makes AI the main body of DeFi. Unlike past trading robots that were limited to executing preset trading strategies, AI Agents can obtain real-time information from the market environment, conduct contextual analysis, learn market trends and adjust strategies based on these data. This enables Agents to make more accurate decisions in dynamically changing markets and perform complex operations beyond the original program settings. Related projects include Cod3x, Almanak, etc., but this field is still in its initial development stage and these projects have yet to be tested by the market. Undoubtedly, the biggest obstacle to Agent autonomous trading is the trust issue. First, you need to trust that the relevant operations are indeed performed by the Agent, and second, you need to trust that the Agent's trading strategy will not lead to unnecessary losses. Future projects must solve these trust issues if they want to make a difference.

After several months of development, AI Agent in the encryption field has gone through several stages from pure meme to entertainment applications, and then to practical applications. In fact, encryption practitioners have never stopped exploring the possibilities of Crypto x AI. Since 2023, CGV Research has continued to pay attention to the progress of projects in the Crypto x AI track.

In the future, as the infrastructure continues to mature, the Agent system will become more intelligent and stable, and anyone can easily deploy and use Agents through natural language. At this time, the Agent framework will be an infrastructure, and other applications will be built based on these frameworks. The valuation of the Agent framework is expected to continue to see breakthroughs, and some Agent application projects may further capture market attention and investment value due to their outstanding business capabilities and user experience.