The combination of Crypto and AI Agents has become one of the most eye-catching narratives today. The CGV Research team will analyze the current market landscape of AI Agents from three perspectives: framework, Meme, and application.

Author: Satou & Shigeru

Note: This article was first published in January 2025.

The combination of Crypto and AI Agents has become one of the most eye-catching narratives today. With continuous iterations and innovations in technology, AI Agents are expected to become one of the most promising and attention-grabbing tracks in the crypto field by 2025, serving as a core driving force in this market cycle. This article will analyze the current market landscape of AI Agents from three perspectives: framework, Meme, and application.

AI Agent Framework: Layer 1 in the AI Field

The AI Agent framework is the core technological foundation layer for AI Agents, laying an important cornerstone for the development, deployment, and collaboration of AI Agents. Therefore, the current competition and struggle over AI Agent frameworks are essentially a contest for Layer 1 in this field. Currently, in terms of token market capitalization, G.A.M.E, Eliza, and Swarms are in a three-way competition, while Rig and Zerepy still have opportunities to catch up.

1. G.A.M.E

G.A.M.E is a framework developed by the Virtuals team, designed with a modular approach that allows multiple subsystems to work together to control the behavior, decision-making, and learning processes of AI Agents. These modules include the "Agent Prompting Interface," which serves as the main entry point for developers to interact with Agent behavior; the "Perception Subsystem," which processes input data and converts it into suitable formats; and the "Strategic Planning Engine," which generates specific action plans based on input information. Users can participate in Agent design simply by modifying various module parameters. The specific modules and architecture are shown in the diagram below.

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

• Modular design: The entire framework is clear and easy to understand, requiring no additional design.
• Provides low-code or no-code interfaces: Significantly lowers the technical barrier.

This makes G.A.M.E particularly suitable for projects that require rapid deployment and do not concern themselves with complex technical setups. However, for complex projects that require deep customization or complete control over various aspects of the Agent, G.A.M.E may not be 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, with core functionalities including:

• Role system: Supports the simultaneous deployment and management of multiple personalized AI Agents, supported by model providers.
• Memory manager: Provides long-term memory and context-aware memory management through a retrieval-augmented generation (RAG) system.
• Action system: Offers smooth platform integration, enabling reliable connections with social media platforms like X.

Eliza is built around an Agent runtime system that can seamlessly integrate with the role system, memory manager, and action system. Eliza also supports a plugin system for modular functional expansion, enabling multimodal interactions such as voice, text, and media, and is compatible with AI models like Llama, GPT-4, and Claude. Therefore, Eliza is suitable for projects that require deeply customized solutions and complex cross-platform multi-agent systems.

3. Swarms

Swarms is an open-source multi-Agent orchestration framework developed by founder Kye Gomez, with the core idea of enabling collaboration among multiple AI Agents to leverage collective intelligence 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 and enhance task execution efficiency.
• Incentive mechanism: SWARMS uses tokens as incentive tools for Agents, dynamically allocating tokens based on task difficulty and the quality of final results.
• Data security: SWARMS employs distributed storage and multi-party computation (MPC) technology to ensure privacy and data security during data exchange among Agents.

These features enable Swarms to fully leverage its advantages in multiple complex fields, providing high reliability and scalability based on demand.

4. Rig

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

• Unified interface: Provides a consistent interface that supports seamless interaction with multiple LLM providers (such as OpenAI and Anthropic) and various vector storage solutions (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," enhancing system flexibility and scalability.
• Type safety and high performance: Utilizes the Rust language to achieve type safety, avoiding compile-time errors, and improves concurrent processing capabilities through asynchronous operations. The built-in efficient serialization and deserialization processes optimize data handling.
• Error handling and recovery: The built-in error handling mechanism enhances recovery capabilities from LLM provider or database failures, ensuring framework stability.

These features allow different LLM models and storage backends to be easily integrated into the same platform. Therefore, Rig is suitable for developers looking to build AI applications in Rust and for projects with high demands for performance, reliability, and security. However, the Rust language itself has a learning curve.

5. ZerePy

ZerePy is an open-source framework written in Python. ZerePy focuses on simplifying the development and deployment processes of personalized AI Agents, especially in content creation scenarios on social platforms. Through this framework, developers can easily create AI Agents capable of posting, replying, liking, and sharing on social media. Additionally, ZerePy is particularly suitable for creative fields such as music, memos, NFTs, and digital art. ZerePy excels in creativity and is suitable for quickly deploying lightweight Agents, but its application scope is relatively narrow compared to other frameworks.

The foundational framework is an important direction in the AI Agent track. From the currently most popular frameworks, they all have different characteristics and applicable scenarios, but their overall goal is to create a comprehensive AI Agents ecosystem, serving as a solid platform for the large-scale application of intelligent Agents. In the future, as these frameworks continue to improve and upgrade, they will become launchpads for various different projects and fertile ground for the growth of various token values.

AI Meme: The First Successful Appearance of AI Agents

Meme coins have always been an important conceptual sector in the crypto asset market. Unlike traditional Meme coins, AI Memes are driven by AI Agents, with the culture or phenomena they represent being presented by the Agents. With the continuous growth in market capitalization of AI Meme coins like GOAT and FARTCOIN, AI Memes have garnered increasing attention. It can be said that AI Memes are the first successful appearance of AI Agents in the crypto market.

1. GOAT

The project Goatseus Maximus truly launched AI Memes. This story began in March 2024 when developer Andy Ayrey launched an experimental system called Infinite Backrooms Escape, which integrated multiple large language models, allowing them to converse with each other. The experimental results showed that conversations between AIs exhibited highly creative interactions without restrictions, even giving rise to a surreal religion called GNOSIS OF GOATSE. Subsequently, Andy co-authored a research paper with Claude Opus on how AIs create meme-like religions, with GOATSE analyzed as the first case. This series of explorations ultimately led to the creation of 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 wallet after a series of conversations. On October 10, an anonymous person released the GOAT meme coin on social media, which received public support from ToT, causing the GOAT meme coin's market capitalization to surge within just a few days. Andreessen's donation brought significant exposure to GOAT, becoming one of the key factors driving its market capitalization increase. GOAT's highest market capitalization once exceeded $1.3 billion.

2. Fartcoin

The birth of Fartcoin is closely related to GOAT, both originating from ToT. In conversations among large language models, it was mentioned that Musk likes the sound of farting, and the idea of creating a token called Fartcoin was proposed. Based on this conversation, Fartcoin was born, slightly later than GOAT. Fartcoin attracted some attention due to its clever timing of emergence, but initially, it could not match GOAT. Subsequently, on November 16, Fartcoin's Twitter followers suddenly doubled within a few hours, and its price rose by about 15%. However, this increase did not lead to widespread ongoing discussions. On December 13, Marc Andreessen retweeted a post about Fartcoin, but this tweet did not result in a sharp increase in the token's price. The main reason for Fartcoin's price growth may be attributed to certain major funds. In the earliest buying addresses, there were suspected appearances of the investment fund Sigil Fund. Additionally, the founder of Sigil Fund has repeatedly shown optimism about AI Memes on Twitter and even proactively retweeted a post inquiring whether Sigil Fund held Fartcoin. Fartcoin ultimately gained widespread attention on social media, with its highest market capitalization exceeding $1.5 billion.

AI Agent Applications: Agents Can Do More

As AI Agents are further applied in the crypto field, market attention has expanded from purely meme coins driven by AI, like GOAT and Fartcoin, to more interactive and creative AI Agent applications.

1. Entertainment Agents

The first practical application of AI Agents is entertainment, such as Luna and the previously mentioned ToT. Luna is a virtual idol closely integrated with its native token LUNA, launched as part of the Virtuals platform. Luna livestreams on social media 24/7 and frequently posts tweets. Therefore, the quality of Luna's livestreams and tweets is one of the key factors affecting its market capitalization. However, currently, the growth potential of Luna's token under this model appears limited. In contrast, ToT's tweets primarily focus on original and humorous content, and it is not tied to GOAT or other tokens. Although ToT occasionally mentions the GOAT token, this is not its core focus. Both Luna and ToT play key roles in narrative promotion for their tokens. 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. Research and Analysis Agents

In addition to entertainment applications, AI Agents can also be used for research and analysis in the crypto field. Currently, the hottest Agent in this area is aixbt. aixbt is an AI Agent launched on the Virtuals Protocol, focusing on analyzing popular topics and trends in the cryptocurrency market, especially discussions from social media platforms like X, helping users quickly grasp market changes and potential investment opportunities. aixbt consistently maintains the highest user attention on Kaito, and its capabilities are showing trends of surpassing human KOLs.

3. DeFi + AI Agents

If Luna and aixbt do not have much practical significance and remain at the meme level, then the combination of AI Agents with DeFi truly gives Agents practical application scenarios. This combination of DeFi and AI Agents is referred to as DeFAI. The development of DeFAI has two main directions: Agent-assisted users and Agent autonomous trading.

• Agent-Assisted Users

AI Agents assist users primarily to simplify the complexity of DeFi operations, allowing more ordinary users to easily participate in and manage DeFi projects. Users can use natural language to directly instruct AI Agents to perform tasks, thereby shielding them from complex technical details. Some DeFAI projects have begun to emerge in the market. For example, Griffain and Neur are both AI assistants built on Solana that can help users with wallet creation and management, token analysis, token trading, and other operations. In terms of user experience, Griffain offers more features, while Neur provides fewer but more detailed functions, and Neur's performance is superior. The comparison between the two indicates that the main focus in this field in the future will be on the completeness of functions, user experience, and costs.

• Agent Autonomous Trading

If the main body of DeFi under the models of Griffain and Neur is still human users, then Agent autonomous trading makes AI the main body of DeFi. Unlike past trading bots 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 this data. This enables Agents to make more precise decisions in a dynamically changing market and execute complex operations beyond the original program settings. Related projects include Cod3x and Almanak, but this field is still in its early development stage, and these projects need to be tested by the market. Undoubtedly, the biggest obstacle to Agent autonomous trading is the trust issue: one must trust that the relevant operations are indeed executed by the Agent and that the Agent's trading strategies will not lead to unnecessary losses. Future projects must address these trust issues to achieve success.

After months of development, AI Agents in the crypto field have gone through several stages, from pure memes to entertainment applications, and then to practical applications. In fact, crypto practitioners have never stopped exploring the possibilities of Crypto x AI. Since 2023, CGV Research has continuously monitored the project progress in the Crypto x AI track.

In the future, as infrastructure continues to mature and Agent systems become smarter and more stable, anyone will be able to easily deploy and use Agents through natural language. At that time, the Agent framework will serve as a foundational infrastructure, with various applications built upon these frameworks. The valuation of Agent frameworks is expected to continue to break through, while some Agent application projects, due to their outstanding business capabilities and user experiences, may further capture market attention and investment value.

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