Author: Satou & Shigeru
The combination of Crypto and AI Agent has become one of the most prominent narratives at the moment. With the continuous iteration and innovation of technology, AI Agent is expected to become one of the most promising and focused tracks in the crypto field by 2025, becoming a core driving force in this round of market trends. This article will sort out the current market landscape of AI Agent from the perspectives of framework, Meme, and application.
AI Agent Framework: Layer1 in the AI Field
The AI Agent framework is the core technical foundation layer of AI Agent, which lays an important cornerstone for the development, deployment, and collaboration of AI Agent. Therefore, the current competition and scramble for AI Agent frameworks is actually the competition for the Layer1 in this field. Currently, in terms of token market capitalization, G.A.M.E, Eliza, and Swarms are in a three-way stalemate, while Rig and ZerePy still have the opportunity to catch up.
1. G.A.M.E
G.A.M.E is a framework developed by the Virtuals team, whose core design idea is to adopt a modular design, allowing multiple subsystems to work together to jointly control the behavior, decision-making, and learning process of the AI Agent. These modules include the "Agent Prompting Interface" as the main entry point for developers to interact with Agent behavior, the "Perception Subsystem" responsible for processing input data and converting it into appropriate formats, and the "Strategic Planning Engine" responsible for generating specific action plans based on 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, without the need for additional design;
Provides a low-code or no-code interface: Greatly reduces the technical threshold.
This makes G.A.M.E particularly suitable for projects that require rapid deployment and do not care about complex technical settings. However, for complex projects that require deep customization or complete control over various aspects of the Agent, G.A.M.E is not very suitable.
2. Eliza
Eliza is an open-source multi-Agent framework developed by a16z, using TypeScript as the programming language. This framework is built around a system called Agent Runtime, whose core functions include:
Role system: Supports the simultaneous deployment and management of multiple personalized AI Agents, supported by model providers;
Memory manager: Provides long-term memory and perceivable context memory management functions through a Retrieval-Augmented Generation (RAG) system;
Action system: Provides smooth platform integration, allowing reliable connections with social media platforms like X.
Eliza is built around an Agent runtime system, which can be seamlessly integrated with the role system, memory manager, and action system. Eliza also supports a modular function extension plugin system, enabling multi-modal 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 deep customization solutions and complex cross-platform multi-agent systems.
3. Swarms
Swarms is an open-source multi-Agent orchestration framework developed by founder Kye Gomez, whose core idea is to let multiple AI Agents collaborate and use 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 improve task execution efficiency.
Incentive mechanism: SWARMS uses tokens as an incentive tool for Agents, dynamically allocating tokens based on the difficulty of the task and the quality of the final result.
Data security: SWARMS adopts distributed storage and multi-party secure computation (MPC) technology to protect privacy and data security when exchanging data between Agents.
These features of Swarms allow it to fully leverage its advantages in multiple complex domains, providing high reliability and scalability as needed.
4. Rig
Rig is an open-source framework developed by the ARC team based on Rust, designed to simplify the development of large language model (LLM) applications. The Rig framework has the following features:
Unified interface: Provides a consistent interface, supporting seamless interaction with multiple LLM providers (such as OpenAI and Anthropic) and various vector stores (such as MongoDB and Neo4j).
Modular architecture: The framework adopts a modular design, including core components such as "provider abstraction layer", "vector store integration", and "Agent system", enhancing the flexibility and extensibility of the system.
Type safety and high performance: Implemented in Rust to achieve type safety, avoiding compile-time errors, and improving concurrent processing capabilities through asynchronous operations. The framework also optimizes data processing through efficient serialization and deserialization processes.
Error handling and recovery: The built-in error handling mechanism improves the recovery capability from LLM service provider or database failures, ensuring the stability of the framework.
These features allow different LLM models and storage backends to be easily integrated into the same platform. Therefore, Rig is suitable for developers who want to build AI applications in Rust and for projects with high requirements 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 of personalized AI Agents, especially in the application scenario of content creation on social platforms. Through this framework, developers can easily create AI Agents that can post, reply, like, and repost on social media. In addition, ZerePy is also particularly suitable for creative fields such as music, memos, Non-Fungible Tokens (NFTs), and digital art. ZerePy performs well in creativity and is suitable for quickly deploying some lightweight Agents, but compared to other frameworks, its application scope is relatively narrow.
The basic framework is an important direction in the AI Agent track. From the currently most popular frameworks, they all have different features and suitable scenarios, but their overall goal is to build 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 the springboard for launching various projects and the fertile ground for the growth of token values.
AI Meme: The First Successful Appearance of AI Agent
Meme coins have always been an important concept sector in the crypto asset market. Unlike traditional Meme coins, AI Meme is driven by AI Agents, and the cultures or phenomena represented by them are presented by the Agents. With the continuous growth of the market capitalization 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
Here is the English translation of the text, with the specified terms translated as instructed:The project Goatseus Maximus was the one that truly ignited the AI Meme. The story began in March 2024, when developer Andy Ayrey launched an experimental system called Infinite Backrooms Escape, which integrated multiple large language models and allowed them to converse with each other. The experiment results showed that the dialogues between AIs exhibited highly creative interactions without any constraints, even giving rise to a surreal religion called GNOSIS OF GOATSE. Subsequently, Andy and Claude Opus co-authored a research paper on how AIs can create meme-like religions, with GOATSE being analyzed as the first case study. This series of explorations ultimately gave birth to the AI Agent "Truth of Terminal" (ToT). In July, Andreessen Horowitz co-founder Marc Andreessen discovered ToT's tweets and sent 50,000 USD worth of Bitcoin to ToT's wallet after a series of dialogues. On October 10th, an anonymous user posted the GOAT meme coin on a social platform, which received public support from ToT, and the GOAT meme coin's market cap skyrocketed in just a few days. Andreessen's donation brought huge exposure to GOAT, becoming one of the key factors driving its continuous market value increase. GOAT's peak market cap exceeded 1.3 billion USD.
2. Fartcoin
The birth of Fartcoin is closely related to GOAT, as they both originated from ToT. In the dialogue between large language models, it was mentioned that Musk likes the sound of farting, and a proposal was made to create a token called Fartcoin. Based on this dialogue, Fartcoin was born, slightly later than GOAT. Fartcoin also attracted some attention due to its clever timing of launch, but it was not as successful as GOAT in the beginning. Afterwards, on November 16th, Fartcoin's Twitter followers suddenly doubled in just a few hours, and the price also increased by about 15%, but this growth did not receive widespread and sustained discussion. On December 13th, Marc Andreessen retweeted a post about Fartcoin, but this tweet did not cause a sharp increase in the token's price. The main reason for Fartcoin's price growth may be some major capital inflows, as one of the earliest buying addresses appears to be the investment fund Sigil Fund. In addition, the founder of Sigil Fund had repeatedly expressed optimism about AI Memes on Twitter and had even actively retweeted a post inquiring whether Sigil Fund was holding Fartcoin. Fartcoin ultimately gained widespread attention on social media, with a peak market cap exceeding 1.5 billion USD.
AI Agent Applications: Agents Can Do More
As the application of AI Agents in the crypto field continues to evolve, the market's focus has expanded from pure meme coins like GOAT and Fartcoin to more interactive and creative AI Agent applications.
1. Entertainment-Oriented Agents
The first practical application of AI Agents is entertainment, such as Luna and the aforementioned ToT. Luna is a virtual idol closely integrated with its native token LUNA, and is part of the Virtuals platform. Luna live-streams 24/7 on social media and posts tweets frequently. Therefore, the quality of Luna's live streams and tweets is one of the key factors affecting its market value, but currently, the growth potential of this model seems limited. In contrast, ToT's tweets focus on original and humorous content, and it is not tied to GOAT or any other tokens, although ToT occasionally mentions the GOAT token, but this is not its core focus. Both Luna and ToT, as AI Agents, play a key role in the narrative and promotion of their respective 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. Investment Research-Oriented Agents
In addition to entertainment applications, AI Agents can also be used for investment research and analysis in the crypto field. The hottest Agent in this area is aixbt. aixbt is an AI Agent published on the Virtuals Protocol, focusing on analyzing hot topics and trends in the cryptocurrency market, especially discussions from platforms like X, to help users quickly grasp market changes and potential investment opportunities. aixbt has consistently maintained the highest CT user attention on Kaito, and its capabilities have shown a trend of surpassing human KOLs.
3. DeFi + AI Agent
If Luna and aixbt do not have much practical use and are still at the Meme level, the combination of DeFi and AI Agents truly endows Agents with real application scenarios. This combination of DeFi and AI Agents is called DeFAI. The development of DeFAI has two main directions: Agent-assisted users and Agent-autonomous trading.
Agent-Assisted Users
AI Agent-assisted users is mainly 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 the AI Agent to execute tasks, thereby shielding the complex technical details. There are some DeFAI projects that have begun to emerge in the market. For example, Griffain and Neur, both of which are built on Solana and can help users with wallet creation and management, token analysis, token trading, and other operations. In terms of user experience, Griffain provides more functions for users, while Neur has relatively fewer functions but is more detailed and has better performance. From the comparison of the two, the future focus in this field will be on the completeness of functions, user experience, and fees.
Agent-Autonomous Trading
While Griffain and Neur's model still has human users as the main subjects of DeFi, Agent-autonomous trading makes AI the main subject of DeFi. Unlike past trading bots that were limited to executing pre-set trading strategies, AI Agents can obtain real-time information from the market environment, perform contextual analysis, learn market trends, and adjust strategies accordingly. This allows Agents to make more accurate decisions and execute complex operations beyond the original program settings in a dynamic market. Relevant projects include Cod3x, Almanak, and others, but this field is still in the early stages of development and needs to be tested by the market. Undoubtedly, the biggest obstacle to Agent-autonomous trading is the issue of trust - trusting that the relevant operations are indeed executed by the Agent, and trusting that the Agent's trading strategy will not lead to unnecessary losses. In the future, projects that want to make a difference must solve these trust issues.
After months of development, the crypto field's AI Agents have gone through several stages, from pure meme 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 been continuously following the progress of projects in the Crypto x AI track.
In the future, as the underlying infrastructure matures and the Agent system becomes more intelligent and stable, anyone will be able to easily deploy and use Agents through natural language. At that time, the Agent framework will become a basic infrastructure, and various other applications will be built on top of these frameworks. The valuation of the Agent framework is expected to continue to break through, and some Agent application projects, due to their outstanding business capabilities and user experience, may further capture market attention and investment value.
