Comparison of the four major Crypto X AI frameworks: ELIZA, GAME, ARC, and ZEREPY.

Author: Deep Value Memetics, Translation: Golden Finance xiaozou

In this article, we will explore the prospects of the Crypto X AI framework. We will focus on the current four major frameworks (ELIZA, GAME, ARC, ZEREPY) and their respective technical differences.

1. Introduction

In the past week, we researched and tested the four major Crypto X AI frameworks: ELIZA, GAME, ARC, and ZEREPY. Our conclusions are as follows.

We believe that AI16Z will continue to dominate. The value of Eliza (with a market share of about 60% and a market capitalization of over $1 billion) lies in its first-mover advantage (Lindy effect) and its increasing adoption by developers. The data, such as 193 contributors, 1800 forks, and over 6000 stars, proves this, making it one of the most popular code repositories on GitHub.

So far, GAME (with a market share of about 20% and a market value of about $300 million) has developed very smoothly, gaining rapid adoption. As VIRTUAL has just announced, the platform has over 200 projects, 150,000 daily requests, and a weekly growth rate of 200%. GAME will continue to benefit from the rise of VIRTUAL and will become one of the biggest winners in its ecosystem.

Rig (ARC, with a market share of approximately 15% and a market capitalization of about 160 million USD) is very prominent due to its modular design, which is very easy to operate, and it can dominate as a "pure-play" in the Solana ecosystem (RUST).

Zerepy (with a market share of about 5% and a market value of approximately 300 million USD) is a relatively niche application aimed at the enthusiastic ZEREBRO community, and its recent collaboration with the ai16z community may yield synergies.

We have noted that our market share calculation covers market capitalization, development records, and the underlying operating system terminal market.

We believe that in this market cycle, the framework sub-market will be the fastest-growing area, with a total market capitalization of $1.7 billion potentially easily growing to $20 billion, which is still relatively conservative compared to the peak valuations of L1 in 2021, when many L1 valuations exceeded $20 billion. Although these frameworks serve different end markets (chains/ecosystems), given that we believe this field is in a continual upward trend, a market capitalization-weighted approach may be the most prudent method.

2. Four Major Frameworks

In the table below, we have listed the key technologies, components, and advantages of each major framework.

(1) Framework Overview

In the intersection of AI and Crypto, there are several frameworks that promote the development of AI. They are AI16Z's ELIZA, ARC's RIG, ZEREPY's ZEREBRO, and GAME's VIRTUAL. Each framework caters to different needs and philosophies in the AI agent development process, ranging from open-source community projects to performance-focused enterprise-level solutions.

This article will first introduce the framework, explaining what they are, what programming languages, technical architectures, and algorithms are used, what unique features they have, and what potential use cases the frameworks can be applied to. Then, we will compare each framework in terms of usability, scalability, adaptability, and performance, exploring their respective advantages and limitations.

ELIZA (developed by ai16z)

Eliza is an open-source multi-agent simulation framework designed to create, deploy, and manage autonomous AI agents. It is developed in the TypeScript programming language, providing a flexible and scalable platform for building intelligent agents that can interact with humans across multiple platforms while maintaining consistent personalities and knowledge.

The core functions of this framework include a multi-agent architecture that supports the simultaneous deployment and management of multiple unique AI personalities, a role system that creates different agents using a role file framework, and memory management capabilities that provide long-term memory and context-aware memory management through a Retrieval-Augmented Generation (RAG) system. Additionally, the Eliza framework offers smooth platform integration for reliable connections with Discord, X, and other social media platforms.

From the perspective of communication and media capabilities of AI agents, Eliza is an excellent choice. In terms of communication, the framework supports integration with Discord's voice channel functionality, X functionality, Telegram, and direct access to APIs for customized use cases. On the other hand, the framework's media processing capabilities can be extended to PDF document reading and analysis, link content extraction and summarization, audio transcription, video content processing, image analysis, and dialogue summarization, effectively handling various types of media input and output.

The Eliza framework provides flexible AI model support through local inference of open-source models, OpenAI's cloud inference, and default configurations (such as Nous Hermes Llama 3.1B), and integrates support for Claude to handle complex tasks. Eliza adopts a modular architecture, has extensive operating system and custom client support, and offers a comprehensive API, ensuring scalability and adaptability between applications.

Eliza's use cases span multiple domains, such as AI assistants for customer support, community moderation, and personal tasks, as well as content automators, interactive bots, and brand representatives in social media roles. It can also serve as a knowledge worker, taking on roles such as research assistant, content analyst, and document processor, and supports interactive roles in forms like role-playing bots, educational mentors, and agent representatives.

Eliza's architecture is built around an agent runtime, which seamlessly integrates with its role system (supported by model providers), memory manager (connected to the database), and operating system (linked to the platform client). The framework's unique features include a plugin system that supports modular functional extensions, multi-modal interactions supporting voice, text, and media, and compatibility with leading AI models such as Llama, GPT-4, and Claude. With its diverse capabilities and robust design, Eliza stands out as a powerful tool for cross-domain AI application development.

G.A.M.E (developed by Virtuals Protocol)

The Generative Autonomous Multimodal Entity framework (G.A.M.E) aims to provide developers with API and SDK access for AI agent experimentation. This framework offers a structured approach to managing the behavior, decision-making, and learning processes of AI agents.

The core components are as follows: First, the Agent Prompting Interface is the entry point for developers to integrate GAME into the agent to access agent behaviors. The Perception Subsystem initiates a session by specifying parameters such as session ID, agent ID, user, and other relevant details.

It synthesizes incoming information into a format suitable for the Strategic Planning Engine, acting as an input mechanism for the AI agent, whether in the form of dialogue or reactions. At its core is the dialogue processing module, which is used to handle messages and responses from the agent and works with the perception subsystem to effectively interpret and respond to inputs.

The strategic planning engine works together with the dialogue processing module and the on-chain wallet operator to generate responses and plans. This engine has two levels of functionality: as a high-level planner, it creates broad strategies based on context or goals; as a low-level strategist, it converts these strategies into actionable plans, which are further divided into action planners for specified tasks and plan executors for executing tasks.

Another independent but important component is the World Context, which references the environment, global information, and game state, providing the necessary context for the agent's decision-making. In addition, the Agent Repository is used to store long-term attributes such as goals, reflections, experiences, and personality, which together shape the agent's behavior and decision-making process.

The framework utilizes a short-term working memory and a long-term memory processor. Short-term memory retains relevant information about past behaviors, results, and current plans. In contrast, the long-term memory processor extracts key information based on criteria such as importance, recency, and relevance. Long-term memory stores knowledge such as the agent's experiences, reflections, dynamic personality, world context, and working memory to enhance decision-making and provide a foundation for learning.

The learning module uses data from the perception subsystem to generate general knowledge, which is fed back into the system to improve future interactions. Developers can input feedback regarding actions, game states, and sensory data through the interface to enhance the AI agent's learning ability and improve its planning and decision-making capabilities.

The workflow begins with developers interacting through the agent prompt interface. Inputs are processed by the perception subsystem and forwarded to the dialogue processing module, which is responsible for managing the interaction logic. Then, the strategic planning engine formulates and executes plans based on this information, utilizing high-level strategies and detailed action plans.

Data from the global context and proxy repositories notifies these processes, while working memory tracks instant tasks. Meanwhile, the long-term memory processor stores and retrieves long-term knowledge. The learning module analyzes the results and integrates new knowledge into the system, allowing for continuous improvement of the agent's behavior and interactions.

RIG (developed by ARC)

Rig is an open-source Rust framework designed to simplify the development of large language model applications. It provides a unified interface for interacting with multiple LLM providers, such as OpenAI and Anthropic, and supports various vector storage options, including MongoDB and Neo4j. The unique aspect of the framework's modular architecture lies in its core components, such as the Provider Abstraction Layer, vector storage integration, and proxy system, to facilitate seamless interaction with LLMs.

The main audience for Rig includes developers building AI/ML applications using Rust, followed by organizations seeking to integrate multiple LLM providers and vector storage into their own Rust applications. The repository uses a workspace architecture with multiple crates, supporting scalability and efficient project management. Its key features include a provider abstraction layer that provides standardization for completing and embedding APIs across different LLM providers, with consistent error handling. The Vector Store Integration component provides an abstract interface for multiple backends and supports vector similarity search. The proxy system simplifies LLM interactions, supporting Retrieval-Augmented Generation (RAG) and tool integration. Additionally, the embedding framework provides batch processing capabilities and type-safe embedding operations.

Rig leverages multiple technical advantages to ensure reliability and performance. Asynchronous operations utilize Rust's asynchronous runtime to efficiently handle a large number of concurrent requests. The framework's inherent error handling mechanism improves the recovery capability from failures in AI providers or database operations. Type safety can prevent errors during the compilation process, thereby enhancing code maintainability. Efficient serialization and deserialization processes support data handling in formats like JSON, which is crucial for AI service communication and storage. Detailed logging and monitoring further assist in debugging and monitoring applications.

The workflow of Rig begins when a request is initiated on the client side, interacting with the appropriate LLM model through a provider abstraction layer. The data is then processed by the core layer, where the agent can use tools or access the vector storage of context. The response is generated and refined through a complex workflow (such as RAG) before being returned to the client, a process that involves document retrieval and context understanding. The system integrates multiple LLM providers and vector storage, adapting to updates in model availability or performance.

The use cases of Rig are diverse, including question-and-answer systems that retrieve relevant documents to provide accurate responses, document search and retrieval systems for efficient content discovery, and chatbots or virtual assistants that provide context-aware interactions for customer service or education. It also supports content generation, enabling the creation of text and other materials based on learning patterns, making it a versatile tool for developers and organizations.

Zerepy (developed by ZEREPY and blorm)

ZerePy is an open-source framework written in Python, designed to deploy agents on X using OpenAI or Anthropic LLM. It is a modular version derived from the Zerebro backend, allowing developers to launch agents with functionalities similar to the core features of Zerebro. While the framework provides the foundation for agent deployment, fine-tuning the model is essential to generate creative outputs. ZerePy simplifies the development and deployment of personalized AI agents, particularly for content creation on social platforms, fostering an AI-driven creative ecosystem focused on art and decentralized applications.

This framework is developed using Python, emphasizes agent autonomy, and focuses on creative output generation, consistent with the architecture of ELIZA and its collaboration with ELIZA. Its modular design supports memory system integration and allows for agent deployment on social platforms. Key features include a command-line interface for agent management, integration with Twitter, support for OpenAI and Anthropic LLMs, and a modular connection system for enhanced functionality.

The use cases of ZerePy cover the field of social media automation, allowing users to deploy AI agents for posting, replying, liking, and sharing, thereby increasing platform engagement. Additionally, it caters to content creation in areas such as music, memes, and NFTs, making it an important tool for digital art and blockchain-based content platforms.

(2) Comparison of the Four Frameworks

In our view, each framework provides a unique approach to artificial intelligence development, catering to specific needs and environments. We shift the focus from the competitive relationship between these frameworks to the uniqueness of each framework.

ELIZA stands out with its user-friendly interface, especially for developers familiar with JavaScript and Node.js environments. Its comprehensive documentation helps set up AI agents across various platforms, although its extensive feature set may present a certain learning curve. Developed using TypeScript, Eliza is an ideal choice for building agents embedded in web applications, as most web infrastructure front-ends are developed with TypeScript. The framework is known for its multi-agent architecture, allowing different AI personalities to be deployed on platforms like Discord, X, and Telegram. Its advanced memory management RAG system makes it particularly effective for AI assistants in customer support or social media applications. While it offers flexibility, strong community support, and consistent cross-platform performance, it is still in the early stages and may pose a learning curve for developers.

GAME is designed specifically for game developers, providing a low-code or no-code interface through APIs, allowing users with lower technical skills in the gaming field to use it. However, it focuses on game development and blockchain integration, which may present a steep learning curve for those without relevant experience. It excels in program content generation and NPC behavior but is limited by the complexity added by its niche focus and blockchain integration.

Due to the use of the Rust language, and given the complexity of this language, Rig may not be very user-friendly, which presents significant learning challenges. However, for those proficient in system programming, it offers intuitive interaction. Compared to TypeScript, this programming language is known for its performance and memory safety. It features strict compile-time checks and zero-cost abstractions, which are essential for running complex AI algorithms. The language is highly efficient, and its low-level control makes it an ideal choice for resource-intensive AI applications. The framework provides high-performance solutions with a modular and scalable design, making it ideal for enterprise applications. However, for developers unfamiliar with Rust, using Rust inevitably entails facing a steep learning curve.

ZerePy utilizes Python, providing high availability for creative AI tasks, with a lower learning curve for Python developers, especially for those with an AI/ML background, and benefits from strong community support due to the Zerebro crypto community. ZerePy excels in creative AI applications such as NFTs, positioning itself as a powerful tool for digital media and art. While it thrives in creativity, its scope is relatively narrow compared to other frameworks.

In terms of scalability, ELIZA has made significant progress in its V2 update, introducing a unified messaging line and a scalable core framework that supports effective management across multiple platforms. However, without optimization, managing these multi-platform interactions may pose scalability challenges.

GAME performs excellently in real-time processing required for games, with scalability managed through efficient algorithms and potential blockchain distributed systems, although it may be limited by specific game engines or blockchain networks.

The Rig framework leverages the scalability performance of Rust, designed for high-throughput applications, which is particularly effective for enterprise-level deployments, although this may mean that achieving true scalability requires complex setups.

Zerepy's scalability is aimed at creative output, supported by community contributions, but its focus may limit its application in a broader AI environment. Scalability may be tested by the diversity of creative tasks rather than the number of users.

In terms of adaptability, ELIZA leads with its plugin system and cross-platform compatibility, while its GAME in gaming environments and Rig for handling complex AI tasks are also outstanding. ZerePy demonstrates high adaptability in the creative field but is less suitable for broader AI applications.

In terms of performance, ELIZA is optimized for quick social media interactions, where fast response times are key, but its performance may vary when handling more complex computational tasks.

The GAME developed by Virtual Protocol focuses on high-performance real-time interactions in gaming scenarios, utilizing efficient decision-making processes and potential blockchain for decentralized AI operations.

The Rig framework, based on the Rust language, offers outstanding performance for high-performance computing tasks, making it suitable for enterprise applications where computational efficiency is crucial.

The performance of Zerepy is customized for the creation of creative content, with its metrics centered around the efficiency and quality of content generation, which may not be as applicable outside the creative field.

The advantage of ELIZA is its flexibility and scalability, which, through its plugin system and role configuration, gives it a high level of adaptability, facilitating cross-platform social AI interactions.

GAME provides a unique real-time interaction feature in the game, enhanced by the integration of novel AI participation through blockchain.

The advantage of Rig lies in its performance and scalability for enterprise artificial intelligence tasks, focusing on providing clean modular code for the health of long-term projects.

Zerepy excels at nurturing creativity and is at the forefront of digital art applications in artificial intelligence, supported by a vibrant community-driven development model.

Every framework has its limitations. ELIZA is still in its early stages and has potential stability issues and a learning curve for new developers. Niche games may limit broader applications, and the complexity added by blockchain also plays a role. The steep learning curve of Rig due to Rust may deter some developers, while Zerepy's narrow focus on creative outputs might restrict its use in other AI fields.

(3) Framework Comparison Summary

Rig (ARC):

Language: Rust, focusing on safety and performance.

Use case: An ideal choice for enterprise-level AI applications, as it emphasizes efficiency and scalability.

Community: Not very community-driven, focusing more on technical developers.

Eliza (AI16Z):

Language: TypeScript, emphasizing the flexibility of web3 and community participation.

Use case: Designed for social interaction, DAOs, and trading, with a particular emphasis on multi-agent systems.

Community: Highly community-driven with extensive GitHub participation.

ZerePy (ZEREBRO)：

Language: Python, making it accessible to a broader base of AI developers.

Use case: Suitable for social media automation and simpler AI agent tasks.

Community: Relatively new, but expected to grow due to the popularity of Python and the support of AI16Z contributors.

GAME (VIRTUAL):

Focus: Autonomous, adaptive artificial intelligence agents that can evolve based on interactions in a virtual environment.

Use case: Most suitable for AI agents to learn and adapt in scenarios such as games or virtual worlds.

Community: An innovative community, but still determining its position in the competition.

3. Star Data Trend on Github

The above chart shows the GitHub star engagement data since the release of these frameworks. It is worth noting that GitHub stars are indicators of community interest, project popularity, and perceived value of the project.

ELIZA (Red Line):

Starting from the low base in July, and then seeing a significant increase in the number of stars by late November (reaching 61,000 stars), this indicates a rapid increase in people's interest, attracting the attention of developers. This exponential growth suggests that ELIZA has gained tremendous appeal due to its features, updates, and community involvement. Its popularity far surpasses that of other competitors, indicating strong community support and broader applicability or interest within the artificial intelligence community.

RIG (Blue Line):

Rig is the oldest among the four major frameworks, with a moderate but consistently growing number of stars. It is likely to see a significant increase in the next month. It has reached 1,700 stars, but it continues to rise. Ongoing development, updates, and a continuously growing user base are the reasons for the accumulating interest from users. This may reflect that the framework has a niche user base or is still building its reputation.

ZEREPY (Yellow Line):

ZerePy was just launched a few days ago and has already accumulated 181 stars. It is worth emphasizing that ZerePy needs more development to improve its visibility and adoption rate. Collaboration with AI16Z may attract more code contributors.

GAME (Green Line):

This project has the least number of stars. It is worth noting that this framework can be directly applied to agents in the virtual ecosystem through the API, eliminating the need for visibility on GitHub. However, this framework was only made available to builders just over a month ago, and more than 200 projects are currently using GAME to build.

4. Framework Bullish Reasons

The V2 version of Eliza will integrate the Coinbase proxy suite. All projects using Eliza will support native TEE in the future, enabling the proxy to run in a secure environment. One of the upcoming features of Eliza is the Plugin Registry, which will allow developers to seamlessly register and integrate plugins.

In addition, Eliza V2 will support automated anonymous cross-platform messaging. The tokenomics white paper is scheduled to be released on January 1, 2025, and is expected to have a positive impact on the underlying AI16Z token of the Eliza framework. AI16Z plans to continue enhancing the utility of the framework and attracting high-quality talent, with the efforts of its main contributors already demonstrating its capabilities.

The GAME framework provides no-code integration for agents, allowing the simultaneous use of GAME and ELIZA within a single project, each serving a specific purpose. This approach is expected to attract builders who focus on business logic rather than technical complexity. Although the framework has only been publicly released for a little over 30 days, it has made substantial progress with the team's efforts to attract more contributors' support. It is anticipated that all projects launched on VIRTUAL will utilize GAME.

The Rig represented by the ARC token has enormous potential, although its framework is still in the early growth stage, and the plan to drive project adoption has only been launched for a few days. However, high-quality projects adopting ARC are expected to emerge soon, similar to Virtual flywheel, but with a focus on Solana. The team is optimistic about collaborating with Solana, comparing the relationship of ARC with Solana to that of Virtual with Base. Notably, the team encourages not only new projects to launch using Rig but also developers to enhance the Rig framework itself.

Zerepy is a newly launched framework that is gaining increasing attention due to its collaboration with Eliza. The framework has attracted contributors from Eliza who are actively improving it. Driven by ZEREBRO fans, it has a dedicated following and offers new opportunities for Python developers who previously lacked representation in the competitive landscape of AI infrastructure. The framework is set to play an important role in AI creativity.