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

Author: Deep Value Memetics, Translator: 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 have 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. Data such as 193 contributors, 1800 forks, and over 6000 stars prove 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 USD) has developed very smoothly and is gaining rapid adoption. As announced by VIRTUAL, the platform has over 200 projects, 150,000 daily requests, and a 200% weekly growth rate. GAME will continue to benefit from the rise of VIRTUAL and will become one of the biggest winners in its ecosystem.

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

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

We have noticed that our market share calculations cover market capitalization, development records, and the underlying operating system terminal market.

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

2. Four Frameworks

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

(1) Framework Overview

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

This article first introduces the framework, telling everyone what they are, what programming languages, technical architecture, and algorithms are used, what unique features they possess, and what potential use cases the frameworks can have. Then, we 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 using the TypeScript programming language and provides a flexible and scalable platform for building intelligent agents that can interact with humans across multiple platforms while maintaining a consistent personality and knowledge.

The core features of this framework include a multi-agent architecture that supports the simultaneous deployment and management of multiple unique AI personalities, a role system for creating different agents using a role file framework, and memory management capabilities that provide long-term memory and context-aware memory management through an advanced retrieval-augmented generation (RAG) system. Additionally, the Eliza framework offers seamless platform integration, enabling reliable connections with Discord, X, and other social media platforms.

From the perspective of AI agent communication and media capabilities, 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 media processing capabilities of the framework can be extended to PDF document reading and analysis, link content extraction and summarization, audio transcription, video content processing, image analysis, and conversation summarization, effectively handling various media inputs and outputs.

The Eliza framework offers 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, with extensive operating system and custom client support, as well as a comprehensive API, ensuring scalability and adaptability between applications.

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

Eliza's architecture is built around the 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 functionality 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 functionalities and robust design, Eliza stands out as a powerful tool for cross-domain development of AI applications.

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

The strategic planning engine works in conjunction 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 that creates broad strategies based on context or goals; and as a low-level strategy that translates 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. Additionally, 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 uses short-term working memory and long-term memory processors. 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 capabilities and improve its planning and decision-making abilities.

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 agent repositories notifies these processes, while working memory tracks immediate tasks. Meanwhile, the long-term memory processor stores and retrieves long-term knowledge. The learning module analyzes results and integrates new knowledge into the system, enabling 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, 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 agent system, to facilitate seamless interaction with LLMs.

The main audience for Rig includes developers building AI/ML applications using Rust, as well as 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, which standardizes the completion 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 agent system simplifies LLM interactions, supporting retrieval-augmented generation (RAG) and tool integration. Additionally, the embedding framework offers batch processing capabilities and type safety for 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 inherent error handling mechanism of the framework enhances the recovery capability from failures in AI provider or database operations. Type safety can prevent errors during the compilation process, thereby enhancing the maintainability of the code. Efficient serialization and deserialization processes support data handling in formats such as JSON, which is critical for AI service communication and storage. Detailed logging and monitoring further assist in debugging and monitoring the application.

The workflow of Rig begins when a request is initiated by the client, which interacts with the appropriate LLM model through the provider abstraction layer. The data is then processed by the core layer, where agents can use tools or access the context's vector storage. 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. A modular version derived from the Zerebro backend, ZerePy allows developers to launch agents with features similar to the Zerebro core. While the framework provides the foundation for agent deployment, fine-tuning the model is essential for generating creative outputs. ZerePy simplifies the development and deployment of personalized AI agents, especially for content creation on social platforms, fostering an AI-driven creative ecosystem focused on art and decentralized applications.

This framework is developed using Python, emphasizing agent autonomy and focusing on creative output generation, in line with ELIZA's architecture and its relationship with ELIZA. Its modular design supports memory system integration and enables the deployment of agents 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.

ZerePy's use cases 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 Major Frameworks

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

ELIZA stands out with its user-friendly interface, especially for developers familiar with JavaScript and Node.js environments. Its comprehensive documentation aids in setting up AI agents across various platforms, although its extensive feature set may present a learning curve. Developed using TypeScript, Eliza is an ideal choice for building agents embedded in web applications, as most web infrastructure frontends are developed with TypeScript. The framework is known for its multi-agent architecture, allowing deployment of different AI personalities 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 its early stages, which 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 complexities introduced by its niche and blockchain integration.

Due to the use of the Rust language, and considering the complexity of this language, Rig may not be very user-friendly, which presents significant learning challenges, but it offers intuitive interaction for those proficient in system programming. 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 an ideal choice for enterprise applications. However, for developers unfamiliar with Rust, using Rust inevitably involves facing a steep learning curve.

ZerePy leverages Python to provide high availability for creative AI tasks, with a lower learning curve for Python developers, especially 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 this multi-platform interaction may pose challenges in terms of scalability.

GAME excels 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 extensibility of Rust, designed for high-throughput applications, which is particularly effective for enterprise-level deployments, although this may imply that achieving true scalability requires complex setups.

The scalability of Zerepy is oriented towards creative output, supported by community contributions, but its focus may limit its application in a broader artificial intelligence environment. Scalability may be challenged 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 the gaming environment 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 fast social media interactions, with quick response times being key, but its performance may vary when handling more complex computational tasks.

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

The Rig framework is based on the Rust language and provides excellent performance for high-performance computing tasks, making it suitable for enterprise applications where computational efficiency is critical.

Zerepy's performance is tailored for the creation of creative content, with its metrics focused on the efficiency and quality of content generation, which may not be very applicable outside the creative field.

The advantage of ELIZA is that it provides flexibility and scalability, with its plugin system and role configuration giving it a high degree of adaptability, which is beneficial for cross-platform social AI interactions.

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

The advantages of Rig lie in its performance and scalability for enterprise artificial intelligence tasks, with a focus on providing clean modular code for the health of long-term projects.

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

Every framework has its own limitations. ELIZA is still in its early stages, with potential stability issues and a learning curve for new developers. Niche games may limit broader applications, and blockchain adds complexity. Rig's steep learning curve due to Rust may deter some developers, while Zerepy's narrow focus on creative output may limit 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 because it emphasizes efficiency and scalability.

Community: Not very community-driven, with more focus 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 available for 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 support from AI16Z contributors.

GAME (VIRTUAL):

Focus: Autonomous, self-adaptive artificial intelligence agents that can evolve based on interactions within virtual environments.

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

Community: An innovative community that is still determining its position in the competition.

3. Trends of Star Data on Github

The above image shows the GitHub star engagement data since the release of these frameworks. It is worth noting that GitHub stars are an indicator 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 in late November (reaching 61,000 stars), this indicates that people's interest is rapidly growing and attracting the attention of developers. This exponential growth suggests that ELIZA has gained substantial appeal due to its features, updates, and community engagement. Its popularity far exceeds 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 continuously growing number of stars. It is likely to see a significant increase in the next month. It has reached 1700 stars, but it continues to rise. Ongoing development, updates, and a growing number of users are reasons for the accumulating user interest. 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 fewest stars, and it is worth noting that this framework can be directly applied to agents in the virtual ecosystem via API, thus eliminating the need for visibility on Github. However, this framework was only made publicly available to builders just over a month ago, and over 200 projects are currently being built using GAME.

4. Bullish Reasons for the Framework

The V2 version of Eliza will integrate the Coinbase proxy suite. All projects using Eliza will support native TEE in the future, allowing the proxy to operate in a secure environment. One upcoming feature 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 for release 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 attract high-quality talent, with the efforts of its main contributors already demonstrating its capabilities.

The GAME framework provides no-code integration for agents, allowing GAME and ELIZA to be used simultaneously within a single project, each serving specific purposes. This approach is expected to attract builders who focus on business logic rather than technical complexities. Although the framework has only been publicly released for about 30 days, it has made substantial progress with the team's efforts to attract more contributors' support. All projects expected to launch on VIRTUAL are anticipated to utilize GAME.

The Rig, represented by the ARC token, has huge potential, although its framework is still in the early stages of growth and the adoption plan for the project 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 the collaboration with Solana, likening the relationship between ARC and Solana to that of Virtual to Base. It is noteworthy that the team not only encourages new projects to launch with Rig but also encourages developers to enhance the Rig framework itself.

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