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 major four 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 USD) lies in its first-mover advantage (Lindy effect) and its increasing use by developers. The data, including 193 contributors, 1800 forks, and over 6000 stars, proves this, making it one of the most popular code repositories on Github.
So far, the development of GAME (with a market share of about 20% and a market value of about $300 million) has been very smooth, gaining rapid adoption. As recently 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, with a market share of about 15% and a market value of approximately $160 million) is very noteworthy because its modular design is very easy to operate and can dominate as a "pure-play" in the Solana ecosystem (RUST).
Zerepy (market share approximately 5%, market value approximately 300 million USD) is a relatively niche application designed specifically for the enthusiastic ZEREBRO community, and its recent collaboration with the ai16z community may generate 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 likely to easily grow to $20 billion, which is still relatively conservative compared to the peak valuations of L1 in 2021, when many L1s had valuations exceeding $20 billion. Although these frameworks serve different end markets (chains/ecosystems), given that we think this field is on a continuous 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.
In the intersection of AI and Crypto, several frameworks have facilitated the development of AI. They are AI16Z's ELIZA, ARC's RIG, ZEREPY's ZEREBRO, and GAME's VIRTUAL. Each framework addresses 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 frameworks, explaining what they are, what programming languages, technical architectures, and algorithms they use, 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 framework for multi-agent simulation, designed to create, deploy, and manage autonomous AI agents. It is developed using the TypeScript programming language, providing 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 that creates different agents using a role file framework, and memory management capabilities that provide long-term memory and context-aware memory through a Retrieval-Augmented Generation (RAG) system. In addition, the Eliza framework offers seamless platform integration, allowing for 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 features, X features, Telegram, and direct access to APIs for customized use cases. On the other hand, the framework's media processing capabilities can extend 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, cloud inference from OpenAI, and default configurations (such as Nous Hermes Llama 3.1B), and integrates support for Claude to handle complex tasks. Eliza uses a modular architecture, offering broad operating system and custom client support, along with 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 auto-creators, interactive bots, and brand representatives in social media roles. 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 agency representatives.
Eliza's architecture is built around the agent runtime, which integrates seamlessly 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, multimodal interactions such as voice, text, and media, and compatibility with leading AI models like Llama, GPT-4, and Claude. With its diverse functionalities and robust design, Eliza stands out as a powerful tool for developing AI applications across various domains.
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 and access agent behavior. 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, serving as an input mechanism for the AI agent, whether in the form of dialogue or reaction. At its core is the dialogue processing module, which handles 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, it creates broad strategies based on context or goals; as a low-level strategy, it converts these strategies into actionable plans, which are further divided into action planners for specified tasks and plan executors for task execution.
Another independent but important component is the World Context, which refers to 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 previous 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 a proxy prompt interface. The input is 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 the agent repository 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, 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 agent system, to facilitate seamless interaction with LLMs.
Rig's primary audience includes developers building AI/ML applications using Rust, followed by organizations seeking to integrate multiple LLM providers and vector stores into their own Rust applications. The repository uses a workspace architecture with multiple crates to support scalability and efficient project management. Its key feature is the provider abstraction layer, which provides standardization for completing and embedding APIs between different LLM providers with consistent error handling. The Vector Store Integration component provides an abstract interface for multiple backends and supports vector similarity searches. The agent system simplifies LLM interactions, supports Retrieval Enhanced Generation (RAG), and tool integration. In addition, the embedding framework provides batch processing capabilities and embedding operations for type safety.
Rig leverages multiple technological advantages to ensure reliability and performance. Asynchronous operations utilize Rust's asynchronous runtime to effectively handle a large number of concurrent requests. The inherent error handling mechanism of the framework enhances the recovery capabilities from failures in AI providers 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 crucial for AI service communication and storage. Detailed logging and monitoring further assist in debugging and overseeing the application.
The workflow of Rig begins when a request is initiated on the client side, 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 vector storage of the 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 model availability or performance updates.
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 modes, making it a versatile tool for developers and organizations.
Zerepy (developed by ZEREPY and blorm)
ZerePy is an open-source framework written in the Python language that aims to deploy agents on X using OpenAI or Anthropic LLMs. Derived from a modular version of Zerebro's backend, ZerePy allows developers to launch agents with core functionality similar to Zerebro. While the framework provides the foundation for agent deployment, fine-tuning the model is essential to generate creative output. ZerePy simplifies the development and deployment of personalized AI agents, especially for content creation on social platforms, fostering an AI-driven creative ecosystem for art and decentralized applications.
This framework is developed using Python, emphasizing agent autonomy and focusing on creative output generation, in line with the architecture of ELIZA 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 Frameworks
In our view, each framework provides a unique approach to artificial intelligence development that meets 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 wide range of features may present a certain learning curve. Developed using TypeScript, Eliza is an ideal choice for building agents embedded in the web, as most web infrastructure front-ends are developed in 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 its 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 expertise in the gaming field to utilize 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 procedural 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, Rig may not be very user-friendly given the complexity of the language, which presents a significant learning challenge, but it has intuitive interaction for those who are proficient in systems programming. In contrast to TypeScripe, the programming language itself is known for its performance and memory safety. It has rigorous compile-time checks and zero-cost abstractions, which are necessary to run complex AI algorithms. The language is highly efficient, and its low degree of control makes it ideal for resource-intensive AI applications. The framework provides a high-performance solution with a modular and scalable design, making it ideal for enterprise applications. However, for developers who are not familiar with Rust, using Rust will inevitably lead to a steep learning curve.
Leveraging Python, ZerePy offers a high level of usability for creative AI tasks, with a low learning curve for Python developers, especially for those with AI/ML backgrounds, and benefits from strong community support thanks to Zerebro's crypto community. ZerePy specializes in creative AI applications such as NFTs, positioning itself as a powerful tool for digital media and art. While it thrives on creativity, it has a relatively narrow scope 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 by games, and scalability is 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 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 setup.
Zerepy's scalability is aimed at creative outputs, supported by community contributions, but its focused emphasis may limit its application in a broader artificial intelligence environment. Scalability might 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 the gaming environment and Rig for handling complex AI tasks are also outstanding. ZerePy shows 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, where quick response time is 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 artificial intelligence operations.
The Rig framework is based on the Rust language, providing excellent performance for high-performance computing tasks, making it suitable for enterprise applications where computational efficiency is crucial.
The performance of Zerepy is tailored for the creation of creative content, with its metrics centered around the efficiency and quality of content generation, which may not be very applicable outside the creative field.
The advantage of ELIZA is that it offers flexibility and scalability, with its plugin system and character configuration providing a high level of adaptability, which is beneficial for 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 specializes in fostering creativity and is at the forefront of digital art AI applications, 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 the blockchain adds complexity. Rig's steep learning curve due to Rust may deter some developers, while Zerepy's narrow focus on creative output might restrict its use in other AI domains.
(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 focuses on efficiency and scalability.
Community: Less driven by the community, more focused 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 support from AI16Z contributors.
GAME (VIRTUAL):
Focus: Autonomous, self-adaptive artificial intelligence agents that can evolve based on interactions in 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, but still determining its position in the competition.
3. Star Data Trends on Github
The above figure shows the GitHub star attention 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, the significant increase in the number of stars in late November (reaching 61,000 stars) indicates that people's interest has rapidly grown, attracting the attention of developers. This exponential growth shows 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 a 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 steadily growing number of stars. It is likely to see a significant increase in the coming month. It has reached 1,700 stars, but it continues to rise. Ongoing development, updates, and a growing user base are the reasons for the continuous accumulation of user interest. This may reflect that the framework has a niche user base or is still building its reputation.
ZEREPY (Yellow Line):
ZerePy was launched just 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. The 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 through the API, thereby 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. Reasons for a Bullish 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 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 attract high-quality talent, as the efforts of its main contributors have already demonstrated its capability.
The GAME framework provides no-code integration for agents, allowing the simultaneous use of GAME and ELIZA in a single project, each serving a specific purpose. 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 significant progress with the team's efforts to attract more contributors. All projects expected to launch on VIRTUAL are anticipated to use GAME.
The Rig, represented by the ARC token, has enormous potential, although its framework is still in the early stages of growth and the plans to drive project adoption have 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, comparing the relationship of ARC with Solana to that of Virtual with Base. It is noteworthy that the team not only encourages new projects to use Rig for launch but also encourages developers to enhance the Rig framework itself.
Zerepy is a newly launched framework that is gaining increasing attention due to its partnership with Eliza. The framework has attracted contributors from Eliza, who are actively improving it. Driven by ZEREBRO fans, it has a passionate following and offers new opportunities for Python developers, who previously lacked representation in the competitive landscape of artificial intelligence infrastructure. The framework is expected to play an important role in AI creativity.
The content is for reference only, not a solicitation or offer. No investment, tax, or legal advice provided. See Disclaimer for more risks disclosure.
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 major four 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 USD) lies in its first-mover advantage (Lindy effect) and its increasing use by developers. The data, including 193 contributors, 1800 forks, and over 6000 stars, proves this, making it one of the most popular code repositories on Github.
So far, the development of GAME (with a market share of about 20% and a market value of about $300 million) has been very smooth, gaining rapid adoption. As recently 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, with a market share of about 15% and a market value of approximately $160 million) is very noteworthy because its modular design is very easy to operate and can dominate as a "pure-play" in the Solana ecosystem (RUST).
Zerepy (market share approximately 5%, market value approximately 300 million USD) is a relatively niche application designed specifically for the enthusiastic ZEREBRO community, and its recent collaboration with the ai16z community may generate 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 likely to easily grow to $20 billion, which is still relatively conservative compared to the peak valuations of L1 in 2021, when many L1s had valuations exceeding $20 billion. Although these frameworks serve different end markets (chains/ecosystems), given that we think this field is on a continuous 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.
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(1) Framework Overview
In the intersection of AI and Crypto, several frameworks have facilitated the development of AI. They are AI16Z's ELIZA, ARC's RIG, ZEREPY's ZEREBRO, and GAME's VIRTUAL. Each framework addresses 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 frameworks, explaining what they are, what programming languages, technical architectures, and algorithms they use, 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 framework for multi-agent simulation, designed to create, deploy, and manage autonomous AI agents. It is developed using the TypeScript programming language, providing 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 that creates different agents using a role file framework, and memory management capabilities that provide long-term memory and context-aware memory through a Retrieval-Augmented Generation (RAG) system. In addition, the Eliza framework offers seamless platform integration, allowing for 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 features, X features, Telegram, and direct access to APIs for customized use cases. On the other hand, the framework's media processing capabilities can extend 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, cloud inference from OpenAI, and default configurations (such as Nous Hermes Llama 3.1B), and integrates support for Claude to handle complex tasks. Eliza uses a modular architecture, offering broad operating system and custom client support, along with 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 auto-creators, interactive bots, and brand representatives in social media roles. 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 agency representatives.
Eliza's architecture is built around the agent runtime, which integrates seamlessly 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, multimodal interactions such as voice, text, and media, and compatibility with leading AI models like Llama, GPT-4, and Claude. With its diverse functionalities and robust design, Eliza stands out as a powerful tool for developing AI applications across various domains.
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 and access agent behavior. 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, serving as an input mechanism for the AI agent, whether in the form of dialogue or reaction. At its core is the dialogue processing module, which handles 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, it creates broad strategies based on context or goals; as a low-level strategy, it converts these strategies into actionable plans, which are further divided into action planners for specified tasks and plan executors for task execution.
Another independent but important component is the World Context, which refers to 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 previous 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 a proxy prompt interface. The input is 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 the agent repository 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, 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 agent system, to facilitate seamless interaction with LLMs.
Rig's primary audience includes developers building AI/ML applications using Rust, followed by organizations seeking to integrate multiple LLM providers and vector stores into their own Rust applications. The repository uses a workspace architecture with multiple crates to support scalability and efficient project management. Its key feature is the provider abstraction layer, which provides standardization for completing and embedding APIs between different LLM providers with consistent error handling. The Vector Store Integration component provides an abstract interface for multiple backends and supports vector similarity searches. The agent system simplifies LLM interactions, supports Retrieval Enhanced Generation (RAG), and tool integration. In addition, the embedding framework provides batch processing capabilities and embedding operations for type safety.
Rig leverages multiple technological advantages to ensure reliability and performance. Asynchronous operations utilize Rust's asynchronous runtime to effectively handle a large number of concurrent requests. The inherent error handling mechanism of the framework enhances the recovery capabilities from failures in AI providers 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 crucial for AI service communication and storage. Detailed logging and monitoring further assist in debugging and overseeing the application.
The workflow of Rig begins when a request is initiated on the client side, 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 vector storage of the 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 model availability or performance updates.
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 modes, making it a versatile tool for developers and organizations.
Zerepy (developed by ZEREPY and blorm)
ZerePy is an open-source framework written in the Python language that aims to deploy agents on X using OpenAI or Anthropic LLMs. Derived from a modular version of Zerebro's backend, ZerePy allows developers to launch agents with core functionality similar to Zerebro. While the framework provides the foundation for agent deployment, fine-tuning the model is essential to generate creative output. ZerePy simplifies the development and deployment of personalized AI agents, especially for content creation on social platforms, fostering an AI-driven creative ecosystem for art and decentralized applications.
This framework is developed using Python, emphasizing agent autonomy and focusing on creative output generation, in line with the architecture of ELIZA 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 Frameworks
In our view, each framework provides a unique approach to artificial intelligence development that meets 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 wide range of features may present a certain learning curve. Developed using TypeScript, Eliza is an ideal choice for building agents embedded in the web, as most web infrastructure front-ends are developed in 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 its 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 expertise in the gaming field to utilize 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 procedural 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, Rig may not be very user-friendly given the complexity of the language, which presents a significant learning challenge, but it has intuitive interaction for those who are proficient in systems programming. In contrast to TypeScripe, the programming language itself is known for its performance and memory safety. It has rigorous compile-time checks and zero-cost abstractions, which are necessary to run complex AI algorithms. The language is highly efficient, and its low degree of control makes it ideal for resource-intensive AI applications. The framework provides a high-performance solution with a modular and scalable design, making it ideal for enterprise applications. However, for developers who are not familiar with Rust, using Rust will inevitably lead to a steep learning curve.
Leveraging Python, ZerePy offers a high level of usability for creative AI tasks, with a low learning curve for Python developers, especially for those with AI/ML backgrounds, and benefits from strong community support thanks to Zerebro's crypto community. ZerePy specializes in creative AI applications such as NFTs, positioning itself as a powerful tool for digital media and art. While it thrives on creativity, it has a relatively narrow scope 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 by games, and scalability is 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 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 setup.
Zerepy's scalability is aimed at creative outputs, supported by community contributions, but its focused emphasis may limit its application in a broader artificial intelligence environment. Scalability might 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 the gaming environment and Rig for handling complex AI tasks are also outstanding. ZerePy shows 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, where quick response time is 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 artificial intelligence operations.
The Rig framework is based on the Rust language, providing excellent performance for high-performance computing tasks, making it suitable for enterprise applications where computational efficiency is crucial.
The performance of Zerepy is tailored for the creation of creative content, with its metrics centered around the efficiency and quality of content generation, which may not be very applicable outside the creative field.
The advantage of ELIZA is that it offers flexibility and scalability, with its plugin system and character configuration providing a high level of adaptability, which is beneficial for 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 specializes in fostering creativity and is at the forefront of digital art AI applications, 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 the blockchain adds complexity. Rig's steep learning curve due to Rust may deter some developers, while Zerepy's narrow focus on creative output might restrict its use in other AI domains.
(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 focuses on efficiency and scalability.
Community: Less driven by the community, more focused 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 support from AI16Z contributors.
GAME (VIRTUAL):
Focus: Autonomous, self-adaptive artificial intelligence agents that can evolve based on interactions in 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, but still determining its position in the competition.
3. Star Data Trends on Github
The above figure shows the GitHub star attention 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, the significant increase in the number of stars in late November (reaching 61,000 stars) indicates that people's interest has rapidly grown, attracting the attention of developers. This exponential growth shows 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 a 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 steadily growing number of stars. It is likely to see a significant increase in the coming month. It has reached 1,700 stars, but it continues to rise. Ongoing development, updates, and a growing user base are the reasons for the continuous accumulation of user interest. This may reflect that the framework has a niche user base or is still building its reputation.
ZEREPY (Yellow Line):
ZerePy was launched just 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. The 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 through the API, thereby 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. Reasons for a Bullish 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 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 attract high-quality talent, as the efforts of its main contributors have already demonstrated its capability.
The GAME framework provides no-code integration for agents, allowing the simultaneous use of GAME and ELIZA in a single project, each serving a specific purpose. 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 significant progress with the team's efforts to attract more contributors. All projects expected to launch on VIRTUAL are anticipated to use GAME.
The Rig, represented by the ARC token, has enormous potential, although its framework is still in the early stages of growth and the plans to drive project adoption have 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, comparing the relationship of ARC with Solana to that of Virtual with Base. It is noteworthy that the team not only encourages new projects to use Rig for launch but also encourages developers to enhance the Rig framework itself.
Zerepy is a newly launched framework that is gaining increasing attention due to its partnership with Eliza. The framework has attracted contributors from Eliza, who are actively improving it. Driven by ZEREBRO fans, it has a passionate following and offers new opportunities for Python developers, who previously lacked representation in the competitive landscape of artificial intelligence infrastructure. The framework is expected to play an important role in AI creativity.