Horizontal comparison of four major AI agent frameworks: adoption status, advantages and disadvantages, and token growth potential

This article comes from: Deep Value Memetics

Compilation｜Odaily Planet Daily (@OdailyChina)

Translator｜Azuma (@azuma_eth)

Summary of key points

In this report, we discuss the development pattern of several mainstream frameworks in the field of Crypto & AI. We will examine the current four mainstream frameworks - Eliza (AI16Z), GAME (GAME), Rig (ARC), ZerePy (ZEREBRO), and analyze their technical differences and development potential.

In the past week, we have analyzed and tested the above four major frameworks, and the conclusions are summarized below.

• We believe that Eliza (with a market share of approximately 60%, a market value of approximately US$900 million when the original author wrote the article, and a market value of approximately US$1.4 billion as of publication) will continue to dominate the market share . Eliza's value lies in its first-mover advantage and accelerated developer adoption, as evidenced by 193 contributors, 1,800 forks, and over 6,000 stars on Github, making it the most popular software repository on Github one.

• GAME (with a market share of about 20%, a market capitalization of about US$300 million when the original author wrote the article, and a market capitalization of about US$257 million as of the time of publication) has been developing very smoothly so far , and is also experiencing rapid adoption, as announced earlier by Virtuals Protocol In this way, there are more than 200 projects built based on GAME, with daily requests exceeding 150,000 and a weekly growth rate exceeding 200%. GAME will continue to benefit from the explosion of VIRTUAL and is likely to become one of the biggest winners in the ecosystem.

• The modular design of Rig (with a market share of about 15%, a market value of about US$160 million when the original author wrote the article, and a market value of about US$279 million as of the time of publication) is very eye-catching and easy to operate , and is expected to occupy an important position in the Solana ecosystem (RUST). dominant position.

• Zerepy (with a market share of about 5%, a market value of about US$300 million when the original author wrote, and a market value of about US$424 million as of publication) is a more niche application, specific to a fanatical ZEREBRO community , and its recent cooperation with the ai16z community There may be some synergy effects.

In the above statistics, "market share" is calculated by taking into account market value, development record, and the breadth of the basic operating system terminal market.

We believe that the AI ​​framework will become the fastest growing sector in this cycle, and the current total market value of the sector of approximately US$1.7 billion will easily grow to US$20 billion . Compared with the Layer1 valuation at the peak in 2021, this number may still be relatively high. Conservative - many single projects were valued at more than $20 billion at the time. Although the above frameworks serve different end markets (chains/ecosystems), given that we believe this sector will grow overall, it may be relatively prudent to adopt a market capitalization weighted approach.

Four major frameworks

At the intersection of AI and Crypto, several frameworks have emerged aimed at accelerating AI development, including Eliza (AI16Z), GAME (GAME), Rig (ARC), and ZerePy (ZEREBRO). From open source community projects to performance-focused enterprise solutions, each framework caters to the different needs and philosophies of agent development.

In the table below, we list the key technologies, components, and advantages of each framework.

This report will first focus on what these frameworks are, the programming languages ​​they use, technical architectures, algorithms, and unique features with potential use cases. We will then compare each framework based on ease of use, scalability, adaptability, and performance, while discussing their strengths and limitations.

Eliza

Eliza is an open source multi-agent simulation framework developed by ai16z, designed to create, deploy and manage autonomous AI agents. Developed with TypeScript as the programming language, it provides a flexible, scalable platform for building intelligent agents that are able to interact with humans across multiple platforms while maintaining consistent personality and knowledge.

Core features of the framework include: a multi-agent architecture that supports simultaneous deployment and management of multiple unique AI personalities; a character system that uses the character file framework to create diverse agents; and an advanced retrieval-augmented generation system (RAG) that provides long-term memory and perceptibility Contextual memory management capabilities. Additionally, the Eliza framework provides smooth platform integration for reliable connections with Discord, X, and other social media platforms.

Eliza is an excellent choice for AI agent communication and media capabilities. On the communication side, the framework supports integration with Discord's voice channel feature, X features, Telegram, and direct API access for custom use cases. On the other hand, the framework's media processing capabilities have been extended to PDF document reading and analysis, link content extraction and summarization, audio transcription, video content processing, image analysis, and dialogue summarization, which can effectively handle various media inputs and outputs.

Eliza provides flexible AI model support by using open source models for local inference, cloud-based inference through default configurations such as OpenAI and Nous Hermes Llama 3.1B, and supports integrating Claude to handle complex queries. Eliza uses a modular architecture with an extensive action system, custom client support, and comprehensive API to ensure cross-application scalability and adaptability.

Eliza's use cases cover many fields, such as AI assistants related to customer support, community management, and personal tasks; and social media roles such as automatic content creators and brand representatives; it can also serve as a knowledge worker, playing the role of research assistant, Roles such as content analysts and document processors; and interactive roles in the form of role-playing bots, educational tutors, and entertainment agents.

Eliza's architecture is built around an agent runtime that works seamlessly with the character system (supported by the model provider), the memory manager (connected to the database), and the action system (linked to the platform client) integrated. Unique features of the framework include a plug-in system that allows for modular functionality expansion, support for multi-modal interactions such as voice, text, and media, and compatibility with leading AI models such as Llama, GPT-4, and Claude. With its versatility and powerful design, Eliza becomes a powerful tool for developing AI applications across domains.

GAME

GAME was developed by the Virtuals official team, and its full name is "The Generative Autonomous Multimodal Entities Framework (The Generative Autonomous Multimodal Entities Framework)". This framework aims to provide developers with application programming interfaces (APIs) and software development kits (SDKs). , so that they can conduct experiments using AI agents. The framework provides a structured approach to managing the behavior, decision-making, and learning process of AI agents.

• The core components of GAME are as follows: First, the "Agent Prompting Interface" is the entrance for developers to integrate GAME into the agent to obtain agent behavior.
• The "awareness subsystem" starts a session by specifying parameters such as session ID, agent ID, user and other relevant details. It synthesizes incoming messages into a format suitable for a "strategic planning engine", acting as a sensory input mechanism for the AI ​​agent, whether in the form of conversations or reactions. The core here is the "dialogue processing module", which is responsible for processing messages and responses from the agent, and collaborates with the "perception subsystem" to effectively interpret and respond to the input.
• The “Strategic Planning Engine” works in conjunction with the “Conversation Processing Module” and the “On-Chain Wallet Operator” to generate responses and plans. The engine operates at two levels: as a high-level planner, which creates broad strategies based on context or goals; and as a low-level strategy, which converts these strategies into executable policies, further broken down into action planners (for specified tasks) and plan executor (for executing tasks).
• A separate but critical component is the "world context", which references the environment, world information, and game state to provide the necessary context for the agent's decisions. Additionally, an “agent repository” is used to store long-term attributes such as goals, reflections, experiences, and personalities, which together shape the agent’s behavior and decision-making process. The framework uses "short-term working memory" and a "long-term memory processor" - short-term memory retains relevant information about previous actions, results and current plans; in contrast, long-term memory processors based on importance, recency and relevance Extract key information based on criteria such as sex. This memory stores knowledge about the agent's experience, reflection, dynamic personality, world context, and working memory to enhance decision-making and provide a basis for learning.
• To increase the layout, the "learning module" takes data from the "perception subsystem" to generate general knowledge, which is fed back into the system to optimize future interactions. Developers can input feedback on actions, game states, and sensory data through the interface to enhance the AI ​​agent's learning and improve its planning and decision-making capabilities.

The workflow starts with the developer interacting through the agent prompt interface; the "awareness subsystem" processes the input and forwards it to the "dialog processing module", which manages the interaction logic; then, based on this information, the "strategic planning engine" uses advanced Strategy and detailed action planning to develop and execute plans.

Data from the "world context" and "agent library" inform these processes, while working memory keeps track of immediate tasks. Meanwhile, "long-term memory processors" store and retrieve knowledge over time. "Learning modules" analyze the results and integrate new knowledge into the system, allowing the agent's behavior and interactions to continuously improve.

Rig

Rig is a Rust-based open source framework designed to simplify the development of large language model (LLM) applications. It provides a unified interface for interacting with multiple LLM providers such as OpenAI and Anthropic, and supports a variety of vector stores, including MongoDB and Neo4j. The framework's modular architecture has core components such as "Provider Abstraction Layer", "Vector Storage Integration" and "Agent System" to facilitate seamless interaction of LLM.

Rig's primary audience includes developers building AI/ML applications using Rust, and its secondary audience includes organizations looking to integrate multiple LLM providers and vector stores into their Rust applications. The resource library is organized using a workspace-based structure and contains multiple crates to achieve scalability and efficient project management. Rig's main features include the "Provider Abstraction Layer", which standardizes the API for completing and embedding LLM providers through consistent error handling; the "Vector Storage Integration" component provides multiple backends An abstract interface and support for vector similarity search; a "proxy system" simplifies LLM interaction, supports retrieval augmentation generation (RAG) and tool integration. Additionally, the embedding framework provides batch processing capabilities and type-safe embedding operations.

Rig leverages several technical advantages to ensure reliability and performance. Asynchronous operations leverage Rust's asynchronous runtime to efficiently handle large numbers of concurrent requests; the framework's inherent error handling mechanism improves resilience to failures in AI providers or database operations; type safety prevents compile-time errors, thereby improving code reliability Maintainability; efficient serialization and deserialization processes help process data in formats such as JSON, which is critical for communication and storage of AI services; detailed logging and instrumentation further help debugging and monitoring applications.

The workflow in Rig starts with the client initiating a request, which flows through the "provider abstraction layer" and interacts with the corresponding LLM model; the data is then processed by the core layer, where the agent can use tools or access the vector store to obtain Context; responses are generated and refined through complex workflows such as RAG, including document retrieval and contextual understanding, before being returned to the client. The system integrates multiple LLM providers and vector stores to adapt to changes in model availability or performance.

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

ZerePy

ZerePy is an open source framework written in Python designed to deploy agents on X using OpenAI or Anthropic LLM. ZerePy is derived from a modular version of the Zerebro backend, allowing developers to launch proxies with functionality similar to Zerebro's core functionality. While the framework provides a foundation for the deployment of agents, the model must be fine-tuned in order to produce creative outputs. ZerePy simplifies the development and deployment of personalized AI agents, especially suitable for content creation on social platforms, promoting AI creative ecosystems targeting art and decentralized applications.

The framework is built in Python language, emphasizing the autonomy of agents and focusing on the generation of creative outputs, consistent with Eliza's architecture + partnership. Its modular design supports in-memory system integration for easy deployment of agents on social platforms. Its key features include a command line interface for agent management, integration with X, support for OpenAI and Anthropic LLM, and a modular connectivity system for enhanced functionality.

ZerePy's use cases cover social media automation, where users can deploy AI agents to post, reply, like, and retweet to increase platform engagement. In addition, it is also suitable for content creation in fields such as music, memos, and NFTs, and is an important tool for digital art and blockchain-based content platforms.

Horizontal comparison

In our opinion, each of the above frameworks provides a unique approach to AI development, catering to specific needs and circumstances, which makes the debate no longer limited to whether these frameworks are competitors, but focuses on each framework. Whether it provides unique utility and value.

• Eliza stands out for its user-friendly interface, especially for developers familiar with JavaScript and Node.js environments. Its comprehensive documentation helps set up AI agents on a variety of platforms, and while its rich feature set may present a modest learning curve, thanks to its use of TypeScript, Eliza is well-suited for building agents embedded in the web as most of the front-end The web infrastructure is all built with TypeScript. The framework is known for its multi-agent architecture, enabling the deployment of diverse AI personality agents across platforms such as Discord, X, and Telegram. Its advanced RAG system for memory management makes it particularly suitable for building AI assistants of the type for customer support or social media applications. While it offers flexibility, strong community support, and consistent cross-platform performance, it's still in its early stages and may pose a learning curve for developers.

• Designed specifically for game developers, GAME provides a low-code or no-code interface through an API that is easily accessible to users with less technical skills in the gaming field. However, it is focused on game development and blockchain integration, and the learning curve may be steep for those without relevant experience. It excels in programmatic content generation and NPC behavior, but is also limited by its niche and the additional complexity that comes with blockchain integration.

• Because Rig uses the Rust language, it may not be user-friendly due to the complexity of the language, which makes learning a big challenge, but for people who are proficient in system programming, it can provide intuitive interaction. Rust itself is known for its performance and memory safety compared to TypeScript. It features strict compile-time checks and zero-cost abstractions, which are necessary to run complex artificial intelligence algorithms. The language's efficient and low-control characteristics make it ideal for resource-intensive AI applications. The framework's modular and scalable design delivers high-performance solutions ideally suited for enterprise applications. However, for developers who are not familiar with the Rust language, using Rust will bring a steep learning curve.

• ZerePy uses the Python language to provide higher usability for creative AI tasks. For Python developers, especially those with an AI/ML background, the learning curve is low, and ZEREBRO has strong community support due to its popularity. ZerePy excels in creative AI applications such as NFTs, and the framework has also positioned itself as a powerful tool in the fields of digital media and art. While it excels in creative areas, its scope of application is relatively narrow compared to other frameworks.

In terms of scalability, the comparison of the four major frameworks is as follows.

• Eliza has made great progress after the V2 version update , introducing a unified message line and an extensible core framework to achieve efficient cross-platform management. However, managing this multi-platform interaction can pose scalability challenges without optimization.

• GAME excels at the real-time processing required for games , and its scalability can be managed through efficient algorithms and an underlying blockchain distributed system, although it may be constrained by specific game engine or blockchain network limitations.

• The Rig framework leverages Rust 's performance advantages for better scalability and is inherently designed for high-throughput applications, which may be particularly effective for enterprise-level deployments, although this may mean complex setup is required to achieve true scalability. .

• ZerePy 's scalability is targeted at creative output and is supported by community contributions, but the framework's focus may limit its application in broader artificial intelligence contexts, and its scalability may be limited by the diversity of creative tasks. The test of user volume.

In terms of applicability, Eliza is far ahead with its plug-in system and cross-platform compatibility, followed by GAME in gaming environments and Rig for handling complex AI tasks. ZerePy has shown high adaptability in creative fields, but it is not suitable for wider AI applications.

In terms of performance, the test results of the four major frameworks are as follows.

• Eliza is optimized for fast interactions on social media, but performance may vary when handling more complex computing tasks.

• GAME focuses on high-performance real-time interaction in game scenarios and can utilize efficient decision-making processes and possible blockchains for decentralized AI operations.

• Rig is based on Rust and can provide excellent performance for high-performance computing tasks and is suitable for enterprise applications where computing efficiency is crucial.

• ZerePy's performance targets the creation of creative content, with metrics centered on the efficiency and quality of content generation that may be less generalizable outside of the creative field.

Combined with the comprehensive analysis of the above advantages and disadvantages, Eliza provides better flexibility and scalability. The plug-in system and role configuration make it highly adaptable, which is conducive to cross-platform social artificial intelligence interaction; GAME can provide unique real-time interaction capabilities, and provides novel AI participation through blockchain integration; Rig’s advantage lies in its performance and scalability, suitable for enterprise-level AI tasks, and focuses on the simplicity and modularity of code to ensure the long-term project Healthy development; Zerepy Specializing in nurturing creativity, it is a leader in AI applications for digital art, supported by a vibrant community-driven development model.

All in all, every framework has its limitations. Eliza is still in its early stages, with potential stability issues and a long learning curve for new developers; GAME’s niche focus may limit its wider application, and the introduction of blockchain will also increase complexity; Rig’s learning The curve is steeper because of the complexity of the Rust language, which may deter some developers; Zerepy 's narrow focus on creative output may limit its use in other areas of artificial intelligence.

Sorting out core comparison items

Rig(ARC)

• Language: Rust, focused on safety and performance.

• Use cases: Focus on efficiency and scalability, ideal for enterprise-level AI applications.

• Community: Less community driven and more focused on technology developers.

Eliza (AI16Z)

• Language: TypeScript, emphasizing Web3 flexibility and community participation.

• Use cases: Designed for social interactions, DAOs and transactions, with a special emphasis on multi-agent systems.

• Community: Highly community driven, with extensive ties to GitHub.

ZerePy (ZEREBRO):

• Language: Python, more easily accepted by a wider group of AI developers.

• Use cases: 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, GMAE):

• Focus: Autonomous, adaptive AI agents that evolve based on interactions in virtual environments.

• Use cases: Best suited for scenarios where agents need to learn and adapt, such as games or virtual worlds.

• Community: Innovative but still defining its position in the competition.

Github data growth

The above chart shows how the star data on GitHub has changed for these frameworks since their launch. In general, GitHub stars serve as an indicator of community interest, project popularity, and the project's perceived value.

• Eliza (red line): The chart shows that the number of stars in this framework has grown significantly and steadily, starting from a low base in July and surging in late November, now reaching 6,100 stars. This shows the rapid surge of interest around the framework, attracting the attention of developers. The exponential growth shows that Eliza has gained huge traction due to its features, updates and community participation. Its popularity far exceeds other products, which shows that it has strong support from the community. In the artificial intelligence community have broader applicability or interest.

• Rig (blue line): Rig is the oldest of the four major frameworks. Its star growth rate is not large, but it is stable, and it has increased significantly in the past month. Its total number of stars has reached 1,700, but it is still in an upward orbit. The steady accumulation of attention is due to continuous development, updates and a growing user base. This may reflect the fact that Rig is still building a reputation as a framework.

• ZerePy (yellow line): ZerePy was launched just a few days ago and the number of stars has grown to 181. It is important to emphasize that ZerePy needs more development to increase its visibility and adoption, and the partnership with ai16z may attract more contributors to its codebase.

• GAME (green line): This framework has a low number of stars, but it is worth noting that the framework can be applied directly to agents in the Virtual ecosystem via API, so there is no need to publish on Github. However, while the framework was made available to builders just over a month ago, more than 200 projects are currently being built using GAME.

Expected upgrades to the AI ​​framework

Version 2.0 of Eliza will include integration with the Coinbase proxy toolkit. All projects using Eliza will receive support for future native TEEs (Trusted Execution Environments), enabling agents to run in a secure environment. The Plugin Registry is an upcoming feature of Eliza that allows developers to seamlessly register and integrate plug-ins.

Additionally, Eliza 2.0 will support automated anonymous cross-platform messaging. The Tokenomics white paper, which is expected to be released on January 1, 2025, will have a positive impact on the AI16Z token supporting the Eliza framework . ai16z plans to continue to enhance the utility of the framework and leverage the efforts of its key contributors to bring in high-quality talent.

The GAME framework provides code-less integration for agents, making it possible to use both GAME and Eliza in a single project, each serving a specific use case. This approach is expected to appeal to builders who focus on business logic rather than technical complexity. Although the framework has only been publicly available for just over 30 days, substantial progress has been made on the framework, supported by the team's efforts to attract additional contributors. It is expected that every project launched on VirtuaI will adopt GAME.

The Rig framework powered by the ARC token has significant potential, although the growth of the framework is in its early stages and the project contract program to drive Rig adoption has only been live for a few days. However, high-quality projects paired with ARC are expected to emerge soon, similar to Virtual Flywheel but focused on Solana . The Rig team is optimistic about cooperation with Solana and positions ARC as Solana’s Virtual. Notably, the team not only incentivizes new projects launched using Rig, but also incentivizes developers to enhance the Rig framework itself.

Zerepy is a newly launched framework that is gaining a lot of attention due to its collaboration with ai16z (Eliza Framework), which has attracted contributors from Eliza who are actively working on improving the framework. Zerepy enjoys enthusiastic support driven by the ZEREBRO community and is opening up new opportunities for Python developers who have previously been underserved in the highly competitive world of AI infrastructure. The framework is expected to play an important role in the creative aspects of AI.