This article is from: Deep Value Memetics
Compiled | Odaily1 (@ OdailyChina)
Translator | Azuma (@ azuma_eth)
Key Points Summary
In this report, we discuss the development landscape of several mainstream frameworks in the Crypto & AI field. We will examine the current four major frameworks - Eliza (Non-Fungible Token16Z), G.A.M.E (GAME), Rig (ARC), and ZerePy (ZEREBRO), analyzing their technical differences and development potential.
In the past week, we have analyzed and tested the above four frameworks, and the conclusions are summarized as follows.
We believe that Eliza (market share around 60%, original author's market value about $900 million, current market value about $1.4 billion) will continue to dominate the market share. The value of Eliza lies in its first-mover advantage and the accelerated adoption by developers, as evidenced by 193 contributors on Github, 1,800 forks, and over 6,000 stars, making it one of the most popular software libraries on Github.
G.A.M.E (market share around 20%, original author's market value about $300 million, current market value about $257 million) has been developing very smoothly so far, and is also experiencing rapid adoption, as evidenced by the announcement previously released by Virtuals Protocol, which stated that there are more than 200 projects built on G.A.M.E, with over 150,000 daily requests and a weekly growth rate exceeding 200%. G.A.M.E will continue to benefit from the explosion of VIRTUAL and may become one of the biggest winners in this ecosystem.
Rig (market share around 15%, original author's market value about $160 million, current market value about $279 million) has a very impressive modular design and is easy to operate, and is expected to dominate the Solana ecosystem (RUST).
ZerePy (market share around 5%, original author's market value about $300 million, current market value about $424 million) is a more niche application, specific to a passionate ZEREBRO community, and its recent collaboration with the ai16z community may generate some synergies.
In the above statistics, "market share" is calculated by comprehensively considering market value, development records, and the breadth of the underlying operating system terminal market.
We believe that AI frameworks will be the fastest-growing sector in this cycle, and the current total market value of about $1.7 billion will easily grow to $20 billion, which may still be relatively conservative compared to the peak valuation of Layer1 in 2021, when many single projects had valuations exceeding $20 billion. Although the above frameworks serve different terminal markets (chains/ecosystems), given that we believe this sector will grow overall, a market value-weighted approach may be relatively the most prudent.
The Four Frameworks
At the intersection of AI and Crypto, several frameworks have emerged to accelerate AI development, including Eliza (Non-Fungible Token16Z), G.A.M.E (GAME), Rig (ARC), and ZerePy (ZEREBRO). From open-source community projects to performance-focused enterprise solutions, each framework caters to 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, technical architectures, algorithms, and unique features with potential use cases they employ. We will then compare each framework in terms of ease of use, scalability, adaptability, and performance, discussing their strengths and limitations.
Eliza
Eliza is an open-source, multi-agent simulation framework developed by Non-Fungible Token16Z, aimed at creating, deploying, and managing autonomous AI agents. It is developed using TypeScript as the programming language, providing a flexible and scalable platform for building intelligent agents that can interact with humans across multiple platforms while maintaining consistent personalities and knowledge.
The core functionalities of the framework include: a multi-agent architecture that supports the simultaneous deployment and management of multiple unique AI personalities; a role system for creating diverse agent personas using a role file framework; and memory management capabilities with advanced retrieval-augmented generation (RAG) to provide long-term memory and contextually aware recollection. Additionally, Eliza offers seamless platform integrations, enabling reliable connections with Discord, X, and other social media platforms.
Eliza is an excellent choice for AI agent communication and media functionalities. In terms of communication, the framework supports integration with Discord's voice channel capabilities, X functionality, Telegram, and direct API access for custom use cases. On the media processing front, the framework has expanded to include PDF document reading and analysis, link content extraction and summarization, audio transcription, video content processing, image analysis, and dialogue summarization, effectively handling a variety of media inputs and outputs.
Eliza provides flexible AI model support, allowing for local inference using open-source models, cloud-based inference through default configurations like OpenAI and Nous Hermes Llama 3.1B, and integration with Claude to handle complex queries. Eliza's modular architecture, with its extensive action system, custom client support, and comprehensive APIs, ensures cross-application scalability and adaptability.
Eliza's use cases span multiple domains, such as AI assistants for customer support, community management, and personal tasks; social media roles like automated content creators and brand representatives; knowledge worker roles like research assistants, content analysts, and document processors; as well as interactive roles like role-playing bots, educational tutors, and entertainment agents.
Eliza's architecture is built around an agent runtime that seamlessly integrates with a role system (supported by model providers), a memory manager (connected to a database), and an action system (linked to platform clients). The framework's unique features include a plugin system that allows for modular functionality extensions, support for multimodal interactions (voice, text, and media), and compatibility with leading AI models like Llama, GPT-4, and Claude. With its multifunctional and robust design, Eliza has become a powerful tool for cross-domain AI application development.
G.A.M.E
G.A.M.E, developed by the official Virtuals team, stands for "The Generative Autonomous Multimodal Entities Framework," and is designed to provide developers with application programming interfaces (APIs) and software development kits (SDKs) to experiment with AI agents. The framework offers a structured approach to managing the behavior, decision-making, and learning processes of AI agents.
Here is the English translation of the text, with the specified translations applied:- The core components of G.A.M.E. are as follows. First, the "Agent Prompting Interface" is the entry point for developers to integrate G.A.M.E. into agents to obtain agent behavior.
- The "Perception Subsystem" then initiates 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 the "Strategic Planning Engine", serving as the sensory input mechanism for the AI agent, whether in the form of dialogue or reaction. The key component here 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 operates at two levels: as a high-level planner, it creates broad strategies based on context or objectives; as a low-level strategy, it translates these strategies into executable policies, further divided into an action planner (for specifying tasks) and a 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 decision-making. Additionally, the "Agent Library" is used to store long-term attributes such as goals, reflections, experiences, and personality, which collectively shape the agent's behavior and decision-making process. This framework utilizes a "Short-Term Working Memory" and a "Long-Term Memory Processor" -- the short-term memory retains relevant information about previous actions, results, and current plans; in contrast, the long-term memory processor extracts key information based on criteria such as importance, recency, and relevance. This memory stores knowledge about the agent's experiences, reflections, dynamic personality, world context, and working memory to enhance decision-making and provide a foundation for learning.
- To enhance the layout, the "Learning Module" receives data from the "Perception Subsystem" to generate general knowledge, which is then fed back into the system to optimize future interactions. Developers can provide 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 prompting interface; the "Perception Subsystem" processes the input and forwards it to the "Dialogue Processing Module", which manages the interaction logic; then, the "Strategic Planning Engine" uses high-level strategies and detailed action planning to formulate and execute plans based on this information.
Data from the "World Context" and the "Agent Library" provides information for these processes, while working memory tracks immediate tasks. Meanwhile, the "Long-Term Memory Processor" stores and retrieves knowledge over time. The "Learning Module" analyzes the results and integrates new knowledge into the system, allowing the agent's behavior and interactions to continuously improve.
Rig
Rig is an open-source framework based on Rust, aimed at simplifying 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 various vector stores, including MongoDB and Neo4j. The framework's modular architecture features core components such as the "Provider Abstraction Layer", "Vector Store Integration", and "Agent System", facilitating seamless LLM interactions.
Rig's primary audience includes Rust-based AI/ML application developers, and its secondary audience comprises organizations seeking to integrate multiple LLM providers and vector stores into their Rust applications. The repository uses a workspace-based structure, containing multiple crates, to enable scalability and efficient project management. Rig's key features include the "Provider Abstraction Layer", which standardizes the APIs used to complete and embed LLM providers through consistent error handling; the "Vector Store Integration" component, which provides an abstract interface for multiple backends and supports vector similarity searches; and the "Agent System", which simplifies LLM interactions, supporting Retrieval-Augmented Generation (RAG) and tool integrations. Additionally, the embedding framework offers batch processing capabilities and type-safe embedding operations.
Rig leverages various technical advantages to ensure reliability and performance. Asynchronous operations utilize Rust's async runtime to efficiently handle a large number of concurrent requests; the framework's inherent error handling mechanisms improve resilience to failures in AI providers or database operations; type safety prevents compile-time errors, enhancing code maintainability; efficient serialization and deserialization processes help handle data in formats like JSON, which is crucial for communication and storage in AI services; and detailed logging and instrumentation further assist in debugging and monitoring applications.
The Rig workflow begins with a client-initiated 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 agents can use tools or access the vector store to obtain context; complex workflows, such as Retrieval-Augmented Generation, generate and refine responses, including document retrieval and context understanding, before returning them to the client. The system integrates multiple LLM providers and vector stores, allowing it to adapt to changes in model availability or performance.
Rig's use cases 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, capable of 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, aimed at deploying agents on X using OpenAI or Anthropic LLMs. ZerePy is derived from a modular version of the Zerebro backend, allowing developers to launch agents with functionality similar to the Zerebro core. While the framework provides a foundation for agent deployment, fine-tuning the models is necessary to produce creative output. ZerePy simplifies the development and deployment of personalized AI agents, particularly suited for content creation on social platforms, fostering an AI creative ecosystem targeting art and decentralized applications.
The framework is built using the Python language, emphasizing agent autonomy and the generation of creative output, consistent with Eliza's architecture and partnership relationships. Its modular design supports memory system integration, facilitating agent deployment on social platforms. Its key features include a command-line interface for agent management, integration with X, support for OpenAI and Anthropic LLMs, and a modular connection system for enhancing functionality.
ZerePy's use cases cover social media automation, where users can deploy AI agents to post, reply, like, and share, thereby increasing platform engagement. Additionally, it is applicable to content creation in domains such as music, memos, and Non-Fungible Tokens (NFTs), serving as an important tool for digital art and blockchain-based content platforms.
Horizontal Comparison
In our view, each of the aforementioned frameworks provides a unique approach to AI development, catering to specific needs and environments, which shifts the discussion from whether these frameworks are direct competitors to whether each framework offers distinct utility and value.
Eliza stands out with its user-friendly interface, particularly suitable for developers familiar with the JavaScript and Node.js environment. Its comprehensive documentation aids in setting up AI agents across various platforms, and while its rich feature set may present a moderate learning curve, its use of TypeScript makes Eliza well-suited for building agents embedded in the web, as most front-end web infrastructure is built using TypeScript. The framework is known for its multi-agent architecture, enabling the deployment of diverse AI personality agents across platforms like Discord, X, and Telegram. Its advanced RAG system for memory management makes it particularly suitable for building customer support or social media assistant-type AI helpers. While it offers flexibility, strong community support, and consistent cross-platform performance, it is still in its early stages, potentially presenting a learning curve for developers.
G.A.M.E. is designed specifically for game developers, providing a low-code or no-code interface through APIs, making it accessible to users with relatively lower technical skills in the game domain. However, its focus on game development and blockchain integration may present a steeper learning curve for those without relevant experience. It excels in procedural content generation and NPC behavior, but is also constrained by its niche focus and the additional complexities involved in blockchain integration.
