Compilation | Odaily
Key Points Overview
In this report, we discuss the development landscape of several mainstream frameworks in the Crypto & AI fields. We will examine the current four mainstream frameworks - Eliza (AI16Z), G.A.M.E (GAME), Rig (ARC), and ZerePy (ZEREBRO), analyzing their technical differences and development potential.
Over the past week, we have analyzed and tested the above four frameworks, with the summary of conclusions as follows.
· We believe that Eliza (market share approximately 60%, market value about $900 million at the time of the original author's writing, and approximately $1.4 billion by the time of publication) will continue to dominate market share. The value of Eliza lies in its first-mover advantage and accelerated adoption by developers, as evidenced by 193 contributors, 1800 forks, and over 6000 stars on GitHub, making it one of the most popular software libraries on GitHub.
· G.A.M.E (market share approximately 20%, market value about $300 million at the time of the original author's writing, and approximately $257 million by the time of publication) has developed smoothly thus far and is experiencing rapid adoption, as indicated by an earlier announcement from Virtuals Protocol, which noted that there are over 200 projects built on G.A.M.E, with daily requests exceeding 150,000 and a weekly growth rate of over 200%. G.A.M.E will continue to benefit from the explosion of VIRTUAL and has the potential to become one of the biggest winners in the ecosystem.
· Rig (market share approximately 15%, market value about $160 million at the time of the original author's writing, and approximately $279 million by the time of publication) has a modular design that is highly attractive and easy to operate, and is expected to dominate the Solana ecosystem (RUST).
· Zerepy (market share approximately 5%, market value about $300 million at the time of the original author's writing, and approximately $424 million by the time of publication) is a more niche application, specific to a fervent ZEREBRO community, and its recent collaboration with the ai16z community may generate some synergy.
In the above statistics, 'market share' takes into account market capitalization, development records, and the breadth of the underlying operating system terminal market in its calculation.
We believe that AI frameworks will become the fastest-growing segment in this cycle, with the current total market capitalization of approximately $1.7 billion expected to easily grow to $20 billion. Compared to the peak valuations of Layer 1 in 2021, this figure may still be relatively conservative - at that time, many single projects were valued at over $20 billion. Although the above frameworks serve different terminal markets (chains/ecosystems), given that we believe this segment will grow as a whole, adopting a market-cap-weighted approach may be relatively cautious.
Four Major Frameworks
At the intersection of AI and Crypto, several frameworks aimed at accelerating AI development have emerged, including Eliza (AI16Z), 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 in 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, their technical architectures, algorithms, and unique features with potential use cases. Then we will compare each framework based on usability, scalability, adaptability, and performance, while discussing their advantages and limitations.
Eliza
Eliza is an open-source multi-agent simulation framework developed by ai16z, designed to create, deploy, and manage 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 personality and knowledge.
The core functionalities of the framework include: supporting the simultaneous deployment and management of multiple unique AI personalities in a multi-agent architecture; creating a diverse agent role system using a role file framework; providing long-term memory and context-aware memory management capabilities through an advanced Retrieval-Augmented Generation (RAG) system. Additionally, the Eliza framework offers smooth platform integration, allowing reliable connections with Discord, X, and other social media platforms.
In terms of communication and media capabilities for AI agents, Eliza is an excellent choice. In communication, the framework supports integration with Discord's voice channel feature, X, Telegram, and direct API access for customized use cases. On the other hand, the framework's media processing capabilities have 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 various media input and output.
Eliza provides flexible AI model support, allowing for local inference using open-source models, cloud-based inference through default configurations such as OpenAI and Nous Hermes Llama 3.1 B, and supports integration with Claude for handling complex queries. Eliza adopts a modular architecture with a wide range of action systems, custom client support, and comprehensive APIs, ensuring cross-application scalability and adaptability.
The use cases of Eliza cover various fields, such as AI assistants related to customer support, community management, and personal tasks; automated content creators, brand representatives, and other social media roles; it can also act as knowledge workers, serving as research assistants, content analysts, and document processors; as well as interactive roles such as role-playing bots, educational mentors, and agency representatives.
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 such as voice, text, and media, and compatibility with leading AI models such as Llama, GPT-4, and Claude. With its versatility and robust design, Eliza becomes a powerful tool for cross-domain AI application development.
G.A.M.E
G.A.M.E is developed by the official Virtuals team, which stands for 'The Generative Autonomous Multimodal Entities Framework.' This framework aims to provide developers with an API and Software Development Kit (SDK) to experiment with AI agents. The framework offers a structured approach to managing AI agent behavior, decision-making, and learning processes.
· The core components of G.A.M.E are as follows: firstly, the 'Agent Prompting Interface' is the entry point for developers to integrate G.A.M.E into agents for obtaining agent behavior.
· The 'Perception Subsystem' initiates sessions 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 responses. The core here is the 'Dialogue Processing Module,' responsible for handling messages and responses from the agent and collaborating with the 'Perception Subsystem' to effectively interpret and respond to inputs.
· The 'Strategic Planning Engine' works in conjunction with the 'Dialogue Processing Module' and 'On-Chain Wallet Operator' to generate responses and plans. This engine operates on two levels: as a high-level planner, creating broad strategies based on context or goals; and as a low-level strategist, converting those strategies into actionable policies, further subdivided into action planners (for specifying tasks) and plan executors (for executing tasks).
· A separate but critical component is the 'World Context,' which refers to the environment, world information, and game state, providing 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 personalities, which collectively shape the agent's behavior and decision-making process. The framework employs a 'Short-term Working Memory' and 'Long-term Memory Processor' - the short-term memory retains relevant information about previous actions, outcomes, 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 related to the agent's experiences, reflections, dynamic personality, world context, and working memory to enhance decision-making and provide a foundation for learning.
· To enhance layout, the 'Learning Module' retrieves 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 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' formulates and executes plans based on this information, utilizing high-level strategies and detailed action planning.
Data from the 'World Context' and 'Agent Library' inform these processes, while working memory tracks immediate tasks. Meanwhile, the 'Long-term Memory Processor' stores and retrieves knowledge over time. The 'Learning Module' analyzes outcomes 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, 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 various vector storage solutions, including MongoDB and Neo4j. The framework's modular architecture includes core components such as 'Provider Abstraction Layer,' 'Vector Storage Integration,' and 'Agent System' to facilitate seamless interactions with LLMs.
Rig's primary audience includes developers building AI/ML applications using Rust, with a secondary audience comprising organizations seeking to integrate multiple LLM providers and vector storage into their Rust applications. The resource library is organized using a workspace-based structure, containing multiple crates that implement scalability and efficient project management. Rig's main features include the 'Provider Abstraction Layer,' which standardizes API completion and embedding for LLM providers through consistent error handling; the 'Vector Storage Integration' component, which provides an abstract interface for multiple backends and supports vector similarity search; and the 'Agent System,' which simplifies LLM interactions and supports Retrieval-Augmented Generation (RAG) and tool integration. Additionally, the embedding framework provides batch processing capabilities and type-safe embedding operations.
Rig leverages several technological advantages to ensure reliability and performance. Asynchronous operations utilize Rust's asynchronous runtime for efficient handling of a large number of concurrent requests; the framework's inherent error handling mechanisms enhance recovery from failures in AI provider or database operations; type safety prevents compile-time errors, improving code maintainability; efficient serialization and deserialization processes aid in handling data formats like JSON, which are crucial for AI service communication and storage; detailed logging and instrumentation further assist in debugging and monitoring applications.
The workflow in Rig begins with the client initiating a request, which flows through the 'Provider Abstraction Layer' to interact with the corresponding LLM model; then, the data is processed by the core layer, where agents can use tools or access vector storage for context; complex workflows such as RAG generate and refine responses, including document retrieval and context understanding, before returning to the client. The system integrates multiple LLM providers and vector storage, adapting to model availability or performance variations.
Rig's use cases are diverse, including retrieval of relevant documents to provide accurate responses in question-answering systems, document searches and retrievals for efficient content discovery, and chatbots or virtual assistants providing 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, designed to deploy 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 functionalities similar to those of Zerebro's core features. While the framework provides a foundation for agent deployment, fine-tuning is necessary to generate creative output. ZerePy simplifies the development and deployment of personalized AI agents, especially suited for content creation on social platforms, fostering an AI creative ecosystem focused on art and decentralized applications.
The framework is built using Python, emphasizing agent autonomy and the generation of creative outputs, aligning with the architecture + partnerships of Eliza. Its modular design supports memory system integration, making it easier to deploy agents on social platforms. Its main features include a command-line interface for agent management, integration with X, support for OpenAI and Anthropic LLMs, and a modular connection system for enhanced functionality.
ZerePy's use cases encompass social media automation, allowing users to deploy AI agents for posting, replying, liking, and sharing, enhancing platform engagement. Additionally, it is suitable for content creation in fields such as music, notes, and NFTs, making it an important tool for digital art and blockchain-based content platforms.
Horizontal Comparison
In our view, each of the aforementioned frameworks offers a unique approach to AI development, catering to specific needs and environments, shifting the debate from whether these frameworks compete with each other to whether each framework provides unique utility and value.
· Eliza stands out with its user-friendly interface, especially suitable for developers familiar with JavaScript and Node.js environments. Its comprehensive documentation facilitates setting up AI agents across various platforms. Although its rich feature set may present a moderate learning curve, Eliza is highly suitable for building agents embedded within the web, as most frontend web infrastructure is built using TypeScript. The framework is known for its multi-agent architecture, capable of deploying diverse AI personality agents across platforms like Discord, X, and Telegram. Its advanced RAG system for memory management makes it particularly suitable for building AI assistants for customer support or social media applications. While it offers flexibility, strong community support, and consistent cross-platform performance, it is still in the early stages, which may pose a learning curve for developers.
· G.A.M.E is designed for game developers, providing a low-code or no-code interface via API, making it accessible for users with lower technical levels in the gaming field. 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 also limited by its niche focus and additional complexities when integrating with blockchain.
· Rig, due to its use of the Rust language, may be less user-friendly due to the complexity of the language, presenting significant challenges for learning. However, for those well-versed in systems programming, it can provide intuitive interactions. Compared to TypeScript, Rust is renowned for its performance and memory safety. It features strict compile-time checks and zero-cost abstractions, essential for running complex AI algorithms. The efficiency and low-level control characteristics of the language make it an ideal choice for resource-intensive AI applications. The framework’s modular and scalable design offers high-performance solutions, making it very suitable for enterprise applications. However, for developers unfamiliar with Rust, using Rust can entail a steep learning curve.
· ZerePy employs Python, providing higher accessibility for creative AI tasks. For Python developers, especially those with AI/ML backgrounds, the learning curve is lower, and strong community support can be gained due to the popularity of ZEREBRO. ZerePy excels in creative AI applications like NFTs, positioning itself as a powerful tool in the digital media and arts domain. While it performs excellently in creativity, its application scope is relatively narrow compared to other frameworks.
In terms of scalability, the comparison of the four frameworks is as follows.
· Eliza has made significant progress following the V2 version update, introducing a unified messaging line and scalable core framework, enabling efficient management across platforms. However, managing this multi-platform interaction may present scalability challenges if not optimized.
· G.A.M.E excels in real-time processing required for gaming, with its scalability managed through efficient algorithms and potential blockchain distributed systems, although it may be constrained by specific game engines or blockchain networks.
· The Rig framework can leverage Rust's performance advantages to achieve better scalability, inherently designed for high-throughput applications, which may be particularly effective for enterprise-level deployments. However, achieving true scalability may require complex setups.
· ZerePy's scalability targets creative outputs and is supported by community contributions, but the framework's focus may limit its application in a broader AI context, with its scalability potentially tested by the diversity of creative tasks rather than user volume.
In terms of applicability, Eliza leads significantly with its plugin system and cross-platform compatibility, followed by G.A.M.E in gaming environments and Rig in handling complex AI tasks. ZerePy shows high adaptability in creative domains but is less applicable in broader AI applications.
In performance, the test results of the four frameworks are as follows.
· Eliza has been optimized for fast interactions on social media, but its performance may vary when handling more complex computational tasks.
· G.A.M.E focuses on high-performance real-time interactions in gaming scenarios, leveraging efficient decision-making processes and potential decentralization of AI operations through blockchain.
· Rig, based on Rust, can provide excellent performance for high-performance computing tasks, suitable for enterprise applications where computational efficiency is critical.
· ZerePy's performance is focused on the creation of creative content, with metrics centered around the efficiency and quality of content generation, which may not be as universally applicable outside of creative domains.
Combining the above advantages and disadvantages, Eliza offers better flexibility and scalability, with its plugin system and role configurations providing strong adaptability for cross-platform social AI interactions; G.A.M.E provides unique real-time interaction capabilities in gaming scenarios and offers novel AI participation through blockchain integration; Rig's strengths lie in its performance and scalability, suitable for enterprise-level AI tasks, emphasizing code simplicity and modularity to ensure the long-term health of projects; Zerepy excels in nurturing creativity, leading in AI applications in digital art, supported by a vibrant community-driven development model.
In summary, each framework has its limitations. Eliza is still in the early stages, with potential stability issues and a long learning curve for new developers; G.A.M.E's niche focus may limit its broader application, and introducing blockchain adds complexity; Rig's learning curve is steeper due to the complexity of the Rust language, which may deter some developers; Zerepy's narrow focus on creative outputs may limit its application in other areas of artificial intelligence.
Core Comparison Items
Rig (ARC)
Language: Rust, focusing on safety and performance.
Use Case: Focused on efficiency and scalability, making it an ideal choice for enterprise-level AI applications.
Community: Less community-driven, more focused on technical developers.
Eliza (AI16Z)
Language: TypeScript, emphasizing flexibility and community participation in Web3.
Use Case: Designed for social interaction, DAOs, and transactions, with a special emphasis on multi-agent systems.
Community: Highly community-driven, with extensive connections to GitHub.
ZerePy (ZEREBRO):
Language: Python, more accessible to a broader AI developer community.
Use Case: Suitable for social media automation and simpler AI agent tasks.
Community: Relatively new but expected to grow due to Python's popularity and support from ai16z contributors.
G.A.M.E (VIRTUAL, GMAE):
Focus: Autonomous, adaptive AI agents that can evolve based on interactions in virtual environments.
Use Case: Best suited for scenarios where agents need to learn and adapt, such as gaming or virtual worlds.
Community: Innovative but still determining its position in competition.
GitHub Data Growth
The above chart shows the changes in star data on GitHub since the launch of these frameworks. Generally speaking, GitHub stars can serve as indicators of community interest, project popularity, and perceived value of projects.
· Eliza (red line): The chart shows a significant and stable growth in the number of stars for this framework, starting from a low base in July, with a surge beginning in late November, now reaching 6,100 stars. This indicates a rapid increase in interest surrounding the framework, attracting developers' attention. The exponential growth suggests that Eliza has gained tremendous appeal due to its features, updates, and community engagement, far surpassing other products in popularity, indicating strong community support and wider applicability or interest in the AI community.
· Rig (blue line): Rig is the oldest of the four frameworks, with modest but stable star growth, and a noticeable rise in the past month. Its total number of stars has reached 1,700 but is still on an upward trajectory. The stable accumulation of attention is attributed to ongoing development, updates, and a growing user base, reflecting that Rig is still building its reputation as a framework.
· ZerePy (yellow line): ZerePy has just launched a few days ago, and the number of stars has grown to 181. It is important to emphasize that ZerePy needs more development to improve its visibility and adoption rate, and collaboration with ai16z may attract more contributors to its codebase.
· G.A.M.E (green line): The framework has a small number of stars, but it is noteworthy that it can be directly applied to agents within the Virtual ecosystem via API, so there is no need for a GitHub release. However, although the framework has only been publicly available for builders for a little over a month, there are currently over 200 projects using G.A.M.E for building.
Expectations for Upgrades of AI Frameworks
Eliza's 2.0 version will include integration with the Coinbase agent toolkit. All projects using Eliza will gain support for future native TEE (Trusted Execution Environment), enabling agents to operate in secure environments. The Plugin Registry is a feature that Eliza will launch soon, allowing developers to seamlessly register and integrate plugins.
Additionally, Eliza 2.0 will support automated anonymous cross-platform messaging. The Tokenomics white paper, expected to be released on January 1, 2025 (with relevant proposals already published), will have a positive impact on the AI16Z token that supports the Eliza framework. ai16z plans to continue enhancing the framework's utility and leverage the efforts of its main contributors to attract high-quality talent.
The G.A.M.E framework provides no-code integration for agents, allowing G.A.M.E and Eliza to be used simultaneously in a single project, each serving specific use cases. This approach is expected to attract builders focused on business logic rather than technical complexity. Although the framework has only been publicly available for just over 30 days, it has made substantial progress with the team's efforts to attract more contributors.
The Rig framework, powered by the ARC token, has significant potential, although its growth is in the early stages, and the project contracts driving Rig adoption have only been launched for a few days. However, high-quality projects are expected to emerge soon in conjunction with ARC, similar to the Virtual flywheel but focusing on Solana. The Rig team is optimistic about collaborating with Solana, positioning ARC as Solana's Virtual. Notably, the team not only incentivizes new projects launched using Rig but also encourages developers to enhance the Rig framework itself.
Zerepy is a newly launched framework that is gaining significant attention due to its collaboration with ai16z (Eliza framework). This framework has attracted contributors from Eliza who are actively working to improve it. Zerepy enjoys fervent support driven by the ZEREBRO community and is opening new opportunities for Python developers who previously lacked space to thrive in the competitive AI infrastructure field. This framework is expected to play an important role in AI's creative aspects.
