Each framework occupies a unique market segment, and the frameworks are more complementary than directly competing.

  • Author:arndxt

  • Compiled by: Shenchao TechFlow

Introduction

Crypto x AI is out of the limelight, and Virtuals is on the rise again (as of the time of compilation and compilation of this article, the market value of Virtuals has exceeded $2.4 billion, with a 24-hour increase of 24%). In addition to Virtuals, what other Crypto x AI frameworks are worth paying attention to? What are the similarities and differences between different frameworks?

DeepChao TechFlow hereby compiles and compiles this article, which provides an in-depth analysis of the technical architecture, market positioning and role of the four major frameworks of Eliza ($AI16Z), GAME ($VIRTUAL), Rig ($ARC) and ZerePy ($ZEREBRO) in the industry. potential impact.

Main Text

In the Crypto x AI field, there are currently four main frameworks:

  • Eliza ($AI16Z)

  • GAME ($VIRTUAL)

  • Rig ($ARC)

  • ZerePy ($ZEREBRO)

These frameworks each have clear positioning, aiming to meet the diverse needs of developers.

Eliza, with its first-mover advantage and active TypeScript community, holds about 60% market share; GAME (~20%) focuses on gaming and metaverse applications and is rapidly gaining popularity.

Rig (~15%) built on Rust offers efficient modular design, highly suited for the Solana ecosystem; while ZerePy (~5%) is an emerging framework based on Python, focusing on creative output and social media automation. Currently, the total valuation of these frameworks is $1.7 billion, and as AI-driven crypto applications continue to expand, the market size may exceed $20 billion, making market capitalization-weighted investment strategies a consideration. Each framework occupies a unique market segment—Eliza focuses on social and multi-agent interactions, GAME focuses on gaming and the metaverse, Rig is dedicated to enterprise-level performance, and ZerePy targets creative community applications. These frameworks complement each other rather than compete directly.

1. Overview and Market Position

(Original English table from @arndxt_xo, compiled by Deep Tide TechFlow) 1.1 Eliza ($AI16Z)

  • Market share: ~60%

  • Market capitalization: $900 million

  • Core language: TypeScript

  • Main advantages: First-mover advantage, large GitHub community (6,000+ stars, 1,800 forks)

  • Key applications: Support for multi-agent simulation, cross-platform social interaction

As one of the earliest AI agent frameworks in the field, Eliza holds a dominant position. Its first-mover advantage is due to a large developer community, which not only accelerates the framework's functional iteration but also drives widespread user adoption. The TypeScript-based tech stack makes it an ideal choice for developers engaged in web development, attracting a broad developer base.

1.2 GAME ($VIRTUAL)

  • Market share: ~20%

  • Market capitalization: $300 million

  • Core language: Language-agnostic design based on API/SDK

  • Main advantages: Rapid adoption in the gaming industry, supports real-time agent interaction

  • Key applications: Procedural content generation, adaptive NPC behavior

GAME is designed for gaming and metaverse applications. Its API-based architecture allows developers to easily integrate into existing projects, while its close connection with the $VIRTUAL ecosystem drives rapid ecological development. To date, over 200 projects have adopted this framework, with an average daily request volume of up to 150,000, and it continues to grow weekly. The no-code integration features of GAME are particularly valued, allowing teams that wish to deploy projects quickly to launch functionalities without deep technical details.

1.3 Rig ($ARC)

  • Market share: ~15%

  • Market capitalization: $160 million

  • Core language: Rust

  • Main advantages: High-performance modular design, optimized for the Solana ecosystem

  • Key applications: Enterprise-level performance needs, complex transaction processing

Rig focuses on performance and is built using the Rust language, fully leveraging Solana's high throughput advantages. Its modular design allows developers to flexibly customize features according to specific needs, making it highly suitable for enterprise-level applications that require high performance and low latency. Although its market share is relatively small, its positioning within the Solana ecosystem makes it particularly appealing to developers engaged in high-frequency trading and complex smart contract execution.

1.4 ZerePy ($ZEREBRO)

  • Market share: ~5%

  • Market capitalization: $300 million

  • Core language: Python

  • Main advantages: Focus on creative output and social media automation

  • Key applications: Generative content, community interaction tools

As an emerging framework in the field, ZerePy, centered on Python, lowers the development threshold and attracts a large number of creative developers and content creators. Its focus on generative content and social media automation makes it an ideal choice for creative communities and marketing teams. Although its current market share is small, its growth potential is significant.

2. Technical Architecture and Core Components

Eliza ($AI16Z)

  • Multi-agent systems: Support multiple AI agents collaborating or competing in the same operational environment, suitable for complex interaction scenarios.

  • Memory management (RAG): Enhances the contextual memory capabilities of generated content by retrieving relevant information, supporting long-term interactions.

  • Plugin system: Allows community development to extend functionality, such as voice, text parsing, and multimedia file processing (like PDF, images).

  • Extensive model support: Compatible with local open-source large language models (LLM) or cloud-based APIs (such as OpenAI, Anthropic).

Eliza's architecture is designed around multimodal communication, making it very suitable for social, marketing, and community-oriented AI applications. It supports easy integration into platforms such as Discord, X (formerly Twitter), and Telegram, providing developers with rich extension options. However, effective management of multiple agents' personalities and memory modules is necessary to ensure the system's stability and efficiency during large-scale deployments.

GAME ($VIRTUAL)

  • API + SDK model: Provides a streamlined integration solution for game studios and metaverse projects.

  • Agent prompt interface: Coordinates the interaction between user input and the agent strategy engine, optimizing player experience.

  • Strategy planning engine: Divides agent logic into high-level goal planning and low-level strategy execution, enhancing the flexibility of character behavior.

  • Blockchain integration: Supports on-chain wallet operations and decentralized agent governance, enhancing asset management capabilities within the metaverse.

The architecture of GAME is optimized for gaming and metaverse scenarios, prioritizing real-time performance needs while supporting the dynamic adaptability of agents. Its strategy planning engine can help game characters set goals and adjust actions in real-time, providing players with a more immersive experience. Although its architecture can be extended to other fields, its design is still primarily aimed at virtual worlds and procedurally generated application scenarios.

Rig ($ARC)

  • Rust workspace structure: To achieve clear and modular design, functionalities are divided into multiple independent packages.

  • Provider abstraction layer: Unifies the interaction methods with various LLM providers (such as OpenAI and Anthropic).

  • Vector storage integration: Supports multiple backends (MongoDB, Neo4j) for contextual retrieval capabilities.

  • Agent systems: Integrates retrieval-augmented generation (RAG) and specialized tool usage.

Rig's high-performance architecture benefits from Rust's concurrency model, making it well-suited for enterprise applications that require strict resource management. Through a layered abstraction design philosophy, Rig offers high reliability, but Rust's steep learning curve may limit developer participation.

ZerePy ($ZEREBRO)

  • Based on Python: Designed for AI/ML developers familiar with Python libraries and development processes, easy to get started.

  • Modular Zerebro backend: Provides creative content generation capabilities, especially suitable for social media and artistic fields.

  • Agent autonomy: Focuses on 'creative output,' including memes (internet pop culture content), music, and NFT generation tasks.

  • Community platform integration: Built-in modules similar to Twitter, such as posting, replying, and retweeting.

ZerePy provides solutions for Python developers who wish to quickly deploy agents on community platforms. Although its application scope is narrower than Eliza or Rig, ZerePy excels in artistic creation or entertainment-driven scenarios, especially having unique advantages in decentralized communities.

3. Comparison Dimensions

3.1 Usability

  • Eliza: The design pursues balance; although the complexity of multi-agents brings a certain learning curve, the strong TypeScript developer community provides good support.

  • GAME: Designed for non-technical users, particularly in the gaming field, providing no-code or low-code development solutions that lower the entry barrier.

  • Rig: Requires a higher level of expertise from developers, and the rigor of Rust necessitates certain professional capabilities, but its high performance and reliability yield rich rewards for diligent developers.

  • ZerePy: Very user-friendly for Python users, particularly suitable for developers involved in creative or media-related AI tasks.

3.2 Scalability

  • Eliza: The V2 version introduces a scalable message bus and optimized concurrency handling, but task scheduling and resource allocation for multi-agents still require careful management.

  • GAME: Scalability depends on real-time gaming demands and the stability of the blockchain network; as long as the limitations of the game engine are effectively controlled, performance remains excellent.

  • Rig: Thanks to Rust's asynchronous runtime, it naturally offers high scalability, making it very suitable for high throughput and enterprise-level workloads.

  • ZerePy: Scalability mainly relies on community-driven efforts, suitable for creative and social media domains, but support for large enterprise-level loads is relatively limited.

3.3 Adaptability

  • Eliza: The most adaptable, with a plugin system, extensive model support, and cross-platform integration capabilities, suitable for various application scenarios.

  • GAME: Highly adaptable in the gaming field, seamlessly integrating with various game engines, but its applicability in other fields is relatively weak.

  • Rig: Suitable for data-intensive or enterprise tasks, supports flexible selection of various large language models and vector storage to meet complex scenario demands.

  • ZerePy: Focuses on creative output, easily scalable with the support of the Python ecosystem, but its application scope is relatively narrow.

3.4 Performance

  • Eliza: Optimized for social media and conversational tasks, its performance relies on the quality and responsiveness of external model APIs.

  • GAME: Provides excellent real-time performance in dynamic in-game contexts, with specific performance depending on the coordination of agent logic and blockchain overhead.

  • Rig: With Rust's concurrency and memory safety, it performs exceptionally well, especially suited for complex large-scale AI processing tasks.

  • ZerePy: Performance primarily depends on Python's execution speed and model invocation efficiency, sufficient to support social and content creation tasks but not suitable for enterprise-level high throughput demands.

4. Advantages and Limitations

(Original English table from @arndxt_xo, compiled by Deep Tide TechFlow) 5. Market Potential and Outlook

The total market capitalization of the four frameworks is currently $1.7 billion. If AI and the cryptocurrency (Crypto) field can achieve explosive growth like L1 blockchains, its market potential may exceed $20 billion. For investors, market capitalization weighting may be a wiser strategy, especially as these frameworks serve different market segments and may benefit together from an overall market uptrend.

  • Eliza ($AI16Z): With a mature ecosystem, strong codebase, and upcoming V2 features (like Coinbase agent toolkit and trusted execution environment (TEE) support), it is expected to maintain its leading market share.

  • GAME ($VIRTUAL): The acceleration of adoption in the gaming and metaverse fields, along with the synergy with the $VIRTUAL ecosystem, ensures continued developer attention.

  • Rig ($ARC): Potentially a 'hidden gem' for enterprise AI on Solana. With the advancement of the handshake project, it is expected to replicate the successful model of other chain-specific frameworks.

  • ZerePy ($ZEREBRO): Despite its niche positioning, it is supported by the Python ecosystem and strong community momentum, focusing on the creative and artistic fields that are often overlooked by more general solutions.

6. Comprehensive Comparative Insights

6.1 Technology Stack and Learning Curve

  • Eliza (TypeScript): Achieves a good balance between usability and feature richness.

  • GAME: Provides a simple and easy-to-use API for game development, but its application scope is relatively limited.

  • Rig (Rust): Pursues extreme optimization of performance at the cost of higher complexity.

  • ZerePy (Python): Operates simply in creative applications but lacks widespread enterprise applicability.

6.2 Community and Ecosystem

  • Eliza: Holds the largest community influence on GitHub, reflecting its wide applicability and strong community support.

  • GAME: Rapid growth in the gaming and metaverse sectors, benefiting from the support of the $VIRTUAL ecosystem.

  • Rig: Although the developer community is smaller, it has strong technical capabilities and focuses on high-performance application scenarios.

  • ZerePy: A niche community built around creativity and decentralized art, further enhancing ecological influence through collaboration with Eliza.

6.3 Future Growth Catalysts

  • Eliza: The upcoming plugin registry and TEE integration may further solidify its market leadership.

  • GAME: Through the expansion of the $VIRTUAL ecosystem, it attracts more non-technical users, driving growth.

  • Rig: Potential Solana partnerships and enterprise-level positioning may lead to significant growth after the developer community expands.

  • ZerePy: Leveraging the popularity of Python in AI development and the trending popularity of community-driven creative projects, it further solidifies its position in niche markets.

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This article is reprinted with permission from Deep Tide TechFlow

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