Author: Arndxt, Threading on the Edge; Translation: Golden Finance xiaozou

There are four main frameworks in the Crypto x AI field: Eliza (AI16Z), GAME (VIRTUAL), Rig (ARC) and ZerePy (ZEREBRO).

These four major frameworks can meet different development needs.

Driven by first-mover advantage and a thriving TypeScript community, Eliza dominates with about 60% market share, while GAME (about 20% market share) is the fastest-adopting game and virtual world applications as targets.

Rig (market share of about 15%) is developed using Rust and provides performance-oriented modularity suitable for the Solana ecosystem, while ZerePy, a new architecture based on Python (market share of about 5% ) focuses on creative output and social media automation. The combined market capitalization of these frameworks is $1.7 billion, and as AI crypto applications expand, the total market capitalization of these frameworks could reach more than $20 billion, making the market-cap-weighted approach potentially attractive. Each framework occupies its own unique niche—social and multi-agent agents (Eliza), games/virtual worlds (GAME), enterprise performance (Rig), and creative community use (ZerePy)—offering complementary options to each other. Not direct competition.

1. Overview of the four major frameworks and market positioning

(1) Eliza ($AI16Z)

● Market share: ~60%

● Market value: US$900 million

● Core language: TypeScript

● Main advantages: first-mover advantage, extensive GitHub community (more than 6,000 stars, 1,800 forks)

● Focus: multi-agent simulation, cross-platform social participation

As the One of the earliest AI agent frameworks in the field, Eliza dominates. Its first-mover advantage is supported by a large community of contributors, which accelerates development and promotes user adoption. Eliza's TypeScript stack makes it ideal for developers working in web-based ecosystems, ensuring broad appeal.

(2) GAME (VIRTUAL)

● Market share: ~20%

● Market value: US$300 million

● Core language: (API/SDK based; using a language-agnostic approach)

● Main advantages: Rapid adoption in the gaming industry, real-time proxy functionality.

● Focus: procedural content generation, adaptive NPC behavior.

GAME is designed for games and virtual world applicationsMade to measure. Its API-driven architecture and close connection with the VIRTUAL ecosystem have inspired huge momentum: more than 200 projects have been harvested, 150,000 requests per day, and rapid weekly growth. GAME's no-code integration further appeals to teams that prioritize rapid deployment over deep technical customization.

(3) Rig (ARC)

● Market share: ~15%

● Market value: US$160 million

● Core language: Rust

● Main advantages: performance, modular design (enterprise level)

● Focus: "pure-play pure game" based on Solana, emphasizing retrieval enhancement generation .

Rig is based on the Rust architecture and caters to developers who value speed, memory safety, and efficient concurrency. It is designed for "enterprise-grade" or heavily data-driven applications, especially those on Solana. Despite the steep learning curve, Rig offers modular performance and reliability that can appeal to system-oriented developers.

(4) ZerePy (ZEREBRO)

● Market share: ~5%

● Market value: US$300 million

● Core language: Python

● Key benefits: Community-driven creativity, social media automation.

● Focus: Agent deployment on social platforms, especially for artistic or niche output.

ZerePy is a latecomer, derived from the core backend of Zerebro. Its Python foundation, coupled with its focus on creative applications (NFTs, music, and digital art), has attracted a cult following. The collaboration with Eliza has increased ZerePy's visibility, but ZerePy's narrow focus may limit adoption by a wide range of enterprises.

2. Technical architecture and core components

(1) Eliza (AI16Z)

● Multi-agent system: deploy multiple AI personalities under a shared runtime.

● Memory Management (RAG): A generation pipeline that implements retrieval enhancements for long-term context.

● Plug-in system: Supports community-developed extensions for voice, text, and media parsing (for example: PDF, images, etc.).

● Extensive model support: Integrate local open source LLM or cloud API (OpenAI, Anthropic).

Eliza's technical design is centered around multi-modal communication, making it ideal for social, marketing, or community-based AI agents. While it excels at easy integration (Discord, X, Telegram), large-scale use also requires careful orchestration of different agent personalities and memory modules.

（2）GAME (VIRTUAL)

● API + SDK model: Simplify agency integration for game companies and virtual world projects.

● Agent Prompt Interface: Coordinates the interaction between user input and the agent strategy engine.

● Strategic planning engine: Splits agent logic into high-level goal planning and low-level strategy execution.

● Blockchain integration: potential on-chain wallet operators for decentralized proxy governance.

GAME's architecture is highly customized for gaming or virtual environments, prioritizing real-time performance and continuous agent adaptation. While its role is not limited to gaming, the system is clearly designed for virtual worlds and procedurally generated scenarios.

(3) Rig (ARC)

● Rust Workspace Structure: To ensure clarity and modularity, functions are separated into multiple crates.

● Provider Abstraction Layer: Standardizes interaction with various LLM providers (OpenAI, Anthropic).

● Vector Store Integration: Supports multiple backends (MongoDB, Neo4j) for context retrieval.

● Agent System: Embed retrieval enhancement generation (RAG) and use of special tools.

Rig's high-performance design benefits from Rust's concurrency model, making it ideal for enterprise environments that require strict resource management. Its conceptual clarity—through layered abstractions—provides strong reliability, but Rust’s learning curve may limit the number of developers.

(4) ZerePy (ZEREBRO)

● Based on Python development: accessible to AI/ML developers who are familiar with Python code base and workflow.

● Modular Zerebro backend: Provides creative content generation, especially for social media and art.

● Agent autonomy: Focus on "creative output" such as memes, music, and NFT generation tasks.

● Social platform integration: Includes built-in commands (post, reply, forward) similar to Twitter functions.

ZerePy fills a gap for Python developers looking to deploy agents directly on social platforms. While ZerePy has a narrower scope than Eliza or Rig, its art- or entertainment-driven use cases thrive, especially in the decentralized community.

3. Four major framework comparison dimensions

(1) Usability

● Eliza: Adopting a balanced approach, due to the complexity of multi-agent, it constitutes a moderate learning curve, but it has a powerfulTypeScript Developer Basics.

● GAME: Designed for non-technical adopters in the gaming space, offering a no-code or low-code approach.

● Rig: more challenging; the Rust language strictly requires professional knowledge, but can gain high performance and reliability.

● ZerePy: Easiest for Python users, especially in creative or media-focused AI tasks.

(2) Scalability

● Eliza: The V2 iteration introduced a scalable message bus and improved concurrency, but multi-broker concurrency may be complex.

● GAME: Scalability is related to real-time game requirements and blockchain networks; if game engine constraints are controlled, performance will remain unchanged.

● Rig: Naturally scalable via Rust’s asynchronous runtime, suitable for high-throughput or enterprise-level workloads.

● ZerePy: Extensions are community driven and primarily tested in creative or social media environments with less emphasis on large enterprise loads.

(3) Adaptability

● Eliza: has the highest adaptability to plug-in systems, has extensive model support, and can be integrated across platforms.

● GAME: Specifically adapted to the game environment and can be integrated into various game engines, but it is not very suitable for other fields outside the game field.

● Rig: Ideal for data-intensive or enterprise tasks; provides flexible vendor tiers for multiple LLMs and vector storage.

● ZerePy: For creative output; easy to expand in the Python ecosystem, but the scope of the field is relatively narrow.

(4) Performance

● Eliza: Optimized for fast-changing social media or conversational tasks, its performance depends on the external model API.

● GAME: A real-time representation of game dynamics; its success depends on the interplay of agent logic and blockchain overhead.

● Rig: Due to Rust’s concurrency and memory safety, it has high performance and is very suitable for complex large-scale AI processes.

● ZerePy: Performance depends on Python speed and model calls; generally sufficient for social/content tasks, but not targeted for enterprise-grade throughput.

4. Advantages and limitations

5. Market potential and prospects

All these four frameworks have a total market value of US$1.7 billion. If the AI ​​x Crypto industry follows what has been presented in the L1 blockchain In the explosive growth model, it is possible to grow to more than 20 billion US dollars. For investors who believe these frameworks, each serving a different market niche, will rise together within a broader "up" trend, the cap-weighted approach may be most prudent.

● Eliza (AI16Z): Because of herWith the ecosystem, strong code base, and upcoming enhanced V2 (e.g., Coinbase proxy suite integration, TEE support), it will likely continue to hold the top market share.

● GAME (VIRTUAL): It is expected to be further popularized in games/virtual worlds. Synergies with the VIRTUAL ecosystem ensure continued developer interest.

● Rig (ARC): Could become a “hidden gem” for enterprise AI on Solana; as its partnership program matures, it could replicate the traction that other chain-specific frameworks have.

● ZerePy (ZEREBRO): Although its scope is small, it benefits from strong community momentum and the Python ecosystem, specifically targeting creative and creative applications that are often overlooked by more general solutions. Artistic use cases.

6. Comparison summary

(1) Technology stack and learning curve

● Eliza (TypeScript) strikes a balance between accessibility and rich functionality.

● GAME provides an accessible API for games, but may be targeted at niche groups.

● Rig (Rust) maximizes performance at the expense of a higher complexity threshold.

● ZerePy (Python) is simple for creative applications but lacks wider enterprise adoption.

(2) Community and Ecosystem

● Eliza: Best performance on GitHub, reflecting strong community participation and broad applicability.

● GAME: Thanks to the support of VIRTUAL, it has achieved rapid growth in the field of games and virtual worlds.

● Rig: For a small community of technically savvy developers, focused on high-performance use cases.

● ZerePy: A growing niche community built around creativity and decentralized art, whose development benefits from a partnership with Eliza.

(3) Future growth catalysts

● Eliza: The new plug-in registry and TEE integration may further consolidate its leadership position.

● GAME: Actively expanding through VIRTUAL’s ecosystem; accessible to non-technical users.

● Rig: Once developer traction increases, cooperation with Solana may be reached, and the focus on enterprises may lead to strong growth.

● ZerePy: Leverage Python’s popularity in the field of artificial intelligence and the cultural momentum around creative, community-driven projects.