Crypto x AI field presents four mainstream frameworks:
- Eliza ($AI16Z)
- GAME ($VIRTUAL)
- Rig ($ARC)
- ZerePy ($ZEREBRO)
They respectively address different developer needs and provide differentiated solutions.
Eliza, with its first-mover advantage and a large TypeScript community, dominates the market with about 60% market share; GAME (about 20%) focuses on gaming and metaverse applications, quickly gaining market favor; Rig (about 15%), based on Rust, emphasizes performance-oriented modular design, fitting the Solana ecosystem; while ZerePy (about 5%), based on Python, is oriented towards creative output and social media automation.
The four frameworks have a total valuation of $1.7 billion, and their market size is expected to exceed $20 billion as AI-driven crypto applications expand. A market cap-weighted investment strategy may be attractive, as they each occupy unique niches and exist in a complementary rather than competitive relationship.
1. Framework Overview and Market Position
Eliza ($AI16Z)
Market Share: About 60%
Market Cap: $900 million
Core Language: TypeScript
Key Advantages: First-mover advantage, large GitHub community (6000+ stars, 1.8K forks)
Focus Areas: Multi-agent simulation, cross-platform social interaction
As one of the earliest AI agent frameworks in the field, Eliza has quickly gained market share thanks to its first-mover advantage and an active developer community. The TypeScript stack makes it a natural choice for Web developers, and it is widely popular.
GAME ($VIRTUAL)
Market Share: About 20%
Market Cap: $300 million
Core Language: Language-agnostic (API/SDK-driven)
Key Advantages: Rapid adoption in the gaming industry, real-time agent capabilities
Focus Areas: Procedural content generation, adaptive NPC behavior
GAME is designed for games and the metaverse, and its API-driven architecture and deep integration with the $VIRTUAL ecosystem have led to significant growth (200+ projects, 150,000 daily requests on average). Its no-code integration approach also appeals to teams prioritizing rapid deployment.
Rig ($ARC)
Market Share: About 15%
Market Cap: $160 million
Core Language: Rust
Key Advantages: High performance, modular design (enterprise-grade)
Focus Areas: Solana-based pure applications, enhanced retrieval-generation technology
Rig's Rust architecture is tailored for developers who prioritize speed and resource management, suitable for data-intensive enterprise applications. Although the learning curve is steeper, its modularity and reliability are highly attractive.
ZerePy ($ZEREBRO)
Market Share: About 5%
Market Cap: $30 million
Core Language: Python
Key Advantages: Community-driven creativity, social media automation
Focus Areas: Agent deployment on social platforms, especially for artistic or niche content generation
ZerePy's Python foundation and focus on creative applications (such as NFTs, music, digital art) have attracted a loyal user base. Its collaboration with Eliza has increased its visibility, but its narrower application scope limits wider enterprise adoption.
2. Technical Architecture and Core Components
Eliza ($AI16Z)
- Multi-Agent System: Deploy multiple AI roles under a shared runtime.
- Memory Management (RAG): Achieve long-term context support through retrieval-augmented generation.
- Plugin System: Support community-developed extensions (e.g., voice, text, media parsing).
- Broad Model Support: Can integrate local open-source LLMs or cloud APIs (OpenAI, Anthropic).
Eliza's technical design focuses on multimodal communication, suitable for social, marketing, or community AI agent applications. Although it is easy to integrate (Discord, X, Telegram), large-scale use requires careful coordination.
GAME ($VIRTUAL)
- API + SDK Model: Simplifies the integration of agents into game and metaverse projects.
- Agent Prompt Interface: Coordinates user input with the agent strategy engine.
- Strategy Planning Engine: Separates high-level goal planning and low-level execution strategies.
- Blockchain Integration: Supports on-chain operations for decentralized agent governance.
GAME's architecture is highly specialized for game or metaverse environments, prioritizing real-time performance and continuous agent adaptation. While it can be extended beyond games, the system's design is clearly oriented towards virtual worlds and procedural generation scenarios.
Rig ($ARC)
- Rust Workspace Structure: Divides functionality into modular units.
- Vendor Abstraction Layer: Unifies interactions with various LLM providers.
- Vector Store Integration: Supports multiple backend retrieval options, such as MongoDB and Neo4j.
- Agent System: Embeds enhanced retrieval-generation and professional tool usage.
Rig's high-performance design benefits from Rust's concurrency model, making it an ideal choice for enterprise environments requiring strict resource management. It achieves conceptual clarity through layered abstraction, providing robust reliability, but Rust's learning curve may limit the number of developers.
ZerePy ($ZEREBRO)
- Python Architecture: Convenient for AI/ML developers familiar with Python workflows.
- Modular Zerebro Backend: Focuses on social media and creative content generation.
- Agent Autonomy: Emphasizes creative outputs such as memes, music, and NFTs.
- Social Platform Integration: Built-in Twitter-like functional commands (post, reply, retweet).
ZerePy meets the needs of Python developers seeking direct agent deployment on social platforms. While its application scope is narrower than Eliza or Rig, ZerePy performs well in art or entertainment-driven use cases, especially in decentralized communities.
3. Comparison Dimensions
3.1 Ease of Use
- Eliza: Good balance, suitable for TypeScript developers, but higher complexity in multi-agent scenarios.
- GAME: Designed for non-technical users in the gaming domain, providing a low-code solution.
- Rig: Rust's rigor brings high performance, but requires professional expertise.
- ZerePy: Most friendly for Python users, especially suitable for creative or media AI tasks.
3.2 Scalability
- Eliza: Achieves scalability through V2 message bus and concurrency improvements.
- GAME: Tied to the real-time demands of blockchain networks.
- Rig: Async runtime based on Rust naturally supports high throughput.
- ZerePy: Community-driven extensions, more focused on creative domains.
3.3 Adaptability
- Eliza: Plugin system, broad model support, and cross-platform integration make it the most adaptable.
- GAME: Focused on gaming scenarios, but has lower flexibility in other domains.
- Rig: Suitable for data-intensive tasks, can flexibly adapt to multiple LLMs and vector stores.
- ZerePy: Fits the Python ecosystem, but has a narrower domain range.
4. Strengths and Limitations
5. Market Potential and Outlook
The total market capitalization of the four major frameworks is currently $170 million. If the AI x Crypto field can replicate the explosive growth pattern of L1 blockchains, the market size is expected to exceed $2 billion.
For investors who believe that these frameworks serving different niche markets will rise together in a broader "bullish" scenario, a market capitalization-weighted investment strategy may be a wise choice.
- Eliza ($AI16Z) is expected to continue to maintain a leading market share, relying on its mature ecosystem, strong code base, and the upcoming V2 feature upgrades (such as Coinbase agent kit integration and TEE support) to further consolidate its dominant position.
- GAME ($VIRTUAL) will see further adoption in the gaming and metaverse fields. The synergy with the $VIRTUAL ecosystem ensures the continued attention of developers, and its low-code integration approach will also attract more non-technical teams.
- Rig ($ARC), as an enterprise AI prospect on Solana, is expected to replicate the success of other chain-specific frameworks as its Handshake Program matures. Its appeal continues to grow for enterprise scenarios that require high performance and reliability.
- ZerePy ($ZEREBRO), although with a narrower application scope, benefits from the strong community momentum and the Python ecosystem, occupying a unique niche market in creative and artistic applications, which are often overlooked by more general-purpose solutions.
6. Conclusion
1. Technology Stack and Learning Curve
Eliza (TypeScript) has struck a balance between accessibility and feature richness.
GAME provides accessible APIs for gaming, but may be more niche.
Rig (Rust) maximizes performance at the cost of a higher complexity threshold.
ZerePy (Python) is simple for creative applications, but lacks broader enterprise functionality.
2. Community and Ecosystem
Eliza: The largest presence on GitHub, reflecting strong community engagement and broad applicability.
GAME: Rapid growth in the gaming and metaverse circles, thanks to the support of $VIRTUAL.
Rig: A smaller but technically proficient developer community, focused on high-performance use cases.
ZerePy: A constantly evolving niche community built around creativity and decentralized art, enhanced through collaboration with Eliza.
3. Future Growth Catalysts
Eliza: New plugin registry and TEE integration may further consolidate its leadership position.
GAME: Aggressive expansion through the $VIRTUAL ecosystem; accessible to non-technical users as well.
Rig: Potential Solana partnerships and enterprise focus may bring strong growth once developer appeal increases.
ZerePy: Leveraging the popularity of Python in the AI field and the cultural momentum around creative, community-driven projects.
