Comparison of the four major AI agent frameworks: Market competition situation of Eliza, GAME, Rig, and ZerePy

Comparative Analysis of Crypto x AI Frameworks

The main crypto and AI frameworks are as follows:

1. Eliza ($AI16Z),
2. GAME ($VIRTUAL),
3. Rig ($ARC),
4. ZerePy ($ZEREBRO).
5. 

These frameworks each cater to different developer needs.

Eliza holds a dominant market share of around 60% due to its first-mover advantage and the thriving TypeScript community, while GAME (around 20%) focuses on gaming and Metaverse applications and is rapidly being adopted.

Rig (around 15%) is built on Rust and is designed for performance-oriented modular architecture to fit the Solana ecosystem; ZerePy (around 5%) is an emerging Python-based framework focused on creative output and social media automation.

The total valuation of these frameworks is $1.7 billion, and as AI-driven crypto applications continue to expand, the market size is expected to exceed $20 billion, making a market-cap-weighted approach potentially attractive. Each framework occupies a unique niche - social and multi-agent systems (Eliza), gaming/Metaverse (GAME), enterprise performance (Rig), creative community applications (ZerePy) - providing complementary rather than directly competing options.

1. Overview and Market Positioning

Eliza ($AI16Z)

• Market Share: Around 60%
• Market Cap: $900 million
• Core Language: TypeScript
• Key Strengths: First-mover advantage, large GitHub community (6,000+ stars, 1,800+ forks)
• Focus Areas: 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 further strengthened by its large contributor community, accelerating development progress and user adoption. Eliza's TypeScript tech stack makes it a natural choice for Web ecosystem developers, ensuring broad appeal.

GAME ($VIRTUAL)

• Market Share: Around 20%
• Market Cap: $300 million
• Core Language: API/SDK-based (language-agnostic)
• Key Strengths: Rapid adoption in the gaming industry, real-time agent capabilities
• Focus Areas: Procedural content generation, adaptive NPC behavior

GAME is designed specifically for gaming and Metaverse applications. Its API-driven architecture and tight integration with the $VIRTUAL ecosystem have driven its rapid growth: over 200+ projects, 150,000+ daily requests, and continuing rapid expansion. GAME's no-code integration further attracts teams prioritizing quick deployment over deep technical customization.

Rig ($ARC)

• Market Share: Around 15%
• Market Cap: $160 million
• Core Language: Rust
• Key Strengths: High performance, modular design (enterprise-grade)
• Focus Areas: Solana-based "pure applications", emphasis on retrieval-augmented generation

Rig's Rust architecture suits developers prioritizing speed, memory safety, and efficient concurrency. It is designed specifically for "enterprise-grade" or data-driven applications, particularly on the Solana platform. While the learning curve is steeper, Rig's modularity and reliability appeal to system-oriented developers.

ZerePy ($ZEREBRO)

• Market Share: Around 5%
• Market Cap: $300 million
• Core Language: Python
• Key Strengths: Community-driven creative capabilities, social media automation
• Focus Areas: Agent deployment on social platforms, particularly for artistic or niche outputs

ZerePy is an emerging framework originating from Zerebro's core backend. Its Python foundation and focus on creative applications (e.g., Non-Fungible Tokens, music, and digital art) attract a specific user base. Collaboration with Eliza ($AI16Z) has boosted its visibility, but ZerePy's narrower scope may limit its broader enterprise adoption.

2. Technical Architecture and Core Components

Eliza ($AI16Z)

• Multi-Agent System: Deploys multiple AI personas within a shared runtime environment.
• Memory Management (RAG): Implements a retrieval-augmented generation pipeline for long-term context handling.
• Plugin System: Supports community-built extensions for handling voice, text, media parsing (e.g., PDF, images).
• Broad Model Support: Can integrate local open-source LLMs or cloud-based APIs (e.g., OpenAI, Anthropic).

Eliza's technical design centers around multimodal communication, making it well-suited for social, marketing, or community-driven AI agent applications. While it excels in integrations with platforms like Discord, X (formerly Twitter), and Telegram, careful coordination of different agent personas and memory modules is required for large-scale deployments.

GAME ($VIRTUAL)

• API + SDK Model: Simplifies agent integration for game studios and Metaverse projects.
• Agent Prompt Interface: Coordinates user input with the agent strategy engine.
• Strategy Planning Engine: Separates agent logic into high-level goal planning and low-level strategy execution.
• Blockchain Integration: Potential support for on-chain wallet operations for decentralized agent governance.

GAME's architecture is highly focused on gaming or Metaverse scenarios, prioritizing real-time performance and continuous agent adaptability. While it can be extended beyond gaming, its system design clearly leans towards virtual worlds and procedural generation scenarios.

Rig ($ARC)

• Rust Workspace Structure: Partitions functionality into multiple crates for improved clarity and modularity.
• Provider Abstraction Layer: Standardizes interactions with various LLM providers (e.g., OpenAI, Anthropic).
• Vector Store Integration: Supports multiple backends (e.g., MongoDB, Neo4j) for context retrieval.
• Agent System: Embeds retrieval-augmented generation (RAG) and specialized tooling usage.

Rig's high-performance design benefits from Rust's concurrency model, making it well-suited for enterprise scenarios requiring strict resource management. Its layered abstraction provides conceptual clarity and reliable performance. However, Rust's steeper learning curve may limit the developer pool.

ZerePy ($ZEREBRO)

• Python-Based: Caters to AI/ML developers familiar with Python libraries and workflows.
• Modular Zerebro Backend: Focuses on creative content generation, particularly in the social media and art domains.
• Agent Autonomy: Emphasizes "creative output" tasks, such as meme, music, and Non-Fungible Token generation.
• Social Platform Integration: Built-in Twitter-like functional commands (post, reply, retweet).

ZerePy fills a unique niche for Python developers seeking a straightforward way to deploy agents on social platforms. While its scope is narrower than Eliza or Rig, ZerePy excels in art or entertainment-driven scenarios, especially within decentralized communities.

3. Comparative Dimensions

3.1 Usability

• Eliza: Adopts a balanced approach, with a moderate learning curve due to the complexity of a multi-agent system, but benefits from a strong TypeScript developer base.
• GAME: Designed for non-technical users in the gaming domain, providing no-code or low-code solutions.
• Rig: More challenging; the rigor of Rust requires expertise, but the rewards are high performance and reliability.
• ZerePy: Most user-friendly for Python users, especially in creative or media-focused AI tasks.

3.2 Scalability

• Eliza: The V2 version introduced a scalable message bus and improved concurrency, but multi-agent concurrency remains complex.
• GAME: Scalability is related to real-time gaming requirements and blockchain networks; performance can be maintained if game engine constraints are well-managed.
• Rig: Naturally scalable through Rust's asynchronous runtime, suitable for high-throughput or enterprise-grade workloads.
• ZerePy: Community-driven expansion, primarily tested in creative or social media scenarios, with limited scalability support for large-scale enterprise loads.

3.3 Adaptability

• Eliza: Most adaptable, with a plugin system, broad model support, and cross-platform integration.
• GAME: Strong professional adaptability in gaming scenarios, can integrate with various game engines, but lower adaptability outside of gaming.
• Rig: Suitable for data-intensive or enterprise tasks; flexible provider layer supports multiple LLMs and vector stores.
• ZerePy: Focused on creative output; easily extensible within the Python ecosystem, but with a narrower domain scope.

3.4 Performance

• Eliza: Optimized for fast social media or conversational tasks, with performance dependent on external model APIs.
• GAME: Supports the real-time performance of game dynamics; performance depends on the combination of agent logic and blockchain overhead.
• Rig: Excels in performance due to Rust's concurrency and memory safety, suitable for complex, large-scale AI processing tasks.
• ZerePy: Performance depends on the speed of Python and model calls; usually sufficient for social/content tasks, but not suitable for enterprise-grade throughput requirements.

4. Strengths and Limitations

5. Market Potential and Outlook

The total market capitalization of the four frameworks is $1.7 billion, and if the AI x Crypto industry follows the explosive growth pattern of L1 blockchains, their market size could potentially exceed $20 billion. For investors, a market-cap-weighted approach may be prudent, especially if one believes these frameworks will grow together in a broader "rising tide lifts all boats" scenario.

• Eliza ($AI16Z): Likely to maintain its market share leadership, thanks to its mature ecosystem, robust codebase, and upcoming V2 enhancements (such as Coinbase agent toolkit integration and TEE support).
• GAME ($VIRTUAL): Expected to see further adoption in the gaming/metaverse space, with synergies from the $VIRTUAL ecosystem ensuring continued developer interest.
• Rig ($ARC): May emerge as the "hidden gem" for enterprise-grade AI on Solana. As the handshake protocol matures, it could replicate the success trajectories of other chain-specific frameworks.
• ZerePy ($ZEREBRO): Although niche, its strong community momentum and the Python ecosystem could enable it to target the creative and artistic use cases often overlooked by more comprehensive solutions.

6. Integrated Comparative Insights

Technology Stack and Learning Curve

• Eliza (TypeScript): Strikes a balance between usability and feature richness.
• GAME: Provides easy-to-use APIs for gaming, but with a more limited application scope.
• Rig (Rust): Prioritizes maximum performance, but with a higher complexity threshold.
• ZerePy (Python): Simple and direct for creative applications, but lacks more comprehensive enterprise-grade capabilities.

Community and Ecosystem

• Eliza: Has the strongest presence on GitHub, reflecting a robust community engagement and broad applicability.
• GAME: Growing rapidly in the gaming and metaverse space, benefiting from the support of $VIRTUAL.
• Rig: Has a smaller developer community, but with strong technical capabilities, focused on high-performance scenarios.
• ZerePy: The niche community around creative and decentralized art is growing, and its collaboration with Eliza further enhances its ecosystem influence.

Future Growth Catalysts

• Eliza: New plugin registry and TEE integration could further consolidate its leadership position.
• GAME: Aggressive expansion through the $VIRTUAL ecosystem, attracting non-technical user participation.
• Rig: Potential Solana partnership and enterprise-grade positioning could bring strong growth as developer appeal increases.
• ZerePy: Leverage the popularity of Python in the AI domain and the cultural momentum around creative and community-driven projects to further develop.