With the dramatic fluctuations in the cryptocurrency industry, the AI Agent sector has also undergone its first large-scale reshuffling. The token prices of most projects have dropped by more than 90% from their peak values. According to data from Coingecko, as of February 28, the market capitalization of the AI Agent sector has fallen to around $5.6 billion. Despite the short-term crash triggering market panic, once a trend is formed, temporary fluctuations will not halt it.

Among the many projects, framework-based projects like Eliza, Swarms, Rig, and ZerePy are more favored by the market than application-based projects. This article will delve into the definition and advantages of AI Agent frameworks and provide a comparative analysis of some of the mainstream frameworks on the market to offer users valuable insights.

What is an AI Agent Framework?

An AI Agent framework is a set of tools, interfaces, and standards used for building, managing, and deploying AI agents. If the core logic of an AI agent is considered the application layer, the AI Agent framework serves as the infrastructure that provides users with standardized development modules, helping them quickly create, deploy, and manage AI agents, thus enabling large-scale application of AI agents.

Compared to individual development tools, AI Agent frameworks typically provide a complete solution. Through modular components and standardized processes, they significantly reduce development complexity while also offering high scalability and compatibility to meet developers’ personalized needs.

Advantages of AI Agent Frameworks

Compared to specific, deployed applications, AI Agent frameworks are more likely to be favored by the market during the early stages of development. This is mainly because they provide a balance, allowing developers to achieve proof of concept with less effort and cost while laying the foundation for future expansion. The specific advantages include:

1) Reduction in Development Complexity
Frameworks adopt a modular architecture, providing comprehensive documentation and ready-to-use SDKs, which significantly reduce the difficulty of developing complex systems. Developers can focus on the core business logic rather than underlying technologies, enabling them to quickly build applications.

2) Promotion of Standardization and Collaboration
Frameworks maintain structural consistency in communication, interaction, and data, which facilitates team collaboration, reduces communication costs, and minimizes development errors.

3) Rapid Ecosystem Building
Open-source frameworks can attract developers to contribute code and improvements, maintaining technological iteration and community activity. Additionally, through third-party integrations, it becomes easier to promote application deployment and adoption.

4) Attracting Capital Attention
Compared to specific applications, capital and the market view the generality and scalability of frameworks as having long-term value. Frameworks’ standardized nature makes them easier to evaluate, while the fragmentation of application scenarios increases investment uncertainty.

Comparison of Mainstream Frameworks

Currently, many AI Agent frameworks have emerged in the market, with popular projects like Eliza, Swarms, Rig, and ZerePy, each having its own advantages in terms of architecture, language, third-party integrations, and more. Below is a brief comparison of several mainstream frameworks:

Eliza ($ELIZA)

Eliza is a multi-agent simulation framework developed by the ai16z team using TypeScript, focused on the creation, deployment, and management of autonomous AI agents.

Source: elizaos.ai

As a modular, lightweight AI agent framework, Eliza’s key strengths lie in its versatility and simplicity while maintaining functionality, making it especially suitable for Web3 beginners. Specifically:

• Multi-platform Integration: Eliza agents can interact across platforms such as X, Telegram, Discord, etc., while maintaining a consistent identity.
• Broad Model Support: Eliza supports various models, including Deepseek, Ollama, Grok, OpenAI, Anthropic, Gemini, LLama, offering great flexibility.
• Scalable Plugin System: Developers can easily add and remove custom agent features through standalone plugin modules, such as image generation plugins, TEE (Trusted Execution Environment) plugins, node plugins, etc.
• Rich Agent Role Support: Eliza is mainly used for building social media roles, knowledge workers, interactive characters, and more.
• Active Developer Community: As of February 28th, GitHub data shows Eliza has 14.8k stars, 4.7k forks, and 550 contributors. According to the team, at least 1,000 teams or projects are currently using the Eliza framework.

Eliza emphasizes practicality and user-friendliness, making it developer-friendly. However, it also faces challenges, such as the lack of a defined workflow system, the need to improve runtime design, and some controversy surrounding the founder. Notably, the team needs to think deeper about the empowerment of $ELIZA to enhance its value further.

Swarms ($SWARMS)

Swarms, developed under the leadership of Kye Gomez and launched in 2022, is the first production-grade multi-agent coordination framework. Aimed primarily at enterprise-level clients, Swarms offers the following core features:

• Flexible Communication Protocols: To ensure efficient collaboration between agents, Swarms provides multiple communication protocol types, including hierarchical, parallel, mesh, and federated, catering to developers’ specific needs across different scenarios.
• Rich Cluster Architecture: Swarms offers a wide range of pre-built cluster types that span several verticals, such as healthcare, IoT, and finance. Developers can quickly choose the appropriate architecture based on specific application scenarios. Swarms also supports highly customizable cluster configurations, allowing developers to adjust architectural details to meet their requirements.
• Ecological Support Resources: Swarms has launched a comprehensive ecosystem, including a marketplace, Swarms API, cloud solutions, and more. In the future, it plans to release wallet APIs, exchanges, and other products. Additionally, Swarms integrates numerous third-party APIs, databases, and platforms, enhancing its versatility and functionality.

Source: swarms.world

The $SWARMS token economic model is relatively clear, serving multiple purposes such as utility and governance. To enhance its value, the team needs to make further efforts in incentivizing ecosystem participants and expanding agent capabilities. Additionally, Swarms’ development complexity may present a challenge for novice developers. To reduce the learning curve, the team should strengthen developer support by providing more detailed documentation, tutorials, and case studies and organizing hackathons and training events to help developers get up to speed quickly.

Rig ($ARC)

Rig is a general-purpose LLM framework developed by Playgrounds, written in Rust. It significantly lowers the barrier for developers building AI products by providing high-level abstractions and unified interfaces.

Source: arc.fun

Key features of Rig include:

1. Unified LLM Interface: Rig provides a consistent API interface for different LLM providers (such as OpenAI, Cohere), improving code maintainability and consistency. This allows developers to easily switch or combine different LLM services.
2. High-level Abstraction: With pre-built modular components, Rig simplifies complex AI workflows, such as creating retrieval-augmented generation (RAG) systems, into just a few lines of code, significantly reducing developer workload and improving efficiency.
3. Safety and High Performance: Based on Rust’s strong type system, Rig offers a safer and more efficient development environment. It utilizes Rust’s asynchronous features and the Tokio runtime to support parallel execution of multiple tasks or LLM requests while ensuring memory safety.
4. Seamless Multi-database Integration: Rig includes support for various databases (such as Neo4j, MongoDB, Rig-LanceDB), enabling efficient similarity searches and real-time retrieval in AI applications. This integration is particularly suitable for applications that need to handle large-scale data.

However, Rig faces several challenges. For example, despite Rust’s performance and safety advantages, its developer community is relatively small, which could slow down Rig’s adoption rate. Ensuring data consistency in a multi-database environment might also be complex. Additionally, Rig is associated with the token $ARC, but the team has not yet disclosed detailed information about its economic model, which may hinder its full potential.

ZerePy ($ZEREBRO)

ZerePy is an open-source Python framework developed by the Blorm team, focusing on creative output and social media applications. It allows users to deploy their own agents on platforms like X (formerly Twitter), supporting multiple LLM-driven operations.

Source: zerebro.org

Key features of ZerePy include:

1. CLI Management for Agents: ZerePy offers a comprehensive command-line interface (CLI) toolset, making it easy for users to deploy and manage agents. This design simplifies the operational flow, enabling even users with little technical background to quickly get started.
2. Multi-platform Integration: ZerePy supports seamless interaction across platforms and blockchains. It has integrated with platforms like X and Farcaster, various blockchains like Solana, Sonic, and Ethereum, and blockchain tools like GOAT (Go Agent Tools) and Allora, allowing agents to interact with various blockchain networks and protocols.
3. One-click Deployment: Users can quickly launch ZerePy through Replit templates and local deployment. This significantly lowers the deployment barrier and enhances the overall user experience.

Source: github.com/blorm-network

Based on GitHub data, ZerePy has 556 stars, 214 forks, and 29 contributors, showing rapid development. However, compared to Eliza, Swarms, and Rig, ZerePy’s community size and activity still fall significantly behind. Since ZerePy focuses on the arts and social media field, its application scope is far less broad than general frameworks, limiting its appeal to developers. Additionally, ZerePy is a modular version of the Zerebro backend with similar core functionalities, but currently, there is no direct connection with the $ZEREBRO token.

In summary, the four frameworks—Eliza, Swarms, Rig, and ZerePy—each have their own strengths and weaknesses:

• Developer Community Activity: Eliza leads by a significant margin, followed by Swarms, Rig, and ZerePy.
• Development Difficulty: Eliza and ZerePy are relatively developer-friendly, while Swarms and Rig require higher technical expertise.
• Application Scenarios: Eliza, Swarms, and Rig are more versatile and compatible, suitable for various scenarios. ZerePy is more focused on art and social media, with a narrower but more targeted application scope.
• Token Economy: Swarms offers a clearer token model with some involvement in community governance, while Eliza, Rig, and ZerePy have more ambiguous token economies and need further clarification on their empowering mechanisms.

Conclusion

AI Agent frameworks play a critical role as core infrastructure, reducing development complexity and facilitating the large-scale implementation of AI Agents. Eliza, Swarms, Rig, and ZerePy, as the leading frameworks in the market today, each demonstrate unique advantages regarding developer friendliness, technical features, and application scenarios.

However, these frameworks also face their own challenges. Expanding developer ecosystems, reducing technical complexity, refining token economic models, and breaking through application boundaries are common issues they need to address. For developers and investors, gaining a deep understanding of these frameworks’ characteristics and potential will be an important step in seizing opportunities in the AI Agent space.