Forward the Original Title: Is the AI Agent Framework the Final Piece of the Puzzle? How to Interpret the “Wave-Particle Duality” of Frameworks?

The AI Agent framework, as a key piece in the industry’s development, may harbor the dual potential to drive both the implementation of technology and the maturation of the ecosystem. Some of the most discussed frameworks in the market include Eliza, Rig, Swarms, and ZerePy. These frameworks attract developers via their GitHub repositories, building a reputation. Through the issuance of tokens via “libraries,” these frameworks, like light, embody both wave and particle-like characteristics. Similarly, Agent frameworks possess both serious externalities and Memecoin traits. This article will focus on interpreting the “wave-particle duality” of these frameworks and explore why the Agent framework can be the final piece in the puzzle.

Externalities Brought by Agent Frameworks Can Leave Lasting Growth After the Bubble Bursts

Since the emergence of GOAT, the Agent narrative has been gaining increasing market attention, akin to a martial arts master delivering a powerful blow — with the left fist representing “Memecoin” and the right palm embodying “industry hope,” you might be defeated by either one of these moves. In reality, the application scenarios of AI Agents are not strictly differentiated, and the boundaries between platforms, frameworks, and specific applications are blurred. However, these can still be roughly categorized according to token or protocol preferences. Based on the development preferences of tokens or protocols, they can generally be classified into the following categories:

• Launchpad: Asset issuance platforms. Examples include Virtuals Protocol and Clanker on the Base chain, and Dasha on the Solana chain.
• AI Agent Applications: These float between Agent and Memecoin, and have standout features in memory configuration, such as GOAT, aixbt, etc. These applications are generally one-way outputs, with very limited input conditions.
• AI Agent Engines: Examples include Griffain on the Solana chain and Spectre AI on the Base chain. Griffain evolves from a read-write mode to a read-write-action mode; Spectre AI is a RAG engine used for on-chain searches.
• AI Agent Frameworks: For framework platforms, the Agent itself is an asset. Therefore, the Agent framework acts as an asset issuance platform and a Launchpad for Agents. Representative projects currently include ai16, Zerebro, ARC, and the much-discussed Swarms.
• Other Smaller Directions: Comprehensive Agent projects like Simmi, the AgentFi protocol Mode, falsification-type Agent Seraph, and real-time API Agent Creator.Bid.

When further discussing the Agent framework, it can be seen that it has significant externalities. Unlike developers on major public chains and protocols who can only choose from different programming language environments, the overall size of the developer community in the industry has not shown a corresponding growth rate in market capitalization. GitHub repositories are where Web2 and Web3 developers build consensus. Building a developer community here is far more attractive and influential for Web2 developers than any “plug-and-play” package developed individually by a protocol.

The four frameworks mentioned in this article are all open-source:

• Eliza framework by ai16z has received 6,200 stars.
• ZerePy framework by Zerebro has received 191 stars.
• RIG framework by ARC has received 1,700 stars.
• Swarms framework by Swarms has received 2,100 stars.

Currently, the Eliza framework is widely used in various Agent applications and is the most widely used framework. ZerePy’s development is not highly advanced, and its development direction mainly lies in X. It does not yet support local LLMs and integrated memory. RIG has the highest relative development difficulty but offers developers the greatest freedom to achieve performance optimization. Swarms, apart from the team’s launch of mcs, has no other use cases yet. However, Swarms can integrate with different frameworks, offering significant potential.

Furthermore, in the aforementioned classification, separating the Agent engine and the framework might cause confusion. But I believe the two are different. First, why is it called an engine? The analogy to search engines in real life is relatively appropriate. Unlike the homogenized Agent applications, the performance of the Agent engine is at a higher level, but it is completely encapsulated, and adjustments are made through API interfaces like a black box. Users can experience the performance of the Agent engine by forking it, but they cannot control the full picture or customization freedom like they can with the basic framework. Each user’s engine is like generating a mirror on a trained Agent and interacting with that mirror. On the other hand, the framework is fundamentally designed to adapt to the chain, because when an Agent builds an Agent framework, the ultimate goal is integration with the corresponding chain. How to define data interaction methods, how to define data validation methods, how to define block size, and how to balance consensus and performance—these are the things the framework needs to consider. As for the engine, it only needs to fine-tune the model and adjust the relationship between data interaction and memory in one direction. Performance is the only evaluation standard, whereas the framework is not limited to this.

Viewing the Agent Framework from the “Wave-Particle Duality” Perspective May Be a Prerequisite for Staying on the Right Track

An Agent’s input-output lifecycle requires three parts. First, the underlying model determines the depth and method of thinking. Then memory is where customization takes place. After the basic model produces an output, it is modified based on memory. Finally, the output operation is completed on different clients.

Source: @SuhailKakar

To confirm that the Agent framework has “wave-particle duality,” the “wave” represents the characteristics of “Memecoin,” which stand for community culture and developer activity, emphasizing the attractiveness and dissemination ability of the Agent. The “particle” represents the characteristics of “industry expectations,” which stand for underlying performance, actual use cases, and technical depth. I will explain this by combining two aspects, using the development tutorials of three frameworks as examples:

Rapid Integration Eliza Framework

1. Set up the environment

Source: @SuhailKakar

1. Install Eliza

Source: @SuhailKakar

1. Configuration file

Source: @SuhailKakar

4.Set Agent personality

Source: @SuhailKakar

The Eliza framework is relatively easy to get started with. It is based on TypeScript, a language that most Web and Web3 developers are familiar with. The framework is simple and avoids excessive abstraction, allowing developers to easily add the features they want. From step 3, we can see that Eliza supports multi-client integration, and can be understood as an assembler for multi-client integration. Eliza supports platforms such as DC, TG, and X, as well as various large language models. It allows for input through the above-mentioned social media and outputs through LLM models, and it also supports built-in memory management, enabling any developer with different habits to quickly deploy an AI Agent.

Due to the simplicity of the framework and the richness of its interfaces, Eliza significantly lowers the access threshold and achieves a relatively unified interface standard.

One-Click Use ZerePy Framework

1.Fork ZerePy’s Repository

Source:https://replit.com/

1. Configure X and GPT

Source:https://replit.com/

3.Set Agent Personality

Source:https://replit.com/

Performance-Optimized Rig Framework

Taking the construction of a RAG (Retrieval-Augmented Generation) Agent as an example:

1. Configure the environment and OpenAI key

source:https://dev.to/0thtachi/build-a-rag-system-with-rig-in-under-100-lines-of-code-4422

1. Set OpenAI Client and Use Chunking for PDF Processing

source:https://dev.to/0thtachi/build-a-rag-system-with-rig-in-under-100-lines-of-code-4422

1. Set Document Structure and Embedding

source:https://dev.to/0thtachi/build-a-rag-system-with-rig-in-under-100-lines-of-code-4422

1. Create Vector Storage and RAG Agent

source:https://dev.to/0thtachi/build-a-rag-system-with-rig-in-under-100-lines-of-code-4422

Rig (ARC) is an AI system construction framework based on the Rust language for LLM workflow engines. It solves lower-level performance optimization issues. In other words, ARC is an AI engine “toolbox” that provides AI calls and performance optimization. , data storage, exception handling and other background support services.

What Rig wants to solve is the “calling” problem to help developers better choose LLM, better optimize prompt words, manage tokens more effectively, and how to handle concurrent processing, manage resources, reduce latency, etc. Its focus is on the AI ​​LLM model How to “make good use of it” when collaborating with the AI ​​Agent system.

Rigis an open source Rust library designed to simplify the development of LLM-driven applications, including RAG Agents. Because Rig is more open, it has higher requirements for developers and a higher understanding of Rust and Agent. The tutorial here is the most basic RAG Agent configuration process. RAG enhances LLM by combining LLM with external knowledge retrieval. In other DEMOs on the official website, you can see that Rig has the following characteristics:

• Unified LLM interface: supports consistent APIs of different LLM providers, simplifying integration.
• Abstract workflow: Pre-built modular components allow Rig to undertake the design of complex AI systems.
• Integrated vector storage: Built-in support for genre storage provides efficient performance in similar search agents such as RAG Agent.
• Flexible embedding: Provides an easy-to-use API for processing embedding, reducing the difficulty of semantic understanding when developing similar search agents such as RAG Agent.

It can be seen that compared to Eliza, Rig provides developers with additional room for performance optimization, helping developers better debug the calls and collaboration optimization of LLM and Agent. Rig delivers Rust-driven performance, taking advantage of Rust’s zero-cost abstractions and memory-safe, high-performance, low-latency LLM operations. It can provide a richer degree of freedom at the underlying level.

Modular Composition Swarms Framework

Swarms aims to provide an enterprise-grade production-level multi-Agent orchestration framework. The official website offers dozens of workflows and parallel/serial architectures for Agent tasks. Below is a brief introduction to a small portion of them.

Sequential Workflow

Source: https://docs.swarms.world

Sequential Swarm architecture processes tasks in a linear sequence. Each Agent completes its task before passing the results to the next Agent in the chain. This architecture ensures orderly processing and is useful when tasks have dependencies.

Use case:

• Each step in the workflow depends on the previous one, such as in an assembly line or sequential data processing.
• Scenarios that require strict adherence to operation sequences.

Hierarchical architecture:

Source: https://docs.swarms.world

This architecture implements top-down control, where a higher-level Agent coordinates the tasks between lower-level Agents. Agents execute tasks concurrently and feed their results back into the loop for final aggregation. This is particularly useful for tasks that are highly parallelizable.

Source: https://docs.swarms.world

This architecture is designed to manage large-scale groups of Agents working simultaneously. It can manage thousands of Agents, with each running on its own thread. It is ideal for supervising the output of large-scale Agent operations.

Swarms is not just an Agent framework but is also compatible with the Eliza, ZerePy, and Rig frameworks mentioned earlier. With a modular approach, it maximizes Agent performance across different workflows and architectures to solve the corresponding problems. The conception and development of Swarms, along with its developer community, are progressing well.

• Eliza: Offers the best ease of use, making it suitable for beginners and rapid prototype development, especially for AI interactions on social media platforms. The framework is simple and easy to integrate and modify, suitable for scenarios that do not require extensive performance optimization.
• ZerePy: One-click deployment, ideal for quickly developing AI Agent applications on Web3 and social platforms. It is suitable for lightweight AI applications, with a simple framework and flexible configuration for fast setup and iteration.
• Rig: Focuses on performance optimization, especially excelling in high-concurrency and high-performance tasks. It is ideal for developers who need detailed control and optimization. The framework is more complex and requires knowledge of Rust, making it suitable for more experienced developers.
• Swarms: Suited for enterprise-level applications, supporting multi-Agent collaboration and complex task management. The framework is flexible, supports large-scale parallel processing, and offers various architecture configurations. However, due to its complexity, it may require a stronger technical background for effective use.

In general, Eliza and ZerePy have advantages in ease of use and rapid development, while Rig and Swarms are more suitable for professional developers or enterprise applications requiring high performance and large-scale processing.

This is why the Agent framework possesses the “industry hope” characteristic. The frameworks mentioned above are still in the early stages, and the immediate priority is to gain first-mover advantage and establish an active developer community. The framework’s performance and whether it lags behind Web2 popular applications are not the primary concerns. The only frameworks that will ultimately succeed are those that can continuously attract developers because the Web3 industry always needs to capture the market’s attention. No matter how strong the framework’s performance or how solid its fundamentals, if it is difficult to use and thus fails to attract users, it will be counterproductive. Provided the framework itself can attract developers, those with a more mature and complete token economy model will stand out.

The “Memecoin” characteristic of Agent frameworks is quite easy to understand. The tokens of the frameworks mentioned above do not have a reasonable token economy design, lack use cases or have very limited ones, and have not validated business models. There is no effective token flywheel. The frameworks are merely frameworks, and there has been no organic integration between the framework and the token. The growth in token price, aside from FOMO, has little to support it from the fundamentals and lacks a strong moat to ensure stable and long-term value growth. At the same time, the frameworks themselves are still somewhat crude, and their actual value does not align with their current market value, thus exhibiting strong “Memecoin” characteristics.

It is worth noting that the “wave-particle duality” of the Agent framework is not a disadvantage and should not be roughly interpreted as a framework that is neither a pure Memecoin nor a halfway solution without token use cases. As I mentioned in the previous article, lightweight Agents are covered by the ambiguous Memecoin veil. Community culture and fundamentals will no longer be a contradiction, and a new asset development path is gradually emerging. Despite the initial bubble and uncertainty surrounding Agent frameworks, their potential to attract developers and drive application adoption should not be ignored. In the future, frameworks with a well-developed token economy model and a strong developer ecosystem may become the key pillars of this sector.

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