Original Author: Kevin, the Researcher at BlockBooster

The AI Agent framework, as a key piece in the industry's development puzzle, may harbor the dual potential of driving technological implementation and ecological maturity. The frameworks currently hotly debated in the market include: Eliza, Rig, Swarms, ZerePy, and others. These frameworks attract developers through GitHub Repos, building their reputation. By issuing tokens in the form of a "library," these frameworks possess both wave and particle characteristics, similar to light; the Agent framework embodies both serious externalities and the traits of Memecoins. This article will focus on interpreting the "wave-particle duality" of the framework and why the Agent framework can become the final piece.

The externalities brought by the Agent framework can leave spring buds after the bubble bursts

Since the birth of GOAT, the narrative of Agents has increasingly impacted the market, akin to a kung fu master, with the left fist representing "Memecoin" and the right palm symbolizing "industry hope," where you are bound to lose in one of the moves. In fact, the application scenarios of AI Agents are not strictly differentiated; the boundaries between platforms, frameworks, and specific applications are blurred, but they can still be roughly classified based on token or protocol preferences. However, based on the development preferences of tokens or protocols, they can be categorized into the following types:

• Launchpad: Asset issuance platforms. Virtuals Protocol and clanker on the Base chain, Dasha on the Solana chain.

• AI Agent Applications: Floating between Agent and Memecoin, excelling in memory configuration, such as GOAT, aixbt, etc. These applications are generally unidirectional outputs with very limited input conditions.

• AI Agent Engines: griffain on the Solana chain and Spectre AI on the Base chain. griffain can evolve from read-write mode to read, write, and action mode; Spectre AI is a RAG engine for on-chain search.

• AI Agent Frameworks: For framework platforms, the Agent itself is an asset, so the Agent framework serves as the asset issuance platform for Agents, acting as the Launchpad for Agents. Currently representative projects include ai16, Zerebro, ARC, and the recently discussed Swarms.

• Other minor directions: Comprehensive Agent Simmi; AgentFi protocol Mode; falsifiable Agent Seraph; real-time API Agent Creator.Bid.

Further discussion of the Agent framework reveals its ample externalities. Unlike developers of major public chains and protocols who can only choose from different development language environments, the overall developer scale in the industry has not shown a corresponding growth in market value. GitHub Repo is where Web2 and Web3 developers build consensus; establishing a developer community here is more powerful and influential than any "plug-and-play" package developed by a single protocol for Web2 developers.

The four frameworks mentioned in this article are all open-source: ai16z's Eliza framework has received 6,200 stars; Zerebro's ZerePy framework has received 191 stars; ARC's RIG framework has received 1,700 stars; Swarms' Swarms framework has received 2,100 stars. Currently, the Eliza framework is widely used in various Agent applications and is the most comprehensive framework. ZerePy's development level is not high, with its development direction mainly focused on X, and it does not yet support local LLM and integrated memory. RIG has the highest relative development difficulty but allows developers the greatest freedom to achieve performance optimization. Apart from the team's launch of mcs, Swarms has no other use cases, but it can integrate different frameworks, offering significant imaginative space.

Additionally, the separation of Agent engines and frameworks in the aforementioned classification may cause confusion. However, I believe there is a distinction between the two. First, why is it called an engine? Drawing a parallel to search engines in real life is relatively fitting. Unlike homogeneous Agent applications, the performance of Agent engines is superior, but they are completely encapsulated, adjusted through API interfaces as black boxes. Users can experience the performance of the Agent engine in a forked manner, but they cannot grasp the overall picture and customization freedom like they can with the foundational framework. Each user's engine is like generating a mirror on a well-tuned Agent, interacting with the mirror. The framework, in essence, is designed to adapt to the chain, as both the Agent and the Agent framework ultimately aim for integration with the corresponding chain. How to define data interaction methods, how to define data validation methods, how to define block sizes, and how to balance consensus and performance are all considerations for the framework. As for the engine? It only needs to fine-tune the model and set the relationship between data interaction and memory in a specific direction; performance is the sole evaluation criterion, while the framework is not.

Evaluating the Agent framework from the perspective of "wave-particle duality" may be a prerequisite for ensuring the correct direction

In the lifecycle of an Agent executing an input-output operation, three components are required. First, the underlying model determines the depth and manner of thinking; then, memory is the customizable part, where modifications are made based on the output from the foundational model; finally, the output operation is completed on different clients.

Source: @SuhailKakar

To validate that the Agent framework possesses "wave-particle duality," "wave" embodies the characteristics of "Memecoin," representing community culture and developer activity, emphasizing the appeal and dissemination capability of the Agent; "particle" represents the characteristics of "industry expectations," reflecting underlying performance, actual use cases, and technical depth. I will illustrate this from two aspects using the development tutorials of three frameworks as examples:

Rapid assembly Eliza framework

1. Set up the environment

Source: @SuhailKakar

2. Install Eliza

Source: @SuhailKakar

3. 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 familiar to most Web and Web3 developers. The framework is simple and not overly abstract, allowing developers to easily add the features they want. Through step 3, it can be seen that Eliza supports multi-client integration, which can be understood as an assembler for multi-client integration. Eliza supports platforms like DC, TG, and X, as well as various large language models, enabling input through the aforementioned social media and output via LLM models, while also supporting built-in memory management, allowing any accustomed developer to quickly deploy an AI Agent.

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

One-click usage ZerePy framework

1. Fork the ZerePy library

Source: https://replit.com/@blormdev/ZerePy?v=1

2. Configure X and GPT

Source: https://replit.com/@blormdev/ZerePy?v=1

3. Set Agent personality

Source: https://replit.com/@blormdev/ZerePy?v=1

Performance optimization 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

2. Set up the 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

3. Set up document structure and embeddings

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

4. 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, aimed at LLM workflow engines, designed to address lower-level performance optimization issues. In other words, ARC is an AI engine "toolbox" that provides backend support services such as AI invocation, performance optimization, data storage, and exception handling.

Rig aims to solve the "invocation" problem to help developers better choose LLMs, optimize prompts, manage tokens more effectively, and handle concurrent processing, resource management, and latency reduction. Its focus is on how to "make good use of it" during the collaboration between AI LLM models and AI Agent systems.

Rig is an open-source Rust library designed to simplify the development of LLM-driven applications (including RAG Agents). Because Rig is more open, it requires a higher level of understanding from developers regarding Rust and Agents. The tutorial here outlines the basic configuration process for a RAG Agent, which enhances LLM by combining it with external knowledge retrieval. In other demos on the official website, Rig exhibits the following features:

• Unified LLM interface: Supports consistent APIs for different LLM providers, simplifying integration.

• Abstract workflows: Pre-built modular components allow Rig to undertake the design of complex AI systems.

• Integrated vector storage: Built-in support for vector storage provides efficient performance in search-type Agents like RAG Agents.

• Flexible embeddings: Offers easy-to-use APIs for handling embeddings, reducing the difficulty of semantic understanding when developing search-type Agents like RAG Agents.

Compared to Eliza, Rig provides developers with additional space for performance optimization, helping them better debug LLM and Agent invocation and collaboration optimization. Rig leverages Rust's performance-driven capabilities, utilizing Rust's advantages of zero-cost abstraction, memory safety, high performance, and low-latency LLM operations. It can provide richer freedom at the lower level.

Decomposable Swarms framework

Swarms aims to provide an enterprise-level production-grade multi-Agent orchestration framework, with dozens of workflows and Agent parallel and serial architectures available on its official website. Here, we introduce a small portion of them.

Sequential Workflow

Source: https://docs.swarms.world

The sequential Swarm architecture processes tasks in a linear order. Each Agent completes its task before passing the result to the next Agent in the chain. This architecture ensures ordered processing and is very useful when tasks have dependencies.

Use cases:

• Each step in the workflow depends on the previous step, such as in assembly lines or sequential data processing.

• Scenarios that require strict adherence to operational order.

Hierarchical architecture:

Source: https://docs.swarms.world

Implements top-down control, with a superior Agent coordinating tasks among subordinate Agents. Agents execute tasks simultaneously and then feed their results back into a loop for final aggregation. This is very useful for highly parallelizable tasks.

Spreadsheet-style architecture:

Source: https://docs.swarms.world

Used for managing large-scale groups of multiple Agents working simultaneously. It can manage thousands of Agents at once, with each Agent running on its own thread. It is ideal for supervising the output of large-scale Agents.

Swarms is not only an Agent framework but also compatible with the aforementioned Eliza, ZerePy, and Rig frameworks. With a modular approach, it maximizes Agent performance across different workflows and architectures to solve corresponding problems. The conception and progress of the developer community for Swarms are promising.

• Eliza: The most user-friendly, suitable for beginners and rapid prototyping, especially for AI interactions on social media platforms. The framework is simple, allowing for quick integration and modification, suitable for scenarios that do not require excessive performance optimization.

• ZerePy: One-click deployment, suitable for quickly developing AI Agent applications for Web3 and social platforms. It is suitable for lightweight AI applications, with a simple framework and flexible configuration, ideal for rapid setup and iteration.

• Rig: Focused on performance optimization, especially excelling in high concurrency and high-performance tasks, suitable for developers who need detailed control and optimization. The framework is relatively complex and requires some knowledge of Rust, making it suitable for more experienced developers.

• Swarms: Suitable for enterprise-level applications, supporting multi-Agent collaboration and complex task management. The framework is flexible, supporting large-scale parallel processing and providing various architectural configurations, but due to its complexity, it may require a stronger technical background for effective application.

Overall, 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 that require high performance and large-scale processing.

This is the reason why the Agent framework possesses the characteristic of "industry hope." The aforementioned frameworks are still in their early stages, and the urgent task is to seize the first-mover advantage and establish an active developer community. The performance of the framework itself and whether it lags behind popular Web2 applications are not the main contradictions. Only frameworks that continuously attract developers will ultimately prevail, as the Web3 industry always needs to capture market attention. Regardless of how strong the framework's performance is or how solid its fundamentals are, if it is difficult to use and leads to a lack of interest, it is counterproductive. Under the premise that the framework itself can attract developers, those with more mature and complete token economic models will stand out.

The "Memecoin" characteristic of the Agent framework is quite understandable. The tokens of the aforementioned frameworks lack reasonable token economic design, have no use cases or very singular use cases, lack validated business models, and do not have effective token flywheels. The frameworks are merely frameworks, and there is no organic integration between the frameworks and the tokens. The growth of token prices, apart from FOMO, struggles to gain support from fundamentals, and there are insufficient moats to ensure stable and lasting value growth. At the same time, the frameworks themselves appear relatively rough, with their actual value not matching their current market capitalization, thus exhibiting strong "Memecoin" characteristics.

It is worth noting that the "wave-particle duality" of the Agent framework is not a flaw; it should not be crudely understood as neither a pure Memecoin nor a half-measure without token use cases. As I mentioned in my previous article: lightweight Agents are cloaked in an ambiguous Memecoin veil, and community culture and fundamentals will no longer be in conflict. A new asset development path is gradually emerging; although the Agent framework initially has bubbles and uncertainties, its potential to attract developers and drive application implementation should not be overlooked. In the future, frameworks with well-developed token economic models and strong developer ecosystems may become key pillars in this sector.

About BlockBooster

BlockBooster is an Asian Web3 venture studio supported by OKX Ventures and other top institutions, dedicated to being a trusted partner for outstanding entrepreneurs. We connect Web3 projects with the real world through strategic investments and deep incubation, helping quality entrepreneurial projects grow.

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