Deconstructing AI Frameworks: From Intelligent Agents to Decentralization Exploration

Preface

Recently, the narrative of AI combined with cryptocurrency has developed rapidly. Market attention has shifted towards technology-driven "framework" projects, which have produced multiple projects with a market capitalization exceeding hundreds of millions and even billions in a short period. These projects have given rise to a new asset issuance model: issuing tokens based on GitHub repositories, where Agents developed on the framework can also issue tokens again. This model, based on frameworks and with Agents as applications, has formed a unique infrastructure for the AI era. This article will explore the significance of AI frameworks in the cryptocurrency field.

1. Framework Overview

AI framework is a foundational development tool or platform that integrates pre-built modules, libraries, and tools to simplify the construction process of complex AI models. It can be viewed as the operating system of the AI era, similar to Windows and Linux in desktop systems, or iOS and Android in mobile devices.

Although the "AI framework" is a new concept in the cryptocurrency field, the development history of AI frameworks has been nearly 14 years. There are mature frameworks available in the traditional AI field, such as TensorFlow, Pytorch, etc. The framework projects in cryptocurrency are developed in response to the demand for agents under the AI boom and are expanding into other fields.

1.1 Eliza

Eliza is a multi-Agent simulation framework designed for creating, deploying, and managing autonomous AI Agents. Developed based on TypeScript, it has good compatibility and API integration capabilities. It is mainly aimed at social media scenarios, supporting multi-platform integration and various media content processing.

1.2 G.A.M.E

The G.A.M.E framework is primarily used for the design of intelligent NPCs in games, supporting low-code or even no-code operations. Its core design features a modular architecture where multiple subsystems work collaboratively, including perception subsystems, strategic planning engines, dialogue processing modules, and more.

1.3 Rig

Rig is an open-source tool written in Rust, designed to simplify the development of large language model applications. It provides a unified interface that supports interaction with multiple LLM service providers and vector databases. Its features include a modular architecture, type safety, and efficient performance.

1.4 ZerePy

ZerePy is an open-source framework based on Python, focusing on deploying and managing AI Agents on the X platform. It provides a command-line interface, supports modular design, and allows flexible integration of different functional modules.

2. Similarities with the BTC Ecosystem

The development path of AI Agents has similarities with the BTC ecosystem, but the narrative of AI Agents is not meant to replicate the history of smart contract chains. AI framework projects provide new ideas for infrastructure development, which can be likened to future public chains, with Agents compared to future Dapps.

3. The Significance of On-Chain

The key issue facing the integration of blockchain and AI is what its significance is. Based on the successful experience of DeFi, the chainization of AI Agents may have significance in the following aspects:

1. Reduce usage costs, improve accessibility and choice.
2. Provide blockchain-based security solutions
3. Create unique blockchain financial gameplay
4. Achieve transparent and traceable reasoning to enhance interoperability.

4. Creative Economy

Framework projects may provide entrepreneurial opportunities similar to the GPT Store in the future. A framework that simplifies the agent construction process and offers complex functionality combinations is expected to gain an advantage in the future, creating a more interesting Web3 creative economy than the GPT Store. This will provide opportunities for ordinary people to participate, and future AI Memes may be smarter and more interesting than current agents.