Deconstructing AI Frameworks: Exploring from Intelligent Agents to Decentralization

Introduction

Recently, the narrative combining AI and cryptocurrency has developed rapidly. Market attention has shifted to technology-driven "framework-type" projects, and this niche track has seen the emergence of multiple projects with market capitalizations exceeding one billion or even ten billion in just a few weeks. These projects have also given rise to new asset issuance models - issuing tokens through GitHub repositories, and Agents built on the framework can also issue tokens again. Based on the framework, Agents serve as upper-layer applications, forming a unique infrastructure model for the AI era. This article will explore the significance of AI frameworks for the cryptocurrency industry, starting from the concept of frameworks.

1. What is a framework?

The AI framework is a foundational development tool or platform that integrates pre-built modules, libraries, and tools, simplifying the process of building complex AI models. The framework can be understood as the operating system of the AI era.

Although the "AI framework" is an emerging concept in the cryptocurrency field, the development of AI frameworks has a history of nearly 14 years. There are mature frameworks available in the traditional AI field, such as TensorFlow and Pytorch. The framework projects that have emerged in cryptocurrency are designed to meet the large demand for Agents under the AI boom and have derived into other tracks, forming AI frameworks in different subfields.

1.1 Eliza

Eliza is a multi-agent simulation framework used to create, deploy, and manage autonomous AI agents. Developed in TypeScript, it has good compatibility and is easy to integrate with APIs.

Eliza mainly targets social media scenarios and supports multi-platform integration. It supports functions such as document reading analysis, content extraction and summarization, and audio and video processing in media content handling.

The use cases currently supported by Eliza are:

1. AI Assistant Applications
2. Role of Social Media
3. Knowledge Workers
4. Interactive Roles

Eliza supports local inference and cloud inference for open-source models, with the default configuration being Nous Hermes Llama 3.1B.

1.2 G.A.M.E

G.A.M.E is an automatically generated and managed multimodal AI framework launched by Virtual, primarily designed for intelligent NPCs in games. The framework is characterized by its usability for low-code or even no-code users.

The core design of G.A.M.E is a modular design that works collaboratively through multiple subsystems, including the Agent prompt interface, perception subsystem, strategy planning engine, world context, dialogue processing module, and several other components.

From a technical architecture perspective, this framework primarily focuses on the decision-making, feedback, perception, and personality of agents in virtual environments, applicable to gaming and metaverse scenarios.

1.3 Rig

Rig is an open-source tool written in Rust that simplifies the development of large language model applications. It provides a unified interface to access multiple LLM service providers and vector databases.

The core features of Rig include:

• Unified Interface
• Modular Architecture
• Type Safety
• High performance

Rig is suitable for building Q&A systems, document search tools, chatbots, virtual assistants, and supporting content creation scenarios.

1.4 ZerePy

ZerePy is an open-source framework based on Python, designed to simplify the process of deploying and managing AI Agents on the X platform. It inherits the core functionality of the Zerebro project but is designed in a more modular and extensible way.

ZerePy provides a command line interface, supports large language models from OpenAI and Anthropic, directly integrates with the X platform API, and employs a modular connection system.

ZerePy focuses on simplifying the process of deploying AI Agents on the X platform, leaning more towards practical applications.

2. The Replica of the BTC Ecosystem

The development path of AI Agents has similarities with the BTC ecosystem and can be summarized as: GOAT/ACT - Social type Agents/Analytical AI Agents - Framework competition. In the future, infrastructure projects focused on the Decentralization and security of Agents may become the main theme of the next stage.

The AI framework project provides a new infrastructure development approach. Compared to the Memecoin Launchpad and the Inscription Protocol, the AI framework resembles a future public chain, while the Agent resembles a future Dapp.

In the era of AI in the cryptocurrency field, the future may shift from the debate between EVM and heterogeneous chains to a competition of frameworks. The key issue is how to achieve Decentralization or chainization, and the significance of developing AI applications on the blockchain.

3. What is the significance of being on-chain?

The core issue that needs to be addressed when combining AI with blockchain is: what is the significance? Referring to the successful experience of DeFi, reasons supporting the chainization of agents may include:

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

4. Creative Economy

Framework projects may offer entrepreneurial opportunities similar to the GPT Store in the future. Simplifying the agent construction process and providing frameworks for complex functionality combinations may have an advantage, creating a more interesting Web3 creative economy than the GPT Store.

Web3 still has many unmet demands, and the economic system can make distribution fairer. Introducing community economics helps improve Agents. The creative economy of Agents will provide opportunities for ordinary people to participate, and future AI Memes may be smarter and more interesting than existing Agents.