Deconstructing AI Frameworks: From Intelligent Agents to Decentralization Exploration

Introduction

Recently, the narrative combining AI and cryptocurrency has developed rapidly. Market attention has shifted towards technology-driven "framework" projects, and this niche track has seen the emergence of several projects with market capitalizations exceeding one hundred million or even one billion in a short period. These types of projects have given rise to a new asset issuance model - issuing tokens based on GitHub repositories, with tokens again issued by Agents developed on the framework. With the framework as the foundation and Agents on top, a unique infrastructure model for the AI era has formed. This article will start from the concept of the framework to explore the significance of AI frameworks in the cryptocurrency industry.

1. What is a framework?

AI frameworks are a type of underlying development tool or platform that integrates pre-built modules, libraries, and tools, simplifying the process of building complex AI models. They can be understood as the operating systems of the AI era, such as Windows, Linux, iOS, or Android. Although "AI frameworks" is a new concept in the cryptocurrency field, the development of AI frameworks has a history of nearly 14 years. The emerging framework projects in the cryptocurrency field are designed to meet the demands of a large number of Agents. We will illustrate this with a few mainstream frameworks.

1.1 Eliza

Eliza is a multi-Agent simulation framework designed for creating, deploying, and managing autonomous AI Agents. Developed in TypeScript, it has good compatibility and API integration capabilities.

Eliza is mainly aimed at social media scenarios, supporting multi-platform integration. In terms of media content processing, it supports functions such as PDF document analysis, link content extraction, audio transcription, video processing, and image analysis.

The current use cases supported by Eliza mainly include:

1. AI assistant applications
2. Role of Social Media
3. Knowledge Workers
4. Interactive Roles

The models supported by Eliza include local inference of open-source models and cloud inference via OpenAI API.

1.2 G.A.M.E

G.A.M.E is a multimodal AI framework for automatic generation and management launched by Virtual, primarily designed for intelligent NPCs in games. This framework supports low-code and even no-code development, allowing users to participate in Agent design simply by modifying parameters.

G.A.M.E adopts a modular design, with its core architecture including the Agent prompt interface, perception subsystem, strategic planning engine, world context, dialogue processing module, on-chain wallet operator, learning module, working memory, long-term memory processor, Agent repository, action planner, and plan executor.

The framework mainly focuses on the decision-making, feedback, perception, and personality of Agents in virtual environments, suitable for gaming and metaverse scenarios.

1.3 Rig

Rig is an open-source tool written in Rust, designed to simplify the development of large language model ( LLM ) applications. It provides a unified interface for easy interaction with 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 question-and-answer systems, document search tools, chatbots, virtual assistants, and 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 functionalities of the Zerebro project but adopts a more modular and extensible design.

ZerePy provides a command line interface (CLI), supports large language models from OpenAI and Anthropic, directly integrates X platform API, and plans to integrate a memory system in the future.

2. The Replica of the BTC Ecosystem

The development path of AI Agents has similarities with the recent BTC ecosystem. The BTC ecosystem has gone through stages such as BRC20, multi-protocol competition, BTC L2, and BTCFi. AI Agents, on the other hand, have developed faster on the foundation of mature traditional AI technology stacks, experiencing stages such as GOAT/ACT, Social-type Agents, and analytical AI Agent framework competition.

In the future, infrastructure projects focusing on Agent Decentralization and security may become the theme of the next phase. AI framework projects provide new ideas for infrastructure development, where the AI framework can be likened to future public chains, and Agents can be compared to future Dapps.

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

The combination of blockchain and AI needs to consider its significance. Based on the successful experience of DeFi, reasons supporting the Agent chain may include:

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

4. Creative Economy

AI framework projects may offer entrepreneurial opportunities similar to GPT Store in the future. Simplifying the agent construction process and providing a framework for complex functionality combinations may have advantages, leading to a more interesting Web3 creative economy than GPT Store.

Web3 can make up for the shortcomings of Web2 in terms of demand and economic systems, introducing community economics to make Agents more complete. The creative economy of Agents will provide opportunities for ordinary people to participate, and future AI Memes may be smarter and more interesting than the existing ones.