The Rise of AI Frameworks: From Intelligent Agents to New Infrastructure for the Web3 Ecosystem

Deconstructing AI Framework: From Intelligent Agents to Decentralization Exploration

Deconstructing AI Framework: From Intelligent Agents to Decentralization Exploration

Foreword

Recently, the narrative combining AI and cryptocurrencies has rapidly evolved. Market attention is focused on technology-driven "framework-type" projects, and this sub-sector has seen multiple projects with market capitalizations exceeding hundreds of millions or even billions in a short period. These types of projects have given rise to new asset issuance models: issuing tokens from Github repositories, and Agents based on frameworks can also issue tokens again. Based on the framework, with Agents on top, a unique infrastructure model for the AI era is formed. This article will explore the potential impact of AI frameworks on the cryptocurrency sector.

I. Framework Overview

The AI framework is a fundamental 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, similar to Windows and Linux in desktop systems, or iOS and Android in mobile devices.

Although the "AI framework" is an emerging concept in the cryptocurrency field, its development history has lasted nearly 14 years. There are mature frameworks available in the traditional AI field, such as TensorFlow and Pytorch. The framework projects emerging in cryptocurrency primarily target Agent needs and extend to other fields. Here are a few examples of mainstream frameworks:

1.1 Eliza

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

Mainly aimed at social media scenarios, supporting multi-platform integration such as Discord, X/Twitter, Telegram, etc. In terms of media content processing, it supports functions such as PDF analysis, link extraction, audio transcription, video processing, image analysis, and more.

Use cases supported by Eliza include: AI assistant applications, social media characters, knowledge workers, and interactive roles. Supports local inference of open-source models and cloud-based inference.

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. The special feature is that low-code or even no-code users can also use it.

The project architecture adopts a modular design, including multiple subsystems such as the Agent prompt interface, perception subsystem, strategic planning engine, world context, and dialogue processing module working in coordination.

In addition to games, this framework is also applicable to metaverse scenarios. Several projects have already adopted G.A.M.E for construction.

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.

Core features include: unified interface, modular architecture, type safety, and high performance. The workflow involves mechanisms such as provider abstraction layer, smart agent invocation, and retrieval-augmented generation.

Suitable for building question and answer systems, document search tools, context-aware chatbots, and other scenarios.

1.4 ZerePy

ZerePy is an open-source framework based on Python that simplifies the process of deploying and managing AI Agents on the X platform. It inherits the core functionalities of the Zerebro project and adopts a more modular and easily extensible design.

Provide command line interface ( CLI ) for management control. The core architecture is based on a modular design, supporting LLM integration, X platform integration, and modular connection system functionalities. Future plans include integrating memory systems to enhance the contextual understanding capabilities of the Agent.

Deconstructing AI Framework: From Intelligent Agents to Decentralization Exploration

2. Comparison with BTC Ecosystem Development Path

The development path of AI Agents has similarities with the recent BTC ecosystem:

Multi-Protocol Competition Multi-type Agent/Framework Competition

The AI Agent track is unlikely to replicate the history of smart contract chains. Existing AI framework projects provide new infrastructure development ideas, where the AI framework can be compared to future public chains, and Agents can be compared to future Dapps.

Future debates may shift from the conflict between EVM and heterogeneous chains to a conflict over frameworks. The key issue is how to achieve Decentralization or chainization, and the significance of developing AI frameworks on the blockchain.

Deconstructing AI Framework: Exploring from Intelligent Agents to Decentralization

3. The Significance of Chainization

The core issue facing the combination of blockchain and AI is its significance. Referring to the success factors of DeFi, the reasons supporting the Agent chainization may include:

  1. Reduce usage costs, improve accessibility and options, allowing ordinary users to participate in AI "rental rights".

  2. Provide blockchain-based security solutions to meet the security needs of Agent's interactions with the real/virtual world.

  3. Create unique decentralized finance models, such as new liquidity provision or investment mechanisms based on Agents.

  4. Achieve a transparent and traceable reasoning process, which may outperform the agent browsers provided by traditional internet giants in terms of interoperability.

Deconstructing AI Framework: From Intelligent Agents to Decentralization Exploration

4. Prospects of the Creative Economy

Framework projects may offer entrepreneurial opportunities similar to the GPT Store in the future. Frameworks that simplify the Agent construction process may have an advantage, creating a more interesting Web3 creative economy than the GPT Store.

Compared to the current GPT Store, which tends to focus on practicality in traditional fields, Web3 has advantages in demand and economic systems. The introduction of community economy may make Agents more refined. The creative economy of Agents will provide ordinary people with opportunities to participate, and future AI Memes may be smarter and more interesting than the Agents on existing platforms.

Deconstructing AI Framework: Exploring from Intelligent Agents to Decentralization

Deconstructing AI Framework: Exploring from Intelligent Agents to Decentralization

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GhostChainLoyalistvip
· 8h ago
followed the AI track for a long time, go!
View OriginalReply0
StakeHouseDirectorvip
· 8h ago
Sigh, here comes the AI hype story again.
View OriginalReply0
tokenomics_truthervip
· 8h ago
Another wave of Be Played for Suckers is coming.
View OriginalReply0
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