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AI Framework Deconstruction: From Intelligent Agents to a Decentralized Future Blueprint
Deconstructing AI Frameworks: From Intelligent Agents to Decentralization Exploration
Introduction
The development speed of the AI Agent track is astounding. Since the "Truth Terminal" ignited the Agent craze, the narrative combining AI and cryptocurrency has seen new changes almost every week. Recently, market attention has shifted to "framework-type" projects dominated by technological narratives. This niche track has produced several dark horse projects with market capitalizations exceeding hundreds of millions and even billions in just a few weeks.
This type of project has given rise to a new asset issuance model—issuing tokens based on GitHub code repositories, while Agents developed based on the framework can also issue tokens again. This "framework at the bottom, Agent on top" model, although superficially similar to asset issuance platforms, is actually a kind of infrastructure model unique to the AI era. This article will start with an overview of the framework and explore the far-reaching impact of AI frameworks on the cryptocurrency field.
1. What is a framework?
AI frameworks are a set of integrated underlying development tools or platforms that include pre-built modules, libraries, and tools, simplifying the process of building complex AI models. They can be understood as operating systems for the AI era, similar to desktop systems like Windows and Linux, or mobile systems like iOS and Android. Each framework has its own characteristics, allowing developers to choose freely based on their needs.
Although the "AI framework" is a emerging concept in the cryptocurrency field, its development history has been nearly 14 years. There are many mature frameworks available in the traditional AI field, such as Google's TensorFlow and Meta's PyTorch.
The framework projects emerging in the cryptocurrency field are mainly designed to cater to the massive demand for Agents brought about by the AI boom, gradually expanding into other tracks and forming AI frameworks in different subfields. Below are introductions to several 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.
Eliza primarily focuses on social media scenarios, supporting multi-platform integration, including Discord, X/Twitter, Telegram, and more. In terms of media content processing, it supports PDF document analysis, link content extraction, audio transcription, video processing, image analysis, and other functions.
Eliza currently supports four main use cases:
In terms of model support, Eliza can use open-source models for local inference and also supports using cloud inference services via API.
1.2 G.A.M.E
G.A.M.E ( Generative Autonomous Multimodal Entities Framework ) is an automatically generated and managed multimodal AI framework launched by Virtual, mainly aimed at the design of intelligent NPCs in games. The framework's feature is that users without a programming background can also use it, as they only need to modify parameters to participate in Agent design.
The core design of G.A.M.E adopts a modular architecture where multiple subsystems work in coordination, including components such as the Agent prompt interface, perception subsystem, strategic planning engine, world context, and dialogue processing module.
The workflow of this framework is: Developers start the Agent through the Agent prompt interface, the perception subsystem receives input and transmits it to the strategic planning engine. The strategic planning engine formulates and executes action plans using various systems and information. The learning module continuously monitors the outcomes of the Agent's actions and adjusts behavior accordingly.
In addition to the gaming field, the G.A.M.E framework is also applicable to metaverse scenarios, and multiple projects have adopted this framework for development.
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 operating interface that allows developers to easily interact with multiple LLM service providers and vector databases.
The core features of Rig include:
The workflow of Rig is as follows: user requests first go through the "Provider Abstraction Layer", then in the core layer, the intelligent agent calls various tools or queries vector storage to obtain information. Finally, through mechanisms such as Retrieval-Augmented Generation (RAG), the system generates precise and meaningful responses back to the user.
Rig is suitable for building question answering systems, document search tools, context-aware chatbots or virtual assistants, and even supports content creation.
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 (formerly Twitter) platform. It inherits the core features of the Zerebro project but adopts a more modular and extensible design.
ZerePy provides a command-line interface (CLI) that allows users to manage and control deployed AI Agents. Its core architecture is based on a modular design, including:
Compared to a16z's Eliza project, ZerePy focuses more on simplifying the process of deploying AI Agents on specific social platforms (X), leaning towards practical applications.
2. The Replica of the BTC Ecosystem
The development path of AI Agents has many similarities with the recent BTC ecosystem. The development of the BTC ecosystem can be summarized as: BRC20 - multi-protocol competition - BTC L2 - BTCFi centered around Babylon. In contrast, AI Agents are developing faster on the foundation of mature traditional AI technology stacks, and their path can be summarized as: GOAT/ACT - competition among Social-type Agents/analytical AI Agent frameworks.
Despite the similarities, the AI Agent track is unlikely to become homogeneous and bubble-like like the BTC ecosystem. AI framework projects provide new ideas for infrastructure development, resembling future public chains, while Agents are more like future Dapps.
In the current cryptocurrency ecosystem, we have thousands of public chains and tens of thousands of Dapps. General chains include BTC, Ethereum, and various heterogeneous chains, while application chains are even more diverse. Future debates may shift from the competition between EVM and heterogeneous chains to the competition of frameworks, with key issues revolving around how to achieve Decentralization or "chainification," and the significance of developing these projects on the blockchain.
3. What is the significance of going on-chain?
When blockchain is combined with anything, it faces a core question: does this combination make sense? Looking back at the successful experience of DeFi, its advantages lie in providing greater accessibility, better efficiency, and lower costs, as well as security without the need for trust in centralization. Based on this idea, there may be several reasons for the chainization of AI Agents:
Reduce usage costs, increase accessibility and choice, allowing ordinary users to participate in AI "rental rights".
Provide blockchain-based security solutions to meet the security needs of Agents when interacting with the real or virtual world.
Create a unique blockchain financial model, such as allowing ordinary users to participate in automatic market making or invest in resources like computing power and data labeling required by Agents.
Achieving a transparent and traceable reasoning process may be more attractive than the agent browsers provided by traditional internet giants.
4. New Opportunities in the Creative Economy
Framework projects may provide entrepreneurial opportunities similar to the GPT Store in the future. Although it is still complex for ordinary users to release Agents through frameworks at present, simplifying the Agent building process and providing frameworks for complex functionality combinations is likely to have an advantage in the future. This will form a more interesting Web3 creative economy than the GPT Store.
Unlike the current GPT Store, which is mainly dominated by traditional Web2 companies, the AI creative economy in the Web3 space may be more equitable and introduce community economics to enhance Agents. This will provide opportunities for ordinary people to participate, and future AI Meme projects may be smarter and more interesting than existing Agents.