The AI ​​Agent framework ARC has surged against the trend. What is the difference between it and ELIZA?

The battle for more AI Agent framework standards is in full swing. The secondary market performance of ARC in the past two days has been particularly eye-catching. How to understand this professional framework for AI application development based on Rust? What is the difference between ARC vs ELIZA framework? Based on the technical logic perspective and business perspective, let me talk about my understanding:

1. ELIZA is a multi-client integration framework based on TypeScript architecture and oriented to Agent development. In other words, ELIZA is an "assembler" that focuses on integrating various large LLM models with the input and output functions of platforms such as Discord and Twitter. When assembled, it provides functions such as memory context management and model fine-tuning algorithm optimization, helping developers quickly deploy AI Agents.

ELIZA solves the problem of "access" to ensure that developers can quickly implement AI Agent. Its focus is on unifying interface standards, simplifying the integration process, lowering the development threshold, and allowing LLM to be used in cross-platform applications. ”.

2. Rig (ARC) is an AI system construction framework based on the Rust language for LLM workflow engines. It solves lower-level performance optimization issues. In other words, ARC is an AI engine "toolbox" that provides AI calls, Backend support services such as performance optimization, data storage, and exception handling.

What Rig wants to solve is the "calling" problem to help developers better choose LLM, better optimize prompt words, manage tokens more effectively, and how to handle concurrent processing, manage resources, reduce latency, etc. Its focus is on the AI ​​LLM model How to "make good use of it" when cooperating with the AI ​​Agent system.

3. The above is a very objective technical logic breakdown. You must be interested in ELIZA vs. ARC, who has greater development potential? Just give some evaluation criteria:

1. AI Agent is in the early stage of ecological explosion, and market reputation and ecological developer activity with first-mover advantages are more important; similar to the early development of the EVM chain operation framework, EOS is a blockchain architecture with more advanced technology and suitable for commercial use. It seemed to be the focus of the market for a short time, but ultimately lost to EVM's huge developer ecosystem;

2. ELIZA’s burden lies in ai16z’s immature Tokenomics design, the “empowerment” issue of ai16z and ELIZA’s open source framework tokens, and whether “newcomers” will be added to FamilyMart in the future, which will inevitably make its tokens lack short-term The potential for substantial growth, in comparison, ARC does not seem to have this burden;

3. The problem with ARC is that it describes a grand, high-performance, enterprise-level commercial framework that is more suitable for the future AI Agent ecosystem. However, in order to prove to the market step by step that this "advanced" is not in vain, some units must be implemented in a timely manner. Only when innovative AI applications and AI Agent innovations that can actually be seen are developed;