Article source: On-chain view
More standards disputes regarding AI Agent frameworks are ongoing fervently. In recent days, ARC's performance in the secondary market has been particularly eye-catching. How should we understand this professional framework for AI application development built on Rust? What are the differences between ARC and ELIZA frameworks? From a technical logic perspective and a business perspective, here's my understanding:
1) ELIZA is a multi-client integration framework based on TypeScript architecture and aimed at Agent development. In other words, ELIZA is an 'assembler' focused on integrating various LLM large models and the input and output functions of platforms like Discord and Twitter, providing features like memory context management and model fine-tuning algorithm optimization to help developers quickly deploy AI Agents.
ELIZA addresses the 'access' issue to ensure that developers can quickly deploy AI Agents. Its focus is on unifying interface standards, simplifying integration processes, and lowering development thresholds, enabling how LLMs can be 'used' during cross-platform application processes.
2) Rig (ARC) is an AI system construction framework based on Rust language aimed at LLM workflow engines. It seeks to solve deeper performance optimization issues. In other words, ARC is an AI engine 'toolbox' that provides backend support services for AI invocation, performance optimization, data storage, error handling, and more.
Rig aims to solve the 'invocation' issue to help developers better choose LLMs, optimize prompts more effectively, manage tokens more efficiently, and address issues like concurrent processing, resource management, and reducing latency. Its focus is on how to 'make good use of it' during the collaboration process between AI LLM models and AI Agent systems.
3) The above is a very objective technical logic breakdown, everyone must be interested in who has greater potential for development, ELIZA or ARC? Here are some evaluation criteria:
1. The AI Agent is in the early stages of ecological explosion, and the market reputation and ecological developer activity of those with first-mover advantages are more important; similar to the early development of the EVM chain operating framework, the more advanced and commercially suitable blockchain architecture like EOS temporarily became the market focus, but ultimately lost to the large developer ecosystem of EVM;
2. ELIZA's burden lies in the immature Tokenomics design of ai16z, the 'empowerment' issue of the ai16z and ELIZA open-source framework tokens, and whether new variables will be added to the subsequent family bundle, which will undoubtedly cause its tokens to lack short-term potential for significant growth, whereas ARC seems to lack this burden;
3. The problem with ARC is that it depicts a grand, high-performance, enterprise-level commercialization framework more suitable for the future AI Agent ecosystem, but it must prove step by step to the market that this 'high-level' is not just a facade. It needs to timely release some standalone AI applications and tangible AI Agent innovative gameplay;