The intelligence of AI Agents depends not only on data processing and computational power but also on an efficient memory system. Silica Chain introduces the RAG (Retrieval-Augmented Generation) memory system, which enables AI Agents to retrieve real-time data and generate knowledge, making them more intelligent in task execution. The RAG system combines real-time knowledge retrieval and generation capabilities, allowing AI Agents to complete tasks faster and more accurately.

The core of the RAG memory system is its real-time knowledge retrieval ability. AI Agents can access on-chain and off-chain data sources to retrieve the latest market dynamics, governance proposals, community feedback, and more. This ability enables AI Agents to adjust their decisions in response to complex market changes in real-time. In the decision-making process, the RAG system not only generates highly relevant content by combining contextual information but also dynamically optimizes AI Agent's execution strategies, improving task accuracy and efficiency.

Additionally, the RAG system combines short-term and long-term memory, enabling AI Agents to call upon historical data across tasks, ensuring continuity and consistency in execution. Short-term memory stores real-time data during task execution, while long-term memory retains key events and decisions. This mechanism allows AI Agents to make more accurate predictions and optimize decision-making by referencing historical data.

By introducing the RAG memory system, Silica Chain enhances the intelligence of AI Agents and further promotes the diversification of Web3 application scenarios. AI Agents can flexibly apply in areas such as DAO governance, investment assistance, resource allocation, and more, providing greater value to users and optimizing resource management.