AI agents primarily manage memory and context through a combination of techniques. For short-term memory, they often rely on the context window of underlying Large Language Models, where recent turns of a conversation or relevant data points are directly included in the input prompt. This allows the model to maintain immediate conversational flow. Long-term memory is typically handled using external storage mechanisms like vector databases, which store embeddings of past interactions or relevant knowledge. When new input arrives, a retrieval mechanism identifies and fetches the most pertinent pieces of information from this long-term memory. Finally, all these retrieved data, along with the current input, are then assembled into a coherent contextual prompt for the AI model, ensuring it has all necessary information to generate a relevant and informed response. This dynamic process of recalling, filtering, and assembling information is crucial for maintaining coherence and providing accurate, context-aware outputs over extended interactions. More details: https://info-line.top