Memory (AI Agents)

Memory in AI agents refers to the system's internal mechanisms for storing, managing, and retrieving information, which can include short-term interaction history (such as current conversation context), long-term accumulated knowledge (such as user preferences and factual data), or past actions and results. Effective memory mechanisms are crucial for agents to maintain conversation coherence, learn from experience, make context-aware decisions, and achieve personalization. Modern LLM agents often combine vector databases and other technologies to implement more powerful memory capabilities, enabling them to maintain context across extended interactions and improve their performance over time.