What is Memory (AI)?
Memory in AI refers to a system's capability to retain information from previous interactions, observations, or experiences and utilize that stored information to inform future behavior. Unlike stateless systems that treat each interaction independently, memory-enabled AI agents can maintain context across conversations, learn from past experiences, build knowledge over time, and provide personalized responses based on historical interactions.
AI memory exists in several forms and operates at different timescales. Working memory corresponds to the immediate context in the model's attention window. Short-term memory maintains recent conversation history or task context. Long-term memory stores information persistently across sessions, enabling agents to remember facts, preferences, and experiences over extended periods. Different memory types serve complementary purposes in creating coherent, contextually-aware agent behavior.
The implementation of memory in AI systems draws inspiration from cognitive science and neuroscience, though with significant differences from biological memory. Common approaches include maintaining conversation buffers, storing embeddings in vector databases for semantic retrieval, building knowledge graphs of facts and relationships, and implementing specialized memory architectures that combine multiple storage and retrieval mechanisms. Effective memory management is crucial for building agents that can engage in extended, meaningful interactions and continuously improve through experience.