Building Better Agents: LLM Memory Types and Trade-Offs

Memory in large language models (LLMs) acts as the central nervous system of an agent, determining its coherence. Building resilient agents requires moving beyond basic chat history to navigate a complex decision surface that impacts scalability and reliability. LLM memory consists of parametric knowledge and dynamic memory. Parametric knowledge is the static information stored in a model's weights, while dynamic memory is injected into the runtime context. This shift in managing state enhances application performance across complex workflows.