How to Use Memory in Agent Builder

LangChain's Agent Builder utilizes memory to improve its performance over time by retaining user feedback and preferences. It features two types of memory: short-term for the duration of a single task and persistent long-term memory stored as files. Users can make agents more effective by explicitly telling them to remember successful approaches, creating specialized "skills" for context-specific knowledge, and directly editing the agent's memory files. These methods allow for continuous learning and refinement, making the agent a more capable teammate.