How we built Agent Builder’s memory system

LangSmith Agent Builder is a no-code platform designed for citizen developers to create specialized agents that automate specific tasks, with a strong emphasis on a memory system to enhance user experience. Unlike general-purpose AI agents, LangSmith's agents are tailored for repetitive tasks where memory continuity is crucial, allowing for more efficient and personalized interactions. The memory system is structured using a virtual filesystem supported by Deep Agents, utilizing files stored in a database for ease of access and management. This setup enables agents to update and refine their functionalities through simple configuration and natural language feedback, without requiring domain-specific language expertise. The system prioritizes procedural and semantic memory, while episodic memory is planned for future implementation. Key learnings in building the memory system include the importance of prompting, validation of file types, and maintaining a human-in-the-loop approach for memory updates to prevent errors and potential security risks. The approach not only simplifies the agent-building process but also ensures portability across various platforms, setting the stage for future enhancements such as background memory processes, semantic search, and multi-level memory structures.