All about memory.
This lesson explores how AI agents utilize different memory types—semantic, episodic, and procedural—similar to human memory, leveraging LangMem and LangGraph libraries within the Langchain framework. By efficiently storing and retrieving this information, agents personalize interactions, learn from past experiences, and adapt their behavior for improved performance.
This lesson demonstrates building intelligent agents with robust memory management using LangChain and LangSmith. By leveraging tools like `manage_memory` and `search_memory`, agents learn and adapt from past interactions, maintaining context across conversations and improving efficiency.
This lesson teaches you to build a sophisticated email assistant agent using LangChain's multi-agent system and prompt optimization. The agent learns from user feedback, improving its email drafting skills (including adding signatures and meeting options) and task routing efficiency through a supervisor agent.
