Long-Term Agentic Memory With LangGraph

Learn to build an agent with long-term memory in Long-Term Agentic Memory with LangGraph! Created in partnership with LangChain, and taught by its Co-Founder and CEO, Harrison Chase.

Imagine you had a human personal assistant who forgot about their previous conversation with you—not ideal. The same goes for Agents.

The main use cases for agents include personal assistance and productivity tasks. One core aspect of these tasks is long-term memory.

In this course, you will learn how to build an agent with long-term memory by creating a personal email agent that can respond, ignore, and notify the user using writing, scheduling, and memory tools. You’ll develop your agent’s memory by adding facts to its memory store, providing examples to learn the user’s preferences, and optimizing system prompts to evolve instructions based on previous responses.

By the end of this course, you will have the foundational mental framework to build an agent with long-term memory using LangGraph.

In detail, you’ll:

• Learn how the three types of memory–semantic, episodic, and procedural–and the two mechanisms–via hot path and in the background– apply to your agent.
• Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user.
• Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions.
• Incorporate episodic memory in the form of few-shot examples in the triage step of your agents to help them learn and update user preferences.
• Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows.

Start building agents with long-term memory with LangGraph.

It’s helpful to be familiar with Python and a basic understanding of LLM prompting and LLM application development.