Learn how memory works and how the three types of memory â semantic, episodic, and procedural â are used in agentic workflows.
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Instructor: Harrison Chase
Learn how memory works and how the three types of memory â semantic, episodic, and procedural â are used in agentic workflows.
Build a personal email agent with routing, writing, scheduling tools to automatically ignore, respond to, or notify about incoming emails.
Add long-term memory to your agent by adding facts, user preferences, and evolving system prompts to a memory store that can be searched for on-going interactions.
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 youânot 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. Youâll develop your agentâs memory by adding facts to its memory store, providing examples to learn the userâs 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, youâll:
Start building agents with long-term memory with LangGraph.
Itâs helpful to be familiar with Python and a basic understanding of LLM prompting and LLM application development.
Introduction
Introduction to Agent Memory
Baseline Email Assistant
Email Assistant with Semantic Memory
Email Assistant with Semantic + Episodic Memory
Email Assistant with Semantic + Episodic + Procedural Memory
Conclusion
Appendix - Tips and Helps
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