Long-term agentic memory: three memory types, two update paths
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New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with @langchain taught by its Co-Founder and CEO, @hwchase17! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts…
Context
An 's long-term memory needs rules for what survives a conversation and how it affects later decisions. The LangGraph memory course, developed with LangChain, illustrates this through an email assistant. Stored facts supply information about the user; saved examples guide decisions about handling messages; revised system instructions change the rules the assistant follows. These correspond to semantic, episodic, and procedural memory. The course also separates updating memory during the agent's active workflow from updating it in the background, making storage and update timing explicit design choices.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
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