Learn to build AI agents with long-term memory with LangGraph, using LangMem for memory management.

Learn how memory works and how the three types of memory - semantic, episodic, and procedural - are used in agentic workflows. Learn how memory works and how the three types of memory – semantic, episodic, and procedural – 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. 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. 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 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.

Long-Term Agentic Memory With LangGraph

Explore Mistral's open-source and commercial models, and leverage Mistral's JSON mode to generate structured LLM responses. Use Mistral's API to call user-defined functions for enhanced LLM capabilities.

Explore Mistral's three open source models (Mistral 7B, Mixtral 8x7B, and the latest Mixtral 8x22B), and three commercial models (small, medium, and large), which Mistral provides access to via web interface and API calls. Explore Mistral’s three open source models (Mistral 7B, Mixtral 8x7B, and the latest Mixtral 8x22B), and three commercial models (small, medium, and large), which Mistral provides access to via web interface and API calls.Leverage Mistral's JSON mode to generate LLM responses in a structured JSON format, enabling integration of LLM outputs into larger software applications. Leverage Mistral’s JSON mode to generate LLM responses in a structured JSON format, enabling integration of LLM outputs into larger software applications.Use Mistral's API to call user-defined Python functions for tasks like web searches or retrieving text from databases, enhancing the LLM's ability to find relevant information to answer user queries. Use Mistral’s API to call user-defined Python functions for tasks like web searches or retrieving text from databases, enhancing the LLM’s ability to find relevant information to answer user queries._In this course, you’ll access Mistral AI’s collection of open source and commercial models, including the Mixtral 8x7B model, and the latest Mixtral 8x22B. You’ll learn about selecting the right model for your use case, and get hands-on with features like effective prompting techniques, function calling, JSON mode, and Retrieval Augmented Generation (RAG).In detail:Access and prompt Mistral models via API calls for tasks, and decide whether your task is either a simple task (classification), medium (email writing), or advanced (coding) level of complexity, and consider speed requirements to choose an appropriate model.Learn to use Mistral’s native function calling, in which you give an LLM tools it can call as needed to perform tasks that are better performed by traditional code, such as querying a database for numerical data. Build a basic RAG system from scratch with similarity search, properly chunk data, create embeddings, and implement this tool as a function in your chat system.Build a chat interface to interact with the Mistral models and ask questions about a document that you upload.By the end of this course, you’ll be equipped to leverage Mistral AI’s leading open source and commercial models._Getting Started with Mistral is a beginner-friendly course and it’s suitable for anyone who wants to learn about and use Mistral AI’s collection of advanced open source and commercial LLMs. If you have taken ChatGPT Prompt Engineering for Developers or Prompt Engineering with Llama 2, this is a great next step!