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

Expand your toolkit with LangChain.js, a JavaScript framework for building with LLMs. Understand the fundamentals of using LangChain to orchestrate and chain modules.

Understand the fundamentals of using LangChain’s JavaScript library to orchestrate and chain different modules together. Understand the fundamentals of using LangChain’s JavaScript library to orchestrate and chain different modules together.Learn the basics of loading and preparing data to provide as context to effectively customize LLM generations. Learn the basics of loading and preparing data to provide as context to effectively customize LLM generations.Learn techniques to retrieve and present data to the LLM in useful ways for a conversational retrieval chain. Learn techniques to retrieve and present data to the LLM in useful ways for a conversational retrieval chain._JavaScript is the world’s most popular programming language, and now developers can program in JavaScript to build powerful LLM apps.This course will show webdevs how to expand their toolkits with LangChain.js, a popular JavaScript framework for building with LLMs, and will cover useful concepts for creating powerful, context-aware applications.By taking this course, you will:Learn to use LangChain’s underlying abstractions to build your own JavaScript appsUnderstand the basics of retrieval augmented generation (RAG)Have the structure of a basic conversational retrieval system that you can use for building your own chatbots_Anyone with intermediate JavaScript knowledge and wants to build machine learning applications. 

Build LLM Apps with LangChain.js

Build a full-stack web application that uses RAG capabilities to chat with your data. Learn to build a RAG application in JavaScript, using an intelligent agent to answer queries.

Learn how to build a RAG application in JavaScript, and use an intelligent agent that discerns and selects from multiple data sources to answer your queries. Learn how to build a RAG application in JavaScript, and use an intelligent agent that discerns and selects from multiple data sources to answer your queries.Build a full-stack web app with an interactive frontend component that interacts and chats with your data. Build a full-stack web app with an interactive frontend component that interacts and chats with your data.Learn how to persist your data, enable chatting with your data and make streaming responses possible, all implemented using the create-llama command-line tool. Learn how to persist your data, enable chatting with your data and make streaming responses possible, all implemented using the create-llama command-line tool._Get started building full-stack RAG web applications. This course introduces the basics of Retrieval Augment Generation (RAG), including its practical implementation with JavaScript. You’ll assemble an intelligent agent capable of running its own queries. This course will guide you through the process of building a full-stack JavaScript web application, from the API server to a React component that queries your data. You’ll also learn to develop persistent chat that streams your answers to an interactive front-end in real time.Learn from Laurie Voss, VP of developer relations at LlamaIndex, web developer and the co-founder of npm, the central registry of JavaScript packages.In this course: Learn to build a RAG application in JavaScript for querying your own data.Develop tools to interact with multiple data sources using a router query engine that intelligently selects the right tool for your queries.Build a full-stack web application step by step, starting with a backend web app and progressing to an interactive React frontend that calls your API to display query results.Learn about production-ready techniques, including persisting your data, chatting with your data, and streaming responses for multiple queries.Create a user-friendly web app that can chat with data using the create-llama command line tool from LlamaIndex.Start building RAG web applications in JavaScript that allow you to interact with your data using LlamaIndex._Anyone who has basic JavaScript knowledge who wants to explore RAG application development and to quickly build a full-stack web application using LlamaIndex!

JavaScript RAG Web Apps with LlamaIndex

Build autonomous agents that intelligently navigate and analyze your data. Learn to develop agentic RAG systems using LlamaIndex, enabling powerful document Q&A and summarization. Gain valuable skills in guiding agent reasoning and debugging.

Learn how to build an agent that can reason over your documents and answer complex questions. Learn how to build an agent that can reason over your documents and answer complex questions.Build a router agent that can help you with Q&A and summarization tasks, and extend it to handle passing arguments to this agent. Build a router agent that can help you with Q&A and summarization tasks, and extend it to handle passing arguments to this agent.Design a research agent that handles multi-documents and learn about different ways to debug and control this agent. Design a research agent that handles multi-documents and learn about different ways to debug and control this agent._Join our new short course and learn from Jerry Liu, co-founder and CEO at LlamaIndex to start using agentic RAG, a framework designed to build research agents skilled in tool use, reasoning, and decision-making with your data.In this course:Build the simplest form of agentic RAG – a router. Given a query, the router will pick one of two query engines, Q&A or summarization, to execute a query over a single document.Add tool calling to your router agent where you will use an LLM to not only pick a function to execute but also infer an argument to pass to the function.Build a research assistant agent. Instead of tool calling in a single-shot setting, an agent is able to reason over tools in multiple steps. Build a multi-document agent where you will learn how to extend the research agent to handle multiple documents.Unlike the standard RAG pipeline—suitable for simple queries across a few documents—this intelligent approach adapts based on initial findings to enhance further data retrieval. You’ll learn to develop an autonomous research agent, enhancing your ability to engage with and analyze your data comprehensively.You’ll practice building agents capable of intelligently navigating, summarizing, and comparing information across multiple research papers from arXiv. Additionally, you’ll learn how to debug these agents, ensuring you can guide their actions effectively. Explore one of the most rapidly advancing applications of agentic AI!_Anyone who has basic Python knowledge and wants to learn how to quickly build agents that can reason over their own documents. 

Building Agentic RAG with Llamaindex

Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.

Learn how to collaborate with AI agents to streamline coding workflows, generate and modify code across multiple files, and enhance development efficiency. Learn how to collaborate with AI agents to streamline coding workflows, generate and modify code across multiple files, and enhance development efficiency.Understand how AI coding agents combine tool usage, context awareness, and human-action tracking to function effectively and get a deep dive into search and discovery as one of the main challenges of AI coding agents. Understand how AI coding agents combine tool usage, context awareness, and human-action tracking to function effectively and get a deep dive into search and discovery as one of the main challenges of AI coding agents.Use Windsurf to do real-world coding tasks, including debugging JavaScript code, updating an old large codebase, and building a Wikipedia analysis app that retrieves, processes, and visualizes data while learning to manage unexpected AI behavior. Use Windsurf to do real-world coding tasks, including debugging JavaScript code, updating an old large codebase, and building a Wikipedia analysis app that retrieves, processes, and visualizes data while learning to manage unexpected AI behavior._Join our latest course Build Apps with Windsurf’s AI Coding Agents, built in partnership with Windsurf and taught by Anshul Ramachandran.AI-assisted Integrated Development Environments, or IDEs, make coding and development workflows faster, more efficient, and more fun.Agentic tools like Windsurf are more than just code autocomplete—they are collaborative coding agents that help you break down complex applications, iterate efficiently, and generate code that spans multiple files. Using an AI coding agent effectively requires a new approach to prompting, iteration, and structuring development tasks.In this course, you’ll learn the inner workings of coding agents and how to use Windsurf to code and build several applications. In details, you’ll:Learn how to get started with Windsurf, and build your first app (a snake game) in a couple of minutes.Look at the evolution of AI coding assistants, from simple code suggestions to agentic systems that collaborate with developers.Use an AI agent to analyze and resolve issues in Javascript code, like debugging test issues and multi-file refactoring.Learn mental models to understand how coding agents combine human-action tracking, tool integration, and context awareness in their agentic coding workflow.Learn the challenges of code search and discovery of AI coding agents and how a multi-step retrieval approach helps overcome them.Use Windsurf to analyze and understand a large, old codebase and update it to the latest versions of the frameworks and packages it uses. Use Windsurf to build a Wikipedia data analysis app — retrieving, parsing, and analyzing word frequencies.Enhance the performance of your Wikipedia app by adding caching and while doing so learn how to course-correct when the AI agent produces unexpected results.Learn tips and tricks such as command, autocomplete, @mentions, and explain functionalities to maximize your interactions with the overall AI system as you build a full-stack app to visualize the data of your Wikipedia analysis app.Leverage the multimodal capabilities of the AI agent to increase your development velocity with an example in which you upload a mockup with sketched-out reference UI features to add new functionalities to your app.By the end of this course, you’ll understand agentic coding in-depth and learn to use it to make your development process faster, more efficient, and more enjoyable._It’s helpful for anyone familiar with coding to have a basic understanding of LLM prompting and AI-assisted application development.