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

Use the powerful and extensible LangChain framework, using prompts, parsing, memory, chains, question answering, and agents.

Learn LangChain directly from the creator of the framework, Harrison Chase Learn LangChain directly from the creator of the framework, Harrison ChaseApply LLMs to your proprietary data to build personal assistants and specialized chatbots Apply LLMs to your proprietary data to build personal assistants and specialized chatbotsUse agents, chained calls, and memories to expand your use of LLMs Use agents, chained calls, and memories to expand your use of LLMs_In LangChain for LLM Application Development, you will gain essential skills in expanding the use cases and capabilities of language models in application development using the LangChain framework.In this course you will learn and get experience with the following topics:Models, Prompts and Parsers: calling LLMs, providing prompts and parsing the responseMemories for LLMs: memories to store conversations and manage limited context spaceChains: creating sequences of operationsQuestion Answering over Documents: apply LLMs to your proprietary data and use case requirementsAgents: explore the powerful emerging development of LLM as reasoning agents.This one-hour course, instructed by the creator of LangChain Harrison Chase as well as Andrew Ng will vastly expand the possibilities for leveraging powerful language models, where you can now create incredibly robust applications in a matter of hours._LangChain for LLM Application Development is a beginner-friendly course. Basic Python knowledge will help you get the most out of this course.

LangChain for LLM Application Development

Learn Microsoft's open source orchestrator, Semantic Kernel and use LLM building blocks such as memory, connectors, chains and planners in your apps.

Learn Microsoft’s open source orchestrator, the Semantic Kernel Learn Microsoft’s open source orchestrator, the Semantic KernelDevelop your business planning and analysis skills while leveraging AI tools Develop your business planning and analysis skills while leveraging AI toolsAdvance your skills in LLMs by using memories, connectors, chains, and more Advance your skills in LLMs by using memories, connectors, chains, and more_Large Language Models (LLMs) are enabling coders and non-coders to build new kinds of applications that harness the power of AI. In this course, you’ll learn how to use and create with Microsoft’s open source orchestrator, Semantic Kernel. Along the way you’ll gain skills in getting the most out of LLMs developing prompts, semantic functions, vector databases and using an LLM for planning.After completing this course you will be able to:Develop sophisticated business applications using LLMsLeverage common LLM building blocks such as memories, connectors, chains and plannersUtilize the open source orchestrator, the Semantic Kernel, in your applicationsUsing an orchestration SDK such as Semantic Kernel means you can avoid having to learn APIs for each individual AI service, and build integrations that can always stay up to date with the latest advances in AI research rather than solutions that quickly get outdated. Through this course, you’ll have all you need to take full advantage of this powerful open source tool._Everyone who wants to learn Semantic Kernel. Recommended skills are basic Python, and an understanding of an Application Programming Interface (API). Familiarity with what a Software Design Kit (SDK) is will be helpful, but not required.

How Business Thinkers Can Start Building AI Plugins With Semantic Kernel

Learn about the latest advancements in LLM APIs and use LangChain Expression Language (LCEL) to compose and customize chains and agents.

Learn about the most recent advancements in LLM APIs. Learn about the most recent advancements in LLM APIs.Use LangChain Expression Language (LCEL), a new syntax to compose and customize chains and agents faster. Use LangChain Expression Language (LCEL), a new syntax to compose and customize chains and agents faster.Apply these new capabilities by building up a conversational agent. Apply these new capabilities by building up a conversational agent._The landscape of LLMs and the libraries that support them has evolved rapidly in recent months. This course is designed to keep you ahead of these changes. You’ll explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called LangChain Expression Language (LCEL) for tasks like tagging, extraction, tool selection, and routing.After taking this course, you’ll know how to: Generate structured output, including function calls, using LLMs;Use LCEL, which simplifies the customization of chains and agents, to build applications;Apply function calling to tasks like tagging and data extraction;Understand tool selection and routing using LangChain tools and LLM function calling – and much more.Start applying these new capabilities to build and improve your applications today._Anyone who’s interested in learning about the latest tools to build LLM-based applications. Basic Python knowledge and a familiarity with writing prompts for LLMs is recommended.

Functions, Tools and Agents with LangChain

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

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!

Getting Started with Mistral

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 a flexible framework to build a variety of complex AI applications.

Learn a flexible framework to Learn how Haystack components, pipelines, and document stores can be used to build custom AI applications.build custom AI applications with components, pipelines, and document stores Learn how Haystack components, pipelines, and document stores can be used to build custom AI applications.Build a RAG pipeline and extend its ability by creating custom components. Build a RAG pipeline and extend its ability by creating custom components.Use conditional branching, self-reflecting agents, and function calling to build complex pipelines and apps. Use conditional branching, self-reflecting agents, and function calling to build complex pipelines and apps._In Building AI Applications with Haystack you will learn a high-level orchestration framework that helps ensure your applications are flexible, extendible, and maintainable, even as the technology stack changes, user needs arise, and new features are added.Using a framework can provide common features out of the box that significantly speeds up the development process. Haystack offers robust and flexible architecture and framework for building AI applications. It manages complexity and helps you focus more on developing your application at a higher level of abstraction.Throughout the course, you will develop several projects, including a RAG app, a news summarization app, a chat agent with function calling, and a self-reflecting agent with loops.What you’ll do: Learn about the core abstractions and unique building blocks of the Haystack framework and see how these elements can be combined for various AI use cases.Build a RAG pipeline by using Haystack components, pipelines, and document stores.Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs.Use conditional routing to create a branching pipeline with a fall back to web-search when the LLM does not have the context needed to fully respond to the user’s query.Build a self-reflecting agent for named entity recognition with a Haystack pipeline that is able to loop using an output validator custom component.Create a chat agent using OpenAI’s function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent’s capabilities.Start building exciting LLM applications and optimizing your development workflow using Haystack._Anyone who has basic Python knowledge and wants to learn to build and customize complex apps using Haystack!

Building AI Applications With Haystack

Build systems with MemGPT agents that can autonomously manage their memory.

Build agents with long-term, persistent memory using Letta to manage and edit context efficiently. Build agents with long-term, persistent memory using Letta to manage and edit context efficiently.Learn how an LLM agent can act as an operating system to manage memory, autonomously optimizing context use. Learn how an LLM agent can act as an operating system to manage memory, autonomously optimizing context use.Apply memory management to create adaptive, collaborative AI agents for real-world tasks like research and HR. Apply memory management to create adaptive, collaborative AI agents for real-world tasks like research and HR._Learn how to build agentic memory into your applications in this short course, LLMs as Operating Systems: Agent Memory, created in partnership with Letta, and taught by its founders Charles Packer and Sarah Wooders.An LLM can use any information stored in its input context window but has limited space. Using a longer input context also costs more and causes slower processing. Managing this context window and what to input becomes very important.Based on the innovative approach in the MemGPT research paper “Towards LLMs as Operating Systems,” its authors, two of whom are Charles and Sarah, proposed using an LLM agent to manage this context window, building a management system that provides applications with managed, persistent memory. Examples of Managing Agent Memory are:Control Conversation Memory. As conversations grow beyond defined limits, move information from context to a persistent searchable database. Summarize information to keep relevant facts in context memory. Restore relevant conversation elements as needed by conversation flow.Persist and edit facts such as names, dates, and preferences, and make them available in context.Persist and track ‘task’ specific information. For example, a research agent needs to keep research information in context memory, swapping the most relevant information from a searchable database with previous information.In this course, you’ll learn: How to build an agent with self-editing memory, using tool-calling and multi-step reasoning, from scratch.Letta, an open-source framework that adds memory to your LLM agents, giving them advanced reasoning capabilities and transparent long-term memory.The key ideas behind the MemGPT paper, the two tiers of memory in and outside the context window, and how agent states comprised of memory, tools, and messages are turned into prompts.How to create and interact with a MemGPT agent using the Letta framework, and how to build and edit its core and archival memory.How core memory is designed and implemented with an example of how to customize it with blocks and memory tools.How to implement multi-agent collaboration both by sending messages and by sharing memory blocks.By the end of this course, you will have the tools to build LLM applications that can leverage virtual context, extending memory beyond the finite context window of LLMs._Anyone who has basic Python skills and is curious about how autonomous agents can manage their own memory.

LLMs as Operating Systems: Agent Memory

Move your LLM-powered applications beyond proof-of-concept and into production with the added control of guardrails.

Learn the common failure modes of LLM-powered applications that guardrails can help mitigate, including hallucinations and revealing sensitive information. Learn the common failure modes of LLM-powered applications that guardrails can help mitigate, including hallucinations and revealing sensitive information.Understand how AI guardrails validate and verify your applications with input and output guards, ensuring reliable and controlled interactions. Understand how AI guardrails validate and verify your applications with input and output guards, ensuring reliable and controlled interactions.Add guardrails to a RAG-powered customer service chatbot to create a new layer of control of the applications behavior. Add guardrails to a RAG-powered customer service chatbot to create a new layer of control of the applications behavior._Join our new short course, Safe and Reliable AI via Guardrails, and learn how to build production-ready applications with Shreya Rajpal, co-founder & CEO of GuardrailsAI.The output of LLMs is fundamentally probabilistic, making it impossible to know in advance, or to guarantee the same response twice. This makes it difficult to put LLM-powered applications into production for industries with strict regulations or clients who require high consistency in application behavior. Fortunately, installing guardrails on your system gives you an additional layer of control in creating safe and reliable applications. Guardrails are safety mechanisms and validation tools built into AI applications, acting as a protective framework that prevents your application from revealing incorrect, irrelevant, or sensitive information. This course will show you how to build robust guardrails from scratch that mitigate common failure modes of LLM-powered applications like hallucinations or revealing personally identifiable information (PII). You’ll also learn how to access a variety of pre-built guardrails on the GuardrailsAI hub and are ready to integrate into your projects.You’ll implement these guardrails in the context of a RAG-powered customer service chatbot for a small pizzeria.In detail, you’ll: Explore the common failure modes of LLM-powered applications including hallucinations, going off-topic, revealing sensitive information, and generating responses that can harm your reputation.Learn to mitigate these failure modes with input and output guards that validate and verify your application’s responses.Create a guardrail to prevent the chatbot from discussing sensitive topics, such as a confidential project at the pizza shop.Develop a guardrail to detect hallucinations using a Natural Language Inference (NLI) model, ensuring responses are grounded in the truth of your trusted documents.Add a Personal Identifiable Information (PII) guardrail to detect and redact sensitive information in user prompts and LLM outputs using tools from the Guardrails hub.Set up a guardrail to limit the chatbot’s responses to topics relevant to the pizza shop, keeping interactions focused and appropriate.Configure a guardrail that prevents your chatbot from mentioning any competitors using a name detection pipeline consisting of conditional logic that routes to an exact match or a threshold check with a named entity recognition.The tools in this course will unlock more opportunities for you to build and deploy safe, reliable LLM-powered applications ready for real-world use._Anyone who has basic Python knowledge looking to enhance the safety and reliability of LLM-powered applications with practical, hands-on guardrail techniques.

Safe and reliable AI via guardrails

Build LLM apps that can process very long documents using the Jamba model

Learn how the Jamba model integrates transformers and the Mamba architecture to efficiently process long contexts while maintaining quality. Learn how the Jamba model integrates transformers and the Mamba architecture to efficiently process long contexts while maintaining quality.Understand the training process for long context models, and the metrics used to evaluate their performance. Understand the training process for long context models, and the metrics used to evaluate their performance.Gain hands-on experience applying Jamba to tasks such as processing large documents, tool-calling, and building large context RAG apps. Gain hands-on experience applying Jamba to tasks such as processing large documents, tool-calling, and building large context RAG apps._Learn to use the Jamba model, a hybrid transformer-Mamba architecture trained to handle long contexts, in this new course, Build Long-Context AI Apps with Jamba, built in partnership with AI21 Labs and taught by Chen Wang and Chen Almagor.The transformer architecture is the foundation of most large language models but it is computationally expensive when handling very long input contexts. There’s an alternative to transformers called Mamba. Mamba is a selective state space model that can process very long contexts with a much lower computational cost. However, researchers found that the pure Mamba architecture underperforms in understanding the context, and the quality of the output can be lower even in tasks as simple as repeating the input in the output of the model.To overcome these challenges, AI21 Labs developed the Jamba model, which combines Mamba’s computational efficiency with the transformer’s attention mechanism to help with the output quality.In this course, you’ll learn about the Jamba architecture, how it works, and how it is trained. You’ll also learn how to prompt Jamba and use it to process long documents and build long-context RAG apps.In detail, you’ll:Learn key information about Jamba’s large context model and its unique, hybrid architecture that allows for high-quality outputs, high throughput, and low memory usage.Learn how Jamba combines transformers with Mamba, a selective state-space model to achieve high performance and quality.Use the AI21SDK, especially its document parameter, with an example of prompting a large over 200k-token annual report.Use Jamba for tool-calling with hands-on examples from calling simple arithmetic functions to a function that returns SEC 10-Q company quarterly report.Learn how the training for long context is done, and the metrics used for evaluating the performance.Create a RAG app using the AI21 Conversational RAG tool and build your own RAG pipeline that uses the Jamba model with LangChain.Start building AI apps that can handle context as long as all of your unread emails from the last 20 years!_Anyone who has basic Python knowledge and wants to learn more about how the Jamba model works and how it is used for building long-context AI apps.