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 to break down complex tasks, automate workflows, chain LLM calls, and get better outputs from LLMs. Evaluate LLM inputs and outputs for safety and relevance.

Efficiently build multi-step systems using large language models.  Efficiently build multi-step systems using large language models.Learn to split complex tasks into a pipeline of subtasks using multistage prompts.  Learn to split complex tasks into a pipeline of subtasks using multistage prompts.Evaluate your LLM inputs and outputs for safety, accuracy, and relevance. Evaluate your LLM inputs and outputs for safety, accuracy, and relevance._In Building Systems with the ChatGPT API, you will learn how to automate complex workflows using chain calls to a large language model. Unlock new development capabilities and improve your efficiency in this brand new short course.You’ll build:Chains of prompts that interact with the completions of prior prompts.Systems where Python code interacts with both completions and new prompts.A customer service chatbot using all the techniques from this course.You’ll learn how to apply these skills to practical scenarios, including classifying user queries to a chat agent’s response, evaluating user queries for safety, and processing tasks for chain-of-thought, multi-step reasoning. This one-hour course, taught by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI), builds on the lessons taught in the popular ChatGPT Prompt Engineering for Developers, though it is not a prerequisite. Hands-on examples make each concept easy to understand. Built-in Jupyter notebooks allow you to seamlessly experiment with the code and prompts presented in the course._Building Systems With The ChatGPT API is beginner-friendly. You only need a basic understanding of Python to be able to complete the course. This course is also suitable for intermediate or advanced machine learning engineers who want to learn cutting-edge prompt engineering skills for LLMs. 

Building Systems with the ChatGPT API

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

Learn how to easily build AI applications using open-source models and Hugging Face tools. Find and filter open-source models on Hugging Face Hub.

Find and filter open source models on Hugging Face Hub based on task, rankings, and memory requirements. Find and filter open source models on Hugging Face Hub based on task, rankings, and memory requirements.Write just a few lines of code using the transformers library to perform text, audio, image, and multimodal tasks. Write just a few lines of code using the transformers library to perform text, audio, image, and multimodal tasks.Easily share your AI apps with a user-friendly interface or via API and run them on the cloud using Gradio and Hugging Face Spaces. Easily share your AI apps with a user-friendly interface or via API and run them on the cloud using Gradio and Hugging Face Spaces._The availability of models and their weights for anyone to download enables a broader range of developers to innovate and create.In this course, you’ll select open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library. Easily package your code into a user-friendly app that you can run on the cloud using Gradio and Hugging Face Spaces.You will:Use the transformers library to turn a small language model into a chatbot capable of multi-turn conversations to answer follow-up questions.Translate between languages, summarize documents, and measure the similarity between two pieces of text, which can be used for search and retrieval.Convert audio to text with Automatic Speech Recognition (ASR), and convert text to audio using Text to Speech (TTS).Perform zero-shot audio classification, to classify audio without fine-tuning the model.Generate an audio narration describing an image by combining object detection and text-to-speech models.  Identify objects or regions in an image by prompting a zero-shot image segmentation model with points to identify the object that you want to select.Implement visual question answering, image search, image captioning and other multimodal tasks.Share your AI app using Gradio and Hugging Face Spaces to run your applications in a user-friendly interface on the cloud or as an API. The course will provide you with the building blocks that you can combine into a pipeline to build your AI-enabled applications!_Anyone who wants to get started building AI applications quickly and easily using open source models.

Open Source Models with Hugging Face

Learn how to make safer LLM apps through red teaming. Learn to identify and evaluate vulnerabilities in large language model (LLM) applications.

Learn to identify and evaluate vulnerabilities in large language model (LLM) applications. Learn to identify and evaluate vulnerabilities in large language model (LLM) applications. Apply red teaming techniques from cybersecurity to ensure the safety and reliability of your LLM application. Use an open source library from Giskard to help automate LLM red-teaming methods._Learn how to test and find vulnerabilities in your LLM applications to make them safer. In this course, you’ll attack various chatbot applications using prompt injections to see how the system reacts and understand security failures. LLM failures can lead to legal liability, reputational damage, and costly service disruptions. This course helps you mitigate these risks proactively. Learn industry-proven red teaming techniques to proactively test, attack, and improve the robustness of your LLM applications.In this course:Explore the nuances of LLM performance evaluation, and understand the differences between benchmarking foundation models and testing LLM applications.Get an overview of fundamental LLM application vulnerabilities and how they affect real-world deployments.Gain hands-on experience with both manual and automated LLM red-teaming methods.See a full demonstration of red-teaming assessment, and apply the concepts and techniques covered throughout the course.After completing this course, you will have a fundamental understanding of how to experiment with LLM vulnerability identification and evaluation on your own applications._Red Teaming LLM Applications is a beginner-friendly course. Basic Python knowledge is recommended to get the most out of this course.

Red Teaming LLM Applications

Try out the features of the new Llama 3.2 models to build AI applications with multimodality.

Explore the features of the new Llama 3.2 model, from image classification, vision reasoning to tool use. Explore the features of the new Llama 3.2 model, from image classification, vision reasoning to tool use.Learn the details of Llama 3.2 prompting, tokenization, built-in and custom tool calling. Learn the details of Llama 3.2 prompting, tokenization, built-in and custom tool calling.Gain knowledge of the Llama stack, which is a standardized interface for building AI applications. Gain knowledge of the Llama stack, which is a standardized interface for building AI applications._Join our new short course, Introducing Multimodal Llama 3.2, and learn from Amit Sangani, Senior Director of AI Partner Engineering at Meta, to learn all about the latest additions to the Llama models 3.1 and 3.2, from custom tool calling to multimodality and the new Llama stack.Open models are a key building block of AI and a key enabler of AI research. With Meta’s family of open models, anyone can download, customize, fine-tune, or build new applications on top of them, allowing AI innovation. The Llama model family now ranges from 1B model parameters to its 405B foundation model, allowing for diverse use cases and applications.In this course, you’ll learn about the new vision capabilities that Llama 3.2 brings to the Llama family. You’ll learn how to leverage this along with tool-calling, and Llama Stack, which is an open-source orchestration layer for building on top of the Llama family of models.In detail, you’ll: Learn about the new models, how they were trained, their features, and how they fit into the Llama family.Understand how to do multimodal prompting with Llama and work on advanced image reasoning use cases such as understanding errors on a car dashboard, adding up the total of three restaurant receipts, grading written math homework, and many more.Learn different roles—system, user, assistant, ipython—in the Llama 3.1 and 3.2 family and the prompt format that identifies those roles.Understand how Llama uses the tiktoken tokenizer, and how it has expanded to a 128k vocabulary size that improves encoding efficiency and enables support for seven non-English languages.Learn how to prompt Llama to call both built-in and custom tools with examples for web search and solving math equations.Learn about ‘Llama Stack API’, which is a standardized interface for canonical toolchain components like fine-tuning or synthetic data generation to customize Llama models and build agentic applications.Start building exciting applications on Llama!_Anyone who has basic Python knowledge and wants to learn to quickly build on Llama and Llama stack.

Introducing Multimodal Llama 3.2

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

Learn how an AI Assistant is built to use and accomplish tasks on computers.

Learn about Anthropic’s family of models, its approach to AI research, and the best way to prompt it, including multi-modal use cases. Learn about Anthropic’s family of models, its approach to AI research, and the best way to prompt it, including multi-modal use cases.Learn effective prompting techniques, implement prompt caching to reduce costs and latency, and build AI applications that can call tools. Learn effective prompting techniques, implement prompt caching to reduce costs and latency, and build AI applications that can call tools.See how you can combine multimodal capabilities, agentic workflows, and tool use to build an AI assistant that can navigate and interact with computer interfaces, executing tasks like web searches. See how you can combine multimodal capabilities, agentic workflows, and tool use to build an AI assistant that can navigate and interact with computer interfaces, executing tasks like web searches._Building Towards Computer Use with Anthropic introduces an innovative capability from Anthropic that enables models to interact with and navigate computer interfaces.Taught by Colt Steele, Anthropic’s Head of Curriculum, this course covers Anthropic’s family of models and the building blocks that lead to the amazing new application – Computer Use. Computer Use utilizes the capabilities of the latest models including image reasoning and tool use to enable an LLM-based agent to use a computer. Like a human user, the model processes an image of the screen, analyzes it to understand what’s going on, and navigates the computer by issuing mouse clicks and generating keyboard strokes to get things done.In this course, you’ll learn the features that lead up to computer use from working with the Anthropic’s API, to multimodal prompting, prompt caching, and tool use, ending in a demo that combines these features to build an AI assistant that uses a computer.In detail, you’ll:Learn Anthropic’s approach to AI research, principles of AI safety, alignment, and interpretability while understanding the key differences between its models.Make API requests to Claude, format messages for better responses, and control API parameters like system prompts, temperature, and max tokens for optimal responses.Write multi-modal prompts that combine text and image content blocks and build with streaming responses.Learn effective prompting techniques such as using prompt templates, structuring prompts in XML, and providing examples to get consistent high-quality responses.Learn to implement prompt caching and see how it can reduce costs and latency.Understand tool-use workflows and build a chatbot that can call different tools in response to users’ queries.See all these concepts come together in a demo that uses Anthropic Computer Use to achieve a task on a computer.Start utilizing Anthropic’s family of models to build towards Computer Use applications._Anyone who has basic Python knowledge, wants to learn how to use all of the features of the Anthropic family of models, and understand the capabilities of computer use applications. 