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.

Create and demo machine learning applications quickly. Share your app with teammates and beta testers on Hugging Face Spaces.

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

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

Build agentic AI workflows using LangChain's LangGraph and Tavily's agentic search.

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

Efficiently handle time-varying workloads with serverless agentic workflows and responsible agents built on Amazon Bedrock.

Build agents that collaborate to solve complex business tasks.

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

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

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

Build AI apps that access tools, data, and prompts using the Model Context Protocol.

Build multimodal and long-context GenAI applications using Llama 4 open models, API, and Llama tools.