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

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

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

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

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.

Learn LLMOps best practices as you design and automate steps to fine-tune and deploy an LLM for a specific task.

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

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

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

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