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

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

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

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

Build agents that collaborate to solve complex business tasks.

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

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

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

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

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

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

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

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

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

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.

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