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

Build responsive, scalable, and human-like AI voice applications.

Build agents that navigate and interact with websites, and learn how to make them more reliable.

Design, build, and deploy apps with an AI coding agent in an integrated web development environment.

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

Build an event-driven agentic workflow to process documents and fill forms using RAG and human-in-the-loop feedback.

Learn to build, debug, and deploy applications with an Agentic AI-powered integrated development environment.

Learn how to systematically evaluate, improve, and iterate on AI agents using structured assessments.

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

Learn how to use and prompt OpenAI's o1 model for complex reasoning tasks.

Learn to use OpenAI Canvas to write, code, and create more effectively in collaboration with AI.

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

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.

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

Systematically improve the accuracy of LLM applications with evaluation, prompting, and memory tuning.

Learn to apply function-calling to expand LLM and agent application capabilities.

Interact with tabular data and SQL databases using natural language, enabling more efficient and accessible data analysis.

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

Use the AutoGen framework to build multi-agent systems with diverse roles and capabilities for implementing complex AI applications.

Automate business workflows with multi-AI agent systems. Exceed the performance of prompting a single LLM by designing and prompting a team of AI agents through natural language.

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

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