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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

Build, debug, and optimize AI agents using DSPy and MLflow.

Build agents that communicate and collaborate across different frameworks using ACP.