Apply your knowledge by building a project

In this project, you'll build a customer support system made up of multiple AI agents working together. A manager agent reviews each support ticket and assigns it to the right specialist — whether it's for technical issues, billing questions, or account management. Through this hands-on project, you'll see how AI agents can cooperate, share tasks, and solve problems as a team.

Get started with OpenAI's Chat Completion API by implementing it in a Python CLI program. Create a CLI assistant with whom you can chat.

In this project, you'll build an interactive CLI assistant that turns a loose folder of Markdown notes into a searchable, AI-powered knowledge base. It loads your documents, indexes them in a vector store, and lets you ask natural-language questions that return answers and source citations. Beyond retrieval, it can generate brand-new notes from a topic prompt, then summarize, extract key points, improve, or translate any existing note, saving every result back to disk through function calling. By the time you're done, you'll have a self-contained terminal tool that reads, writes, and reasons over your personal notes.

Movies whisk us away to magical realms and ignite our imagination. So why settle for just watching your favourite films when you can re-imagine them? In this project, you'll feed a large language model a dataset of movie scripts to craft brand-new scenes, character dialogues, and even invent entirely new twists.

The rise of large language models is changing how we process data and develop software. LangChain provides the essential tools to bridge the gap, making it easy to incorporate LLMs into your applications. This project offers a practical, hands-on introduction to LangChain. You'll learn the core concepts and techniques for developing intelligent, LLM-driven applications by building a system that answers questions about planets.