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This tutorial guides you through building your first AI agent using LangGraph in just 11 minutes. Learn why agent frameworks are valuable for specific problems and how they leverage AI roles for decision-making. Discover why LangGraph was selected for this tutorial, with its graph-based workflow orchestration and fine-grained control capabilities that make AI agents deployment-ready. Access recommended learning resources from the free "Intro to LangGraph" course (Modules 1-4) to understand key concepts like state, memory, and human-in-the-loop integration. Examine a practical research assistant example as a foundation, then follow along to build a passive-aggressive budget coach AI agent that defines tasks and roles, uses non-public data sources (credit card data via Plaid API sandbox), sets up nodes and edges, visualizes agent structure with LangGraph Studio, handles data securely, and integrates with Python libraries. See how to create a Streamlit application interface for interaction and explore enhancement options including human-in-the-loop features and deployment strategies. Perfect for beginners and those interested in exploring frameworks like LangGraph. Join the MLOps community at mlops.community/join for more resources.
