Building an AI agent with langGraph (step by step tutorial)
๐ Ready to build your first AI Agent? If you've heard about AI agents but don't know where to start, this tutorial is for you! Join Annie from the MLOps community as she guides you through picking a topic and building your very first agent using LangGraph.
In this video, you'll learn:
Why Use an Agent Framework: Understand the types of problems best suited for AI agents and why frameworks like LangGraph are powerful tools.
Discover how agents use AI roles for decision-making.
Getting Started with LangGraph: Learn why LangGraph was chosen for this tutorial, focusing on its graph-based workflow orchestration and fine-grained control, making your AI agents deployment-ready.
Learning Resources: Leverage the free "Intro to LangGraph" course from LangGraph Academy (Modules 1-4 recommended) to grasp concepts like state, memory, and human-in-the-loop integration.
Practical Example - Research Assistant: See how the LangGraph research assistant example provides a solid foundation for building your own AI agents.
Building a Budget Coach Agent: Follow along as we build a unique, passive-aggressive budget coach AI agent.
This example demonstrates:
Defining tasks and roles for the agent.
Using non-public data sources (like credit card data via the Plaid API sandbox).
Setting up nodes and edges in LangGraph.
Visualizing the agent's structure with LangGraph Studio.
Handling data securely (using Plaid's sandbox environment).
Integrating with Python libraries (Plaid).
Creating an Interface: See how to bring your AI agent to life with a Streamlit application for interaction.
Next Steps: Get ideas for enhancing your AI agent with human-in-the-loop features and deployment strategies.
Whether you're new to AI agents or looking to explore frameworks like LangGraph, this video provides a practical walkthrough to get you started.
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