LangGraph Tutorial: Integrating External Tools and APIs into AI Agents

Description

In this lesson of our LangGraph Foundations series, we explore how to give your AI agents "superpowers" by integrating External Tools. 🚀 Large Language Models are excellent at text generation, but they need tools to interact with the real world—whether it's performing precise calculations, searching the web, or querying a database. In this video, we move beyond pure text and build a workflow where an agent can decide when to call a function to solve a problem. What you will learn in this video: 1. The Concept of Tools: Why agents need external functions for tasks like math, file reading, and web search. 2. Designing Tool Nodes: How to structure a node with specific inputs (file paths, expressions, SQL) and core logic. 3. The Planner Agent: Building a "brain" node that inspects the state and decides if a tool is needed. 4. Agent-Tool Interaction: How to route the workflow from the Planner to the Tool and back to the Agent for a final natural language response. 5. Hands-on Demo: A step-by-step walkthrough of building a Calculator Tool and integrating it into a LangGraph workflow with conditional logic. Key Highlights: 1. Using the eval logic in Python safely within a node. 2. Implementing a JSON-based Planner that outputs tool requests. 3. Visualizing a workflow that handles both chitchat and functional tool calls. Prerequisites: Basic Python knowledge and an OpenAI API key. Check out our previous videos in the series for context on LangGraph states and nodes! #LangGraph #AIAgents #LangChain #ToolCalling #PythonAI #GenerativeAI #OpenAI #MachineLearning #aitutorial We will discuss the following- langgraph multi agent langgraph agents langgraph project langgraph playlist langgraph and langchain langgraph studio langgraph ai agents langgraph multi agent tutorial langgraph chatbot langgraph multi agent project langgraph memory langgraph js