Part 4 of an Agentic AI series covering the ReAct (Reasoning + Acting) pattern for building multi-step AI agents. Explains the ReAct cycle conceptually, implements a vanilla ReAct loop in Python using asyncio for parallel tool execution, then shows how LangGraph simplifies the pattern using ToolNode, StateGraph, tools_condition conditional edges, and typed state management. Includes complete code for a weather assistant that chains tool calls to answer multi-step queries.
Table of contents
🧰 Key LangGraph Concepts for ReAct Agents1. System Configurations2. Project Structure3. ReAct Pattern3.1. ReAct Pattern Concept3.2. Vanilla ReAct Loop Implementation4. ReAct with LangGraph4.1. Define a GraphState Class4.2. Define Nodes4.2. Build Graph4.3. Compile and Invoke Graph5. Closing Summary285 Impressions