Getting Started with LangGraph | Build Local Agentic Workflows and AI Agents with Ollama

LangGraph is presented as a practical way to turn brittle, demo-only AI prototypes into maintainable agentic systems by replacing nested if/else logic with explicit state and a graph of small, testable steps. The core idea is to model an AI support process as a workflow graph: each node performs one job (classify, retrieve, draft, evaluate, revise), while edges define the execution path. That structure makes behavior predictable, easier to debug, and simpler to iterate—especially once the number of steps grows beyond a handful.

The tutorial uses an intelligent support ticket scenario to show why workflows often beat “free-form” agent behavior at first. The workflow starts by defining a state object (a data class) holding fields like ticket classification, retrieved documents, the drafted response, evaluation feedback, and a revision counter. It then runs a sequence of language-model calls: classify the ticket text, retrieve relevant knowledge base snippets using an in-memory vector store, draft a response grounded in the retrieved context, and evaluate whether the draft fully addresses the ticket. If evaluation returns “fail,” a conditional edge routes execution back into a revision step; if it returns “pass,” the graph ends. LangGraph’s state graph also supports visual debugging via Mermaid diagrams exported as PNG, and the compiled graph can be invoked with an initial state containing the ticket text (example: “my login is broken please help”).

A key takeaway is the decision framework for when to use a workflow versus an AI agent. Workflows keep the developer in control: every step and branch is explicitly defined, which improves reliability and troubleshooting. Agents, by contrast, delegate tool selection and step ordering to the model, which can be harder to constrain when a business process requires a specific sequence. To demonstrate an agent conversion, the tutorial applies the Reason Act Observe (ReAct) pattern (referenced as proposed in a Google paper) and builds custom tools that correspond to the workflow’s nodes—classification, retrieval, and drafting. The agent runs a loop where it reasons about the ticket, calls tools in sequence, and produces a final draft response. In this agent version, state is simplified to a running list of messages, and a conditional edge determines whether the agent should continue calling tools or stop.

The result is two implementations of the same support system: a controlled workflow with revision cycles and evaluation gates, and a ReAct-style agent that performs tool-driven steps with less direct flow control. The closing guidance is pragmatic: start with a workflow for most real-world processes, add sub-agents only when needed, and switch to agentic autonomy only after extensive testing, debugging, and prompt/tool tuning—often with human oversight still in the loop for production-grade reliability.