LangGraph Core Components Explained with a Simple Graph
Last Updated on August 28, 2025 by Editorial Team
Author(s): A.Venkatesh
Originally published on Towards AI.
What Are We Building?
If you’ve been curious about LangGraph and how it can help you build smart, dynamic AI workflows, you’re in the right place.
This article explains LangGraph’s core components—State, Nodes, Edges, and Graph Execution—by illustrating how to build a simple example with three nodes and one conditional edge that demonstrates data flow and decision-making in LangGraph. The post covers details of each component through Python code, highlights their functionalities, and ultimately shows how to create and execute a full graph, making it easy to build modular workflows effectively.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
