langgraph-fundamentals

Teach LangGraph state management, nodes, edges, and conditional routing.

Updated Aug 23, 2026

Search Tags:#workflow #state management #agent #orchestration #langgraph #graph

Ready to run

npx skills add https://github.com/JosephRobles23/Vora.IA --skill langgraph-fundamentals-josephrobles23

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langgraph-fundamentals
Source: https://github.com/JosephRobles23/Vora.IA/tree/main/backend/.windsurf/skills/langgraph-fundamentals
Command: npx skills add https://github.com/JosephRobles23/Vora.IA --skill langgraph-fundamentals-josephrobles23

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a comprehensive guide to building and managing agentic workflows using LangGraph, enabling developers to create complex, stateful applications with fine-grained control over orchestration.

Core Features & Use Cases

State Management: Understand how to define and update graph state using reducers for accumulation.
Node & Edge Design: Learn to implement nodes for discrete tasks and wire them with conditional edges for dynamic routing.
Advanced Concepts: Explore Command for combined state updates and routing, and Send for fanning out parallel tasks.
Error Handling: Implement robust error handling strategies including retries and LLM-recoverable tool errors.
Use Case: Building a multi-step agent that needs to research a topic, synthesize information, and then present findings, with robust error handling for external API calls.

Quick Start

Use the langgraph-fundamentals skill to learn how to define a StateGraph with nodes and conditional edges.

Frequently Asked Questions about langgraph-fundamentals

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build stateful agentic workflows with LangGraph?▼

LangGraph provides foundational knowledge for developing agentic workflows by covering state management, node and edge definitions, and conditional routing. It enables fine-grained control over execution flow and state transitions for complex agent applications.

How do I manage state updates and routing in LangGraph?▼

LangGraph manages state updates and routing through the Command object for combined updates and transitions, and the Send object for fanning out parallel tasks. State is defined and accumulated using reducers across the graph execution.

What is the best way to handle errors and API failures in LangGraph?▼

LangGraph handles errors through robust strategies including retry policies and mechanisms for LLM-recoverable tool errors. This ensures complex, stateful agent applications can manage failures during external API calls and maintain execution flow.

How do I fan out parallel tasks in a LangGraph workflow?▼

LangGraph fans out parallel tasks using the Send object to dispatch multiple state instances concurrently. This enables stateful agent applications to execute parallel operations and gather synthesized information efficiently.

Do I need prior agent orchestration experience to use LangGraph?▼

LangGraph is suited for developers building complex, stateful agent applications requiring fine-grained orchestration control. It covers foundational concepts like StateGraph, nodes, edges, and streaming, making it accessible for those learning advanced agent workflow design.

When should I use conditional edges in a LangGraph StateGraph?▼

Conditional edges in LangGraph are used when workflows require dynamic routing based on runtime state. This enables stateful agent applications to adapt execution flow, directing tasks to specific nodes depending on agent decisions and accumulated data.