langgraph-fundamentals

Design and execute LangGraph agent workflows as directed state graphs.

Updated Nov 16, 2025

Search Tags:#workflow #runtime #langgraph #graphs #state-machine #edge-routing #node-signatures

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npx skills add https://github.com/daniel-dihardja/menuyukti --skill langgraph-fundamentals-daniel-dihardja

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langgraph-fundamentals
Source: https://github.com/daniel-dihardja/menuyukti/tree/main/.agents/skills/langgraph-fundamentals
Command: npx skills add https://github.com/daniel-dihardja/menuyukti --skill langgraph-fundamentals-daniel-dihardja

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LangGraph helps engineers model and run complex agent workflows as directed graphs with clear state management and deterministic execution.

Core Features & Use Cases

StateGraph, Nodes, Edges, and START/END markers for structured workflows.
Conditional routing, Commands, and Send parallelism for scalable orchestration.
Real-world use: design multi-step agents that process inputs, branch logic, and aggregate results.

Quick Start

Create a simple two-node LangGraph, compile it, and run it with an initial state to observe the final output.

Frequently Asked Questions about langgraph-fundamentals

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

FAQPage Schema

How do I build a LangGraph agent workflow with branching logic?▼

You can build a LangGraph agent workflow by modeling it as a directed state graph using StateGraph, Nodes, and Edges. This structure enforces clear state management and deterministic execution for branching logic and parallel task orchestration.

What is conditional edge routing in LangGraph state machines?▼

Conditional edge routing in LangGraph state machines directs execution flow based on specific state conditions. It enables scalable orchestration by allowing multi-step agents to branch logic dynamically during runtime execution.

How do I run parallel tasks in a LangGraph workflow?▼

You run parallel tasks in a LangGraph workflow by using the Send parallelism feature. This allows the state machine to orchestrate scalable parallel processing and aggregate the results back into the final state.

Can I use LangGraph for multi-step campaign orchestration?▼

Yes, LangGraph is designed for multi-step campaign orchestration. It models AI-driven processes as directed graphs, allowing you to process inputs, apply branching logic, and aggregate results reliably across multiple steps.

Does LangGraph enforce node signatures and error handling requirements?▼

LangGraph enforces node signatures, edge types, reducers, streaming, and error handling requirements. These constraints ensure reliable execution and state management across complex agent workflows.

What is the best way to structure a simple two-node LangGraph?▼

The best way to structure a simple two-node LangGraph is to define START and END markers, connect the nodes with edges, compile the graph, and run it with an initial state to observe the final output.