langgraph-fundamentals

Guide LangGraph workflow construction with StateGraph, nodes, edges, Command, and Send.

Updated May 26, 2026
One-click install
npx skills add https://github.com/anukkrit149/anukkrit-skills --skill langgraph-fundamentals-anukkrit149
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: langgraph-fundamentals
Source: https://github.com/anukkrit149/anukkrit-skills/tree/main/cloud/skills/langgraph-fundamentals
Command: npx skills add https://github.com/anukkrit149/anukkrit-skills --skill langgraph-fundamentals-anukkrit149

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Prevents common LangGraph mistakes by teaching you the right mental model for building directed graphs with shared state, reducers, and safe routing.

Core Features & Use Cases

  • StateGraph fundamentals: Learn how to structure a graph as a compiled, executable workflow with clear entry and exit points.
  • State and reducers: Use reducers to control how state updates merge—especially for list accumulation and streaming histories.
  • Nodes, edges, Command, and Send: Choose the right orchestration primitive for sequential flow, conditional routing, state+route bundling (Command), and parallel fan-out (Send).
  • Reliability patterns: Apply compile-before-invoke discipline and error-handling guidance to reduce runtime surprises.

Quick Start

Use this skill to guide you when you are writing LangGraph code by asking for an example that matches the specific graph type you want to build (linear, conditional, Command-based routing, or Send-based fan-out).

Frequently Asked Questions about langgraph-fundamentals

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

FAQPage Schema
How do I manage state and reducers in a LangGraph StateGraph?

To maintain stateful message history in LangGraph, define reducers for list-accumulating fields in your StateGraph. Reducers ensure that partial state updates from sequential nodes append to the history correctly rather than overwriting previous entries.

When should I use Command versus Send for conditional routing in LangGraph?

Use Command for conditional routing by bundling state updates and route selection together, while use Send for parallel worker fan-out to dispatch multiple asynchronous tasks simultaneously. Choose Send when you need concurrent execution across multiple nodes.

Why does my LangGraph workflow fail at runtime before execution?

Your LangGraph workflow fails because the StateGraph requires compiling the graph before execution. Applying compile-before-invoke discipline establishes clear entry and exit points, reducing runtime surprises and ensuring the directed graph is safely executable.

What is the best way to structure parallel worker fan-out in LangGraph?

The best way to structure parallel worker fan-out in LangGraph is by using the Send orchestration primitive. Send dispatches multiple asynchronous workers simultaneously, while defined reducers safely merge the resulting partial state updates back into the shared state.

Does LangGraph support error handling and streaming for agent orchestration?

Yes, LangGraph supports error handling and streaming for agent orchestration. By applying reliability patterns during StateGraph construction, you can safely stream stateful message histories and handle runtime exceptions within your directed graph workflows.