langgraph-graphs

Define stateful LangGraph workflows with nodes, edges, and conditional routing.

20|4|Updated Nov 9, 2025

Search Tags:#python #workflow orchestration #ai agents #langgraph #agentic #state machine #graph database

One-click install

npx skills add https://github.com/anderskev/amelia --skill langgraph-graphs

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: langgraph-graphs
Source: https://github.com/anderskev/amelia/tree/main/.claude/skills/amelia/langgraph-graphs
Command: npx skills add https://github.com/anderskev/amelia --skill langgraph-graphs

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Orchestrating complex, multi-step AI agents or data processing pipelines often leads to tangled, hard-to-manage code. LangGraph provides a structured way to define stateful workflows, making them robust and observable.

Core Features & Use Cases

Stateful Workflow Definition: Design AI agent interactions and data flows using nodes, edges, and conditional routing.
Checkpointing & Persistence: Integrate with checkpointers to save and resume workflow state, enabling human-in-the-loop processes.
Error Handling & Retries: Implement sophisticated error recovery and retry logic directly within the graph structure.
Use Case: Create a multi-agent system where a "planner" agent decides the next step, an "executor" agent performs tasks, and a "reviewer" agent provides feedback, with the ability to pause for human approval at critical junctures.

Quick Start

Explain how to define a simple LangGraph StateGraph with two nodes and a conditional edge, using a TypedDict for state.