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langgraph

Orchestrate stateful AI agent workflows with LangGraph for durable execution and conditional routing.

44|9|Updated May 7, 2026

Search Tags:#workflow orchestration #checkpointing #langgraph #human-in-the-loop #stateful agents #multi-agent routing #typed state

Ready to run

npx skills add https://github.com/Omar-Obando/qwen-orchestrator --skill langgraph-omar-obando

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langgraph
Source: https://github.com/Omar-Obando/qwen-orchestrator/tree/main/skills/langgraph
Command: npx skills add https://github.com/Omar-Obando/qwen-orchestrator --skill langgraph-omar-obando

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LangGraph Skill solves the challenge of building agent workflows that need durable state, resumability, and multi-agent routing without losing track of conversation context.

Core Features & Use Cases

Stateful agent orchestration: Define typed state (e.g., message history, current agent) and run deterministic graph executions.
Graph-based control flow: Use sequential edges, conditional routing, and multi-node patterns to model complex agent behaviors.
Persistence and human-in-the-loop: Add checkpointing (in-memory, SQLite, PostgreSQL) and interrupts to pause for approvals or feedback.
Multi-agent patterns: Implement swarm-style collaboration, agent handoffs, and active-agent routers.

Quick Start

Use the langgraph skill to design a stateful LangGraph workflow with checkpoints and conditional routing for a human-approved multi-step agent task.

Frequently Asked Questions about langgraph

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

FAQPage Schema

How do I build stateful AI agents with durable execution and resumability?▼

You build stateful AI agents by defining typed state and compiling graph executions with a configured checkpointer, enabling durable execution, resumability, and thread persistence to maintain conversation context without losing track of agent history.

How do I implement human-in-the-loop interrupts for agent approval workflows?▼

Human-in-the-loop interrupts are implemented by adding checkpointing storage like SQLite or PostgreSQL to your graph, pausing stateful agent executions to wait for human feedback or approvals before resuming the workflow.

What is the best way to route tasks between multiple agents in a workflow?▼

Multi-agent routing is handled using conditional edges and active-agent routers, enabling swarm-style collaboration and agent handoffs within the stateful graph to direct tasks to the appropriate agent node dynamically.

Can I use LangGraph for both Python and JavaScript agent architectures?▼

Yes, LangGraph supports both Python and JavaScript implementations for building sequential and graph-based agent architectures, allowing you to define typed state and compile graphs with configured checkpointers in either environment.

How do I add conditional routing to a multi-step agent workflow?▼

Conditional routing is added by defining sequential edges and conditional logic between graph nodes, allowing the stateful workflow to model complex agent behaviors and direct execution flow based on the current typed state.