# LangGraph — Stateful Multi-Agent Orchestration

Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. The foundation for agents in the LangChain ecosystem.

## Why Install This Skill

When your agent loads this skill, it becomes a **LangGraph architect** who can:

- **Design graph topologies** — nodes, edges, state schemas, reducers
- **Implement multi-agent patterns** — supervisor, swarm, and hierarchical orchestration
- **Add persistence** — checkpointers and stores for long-running agents
- **Handle production complexity** — branching, cycles, parallel execution, human-in-the-loop
- **Evaluate agent performance** — systematic eval methodology
- **Debug production failures** — common failure modes and how to trace them

## What You Get

| Directory | Purpose |
|-----------|---------|
| `SKILL.md` | Quick start, design principles, pattern selection guide |
| `scripts/` | Supervisor scaffold, swarm scaffold, eval generator |
| `templates/` | 3 runnable template implementations |
| `references/` | 8 reference files: architecture, each pattern in depth, evals, production failures, troubleshooting |

## Triggers

Load this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns.

## Requirements

Python 3.8+ with `langgraph`, `langchain`, and `langchain-openai` packages.


## Quick Start

Start with the setup and first workflow in SKILL.md, then use the linked resources for the specific task you need to complete.