Build stateful AI agents and agentic workflows with LangGraph in Python. Covers tool-using agents, branching workflows, memory persistence, human-in-the-loop, multi-agent systems, and production deployment. Supports 14+ AI coding agents via Agent Skill Standard.

Mastering LangGraph Agent Skill

Build stateful AI agents and agentic workflows with LangGraph in Python. This skill provides comprehensive guidance for tool-using agents, branching workflows, conversation memory, human-in-the-loop oversight, multi-agent systems, and production deployment.

Overview
Key Concepts
Quick Start
Reference Documentation
Requirements
Installation
License

Overview

This skill covers essential LangGraph patterns for building production-ready AI agents:

Topic	Description
Tool-Using Agents	LLM-tool loops that continue until task completion
Branching Workflows	Multi-step pipelines with conditional routing
Persistence & Memory	Checkpointers for conversation context across sessions
Human-in-the-Loop	Pause workflows for human approval with `interrupt()`
Multi-Agent Systems	Supervisor and swarm patterns for agent collaboration
Production Deployment	LangGraph Platform, Docker, and self-hosted options
Debugging	Time-travel, LangSmith tracing, and testing strategies

Key Concepts

Concept	Description
`StateGraph`	Core graph construction API
Nodes & Edges	Define steps and transitions
Conditional Edges	Route based on state values
`MessagesState`	Built-in state for chat applications
Checkpointers	Enable memory and time-travel
`Command` Objects	Control flow from within nodes
`ToolMessage`	Handle tool call results

Quick Start

from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import InMemorySaver
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AnyMessage
from typing_extensions import TypedDict, Annotated
import operator

# Define state with append-mode messages
class State(TypedDict):
    messages: Annotated[list[AnyMessage], operator.add]

# Create chat node
llm = ChatOpenAI(model="gpt-4")

def chat(state: State) -> dict:
    response = llm.invoke(state["messages"])
    return {"messages": [response]}

# Build and compile graph
graph = StateGraph(State)
graph.add_node("chat", chat)
graph.add_edge(START, "chat")
graph.add_edge("chat", END)

chain = graph.compile(checkpointer=InMemorySaver())

# Invoke with thread_id for memory persistence
result = chain.invoke(
    {"messages": [HumanMessage(content="Hello!")]},
    config={"configurable": {"thread_id": "user-123"}}
)

Reference Documentation

This skill includes detailed reference guides for each major topic:

Reference	Topic
core-api.md	StateGraph, nodes, edges, compilation
tool-agent-pattern.md	ReAct agents, tool integration
workflow-patterns.md	Branching, parallel execution, prompt chaining
persistence-memory.md	Checkpointers, thread_id, time-travel
hitl-patterns.md	interrupt(), breakpoints, human approval
multi-agent-patterns.md	Supervisor, swarm, nested hierarchies
production-deployment.md	LangGraph Platform, Docker, RemoteGraph
debugging-monitoring.md	Testing, LangSmith, visualization
official-resources.md	150+ official documentation links

Requirements

Python: >= 3.9
LangGraph: pip install langgraph
LLM Provider: OpenAI, Anthropic, or other supported providers

Installing with Skilz (Universal Installer)

The recommended way to install this skill across different AI coding agents is using the skilz universal installer. This skill supports the Agent Skill Standard, which means it works with 14+ coding agents including Claude Code, OpenAI Codex, Cursor, and Gemini.

Install Skilz

pip install skilz

Install from Git

You can use either -g or --git with HTTPS or SSH URLs:

# HTTPS URL
skilz install -g https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill

# SSH URL
skilz install --git git@github.com:SpillwaveSolutions/mastering-langgraph-agent-skill.git

Install from SkillzWave Marketplace

skilz install SpillwaveSolutions_mastering-langgraph-agent-skill/mastering-langgraph

Agent-Specific Installation

Claude Code

# Install to user home (available in all projects)
skilz install -g https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill

# Install to current project only
skilz install -g https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill --project

OpenCode

# User-level install
skilz install -g https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill --agent opencode

# Project-level install
skilz install -g https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill --project --agent opencode

Gemini CLI

# Project-level install (Gemini only supports project level)
skilz install -g https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill --agent gemini

OpenAI Codex

# User-level install
skilz install -g https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill --agent codex

# Project-level install
skilz install -g https://github.com/SpillwaveSolutions/mastering-langgraph-agent-skill --project --agent codex

Other Supported Agents

Skilz supports 14+ coding agents including Windsurf, Qwen Code, Aidr, and more. For the full list of supported platforms, visit:

License

MIT

SkillzWave - Largest Agentic Marketplace for AI Agent Skills | SpillWave - Leaders in AI Agent Development

mastering-langgraph-agent-skill