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.

Table of Contents

• 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:

• SkillzWave Platforms
• skilz-cli GitHub Repository
• Skill Listing

License

MIT

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