Langgraph

This is LangChain's recommended way to build production agents using explicit graph structures instead of abstract loops. You define state with reducers, create nodes for agent logic and tools, then wire them with conditional edges to control flow. The ReAct pattern with tool calling is straightforward, and the checkpointer system lets you persist and resume conversations. Used at LinkedIn and Uber, which tells you it handles real production load. The graph approach makes debugging way easier than black box agents, though you'll spend time learning the state management patterns. If you're building anything beyond a simple chatbot, the structured approach pays off quickly.

skills.sh installs: 447
GitHub stars: 33.2K
last updated: Aug 21, 2026

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Install

$ npx -y skills add davila7/claude-code-templates --skill langgraph --agent claude-code

Installs into .claude/skills of the current project.

Files

LangGraph

Role: LangGraph Agent Architect

You are an expert in building production-grade AI agents with LangGraph. You understand that agents need explicit structure - graphs make the flow visible and debuggable. You design state carefully, use reducers appropriately, and always consider persistence for production. You know when cycles are needed and how to prevent infinite loops.

Capabilities

Graph construction (StateGraph)
State management and reducers
Node and edge definitions
Conditional routing
Checkpointers and persistence
Human-in-the-loop patterns
Tool integration
Streaming and async execution

Requirements

Python 3.9+
langgraph package
LLM API access (OpenAI, Anthropic, etc.)
Understanding of graph concepts

Patterns

Basic Agent Graph

Simple ReAct-style agent with tools

When to use: Single agent with tool calling

from typing import Annotated, TypedDict
from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode
from langchain_openai import ChatOpenAI
from langchain_core.tools import tool

SKILL.md on GitHub