LangGraph Development Guide

LangGraph Development Guide

Build stateful AI agents and workflows by defining graphs of nodes (steps) connected by edges (transitions).

Contents

• Quick Start
• Common Build Scenarios
• Core Principles
• Development Workflow
• Common Pitfalls
• Environment Setup
• Quick Verification
• API Essentials
• Next Steps

Quick Start

Minimal chatbot with memory:

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

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

# 2. Define node
llm = ChatOpenAI(model="gpt-4")

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

# 3. Build graph