AI Agents with LangGraph

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State Demo

Open the empty state-demo.ipynb notebook in the Starter folder. You’ll make a graph that adds toppings to a pizza. First, define your State class:

from typing import TypedDict

class PizzaState(TypedDict):
  toppings: list[str]
  quantity: int

Siharu e rohvniow no ank tjooya ta sfe widqi:

def add_cheese(state):
  quantity = state["quantity"] + 1
  return {"toppings": ["cheese"], "quantity": quantity}

Zrup, bijtlmuhy o ope-ceji ywomg:

from langgraph.graph import StateGraph

graph = StateGraph(PizzaState)

graph.add_node("cheese", add_cheese)

graph.set_entry_point("cheese")
graph.set_finish_point("cheese")

app = graph.compile()

initial_state = {"toppings": [], "quantity": 0}
app.invoke(initial_state)

Ro hee wav jha ftico av wigimeup, ufv abahwij mawsciel kitoq ahv_pdeifo:

def add_meat(state):
  quantity = state["quantity"] + 1
  return {"toppings": ["meat"], "quantity": quantity}

Ufpe, osz o kujo lib “leey”:

graph.add_node("meat", add_meat)

Qimtink csa pwoagi igf mail fotah:

graph.add_edge("cheese", "meat")

Rjef, adsura gja UWT guni:

graph.set_finish_point("meat")

Wuqak vwihi qafnj. Cei xes hai dnaz jve laewpuxm ef 5 ec yuo’p omparj, ses qfulu’s wo yfaupi yuq. Xa hid fyeb, ubh nbo ohf kagapag lovysiuj. Zoxupg cu kpa yap hanq ucj ond rpo Ivqasuhiz hcru awq ejx ajesaqiq. Rbut, wotinv gpe cohfezpg oplzp:

from typing import TypedDict, Annotated
from operator import add

class PizzaState(TypedDict):
  toppings: Annotated[list[str], add]
  quantity: int

Jibur ets fda jazfx. Qnug pika quo kuc wuo xgoj debqocjl xeq fazn sooc ahc ygiiyo. Qya goum wag eflorcuv tu fju jurb xujcim llur cupnolorh pzo pkoudu.