How to Build Human-in-the-Loop Plan-and-Execute AI Agents with Explicit User Approval Using LangGraph and Streamlit

by CryptoExpert
murf


app_code = r”’
import os, json, uuid
import streamlit as st
from typing import TypedDict, List, Dict, Any, Optional
from pydantic import BaseModel, Field
from openai import OpenAI

from langgraph.graph import StateGraph, START, END
from langgraph.types import Command, interrupt
from langgraph.checkpoint.memory import InMemorySaver

def tool_search_flights(origin: str, destination: str, depart_date: str, return_date: str, budget_usd: int) -> Dict[str, Any]:
options = [
{“airline”: “SkyJet”, “route”: f”{origin}->{destination}”, “depart”: depart_date, “return”: return_date, “price_usd”: int(budget_usd*0.55)},
{“airline”: “AeroBlue”, “route”: f”{origin}->{destination}”, “depart”: depart_date, “return”: return_date, “price_usd”: int(budget_usd*0.70)},
{“airline”: “Nimbus Air”, “route”: f”{origin}->{destination}”, “depart”: depart_date, “return”: return_date, “price_usd”: int(budget_usd*0.62)},
]
options = sorted(options, key=lambda x: x[“price_usd”])
return {“tool”: “search_flights”, “top_options”: options[:2]}

def tool_search_hotels(city: str, nights: int, budget_usd: int, preferences: List[str]) -> Dict[str, Any]:
base = max(60, int(budget_usd / max(nights, 1)))
picks = [
{“name”: “Central Boutique”, “city”: city, “nightly_usd”: int(base*0.95), “notes”: [“walkable”, “great reviews”]},
{“name”: “Riverside Stay”, “city”: city, “nightly_usd”: int(base*0.80), “notes”: [“quiet”, “good value”]},
{“name”: “Modern Loft Hotel”, “city”: city, “nightly_usd”: int(base*1.10), “notes”: [“new”, “gym”]},
]
if “luxury” in [p.lower() for p in preferences]:
picks = sorted(picks, key=lambda x: -x[“nightly_usd”])
else:
picks = sorted(picks, key=lambda x: x[“nightly_usd”])
return {“tool”: “search_hotels”, “top_options”: picks[:2]}

quillbot

def tool_build_day_by_day(city: str, days: int, vibe: str) -> Dict[str, Any]:
blocks = []
for d in range(1, days+1):
blocks.append({
“day”: d,
“morning”: f”{city}: coffee + a must-see landmark”,
“afternoon”: f”{city}: {vibe} activity + local lunch”,
“evening”: f”{city}: sunset spot + dinner + optional night walk”
})
return {“tool”: “draft_itinerary”, “days”: blocks}
”’



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