This content originally appeared on DEV Community and was authored by Aniket Hingane
Building a Weather App with Advanced State Management for Seamless Long-Running Interactions
TL;DR
I built a Weather app that uses LangGraph and the Groq API to create a weather assistant that remembers your previous questions. The app demonstrates how to implement state management for AI assistants, allowing for natural conversations where the AI maintains context across multiple interactions. The code shows how to structure a graph-based agent that can use search tools and persist conversation history in a database.
Introduction
Have you ever been frustrated when a chatbot forgets what you just talked about? I built a solution that fixes that problem. This Weather Assistant remembers your entire conversation, letting you ask follow-up questions naturally. If you ask “What’s the weather in New York?” and then “How about tomorrow?”, it understands you’re still talking about New York.
What’s This Article About?
This article walks through building a stateful AI assistant using modern tools and techniques. I’ve created a Streamlit web application where users can ask questions about weather anywhere in the world. What makes this assistant special is its ability to maintain context throughout a conversation.
Behind the scenes, I’m using LangGraph to create a flexible agent architecture that:
Remembers conversation history using SQLite storage
Uses the Tavily search API to find real-time weather information
Powers natural language understanding with Groq’s Llama-3.3–70b model
Provides a clean, responsive UI through Streamlit
The application passes a unique conversation ID with each interaction, allowing it to retrieve past messages from the database. This creates the illusion of a continuous conversation even if the user closes their browser and returns later.
Why Read It?
AI is transforming how businesses interact with customers. According to industry reports, by 2025, 95% of customer interactions will be handled by AI. This article demonstrates how even our fictional “Weather App Inc.” can implement modern conversational AI that:
Delivers more natural, human-like interactions
Reduces user frustration by maintaining context
Scales to handle many simultaneous conversations
Creates a foundation for more complex AI assistants
The techniques shown here apply far beyond weather information — they can power customer service, internal knowledge bases, technical support, and any application where contextual conversation improves the user experience.
This content originally appeared on DEV Community and was authored by Aniket Hingane
Aniket Hingane | Sciencx (2025-02-22T02:29:55+00:00) Weather App With State Management for Long Running Conversations Using AI Agents. Retrieved from https://www.scien.cx/2025/02/22/weather-app-with-state-management-for-long-running-conversations-using-ai-agents/
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