Travel Planning Agent
This tutorial guides you through the process of creating a simple Travel Planner using LangGraph, a library for building stateful, multi-step applications with language models. The Travel Planner demonstrates how to structure a conversational AI application that collects user input and generates personalized travel itineraries.
In the realm of AI applications, managing state and flow in multi-step processes can be challenging. LangGraph provides a solution by allowing developers to create graph-based workflows that can handle complex interactions while maintaining a clear and modular structure. This Travel Planner serves as a practical example of how to leverage LangGraph's capabilities to build a useful and interactive application.
What you'll learn
- 1StateGraph: The core of our application, defining the flow of our Travel Planner.
- 2PlannerState: A custom type representing the state of our planning process.
- 3Node Functions: Individual steps in our planning process (input_city, input_interests, create_itinerary).
- 4LLM Integration: Utilizing a language model to generate the final itinerary.
About this tutorial
This hands-on Jupyter notebook is part of GenAI Agents, a free open-source repository by Nir Diamant covering ai agents techniques with runnable code examples and detailed explanations.
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