Building Production-Ready AI Agents with LangGraph: A Real-Life Use Case

[nirbar1985]

🚀New Blog Post Alert🚀

In my latest blog post, I discussed the role of AI Agents and demonstrated an implementation using the LangChain framework. While LangChain is great for proofs of concept (POCs), it only sometimes meets the demands of production environments. In this post, I show you how I built production-ready AI agents using LangGraph, designed to be scalable and efficient for real-world applications.
🔑 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀:
• How to split tasks into smaller, manageable prompts for better control and optimization.
• How 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 empowers you to build stateful, multi-actor applications with cyclical graphs for more sophisticated agent runtimes.
• Features like 𝗽𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝘀𝘁𝗮𝘁𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁, 𝗵𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, and 𝘀𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝘀𝘂𝗽𝗽𝗼𝗿𝘁.
💡 𝗪𝗵𝗮𝘁 𝗗𝗶𝗱 𝗜 𝗕𝘂𝗶𝗹𝗱? An AI travel agent that helps you plan your next vacation or business trip by fetching real-time flight and hotel options using the Google Flights and Hotels APIs. It also gives you the option to send the travel information via email using SendGrid!

📂 𝗖𝗼𝗱𝗲 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗼𝗻 𝗚𝗶𝘁𝗛𝘂𝗯! I'm sharing the full implementation code in the GitHub repository for anyone interested in exploring or building on this project

Curious to learn more? Read the full blog post here and explore how you can build 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀.
👉
https://medium.com/cyberark-engineering/building-production-ready-ai-agents-with-langgraph-a-real-life-use-case-7bda34c7f4e4