I Replaced APIs with MCP in My Multi-Agent AI Travel Planner | LangGraph | Part 2

In this video, we extend our Multi-Agent Travel Planning System built with LangGraph by integrating MCP (Model Context Protocol) servers for real-time flight and weather information. 📌GitHub Code: https://github.com/codewithaarohi/AI-... 📌 Part 1 Project Repository: https://github.com/codewithaarohi/AI-... 📌 Part 1 Project Video:    • I Built an AI Travel Planner with Multiple...   📌 Learn about Model Context Protocol (MCP) -    • MCP Explained: How AI Agents Connect to To...   📌 AviationStack MCP Repository: https://github.com/Pradumnasaraf/avia... 📌 Tavily MCP Documentation: https://docs.tavily.com/documentation... You will learn how to: ✅ Connect LangGraph Agents with MCP Servers ✅ Integrate AviationStack MCP Server ✅ Build a Weather MCP Server ✅ Use Real-Time Flight Data in AI Agents ✅ Use Real-Time Weather Data in AI Agents ✅ Add Long-Term Memory using PostgreSQL ✅ Create a Multi-Agent Travel Planning System ✅ Run the application in Terminal and Streamlit By the end of this tutorial, you'll have a production-style Agentic AI application that combines: • LangGraph • MCP (Model Context Protocol) • PostgreSQL Memory • Tavily Search • AviationStack API • OpenWeatherMap API • Streamlit UI #MCP #LangGraph #AgenticAI #AIAgents #MultiAgentSystems #Python #GenerativeAI #LLM #AIEngineering #TravelAI 📸 Follow me on Instagram: @codewithaarohi 🔗   / codewithaarohi   📧 You can also reach me at: aarohisingla1987@gmail.com