Travel Planning Assistant with MongoDB Atlas Gemini LLM and Vector Search

Say goodbye to scattered memory and disconnected AI agents!

This automation uses native MongoDB Atlas support in n8n to build smarter, memory-powered agents—perfect for tasks like travel planning. By combining long-term memory and vector search, your AI can reason with context and recall relevant details in real time.

Whether you're building travel bots, AI planners, or customer assistants, this tool helps your agent think more like a human.

What it does:

📥 Ingests documents (like travel points of interest) and stores them in MongoDB Atlas

🧠 Converts titles/descriptions into vector embeddings using OpenAI (or your preferred provider)

🗂 Stores vector data in a MongoDB Atlas collection for semantic search

💬 Uses AI Agent node with chat memory stored in MongoDB

🔍 Retrieves relevant context from Atlas Vector Search when prompted

🌍 Perfect for building contextual AI travel planners or memory-augmented assistants

✅ Setup guide & importable automation included

Need help setting it up? We offer full configuration and testing for a one-time fee.