Travel planning assistant with MongoDB Atlas, Gemini LLM and vector search
Needs changesNeeds changes. This template uses 1 thing(s) that n8n 3.0 removes: AI Agent v1 (old agent modes). 1 breaking item(s) found. Static check, last run 2026-10-19.
What this workflow does
This AI workflow has 11 nodes and runs when a chat message arrives (plus 1 other trigger). Along the way it connects MongoDB Chat Memory, Google Gemini Chat Model, MongoDB Atlas Vector Store and Embeddings OpenAI.
How it runs
Trigger: When a chat message arrives (plus 1 other trigger).
- AI Traveling Planner Agent (AI Agent): runs an AI agent, with model: Google Gemini Chat Model; 1 tool (MongoDB Atlas Vector Store); memory: MongoDB Chat Memory
- MongoDB Atlas Vector Store: stores documents in the MongoDB Atlas vector store, with embeddings: Embeddings OpenAI; document loader: Default Data Loader
Services it uses
Before you upgrade to n8n 3.0
1 thing in this workflow is removed in n8n 3.0. Fix it before you upgrade:
- AI Agent v1 (old agent modes) in AI Traveling Planner Agent (v1.8): replace with Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent.
Rather not rewrite it? Many of these flows can be rebuilt in Make (free plan).
| Removed in 3.0 | Replace with |
|---|---|
| AI Agent v1 (old agent modes) AI Traveling Planner Agent (v1.8) | Latest AI Agent node (Tools Agent). For SQL Agent, use a Postgres or MySQL tool with a recent AI Agent. |
Nodes used (9)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - MongoDB Chat Memory
@n8n/n8n-nodes-langchain.memoryMongoDbChat - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - MongoDB Atlas Vector Store
@n8n/n8n-nodes-langchain.vectorStoreMongoDBAtlas - Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi - AI Agent
@n8n/n8n-nodes-langchain.agent - Webhook
n8n-nodes-base.webhook - Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - Recursive Character Text Splitter
@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter
From the author
Building agentic AI workflows often requires multiple moving parts: memory management, document retrieval, vector similarity, and orchestration. Until now, these pieces had to be custom-wired. But with the new native n8n nodes for MongoDB Atlas, we reduce…