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Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI

13 nodes · chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow

Engine
n8n
Nodes
13
Trigger
scheduled/triggered
Source file
1703_Stickynote_Webhook_Automation_Webhook.json

Workflow flow

IntegrationsChatLmChatGoogleGeminiWebhookDocumentDefaultDataLoaderTextSplitterRecursiveCharacterTextSplitterMemoryBufferWindowAgentVectorStoreCouchbaseSearchEmbeddingsOpenAi@n8n/n8n-nodes-langchain.chat@n8n/n8n-nodes-langchain.lmChatGoogleGemini@n8n/n8n-nodes-langchain.documentDefaultDataLoader+5 more
100%
TRIGGER

When chat message received

@n8n/n8n-nodes-langchain.chat

Google Gemini Chat Model

@n8n/n8n-nodes-langchain.lmChatGoogleGemini

TRIGGER

Webhook

Webhook

Default Data Loader

@n8n/n8n-nodes-langchain.documentDefaultDataLoader

Recursive Character Text Splitter

@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter

Simple Memory

@n8n/n8n-nodes-langchain.memoryBufferWindow

Retrieve docs with Couchbase Search Vector

N8n-nodes-couchbase.vectorStoreCouchbaseSearch

Generate OpenAI Embeddings using text-embedding-3-small

@n8n/n8n-nodes-langchain.embeddingsOpenAi

Generate OpenAI Embeddings using text-embedding-3-small1

@n8n/n8n-nodes-langchain.embeddingsOpenAi

AI Travel Agent

@n8n/n8n-nodes-langchain.agent

Insert docs with Couchbase Search Vector

N8n-nodes-couchbase.vectorStoreCouchbaseSearch

11 steps · flows top to bottom, branches left to right

Browse by integration

chatlmChatGoogleGeminiwebhookdocumentDefaultDataLoadertextSplitterRecursiveCharacterTextSplittermemoryBufferWindowagentvectorStoreCouchbaseSearchembeddingsOpenAi
Browse all integrations →
Download n8n JSONDownload sanitized JSONDownload flow graph

Import into n8n

  1. Download the n8n JSON file (full or sanitized).
  2. In n8n: Workflows → Import from file.
  3. Open each node and reconnect credentials to your accounts.
  4. Test with manual execution before enabling triggers.

Sanitized JSON removes credential IDs — safer for sharing. Flow graph JSON is OpenRuna's lightweight format for diagrams and other tools, not n8n import.

View source corpus ↗

Overview

Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI (13-node n8n automation, chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow, agent, vectorStoreCouchbaseSearch) is free on OpenRuna — view the graph and export the JSON to import into n8n.

What this workflow does

"Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI" automates a chat-to-lmChatGoogleGemini flow in n8n across 13 nodes. 13 nodes · chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow Execution begins at the scheduled/triggered trigger and flows through the 13 configured nodes. On OpenRuna you get the visual graph, the integration list, and a downloadable JSON ready to import into n8n. Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.

Use cases

  • Share it with your team as a documented reference for scheduled/triggered automations in the chat stack.
  • Study how data moves from chat to lmChatGoogleGemini before building your own variant.
  • Use it as a starting template when wiring chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow, agent, vectorStoreCouchbaseSearch — adjust node parameters to your accounts and record IDs.
  • Import "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI" into n8n, reconnect credentials for chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow, agent, vectorStoreCouchbaseSearch, and run the same automation in your own workspace.

Example output

Imported into n8n, "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI" exposes 13 configured nodes. Trigger type: scheduled/triggered. Integrations: chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow, agent, vectorStoreCouchbaseSearch. Reconnect the chat credential first, then walk the remaining nodes top to bottom. Run it once in test mode to verify any branch logic (Switch/IF nodes) before enabling production triggers.

Tips by platform

Claude

Paste the exported JSON for "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI" into Claude and ask it to explain each of the 13 nodes. Claude is good at suggesting credential mappings for chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow, agent, vectorStoreCouchbaseSearch, plus error-handling and idempotency improvements before you run it in production.

ChatGPT

Have ChatGPT generate a checklist for adapting "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI" to your accounts — which chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow, agent, vectorStoreCouchbaseSearch credentials to create and which node parameters to change first.

Cursor

Open the downloaded JSON in Cursor and use Agent mode to refactor hard-coded URLs into env vars, or to generate tests for any Code nodes. Keep the OpenRuna flow diagram open so you do not break connection order across the 13 nodes.

Frequently asked questions

How do I import "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI" into n8n?
Click Download n8n JSON on this page, then in n8n choose Workflows → Import from file and select the downloaded file. Reconnect credentials on each node — the export carries credential IDs from the original author's instance, not working secrets.
Which integrations does "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI" use?
It uses: chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow, agent, vectorStoreCouchbaseSearch. The flow diagram above shows how each service connects across the 13 nodes. Filter OpenRuna by integration to find related workflows and tools.
Can I import a sanitized export without credentials?
Yes. Use "Download sanitized JSON" to strip credential references before sharing internally. You will still add your own chat and lmChatGoogleGemini credentials in n8n after import.
What trigger type does "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI" use?
Trigger type: scheduled/triggered. After import, confirm the trigger node (webhook, schedule, or app event) matches your environment, then activate the workflow when you are ready to run it.
Is "Travel Planning Agent with Couchbase Vector Search, Gemini 2.0 Flash and OpenAI" free to use?
OpenRuna catalogs community n8n workflows under open licenses. Check the source corpus link for the original license; commercial use may require verifying the third-party API terms for chat, lmChatGoogleGemini, webhook, documentDefaultDataLoader, textSplitterRecursiveCharacterTextSplitter, memoryBufferWindow, agent, vectorStoreCouchbaseSearch.

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