0714_Splitout_Zendesk_Update_Triggered
26 nodes · chat, agent, lmChatOpenAi, splitOut, embeddingsOpenAi, documentDefaultDataLoader
- Engine
- n8n
- Nodes
- 26
- Trigger
- scheduled/triggered
- Source file
- 0714_Splitout_Zendesk_Update_Triggered.json
Workflow flow
When chat message received
@n8n/n8n-nodes-langchain.chat
OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
Embeddings OpenAI1
@n8n/n8n-nodes-langchain.embeddingsOpenAi
Default Data Loader
@n8n/n8n-nodes-langchain.documentDefaultDataLoader
Token Splitter1
@n8n/n8n-nodes-langchain.textSplitterTokenSplitter
Window Buffer Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow
Embeddings OpenAI2
@n8n/n8n-nodes-langchain.embeddingsOpenAi
When clicking ‘Test workflow’
Manual
AI Agent1
@n8n/n8n-nodes-langchain.agent
OpenAI Chat Model1
@n8n/n8n-nodes-langchain.lmChatOpenAi
Embeddings OpenAI
@n8n/n8n-nodes-langchain.embeddingsOpenAi
Loop Over Items
SplitInBatches
Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured
Query Qdrant Vector Store
@n8n/n8n-nodes-langchain.vectorStoreQdrant
Qdrant Vector Store query
@n8n/n8n-nodes-langchain.vectorStoreQdrant
Get all Zendesk Tickets
Zendesk
Update Zendesk with Mitre Data
Zendesk
Move on to next ticket
NoOp
AI Agent
@n8n/n8n-nodes-langchain.agent
Pull Mitre Data From Gdrive
Google Drive
Extract from File
ExtractFromFile
Split Out
Split Out
Embed JSON in Qdrant Collection
@n8n/n8n-nodes-langchain.vectorStoreQdrant
23 steps · flows top to bottom, branches left to right
Browse by integration
Import into n8n
- Download the n8n JSON file (full or sanitized).
- In n8n: Workflows → Import from file.
- Open each node and reconnect credentials to your accounts.
- 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.
Overview
Free n8n workflow: 0714_Splitout_Zendesk_Update_Triggered. 26 nodes across chat, agent, lmChatOpenAi, splitOut, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, memoryBufferWindow, with a downloadable JSON and a visual flow diagram on OpenRuna.
What this workflow does
Built in n8n, "0714_Splitout_Zendesk_Update_Triggered" chains 26 nodes to move data between chat, agent, lmChatOpenAi, splitOut, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, memoryBufferWindow. 26 nodes · chat, agent, lmChatOpenAi, splitOut, embeddingsOpenAi, documentDefaultDataLoader Execution begins at the scheduled/triggered trigger and flows through the 26 configured nodes. OpenRuna renders the node graph and offers the JSON export so you can import and adapt it in minutes. Use it to learn integration patterns, fork it for your own use case, or deploy it quickly after reconnecting credentials.
Use cases
- Fork it to add error handling, retries, or a notification step without designing the 26-node flow from scratch.
- Benchmark it against your current chat integration to spot missing steps or redundant nodes.
- Study how data moves from chat to agent before building your own variant.
- Share it with your team as a documented reference for scheduled/triggered automations in the chat stack.
Example output
Imported into n8n, "0714_Splitout_Zendesk_Update_Triggered" exposes 26 configured nodes. Trigger type: scheduled/triggered. Integrations: chat, agent, lmChatOpenAi, splitOut, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, memoryBufferWindow. Set your credentials on the chat node, then verify each downstream node picks them up. Run it once in test mode to verify any branch logic (Switch/IF nodes) before enabling production triggers.
Tips by platform
Claude
Ask Claude to turn the "0714_Splitout_Zendesk_Update_Triggered" JSON into a plain-English runbook, node by node. It can flag where chat rate limits or missing retries might bite before you go live.
ChatGPT
Drop the workflow JSON or this OpenRuna page URL into ChatGPT and ask for a step-by-step runbook: triggers, branches, and which fields to customise for chat, agent, lmChatOpenAi, splitOut, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, memoryBufferWindow. Handy for onboarding teammates new to n8n.
Cursor
In Cursor, ask Agent mode to parameterise "0714_Splitout_Zendesk_Update_Triggered" — extract chat IDs and secrets into environment variables without changing the 26-node structure.
Frequently asked questions
- How do I import "0714_Splitout_Zendesk_Update_Triggered" 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 "0714_Splitout_Zendesk_Update_Triggered" use?
- It uses: chat, agent, lmChatOpenAi, splitOut, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, memoryBufferWindow. The flow diagram above shows how each service connects across the 26 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 agent credentials in n8n after import.
- What trigger type does "0714_Splitout_Zendesk_Update_Triggered" 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 "0714_Splitout_Zendesk_Update_Triggered" 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, agent, lmChatOpenAi, splitOut, embeddingsOpenAi, documentDefaultDataLoader, textSplitterTokenSplitter, memoryBufferWindow.
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