Auto-Tag Blog Posts in WordPress with AI
32 nodes · lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches
- Engine
- n8n
- Nodes
- 32
- Trigger
- scheduled/triggered
- Source file
- 1323_Splitout_Code_Automation_Webhook.json
Workflow flow
OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
Auto-fixing Output Parser
@n8n/n8n-nodes-langchain.outputParserAutofixing
OpenAI Chat Model1
@n8n/n8n-nodes-langchain.lmChatOpenAi
Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured
RSS Feed Trigger
RssFeedRead
Demo Usage in Another Workflow
ExecuteWorkflow
Generate tags for article
@n8n/n8n-nodes-langchain.chainLlm
MOCK article
Set
Auto-Tag Posts in WordPress
ExecuteWorkflow
Return article details
Set
Wordpress
WordPress
Combine slugs
Aggregate
Return missing tags
Code
If
IF
Split Out
Split Out
POST WP tags
HTTP
SET initial record
Set
GET updated WP tags
HTTP
Combine tag_ids
Aggregate
Loop over articles
SplitInBatches
GET WP tags
HTTP
Keep matches
Filter
22 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: Auto-Tag Blog Posts in WordPress with AI. 32 nodes across lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches, httpRequest, filter, with a downloadable JSON and a visual flow diagram on OpenRuna.
What this workflow does
"Auto-Tag Blog Posts in WordPress with AI" is an n8n automation built from 32 nodes spanning lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches, httpRequest, filter. 32 nodes · lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches Execution begins at the scheduled/triggered trigger and flows through the 32 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
- Benchmark it against your current lmChatOpenAi integration to spot missing steps or redundant nodes.
- Fork it to add error handling, retries, or a notification step without designing the 32-node flow from scratch.
- Use it as a starting template when wiring lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches, httpRequest, filter — adjust node parameters to your accounts and record IDs.
- Import "Auto-Tag Blog Posts in WordPress with AI" into n8n, reconnect credentials for lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches, httpRequest, filter, and run the same automation in your own workspace.
Example output
Imported into n8n, "Auto-Tag Blog Posts in WordPress with AI" exposes 32 configured nodes. Trigger type: scheduled/triggered. Integrations: lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches, httpRequest, filter. Open each node after import to map credentials to your own lmChatOpenAi and outputParserAutofixing accounts. 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 "Auto-Tag Blog Posts in WordPress with AI" into Claude and ask it to explain each of the 32 nodes. Claude is good at suggesting credential mappings for lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches, httpRequest, filter, plus error-handling and idempotency improvements before you run it in production.
ChatGPT
Have ChatGPT generate a checklist for adapting "Auto-Tag Blog Posts in WordPress with AI" to your accounts — which lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches, httpRequest, filter 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 32 nodes.
Frequently asked questions
- How do I import "Auto-Tag Blog Posts in WordPress with AI" 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 "Auto-Tag Blog Posts in WordPress with AI" use?
- It uses: lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches, httpRequest, filter. The flow diagram above shows how each service connects across the 32 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 lmChatOpenAi and outputParserAutofixing credentials in n8n after import.
- What trigger type does "Auto-Tag Blog Posts in WordPress with AI" 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 "Auto-Tag Blog Posts in WordPress with AI" 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 lmChatOpenAi, outputParserAutofixing, outputParserStructured, rssFeedRead, splitOut, splitInBatches, httpRequest, filter.
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