Yet another question about the “right” way to do things.
I’ll start from the beginning - feel free to criticize any part of this…
The background: I have a particular service (ClickUp) that I integrate with.
The requirement: I’d like to keep all my ClickUp integration nodes in one place, so that I don’t have “config sprawl” across multiple workflows with their own ClickUp integration nodes.
My solution: Approach this like traditional software engineering - create a single “interface” for other workflows to use to call ClickUp, in the form of a sub-workflow.
The implementation…
Originally, I was thinking I would implement this like a traditional, “structured” API, where the sub-workflow would get an operation field (create, read, etc.), a resourceType field (task, list, etc.), and the appropriate data for the operation - and then I’d just route to the appropriate integration node with Switch nodes.
The problems I can see with this approach:
Since this sub-workflow covers all ClickUp operations, I can’t pre-define input fields for it, because I don’t know what the input data will be.
Possible solution: define a string “data” input field, which accepts a JSON object, then de-serialize that inside my workflow - but then I have to explain to every calling agent how to format the JSON input.
The sub-workflow would get messy and complicated, with a web of routing decisions and mapping JSON objects around.
The alternative?
The approach I’m considering instead is to:
Have the sub-workflow accept a natural-language prompt as input.
Use a basic LLM chain to figure out the operation and resourceType.
Define a data table with a JSON schema for each operation and resourceType combination. Retrieve the JSON schema and pass it as input to the next node.
Within an Information Extractor node, use “JSON schema” as the schema type. Use an expression to use the received from the previous node.
Route the item created by the Information Extractor to the appropriate ClickUp node.
Does this seem like a reasonable approach? Or am I over-engineering this way too much?
Hello again @jfc ! Don’t worry about asking questions, thats what this place is for!
The instinct to centralize your ClickUp nodes is solid — it’s basically the facade pattern applied to n8n, and it’ll save you a lot of headaches vs. having ClickUp configs scattered across 15 workflows.
For the structured vs. LLM approach — I’d go structured, but with a twist. The LLM layer is over-engineering it here. You’re adding latency, cost, and non-determinism to solve a problem that doesn’t really exist — if your callers are other workflows (not humans typing free text), they can just pass structured JSON. You don’t need an LLM to figure out that operation is create and resourceType is task when the calling workflow already knows that.
The real problem with your structured approach isn’t the concept, it’s the scope. One sub-workflow for all of ClickUp is going to turn into a monster. Instead, break it up by resource type — clickup-tasks, clickup-lists, clickup-spaces, etc. Each one gets its own sub-workflow with well-defined inputs that make sense for that resource. You still get the centralization benefit (all task operations live in one place), but you skip the giant Switch node web and the “I can’t pre-define inputs because they could be anything” problem. Because now they can’t be anything — clickup-tasks always expects task-related fields.
For the JSON input concern — use the “Define using JSON example” input mode on your Execute Sub-workflow Trigger. You give it an example JSON object and n8n generates the schema from it automatically. The calling workflow can build that object with a Set node before hitting the Execute Sub-workflow node. No manual serialization/deserialization needed, n8n passes JSON objects natively between workflows.
The only scenario where the LLM approach makes sense is if your caller is literally an AI agent that’s generating natural language and you can’t control its output format. Even then, I’d do the structured extraction on the agent side with a tool schema rather than inside the sub-workflow — keep your integration layer dumb and deterministic, and let the smart stuff happen upstream.
Hope that helps! Feel free to ask if you have more questions.
Hi @jfc It’s a good pattern to consolidate clickup in a reusable sub workflow. Do not make the interface complex. An AI driven routing layer except for the true need for flexible agent style inputs are usually harder to maintain and debug than a simple structured input contract. The more AI you are going to incorporate AI into your sub workflows the more prone it would be to make mistakes and not work as it should, your approach is pretty much fine and easy to scale up , i recommend going through some of these to get started as i have used a lot of these:
Your approach make things simpler and easily maintainable no matter the size, just make sure it is less prone to errors by incorporating AI only where it needs in the clickup sub flows else it can turn into a headache to debug. Cheers!
@Anshul_Namdev I agree about the ai generated replies right now I’m making a ai generation detector for the forum. What i do is I’ll usually gather all my info, use ai and websearch to fact check it and format it into a response and then ill edit the response and fact check it myself and then I’ll post.
I started on the “monolith sub-workflow” approach and it seems to be working fairly well, but at some point, I’ll probably do what you suggested and break it into resource-specific sub-workflows.
Though I’m leaning towards keeping some kind of “natural-language” AI interface to intelligently handle extra API operations needed to fulfill a request - like, “human refers to the list by name, but the API expects a list ID, so I have to go look up the list ID before I can call it.”
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