How to prevent duplicate LLM calls in the same n8n workflow?

The simplest reliable way to prevent duplicate LLM calls in the same n8n workflow is to store a “fingerprint” of the request (usually a hash of the input), check whether you’ve already processed it, and only call the LLM if that fingerprint does not already exist. You do this by using a database, the n8n Data Store, or a cache-like system before the LLM node.

 

Why this works

 

n8n does not automatically deduplicate LLM calls. Each time execution reaches your LLM node, it will fire. So you must explicitly add logic before the LLM node to detect whether you’ve seen this exact input already. A “fingerprint” is just a reproducible unique key for that input, usually a hash like SHA‑256 of the prompt text. If the workflow sees that the fingerprint already exists, it skips the LLM call; otherwise it writes the fingerprint into storage and proceeds normally.

 

How to implement it in a clean, production‑safe way

 

You can use any persistent storage you prefer: the n8n Data Store (built‑in key/value storage), a Postgres/MySQL table, or a Redis instance. Here’s a very practical version using n8n’s Data Store because it requires no external infra.

• Step 1: Compute a fingerprint. Use a Function node to create a hash of the LLM input. This turns the input text into a unique fixed string.
• Step 2: Check storage. Use the Data Store node to Query by that key. If it exists, skip the LLM call.
• Step 3: Call LLM only if needed. If no record is found, run the LLM node.
• Step 4: Save the fingerprint. After a successful LLM response, store the fingerprint back into the Data Store.

 

A real n8n Function node hashing example

 

This Function node generates a reproducible SHA‑256 hash for the LLM input text stored in items[0].json.prompt:

const crypto = require("crypto");

const prompt = $json.prompt; // The LLM input text
const hash = crypto.createHash("sha256").update(prompt).digest("hex");

return [
  {
    json: {
      prompt,
      fingerprint: hash,
    },
  },
];

 

How the Data Store check typically looks

 

After the Function node, add a Data Store node in “Get” mode and set the key to {{ $json.fingerprint }}. If the store returns an item, you know this input was already processed.

• If the Data Store result has data, use an IF node to route execution around the LLM node.
• If it does not exist, continue into the LLM node.

 

Why this pattern is production‑safe

 

• Idempotency: Running the workflow multiple times with the same input won’t cause multiple LLM charges.
• Resilience: If an execution restarts or retries, the fingerprint check still prevents duplicate LLM calls.
• Predictability: The workflow logic stays simple and transparent — check → call → store.
• Performance: Hash lookups in a Data Store or database are cheap compared to an LLM call.

 

Extra tip: stop duplicates from branches

 

In complex workflows where multiple branches might reach the same LLM node, the same fingerprint check works. Each branch passes through the “check → skip if exists” stage, so even if two branches try to call the LLM at the same moment, only the first one succeeds; the others see the stored fingerprint and skip.

 

In summary: create a hash of the prompt, check storage before the LLM node, call only if missing, and write it afterward. This is the standard production pattern for preventing duplicate LLM calls in n8n.