How to handle concurrency issues with parallel LLM calls in n8n?

The short, practical answer: you avoid concurrency issues with parallel LLM calls in n8n by serializing the calls (Split In Batches), limiting parallel executions (Concurrency settings, Queue mode), or isolating each LLM call in its own execution (Call Workflow). LLMs don’t like heavy parallel bursts, and n8n can overwhelm APIs if you let all items run at once, so you intentionally slow or separate work to keep it stable and predictable.

 

Why concurrency becomes a problem

 

LLM API providers (OpenAI, Anthropic, etc.) usually impose strict rate limits and are sensitive to sudden bursts. When an n8n workflow receives an array of items, it normally processes them in parallel inside a single execution. This means if you have 50 items, n8n might fire 50 LLM requests instantly. That causes:

• 429 rate‑limit errors
• time‑outs inside n8n
• run‑away costs if retries hit unexpectedly
• n8n workflow stuck states when the LLM node takes too long

This is why production setups need to control concurrency explicitly.

 

Technique 1: Split In Batches (most common and reliable)

 

This is the simplest and most production‑friendly approach. You tell n8n to process only a few items at a time. For example, batches of 1, 2, or 5.

Typical pattern:

• Use Split In Batches right before the LLM node.
• Set batch size = 1 (or 2–3 if API allows).
• Loop until no items remain.

This prevents parallel calls and ensures predictable API behavior.

Example of batch loop control:

{{$json["continue"] !== false}} // Common check inside a Loop node

This works because Split In Batches emits items one batch at a time and pauses until the loop returns.

 

Technique 2: Use Queue Mode in n8n

 

If you're using n8n in production (Docker or cloud), you can enable queue mode. This moves workflow execution into a background worker and allows you to control:

• how many workers run in parallel
• how many workflow executions run simultaneously

With queue mode, if you trigger many parallel tasks (e.g., from webhook), only a safe number of executions run at once. This prevents LLM storms.

In Docker, you typically set:

EXECUTIONS_MODE=queue
EXECUTIONS_CONCURRENCY=2   // workers run 2 executions at a time

This is extremely reliable for high-volume systems.

 

Technique 3: Use “Call Workflow” + single-call subworkflow

 

You create one workflow whose only job is to make one LLM request. Then the main workflow loops over items and triggers that subworkflow using a Call Workflow node.

This effectively isolates each LLM call into its own execution, which makes:

• rate limiting easier
• timeouts isolated (only one call fails)
• error retries independent

In real production, this pattern is extremely popular when batch loops still produce too much load.

 

Technique 4: Add manual delays or rate limit nodes

 

If the LLM provider gives you a rate like “60 requests / minute”, you can enforce that with:

• Wait node
• Throttle (Rate Limit) node

Example expression in Wait node:

={{60 / 60}} // wait 1 second per call

It's crude, but works well for stable throughput when other methods are overkill.

 

Technique 5: Set retry logic correctly

 

For any node that talks to an LLM API, configure:

• Retry On Fail = on
• Max Attempts = 3
• Delay Between Attempts = 2000–5000 ms

This prevents concurrency pile-ups where dozens of failed calls retry at the same time.

 

How to choose the right method

 

If you want the safest and simplest: Split In Batches with batch=1. If you want more scalability: queue mode + Call Workflow isolation. If you're working with unpredictable input volume (like webhook bursts): queue mode is non‑negotiable for real stability.

In real production, you often combine methods: e.g., queue mode + isolated subworkflow + retry logic. The overall goal is always to avoid letting large item arrays trigger uncontrolled parallel LLM calls.