How to handle n8n timeout errors on long completions from Mistral?

The direct answer: you avoid n8n timeout errors on long Mistral completions by not waiting synchronously. Instead, you switch to an asynchronous pattern: trigger the Mistral job, return immediately, and then receive the final result via Webhook or Polling. n8n’s default request timeouts (especially on webhook workflows and HTTP Request nodes) simply cannot survive very long LLM completions, so you must restructure the workflow.

 

Why This Happens

 

When you call an LLM provider like Mistral directly from n8n using an HTTP Request node, n8n waits for the response. If the model takes longer than n8n’s allowed timeout window, n8n kills the execution and you get a timeout error. This is not about credentials or configuration — it's a runtime limitation. Workflow executions, especially those started by a Webhook Trigger, cannot hold the connection open for minutes waiting on a slow LLM response.

To run long inference safely, you must not keep the connection open — you let the model work in the background, and n8n resumes the workflow later when the result arrives.

 

The Production-Safe Fix

 

You move to a job-style pattern:

• Step A: Send a request to Mistral that creates a job/task (asynchronous endpoint). This should return quickly with a job ID.
• Step B: End the current execution (important: no waiting!).
• Step C: Resume when the job is done, either via:
  • Webhook Callback (ideal) — if Mistral can call you back.
  • Polling — n8n periodically checks job status until it's completed.

This pattern removes long synchronous waiting entirely, so there’s no timeout, even if the model runs for minutes.

 

If Using Mistral’s Standard Completion Endpoint

 

Today, Mistral’s public APIs behave synchronously — they return the completion in the same request. That means they don’t offer a native async job endpoint. In that case, your only safe production option in n8n is:

• Put a tiny microservice/proxy between n8n and Mistral that:
  • accepts your request
  • queues a background Mistral call
  • returns immediately with a job ID
  • later calls your n8n webhook with the result

This removes the long-running step from n8n entirely. n8n only gets a quick response, and the long-running part happens outside.

 

If You Prefer Polling (No Proxy)

 

You can keep everything inside n8n using a Split In Batches loop or a Wait node:

• Your first workflow receives the request and creates a “job” record in a DB (the input).
• A second scheduled workflow (Cron Trigger) runs every few seconds/minutes, processes pending jobs, calls Mistral, and stores results.
• Clients fetch results from your system whenever they want.

This offloads the long request to a separate background workflow, so your initial workflow never waits.

 

Useful n8n Settings (But They Will Not Solve Long LLM Waits)

 

There are a few configuration tweaks you can apply, but they do not remove the fundamental problem — they only help with small delays:

• HTTP Request Node timeout can be set manually (e.g. 300000 ms)
• N8N_DEFAULT_TIMEOUT env var adjusts the overall execution timeout
• N8N_EXECUTIONS_TIMEOUT can keep long background executions alive

But these still cannot keep an incoming webhook connection alive for long durations. Browsers, proxies, clients, and n8n’s own run‑time will break the connection sooner or later. That’s why async design is required.

 

Simple Example of an Async Pattern (Webhook → Respond Immediately → Process Later)

 

Imagine a Webhook workflow that receives a request:

• Store the prompt in a DB or Redis
• Generate a job ID
• Return immediately so no timeout happens

The workflow could respond like this using a Function node:

// Return the job ID to the webhook caller
return [
  {
    json: {
      job_id: $json.jobId,
      status: "queued"
    }
  }
];

Then a separate Cron workflow picks it up, runs the slow Mistral request, and stores the output. No single n8n execution waits too long.

 

The Core Truth to Remember

 

You cannot reliably run long synchronous LLM completions inside a single n8n execution. n8n is not designed for long-held workflows, especially not web-triggered ones. The reliable, production-safe method is always the same: make the LLM call asynchronous — via webhook callbacks, polling, or a small async proxy.

Once you redesign the workflow that way, timeout errors disappear completely.