How to avoid exceeding token limits when chaining LLM calls in n8n?

The most reliable way to avoid token‑limit errors when chaining multiple LLM calls in n8n is to strictly control what you pass between nodes: keep only the data the next prompt truly needs, aggressively trim or summarize previous messages, and enforce max length checks before every LLM node. In real production n8n workflows, you never pass the full history through the chain; instead, you pass small distilled objects. This alone prevents 90% of token‑limit issues.

 

Why Token Limits Become a Problem in n8n

 

Each LLM node sends some text to the model. If your workflow chains multiple LLM calls, the output of each node becomes input for the next one. Because n8n passes JSON from node to node, it's easy to accidentally keep full conversation history, huge prompts, or large context fields alive in the JSON. That JSON grows quietly until the LLM request exceeds the model’s token limit and fails.

So the real job is to intentionally cut down the payload between nodes.

 

Practical Strategies That Actually Work in Production

 

• Pass only the fields the next step needs. Use a Set node to delete everything else. This is the most important trick.
• Summarize, don’t forward raw text. If a step produces large text, add another LLM node to summarize it to a short version (e.g., “summarize into ≤300 words”).
• Implement max length checks. Use a Function node to cut long text before hitting the next LLM node.
• Avoid passing whole prior LLM responses. Keep prompts stateless when possible. Instead of “here’s everything so far”, send the model a compact structured context.
• Store history outside the chain (database, KV store) and load only what is needed per step.
• Use embeddings instead of raw text if the workflow relies on context retrieval. Embedding vectors never hit token limits.

 

Simple example: trimming before an LLM node

 

The Function node below trims a field called content to 4,000 characters before the next LLM call. This protects the workflow from blowing up the request size.

// This Function node keeps payload small before sending to LLM

return items.map(item => {
  const text = item.json.content || "";
  
  const trimmed = text.length > 4000 
    ? text.substring(0, 4000) 
    : text;

  return {
    json: {
      content: trimmed   // safe to pass forward
    }
  };
});

 

Use Set nodes to strip unnecessary JSON

 

In n8n, each node passes an array of items, and each item can contain lots of leftover fields. A Set node can clean this up. For example, if the previous LLM node output includes metadata you don't need:

• Enable Keep Only Set.
• Define only the fields the next LLM node requires.

This prevents hidden JSON bloat, which is a very common source of token overflows.

 

When you chain multiple LLM calls

 

• Think of each step as isolated. Build it so the next step doesn’t depend on full history.
• Convert long context into short structured objects. Example: instead of forwarding a 5,000‑word description, create a JSON with topic, key facts, and main decision.
• Use summaries as the “state” that travels through the workflow.

 

Example: compressing a multi‑step chain

 

• LLM Node A produces a long analysis.
• Summarize Node → compress output to max 500 words.
• Set Node → keep only { summary: ..., status: ... }.
• LLM Node B → uses only the small summary.

This pattern keeps token usage consistent even with 6–10 chained calls.

 

Final Guidance (the part you should remember)

 

The safest long‑term strategy in n8n is to never let raw, growing text flow freely through your nodes. Summarize early, strip aggressively, enforce max size checks, and pass only minimal JSON between steps. Treat every LLM node as if it’s the first conversation turn. This keeps your workflow stable and prevents token‑limit errors in production.