How to handle “context length exceeded” errors in n8n AI workflows?

The short version: In n8n, a “context length exceeded” error in an AI node (OpenAI, Anthropic, etc.) means you are sending too much text into the model at once, beyond what the model’s max tokens/characters allow. The fix is always to reduce the input before it reaches the AI node — usually by trimming unnecessary fields, summarizing earlier parts, chunking large text, or running a retrieval flow instead of dumping raw data into the prompt. n8n won’t magically shrink your payload; you must preprocess it with Code, Function, or helper nodes before calling the model.

 

What the error really means

 

All LLMs have a context window. This is the maximum amount of text (your prompt + the model’s output) that the model can handle at once. When n8n sends the request, the model checks the size. If it’s too big, the provider responds with an error, and n8n just passes that upstream.

Common root causes in n8n:

• Sending full webhook payloads or full database query results into the AI node.
• Accidentally passing binary data or large JSON objects into a prompt template.
• Chat-style workflows where conversation history keeps growing.
• Trying to embed extremely large documents in a single call.

 

How to fix it in a real n8n workflow

 

The most reliable way to prevent context length errors is to control and shrink what goes into the AI node. Below are the production‑ready strategies that actually work.

• Trim fields before they hit the AI node. Use a Set node to explicitly keep only the text fields you need. Anything unnecessary should be removed.
• Guard against huge content. Add an IF node that checks input length (like: {{ $json.text.length < 5000 }}) and either summarize first or chunk into multiple calls if it’s too large.
• Use a Function node to chunk text. Break large documents into smaller parts and process them sequentially or via Looping. LLMs handle smaller chunks much better.
• Summarize progressively. Instead of feeding the entire history into the AI prompt each time, store a concise summary and update only that summary.
• Use vector search instead of dumping entire documents. If you need retrieval, store chunks in something like Pinecone, Supabase, or Qdrant, then only feed the top few relevant chunks to the LLM.
• Watch for hidden large fields. Webhook nodes or database nodes often include metadata you don’t expect. Use a Set/Function node to strip down to only what you want.

 

Example: Safely limiting text before AI node

 

Here’s a Function node snippet you can drop in before your AI node to make sure nothing larger than a safe size reaches the model:

return items.map(item => {
  const text = item.json.text || "";

  // Limit to 4000 characters
  item.json.text = text.slice(0, 4000);

  return item;
});

This prevents accidental oversize payloads when users paste huge content.

 

Example: Chunking a large document properly

 

const text = $json.text || "";
const chunkSize = 3000; // characters per chunk

const chunks = [];
for (let i = 0; i < text.length; i += chunkSize) {
  chunks.push(text.slice(i, i + chunkSize));
}

// Output chunks so Split In Batches or Looping can process them
return chunks.map(c => ({ json: { chunk: c } }));

This is a real working chunker that turns one big item into many safe-sized items. Then each chunk can be passed individually to an AI node.

 

When the conversation history is too long

 

If you’re doing a chat-like workflow and storing the whole conversation in something like an n8n variable, Airtable, or database, don’t send all messages each time. Instead:

• Keep only the last few messages.
• Store a summary and update it instead of keeping all history.
• Compress earlier messages into a single short explanation.

Everything going into the prompt must be intentionally sized.

 

When to move logic outside n8n

 

If your use case requires repeatedly processing extremely large documents or hundreds of megabytes of text, n8n is not the right place for the heavy lifting. You can offload big document preprocessing to a separate service (Node.js, Python, or a specialized text-processing service) and pass only the cleaned, small chunks back into n8n.

 

The bottom line

 

n8n AI nodes never fix context size problems for you. You must reduce, summarize, or chunk the inputs before they hit the model. Once you do that intentionally, the “context length exceeded” errors disappear completely, and your workflow becomes stable enough for production use.