The n8n Information Extractor node is a powerful AI tool that uses large language models (LLMs) to parse unstructured text and output structured data, like a clean JSON object. It’s designed to take messy, free-form text from sources like emails, PDFs, or web scrapes and intelligently pull out specific details based on a schema you define. This allows you to transform chaotic information into organized, actionable data for your workflows.
What Exactly is the n8n Information Extractor Node?
Imagine you get hundreds of customer support emails a day. Each one is written differently, but they all contain a few key pieces of information: the customer’s name, their order number, and a summary of their issue. Reading each one manually would be a nightmare, right? The n8n Information Extractor node acts like a super-smart assistant. You can feed it the raw email text, and it will read, understand, and pull out just the pieces you need, neatly organizing them for the next step in your automation.
At its core, this node is a bridge between the unpredictable world of human language and the structured world of databases and applications. It leverages the power of LLMs (like those from OpenAI, Google, or Mistral AI) to make sense of the chaos, saving you from writing complex regex patterns or custom code that would break with the slightest variation in the input text. It’s a true game-changer for anyone dealing with text-based data.
Getting Started: Configuring the Node
When you first drop the Information Extractor onto your canvas, you’ll see a few key parameters. Let’s be honest, this is where the magic really happens. Getting this part right is crucial for success.
Feeding it the Text
The first field, Text, is where you tell the node what to analyze. This is almost always going to be an expression that pulls data from a previous node. For example:
- If you’re reading a PDF invoice, you might use an expression like
{{ $json.text }}after an Extract From File node. - If you’re scraping a webpage, you might use data from an HTTP Request or Jina AI node.
Think of this as simply pointing the node to the block of text you want it to work on.
Defining Your Desired Output (The Schema)
This is the most important part of the setup. You need to tell the AI what information to look for and how to format it. The Use Schema Type parameter gives you three ways to do this, each with its own pros and cons.
-
From Attribute Descriptions: This is the most straightforward method. You create a list of fields (attributes) you want and write a simple description for each. For example, you might have an attribute named
customer_namewith the description “The full name of the person who wrote the email.” -
Generate From JSON Example: This is my personal favorite for its speed. You provide a sample JSON object of what you want your final output to look like. The node intelligently infers the structure and data types from your example. A word of caution: n8n treats every field in your example as mandatory, so only include what you expect to find every time.
-
Define using JSON Schema: This is the power-user option. It gives you the ultimate control over the output structure, allowing you to specify data types, required fields, nested objects, and complex validation rules. It’s more complex, but incredibly flexible for advanced use cases.
A Quick Comparison of Schema Methods
| Schema Method | Ease of Use | Flexibility | Best For… |
|---|---|---|---|
| Attribute Descriptions | Very Easy | Low | Simple, flat data structures where you just need a few key-value pairs. |
| Generate From JSON | Easy | Medium | Quickly defining nested structures and arrays without writing a full schema. |
| Define using JSON Schema | Advanced | High | Complex, mission-critical outputs that require strict validation and data typing. |
Real-World Magic: A Practical Case Study
Theory is great, but let’s see how this works in practice. A classic use case I’ve built for clients is automated invoice processing.
The workflow looks something like this:
- Email Trigger (IMAP): Kicks off the workflow whenever an email with an invoice attached arrives in a specific folder.
- Extract From File: Reads the attached PDF and extracts all the raw text from it. This text is a jumbled mess.
- n8n Information Extractor: This is our star player. It takes the jumbled text and, using a schema, extracts the key details:
invoice_number: The unique identifier for the invoice.vendor_name: Who the invoice is from.due_date: When the payment is due.total_amount: The total monetary value.
- Google Sheets: Takes the clean, structured JSON output from the extractor and appends it as a new row in an “Invoices to Pay” spreadsheet.
In just four steps, we’ve built a system that automatically logs invoices, saving hours of manual data entry and reducing human error. This same pattern can be applied to parsing CVs, analyzing customer reviews, or enriching CRM contacts from website text.
“Help! It’s Only Processing Some of My Data!” – Troubleshooting Common Issues
Now, here’s where experience really counts. You’ve built your workflow, you’ve run a test, and the output is… incomplete. You know there are 100 comments in your source text, but the Information Extractor only returns 28. What gives?
I’ve seen this exact issue pop up in the n8n community forums, and it’s almost always due to one of two things.
The “Partial Data” Problem & The Batching Solution
Large language models have a context window, which is like a short-term memory limit. If you feed the Information Extractor a massive wall of text (like a long webpage or a full document), it might only process the beginning before hitting its limit. The model doesn’t always throw an error; sometimes it just silently gives up.
The solution is batching! Before the Information Extractor, insert a Loop Over Items (Split in Batches) node. This node will break your massive input into smaller, manageable chunks. The Information Extractor then runs on each chunk individually. You’ll get multiple output items instead of one, but they will be complete. You can then use a Merge node if you need to combine them back into a single list later.
Schema Mismatches and AI Confusion
Remember: the AI is smart, but it’s not a mind reader. If your schema asks for a product_id but the text only ever mentions a SKU, the AI might get confused and fail to extract it. Always look at your raw input data. Run the node before the extractor and examine its output. Does the information you’re asking for actually exist in the text? Is it named something different? A small tweak to your schema’s descriptions can often fix this instantly.
