Description
n8n-nodes-pdf-redaction
This is an n8n community node. It lets you detect and redact PII (Personally Identifiable Information) in PDF documents using the PDF Redaction API in your n8n workflows.
PDF Redaction is an AI-powered service that automatically finds and redacts sensitive information in PDFs — both digital and scanned (image-based) documents. See the pdf-redaction-api documentation for the underlying REST API, and example notebooks.
Installation
Operations
Credentials
Compatibility
Usage
Resources
Installation
Follow the installation guide in the n8n community nodes documentation, or the step-by-step PDF Redaction install & setup tutorial for a walkthrough with screenshots.
Operations
- Anonymize — redact PII in a PDF, choosing which PII types (tags) to detect and redact (dates, names, emails, addresses, credit cards, etc.)
- Anonymize with Custom Prompt — redact information described by a free-text prompt instead of predefined tags (e.g. “Redact all dates, names, and email addresses”)
- Detect PII — scan a PDF for PII and return the detected entities without redacting the document
- Install & configure the node
- Anonymize a PDF, end to end
- Detect PII without redacting
- n8n community nodes documentation
- PDF Redaction website
- PDF Redaction API docs (Swagger)
- PDF Redaction API key management
- pdf-redaction-api — Python client, example notebooks, and self-hosting instructions for the underlying API
All operations read the input PDF from a binary property on the input item and, for the anonymize operations, write the redacted PDF back to a binary property on the output item. Every operation also returns detectedpii (entities with bounding boxes) and processingtime metrics in the output JSON.
What it can detect and redact
Dates, person names, organizations, locations, emails, phone numbers, IDs, account numbers, zip codes, addresses, IP addresses, URLs, SSNs, driver licenses, passports, passwords, ages, credit card numbers, money amounts, signatures, QR codes, and faces — plus your own custom tags. Detection works on both digital PDFs and scanned/image-based PDFs (via OCR, with support for multiple languages), including rotated text.
Limits
Only the first 10 pages of a document are processed per request, and free-tier accounts are additionally capped (at the time of writing: 10 pages/request, 100 requests/month, 5 requests/minute) — check your plan at pdf-redaction.com/apikeys for current limits.
Credentials
This node uses an API key credential (PDF Redaction API):
1. Go to pdf-redaction.com/apikeys and generate an API key.
2. In n8n, create a new PDF Redaction API credential and paste the key into the API Key field.
The credential form inside n8n — paste the key you generated above into the API Key field and save.
Once your key is active, the API Usage tab on the same page tracks consumption against your plan — a running total plus a day-by-day breakdown — so you can see how close you are to your monthly cap before a workflow starts failing.
Compatibility
Compatible with n8n@1.60.0 or later
Usage
The example below wires up a small workflow that pulls a PDF from a URL and blacks out any faces it contains, then inspects what came back.
1. Fetch a file. An HTTP Request node (GET, Response Format set to File) grabs the source PDF and hands it downstream as binary data on its data field.
2. Configure the node. Drop a PDF Redaction node after it, pick your credential, leave Operation on Anonymize, and add Face under Additional Fields → Tags. Since both nodes default to a data binary field, no field mapping is needed.
3. Run it. Executing the node returns a processed file plus a JSON payload describing every match — here, the two faces it located, each with a bounding box, alongside a per-stage timing breakdown.
4. Check the result. Opening the output file confirms both faces are blacked out on the page.
5. The finished workflow. Three nodes, each with a green checkmark after a successful run.
Any node that produces binary data works as the source — a webhook payload, Read/Write File from Disk, an email attachment, or a cloud-storage node — as long as its output field name matches the Input Binary Field you set on PDF Redaction.
For the full step-by-step tutorials this walkthrough is based on, see:
