How to Use Claude with n8n: readPDF, Anthropic Nodes, and Workflow Automation

Why These Two Nodes Together?

n8n ships native integrations for both Anthropic and PDF reading, and they compose naturally. The readpdf node handles binary-to-text conversion; the anthropic node handles the API call. You connect them with a single wire and the workflow does the rest.

This combination unlocks a large class of document-processing automations that previously required Python scripts or dedicated document-intelligence SaaS tools:

• Summarize incoming contracts or invoices the moment they land in a shared inbox
• Classify uploaded research papers by topic and route them to the right Notion database
• Extract structured data from PDF reports and write the results to a Google Sheet
• Answer questions about a PDF using Claude, then post the answer to Slack

Setting Up the Anthropic Node (n8n-nodes-base.anthropic)

The Anthropic node is bundled in n8n's standard node library — no extra install needed. It supports Text and Chat operations and maps directly to the Anthropic Messages API.

Step 1 — Add credentials

Go to Settings → Credentials → New in n8n, search for "Anthropic", and paste your API key. The credential type is simply "Anthropic API" — one field.

Step 2 — Configure the node

In the node editor, the key fields are:

Minimal workflow JSON — Anthropic node only

Here is the n8n workflow JSON for a minimal "send a prompt, get a response" setup. Import this via Workflows → Import from JSON:

{
  "nodes": [
    {
      "parameters": {
        "prompt": "Summarize the following text in three bullet points:\n\n{{ $json.body }}"
      },
      "type": "n8n-nodes-base.anthropic",
      "typeVersion": 1,
      "position": [460, 300],
      "name": "Claude – Summarize",
      "credentials": {
        "anthropicApi": {
          "name": "Anthropic API"
        }
      }
    }
  ]
}

The "type": "n8n-nodes-base.anthropic" field is how n8n identifies the node internally. You'll see this string in exported workflow JSON whenever the Anthropic node appears.

Reading PDFs Before Sending to Claude (n8n-nodes-base.readpdf)

The n8n-nodes-base.readpdf node converts a binary PDF attachment into plain text. It has no configuration options — you pass it a binary item and it outputs a text field. That text field is what you reference in the Anthropic node's prompt expression.

Where the binary comes from

The most common upstream nodes that produce PDF binary data are:

• Gmail / Outlook — attachment extraction from incoming emails
• HTTP Request — fetching a PDF from a URL, with response format set to file
• Google Drive / Dropbox — downloading a file by ID
• Webhook — receiving a multipart/form-data upload

In all cases, the binary field is typically called data. The readPDF node picks this up automatically unless you override the binary property name.

The "type": "n8n-nodes-base.readpdf" node in workflow JSON

When you export a workflow containing a readPDF node, the JSON fragment looks like this:

{
  "parameters": {
    "options": {}
  },
  "type": "n8n-nodes-base.readpdf",
  "typeVersion": 1,
  "position": [240, 300],
  "name": "Extract PDF Text"
}

There is no required parameter beyond the node type itself. The node reads whatever binary is in the input item's data property and writes the extracted text to $json.texton the output item.

Full Workflow: PDF → readPDF → Claude → Slack

Here is a complete, importable n8n workflow JSON that: watches a Gmail inbox for PDF attachments, extracts their text, sends the text to Claude for summarization, and posts the summary to Slack.

{
  "name": "PDF Summarizer via Claude",
  "nodes": [
    {
      "parameters": {
        "pollTimes": { "item": [{ "mode": "everyMinute" }] },
        "filters": { "readStatus": "unread" },
        "downloadAttachments": true
      },
      "type": "n8n-nodes-base.gmailTrigger",
      "typeVersion": 1,
      "position": [0, 300],
      "name": "Gmail – New Email with Attachment"
    },
    {
      "parameters": {
        "conditions": {
          "string": [{
            "value1": "={{ $binary.data.mimeType }}",
            "operation": "contains",
            "value2": "pdf"
          }]
        }
      },
      "type": "n8n-nodes-base.if",
      "typeVersion": 1,
      "position": [220, 300],
      "name": "Is PDF?"
    },
    {
      "parameters": { "options": {} },
      "type": "n8n-nodes-base.readpdf",
      "typeVersion": 1,
      "position": [440, 240],
      "name": "Extract PDF Text"
    },
    {
      "parameters": {
        "model": "claude-haiku-4-5-20251001",
        "prompt": "Summarize the following document in 3-5 bullet points. Be concise and focus on the key facts.\n\n{{ $json.text }}",
        "maxTokensToSample": 1024
      },
      "type": "n8n-nodes-base.anthropic",
      "typeVersion": 1,
      "position": [660, 240],
      "name": "Claude – Summarize PDF",
      "credentials": {
        "anthropicApi": { "name": "Anthropic API" }
      }
    },
    {
      "parameters": {
        "channel": "#document-summaries",
        "text": "=*New PDF summary:*\n{{ $json.completion }}"
      },
      "type": "n8n-nodes-base.slack",
      "typeVersion": 2,
      "position": [880, 240],
      "name": "Post to Slack"
    }
  ],
  "connections": {
    "Gmail – New Email with Attachment": {
      "main": [[{ "node": "Is PDF?", "type": "main", "index": 0 }]]
    },
    "Is PDF?": {
      "main": [[{ "node": "Extract PDF Text", "type": "main", "index": 0 }]]
    },
    "Extract PDF Text": {
      "main": [[{ "node": "Claude – Summarize PDF", "type": "main", "index": 0 }]]
    },
    "Claude – Summarize PDF": {
      "main": [[{ "node": "Post to Slack", "type": "main", "index": 0 }]]
    }
  }
}

Key things to observe in this workflow:

• The readpdf output is accessed via {{ $json.text }} in the Anthropic node's prompt
• Claude's response is in {{ $json.completion }} for the Text operation (or {{ $json.content[0].text }} for the Chat operation)
• Claude Haiku is used here for cost efficiency — swap to Sonnet for complex extraction

Structured Extraction: Getting JSON Out of Claude

For workflows that need to write data to a spreadsheet or database, you want Claude to return structured JSON rather than prose. Here's the prompt pattern that works reliably with the n8n-nodes-base.anthropic node:

Extract the following fields from the invoice text and return ONLY valid JSON.
Do not include markdown code fences. Do not include any explanation.

Fields to extract:
- invoice_number (string)
- vendor_name (string)
- total_amount (number)
- due_date (ISO 8601 date string)
- line_items (array of { description, quantity, unit_price, total })

Invoice text:
{{ $json.text }}

After the Anthropic node, add a n8n-nodes-base.code node to parse the output:

// Code node — parse Claude's JSON response
const raw = $input.first().json.completion;
const parsed = JSON.parse(raw.trim());
return [{ json: parsed }];

The parsed object then flows downstream as normal n8n item data — ready to map into a Google Sheets row, an Airtable record, or a database insert.

Handling Long PDFs (Token Limits)

Claude Haiku supports up to 200k input tokens; Claude Sonnet the same. Most PDFs fall well within that limit. However, n8n-nodes-base.readpdf extracts all text at once — for very long documents (500+ pages), you may want to truncate or chunk the text before passing it to Claude.

Add a Code node between readPDF and the Anthropic node to cap the input:

// Truncate to first ~100k characters (~75k tokens) to stay well within limits
const MAX_CHARS = 100_000;
const text = $input.first().json.text ?? '';
return [{ json: { text: text.slice(0, MAX_CHARS) } }];

For genuine multi-part documents, consider a map/loop pattern: split the text into overlapping chunks, run Claude on each, then combine the results with a final merge-and-consolidate call.

Common Errors and Fixes

const raw = $input.first().json.completion
  .replace(/^```json\n?/i, '')
  .replace(/```$/, '')
  .trim();
const parsed = JSON.parse(raw);

More n8n + Claude Workflow Patterns

The readPDF → Anthropic chain is one node pattern. The broader playbook for n8n + Claude automation covers webhook triggers, scheduled document processing, multi-step reasoning chains, and memory across workflow runs.

If you want to go further with Claude in agentic pipelines — tool use, multi-step reasoning, or building your own Claude-powered integrations beyond n8n — the MCP servers guide and the AI agent walkthrough cover those patterns in detail.