How to Compress PDFs via REST API — curl, Node.js & Python Examples

the PDF to compress

Response: compressed PDF as a binary download.

curl

curl -X POST https://www.forgelab.africa/api/pdf/compress \
  -H "X-API-Key: $FORGELAB_API_KEY" \
  -F "file=@report.pdf" \
  --output compressed.pdf

A 10 MB PDF typically comes back under 2 MB.

Node.js

import fs from 'fs';
import fetch from 'node-fetch';
import FormData from 'form-data';

async function compressPdf(inputPath, outputPath) {
  const form = new FormData();
  form.append('file', fs.createReadStream(inputPath));

  const res = await fetch('https://www.forgelab.africa/api/pdf/compress', {
    method: 'POST',
    headers: {
      'X-API-Key': process.env.FORGELAB_API_KEY,
      ...form.getHeaders(),
    },
    body: form,
  });

  if (!res.ok) throw new Error(`Compress failed: ${res.status}`);

  const buffer = await res.buffer();
  fs.writeFileSync(outputPath, buffer);
  console.log(`Saved: ${outputPath}`);
}

compressPdf('invoice.pdf', 'invoice-compressed.pdf');

Python

import os
import requests

def compress_pdf(input_path: str, output_path: str) -> None:
    with open(input_path, "rb") as f:
        res = requests.post(
            "https://www.forgelab.africa/api/pdf/compress",
            headers={"X-API-Key": os.environ["FORGELAB_API_KEY"]},
            files={"file": f},
        )
    res.raise_for_status()
    with open(output_path, "wb") as out:
        out.write(res.content)
    print(f"Compressed PDF saved to {output_path}")

compress_pdf("report.pdf", "report-compressed.pdf")

Pitfall Guide

1. Hardcoding API Keys: Embedding X-API-Key directly in source code or frontend bundles exposes credentials. Always inject via environment variables or secret managers (AWS Secrets Manager, HashiCorp Vault, GitHub Secrets).
2. Ignoring Gateway Payload Limits: Reverse proxies (Nginx, AWS ALB, Cloudflare) often default to 1-10 MB request limits. Configure client_max_body_size or equivalent to match your largest expected PDF.
3. Blocking I/O on Large Files: Loading entire files into memory before upload (fs.readFileSync, open().read()) causes OOM errors on files >50 MB. Use streaming APIs (createReadStream, chunked uploads) to maintain constant memory usage.
4. Missing Error & Status Validation: Assuming success without checking HTTP status codes leads to corrupted output files. Always validate res.ok (Node) or call raise_for_status() (Python) before writing to disk.
5. No Retry/Backoff Logic: Transient network drops or API rate limits (429) will break naive implementations. Implement exponential backoff with jitter for retries, and respect Retry-After headers when present.
6. Skipping File Type Validation: Uploading non-PDF files triggers silent failures or API errors. Validate MIME type (application/pdf) and file magic bytes client-side before initiating the request.
7. Neglecting Output Integrity Checks: The API may return a valid HTTP 200 but deliver a truncated or corrupted stream. Verify the output file's PDF header (%PDF-) and run a lightweight validation (e.g., qpdf --check or PyPDF2) before downstream processing.

Deliverables

• 📦 Integration Blueprint: Architecture diagram showing client streaming → API gateway → Forgelab compression engine → binary return flow, including timeout, retry, and fallback configurations.
• ✅ Pre-Flight Checklist: Environment variable setup, payload size validation, MIME verification, error handling coverage, and serverless compatibility verification steps.
• ⚙️ Configuration Templates: Ready-to-use .env examples, Nginx/AWS API Gateway payload limit overrides, and exponential backoff retry wrappers for Node.js and Python.
• 🔑 Pricing & Scaling Reference: Tiered usage mapping (Free: 5 calls/mo → Business: 10,000 calls/mo) with cost-per-compression calculations to optimize batch processing vs. real-time triggers.