cURL to Python: stop writing boilerplate | cloro Blog

We have all been there.

You are reading the documentation for a new API. The examples are all in curl.

You copy the command, paste it into your terminal, and it works. Great. Now you need to get that data into your Python application.

The struggle begins.

You start typing import requests. You manually copy the URL. You painstakingly reformat the headers from -H "Authorization: Bearer..." into a Python dictionary. You mess up a comma. You forget to handle the JSON encoding.

It is tedious. It is error-prone. And in 2025, it is completely unnecessary.

Converting curl to Python is a fundamental skill for backend developers, data engineers, and anyone building AI web scrapers.

This guide will show you how to stop guessing and start automating your API workflows.

Why cURL is the universal language
The anatomy: curl vs python
Method 1: The manual translation (understand the basics)
Method 2: The automated way (tools & shortcuts)
Handling complex scenarios
Going async: curl to aiohttp
Monitoring your API footprint

Why cURL is the universal language

curl (Client URL) has been around since 1996. It is installed on almost every server in the world.

API documentation uses it because it is:

Agnostic: It doesn’t care what language your backend is written in.
Copy-pasteable: You can run it instantly in a terminal.
Explicit: It shows exactly what is being sent over the wire.

But curl is for testing. Python is for building.

To move from a “Hello World” API call to a robust application, you need to map the raw HTTP concepts of curl into the structured objects of Python.

The anatomy: cURL vs Python

Before we use tools, we need to understand the mapping. The industry standard library for this in Python is requests.

If you are still using urllib3 directly, stop. requests is built on top of it and provides a much more human-friendly API.

The Translation Matrix:

Feature	cURL Flag	Python (Requests)
Method	`-X POST`	`requests.post()`
URL	`"https://api.com"`	`'https://api.com'`
Headers	`-H "Content-Type: application/json"`	`headers={'Content-Type': 'application/json'}`
JSON Data	`-d '{"key": "value"}'`	`json={'key': 'value'}`
Form Data	`-F "[email protected]"`	`files={'file': open('photo.png', 'rb')}`
Auth	`-u "user:pass"`	`auth=('user', 'pass')`
Insecure	`-k`	`verify=False`
Follow Redirects	`-L`	`allow_redirects=True`

Method 1: The manual translation (understand the basics)

Let’s look at a real-world example.

Here is a curl command to get a completion from the OpenAI API (a common task for those building AI search engines).

The cURL Command:

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-4",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }'

Step-by-Step Translation:

1. The Method and URL The command doesn’t explicitly say -X POST, but the presence of data (-d) implies it.

import requests

url = "https://api.openai.com/v1/chat/completions"
# We will use requests.post(url, ...)

2. The Headers Headers need to be a dictionary. Note that requests handles Content-Length automatically, so you rarely need to set it manually.

headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_API_KEY_HERE"
    # Best practice: os.getenv("OPENAI_API_KEY")
}

3. The Body (Payload) This is where most people trip up.

If the header is application/json, use the json= parameter.
If the header is application/x-www-form-urlencoded, use the data= parameter.

Since we are sending JSON:

payload = {
    "model": "gpt-4",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ]
}

4. The Final Assembly

import requests
import os

response = requests.post(
    "https://api.openai.com/v1/chat/completions",
    headers={
        "Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}"
    },
    json={
        "model": "gpt-4",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello!"}
        ]
    }
)

print(response.json())

Notice we removed Content-Type from the headers? When you use the json= argument, requests adds that header for you automatically. Cleaner code, fewer bugs.

Method 2: The automated way (tools & shortcuts)

Manual translation is fine for learning. It is terrible for productivity.

If you have a complex curl command with 20 headers and nested cookies, do not type it out. Use a transpiler.

1. CurlConverter.com

This is the gold standard. It is an open-source tool (also available as a CLI) that parses curl commands and outputs idiomatic code for Python, JavaScript, Go, Rust, and more.

How to use it:

Copy your cURL command.
Paste it into curlconverter.com.
Copy the Python code.

It even handles complex edge cases like multipart file uploads and basic auth encoding.

2. Postman Code Generation

If you use Postman to test your APIs:

Set up your request in the UI.
Click the </> (Code) icon on the right sidebar.
Select “Python - Requests”.

3. Insomnia

Similar to Postman, Insomnia allows you to right-click a request -> “Copy as Curl” or “Generate Code” -> Python.

Warning: Automated tools often hardcode secrets. Always replace API keys with environment variables (os.getenv) before committing the code to Git.

Handling complex scenarios

Real-world usage is rarely just a simple GET request. Here is how to handle the messy stuff.

1. Insecure SSL (`-k` or `--insecure`)

Sometimes you are testing against a local server or an internal tool with a self-signed certificate.

cURL:

curl -k https://local-dev.internal/api

Python:

requests.get("https://local-dev.internal/api", verify=False)

Note: This will print a warning to your console. You can suppress it with urllib3.disable_warnings().

2. Custom Timeouts

curl will hang forever by default (or until the TCP stack gives up). Python requests does the same. This is dangerous for production apps.

cURL:

curl --max-time 10 https://api.com

Python:

# Raises ReadTimeout if the server doesn't respond in 10s
requests.get("https://api.com", timeout=10)

3. Session Objects (Cookies & Keep-Alive)

If you are making multiple requests to the same host (e.g., scraping a website), you should use a Session.

A Session keeps the underlying TCP connection open (Keep-Alive), which significantly speeds up subsequent requests. It also persists cookies automatically—just like a browser.

The “Pro” pattern:

s = requests.Session()

# First request sets the cookies
s.post("https://site.com/login", data={"user": "me", "pass": "secret"})

# Second request sends those cookies back automatically
response = s.get("https://site.com/dashboard")

Going async: cURL to aiohttp

requests is synchronous. If you request 10 pages, it does them one by one.

If you need to scrape thousands of pages or perform query fanout, you need asynchronous Python.

The standard library for this is aiohttp.

cURL:

curl https://api.com/users/1

Python (Async):

import aiohttp
import asyncio

async def fetch_user(session, user_id):
    url = f"https://api.com/users/{user_id}"
    async with session.get(url) as response:
        return await response.json()

async def main():
    async with aiohttp.ClientSession() as session:
        # Fetch 10 users in parallel
        tasks = [fetch_user(session, i) for i in range(10)]
        results = await asyncio.gather(*tasks)
        print(results)

asyncio.run(main())

This is significantly more verbose than requests, but it allows you to handle thousands of concurrent connections—essential for high-performance scraping and AI data gathering.

Monitoring your API footprint

Converting curl to Python is step one. Step two is managing what those scripts actually do.

When you automate requests, you lose the visibility you had in your terminal.

Are your scripts hitting rate limits?
Are they returning 404s?
Is the API response changing format unexpectedly?

This is why observability matters.

If you are scraping public data or monitoring your brand’s presence on search engines, you need to know what the “other side” is seeing.

cloro allows you to close this loop.

Just as you use Python to automate your requests, cloro automates the monitoring of how AI engines (like ChatGPT and Perplexity) are responding to your brand.

It essentially “curls” the AI models on your behalf, parsing their complex, streaming responses into structured data you can actually use.

Stop writing boilerplate. Start building logic. Convert your scripts, clean up your headers, and let the automation do the heavy lifting.

Table of contents

Why cURL is the universal language

The anatomy: cURL vs Python

Method 1: The manual translation (understand the basics)

Method 2: The automated way (tools & shortcuts)

1. CurlConverter.com

2. Postman Code Generation

3. Insomnia

Handling complex scenarios

1. Insecure SSL (-k or --insecure)

2. Custom Timeouts

3. Session Objects (Cookies & Keep-Alive)

Going async: cURL to aiohttp

Monitoring your API footprint

1. Insecure SSL (`-k` or `--insecure`)