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How to Use Python in n8n: Unlocking Powerful Automation with Code

Automation is no longer just a luxury for businesses and professionals—it’s a necessity. Every day, companies and individuals seek to save time, reduce errors, and streamline operations by automating repetitive tasks. This is where tools like n8n shine.

n8n is an open-source workflow automation tool that lets you connect various apps, services, and databases to create custom workflows without writing a ton of code. It supports over 200 integrations and comes with many built-in nodes designed for common automation tasks.

However, sometimes your automation needs go beyond what standard nodes can handle. You may need to apply advanced data processing, integrate machine learning, call APIs without dedicated nodes, or simply add custom business logic. This is where using Python in your n8n workflows becomes extremely valuable.

In this comprehensive guide, you will learn how to integrate Python with n8n effectively. We’ll cover why you might want to do this, the best ways to run Python code from within n8n, step-by-step examples, security considerations, and some advanced use cases to inspire your automation journey.

Why Use Python in n8n?

Before diving into technical details, it’s worth understanding why Python is a powerful choice for extending n8n workflows.

1. Python Is Powerful and Easy to Learn

Python’s clean, readable syntax and gentle learning curve have made it one of the world’s most popular programming languages. Its rich ecosystem of libraries lets you handle everything from web requests, data analysis, and image processing to AI and automation.

For example:

  • Pandas and NumPy for data manipulation and numeric computation.
  • Requests for making HTTP requests.
  • TensorFlow and PyTorch for machine learning.
  • BeautifulSoup and Scrapy for web scraping.

Because of this flexibility, Python can tackle almost any problem in automation workflows.

2. Custom Logic and Complex Data Processing

n8n’s built-in JavaScript Code node can handle many custom logic scenarios. But sometimes, Python’s simpler syntax or your existing Python codebase makes it a better option. Complex parsing, scientific calculations, or using existing Python scripts becomes easier when you can plug Python into your workflow.

3. Use Libraries Not Available in n8n

Sometimes you need to interact with APIs or perform actions that n8n doesn’t yet support. Python’s vast libraries mean you can easily integrate third-party services or process data in ways not possible with default nodes.

4. Leverage Machine Learning and Data Science

Python dominates in AI and data science. You can call Python scripts that use machine learning models to predict outcomes, classify data, or generate insights—all within your automation workflows.

How to Run Python in n8n: Your Options

Unlike JavaScript, which n8n supports natively through its Code node, Python integration isn’t built-in. But there are several practical ways to bring Python into your workflows:

1. Use Python via HTTP Request Node

This method involves running your Python code as a web service (API) that listens for requests from n8n, processes the data, and returns a response. Here’s how it works:

  • You create a Python web app using frameworks like Flask or FastAPI.
  • Your app exposes an endpoint (URL) where it accepts data in JSON or other formats.
  • n8n’s HTTP Request node sends data to the Python app and waits for the response.
  • The workflow continues with the processed data.

Advantages:

  • Works with any n8n setup (cloud, local, Docker).
  • Scales well—many workflows can call the same Python service.
  • Easy to update and maintain Python code independently.

Drawbacks:

  • Requires running and managing a separate Python service.
  • Needs secure network setup if exposed externally.

2. Use Execute Command Node to Run Python Scripts Locally

If your n8n runs on a machine where Python is installed (like your laptop or a server), you can execute Python scripts directly using the Execute Command node. This node runs shell commands and returns their output.

Advantages:

  • Simple for local or self-hosted setups.
  • No need to build a web API.

Drawbacks:

  • Less portable—won’t work if n8n is hosted in a cloud environment without shell access.
  • Requires careful handling of paths and security.

3. Use Docker and Mount Python Scripts

If you use n8n in Docker containers, you can mount your Python scripts inside the container and execute them with the Execute Command node. This blends containerization benefits with script execution.

Advantages:

  • Maintains containerized environment.
  • Scripts are version-controlled and easily deployed.

Drawbacks:

  • Requires Docker knowledge.
  • More complex setup.

4. Use Community or Custom Nodes

Some community projects may have created Python execution nodes, or you can build a custom n8n node in TypeScript that wraps Python execution logic. This is more advanced and requires development skills.

Step-by-Step Guide: Running Python via HTTP Request Node

Let’s dive into a practical example using the most versatile method: creating a Python API service and calling it from n8n.

Scenario

You want to send a list of numbers from n8n to Python, calculate their sum in Python, and return the result back into the workflow.

Step 1: Create a Python Web API Using Flask

Flask is a lightweight Python web framework ideal for small APIs.

Python
from flask import Flask, request, jsonify

app = Flask(__name__)

@app.route('/process', methods=['POST'])
def process_data():
    data = request.json
    numbers = data.get('numbers', [])
    # Sum the numbers
    result = sum(numbers)
    return jsonify({'result': result})

if __name__ == '__main__':
    app.run(port=5000)

Save this as app.py.

Explanation:

  • The /process endpoint accepts POST requests.
  • It expects JSON data containing a “numbers” list.
  • Returns the sum as JSON.

Step 2: Run Your Python API

Open a terminal and run:

Bash
python app.py

The service will listen on http://localhost:5000/
process.

Step 3: Create n8n Workflow to Call Python API

  1. Start a new workflow in n8n.
  2. Add an HTTP Request node.
  3. Configure it as:
    • Method: POST
    • URL: http://localhost:
      5000/process
    • Content-Type: application/json
    • Body Parameters: Raw JSON

Example body:

JSON
{
  "numbers": [10, 20, 30, 40]
}
  1. Set Response Format to JSON.

Step 4: Use the Output Data

The HTTP Request node will return:

JSON
{
  "result": 100
}

You can add further nodes to:

  • Send this result in an email.
  • Save it to a spreadsheet.
  • Use it as a condition for another workflow branch.

Step 5: Error Handling

Make sure your Python app returns meaningful errors (e.g., if input is missing or malformed). In n8n, you can add error triggers or conditional checks to handle failures gracefully.

Step-by-Step Guide: Running Python Script Locally Using Execute Command Node

For users running n8n locally or on private servers, executing Python scripts directly may be simpler.

Step 1: Write a Python Script

Create script.py:

Python
import sys
import json

def main():
    # Read JSON string passed as command-line argument
    data = json.loads(sys.argv[1])
    numbers = data.get('numbers', [])
    print(sum(numbers))

if __name__ == "__main__":
    main()

Step 2: Configure Execute Command Node in n8n

  • Add an Execute Command node.
  • Set the command:
Bash
python /path/to/script.py '{"numbers": [5, 15, 25]}'

Replace /path/to/ with your actual script location.

Step 3: Process Output

The output will be the sum printed by the Python script. You can capture this and pass it along your workflow.

Notes:

  • Ensure Python is installed on the machine running n8n.
  • Pass JSON strings carefully—watch out for shell escaping issues.
  • For complex data, consider writing input and output files or use environment variables.

Security and Best Practices When Using Python in n8n

When running Python in automation workflows, especially involving networked services, security is critical.

1. Protect Your Python API

If your Python service is exposed to the internet or a large network:

  • Use API keys or token-based authentication to restrict access.
  • Consider IP whitelisting.
  • Use HTTPS to encrypt traffic.
  • Avoid running your Python service as a privileged user.

2. Input Validation

Always validate inputs in your Python code to prevent injection attacks or crashes due to malformed data.

3. Monitor and Log

Keep logs on both n8n and Python sides for debugging and auditing.

4. Handle Failures Gracefully

Implement retry logic, timeouts, and error notifications in n8n when calling Python services.

5. Separate Concerns

Avoid mixing critical infrastructure scripts with your Python API. Keep the Python environment isolated and manage dependencies carefully using tools like venv or pipenv.

Advanced Use Cases for Python in n8n Workflows

Once you’re comfortable with the basics, here are some powerful ways to use Python with n8n:

1. Machine Learning Predictions

Use Python ML models to classify customer feedback, predict sales, or detect anomalies in your data. For example, send customer reviews from n8n to a Python API that returns sentiment scores.

2. Data Visualization

Generate charts or graphs in Python (using Matplotlib or Seaborn) and return image files to n8n, which can then email reports automatically.

3. Web Scraping

Trigger Python scripts that scrape data from websites and send clean data back to n8n for further processing or storage.

4. Image and Video Processing

Use Python libraries like OpenCV or Pillow to process images or video frames and automate tasks like resizing, filtering, or object detection.

5. Complex Data Transformations

Use Python to transform nested JSON, XML, or CSV files into formats compatible with your downstream apps.

6. Custom Webhook Handlers

Create Python services that act as webhook receivers, preprocess data, and forward it to n8n workflows via HTTP requests.

Conclusion

Integrating Python with n8n unlocks a whole new level of automation power. Whether you need to run simple scripts locally or develop scalable Python web services, you can extend your workflows with custom logic, advanced data processing, and AI capabilities.

Key Takeaways:

  • Python is a versatile, powerful language that complements n8n’s automation.
  • Running Python as a web service is the most flexible approach.
  • The Execute Command node is great for local script execution.
  • Security and input validation are critical when exposing Python services.
  • Advanced use cases include machine learning, scraping, and data visualization.

Start small by creating simple Python integrations, then build up to more complex solutions that save time, reduce errors, and drive value in your automation workflows.