n8n-nodes-python-raw
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π¨ WARNING: 100% AI-GENERATED EXPERIMENTAL CODE - HIGH RISK! Use at own risk, not for production. Python execution for n8n with file processing, multiple credentials, debug tools. Personal use only - Commons Clause license.
π¨ CRITICAL DANGER: 100% AI-GENERATED EXPERIMENTAL CODE π¨: ALL modifications and features in this fork were created using AI assistance. This code may contain CRITICAL ERRORS, security vulnerabilities, or unexpected behavior. ABSOLUTELY NOT FOR PRODUCTION USE. HIGH RISK - USE AT YOUR OWN EXTREME CAUTION.
β οΈ DISCLAIMER: This is an unofficial fork of n8n-nodes-python, not affiliated with or endorsed by n8n GmbH. The name "n8n" is used solely to indicate compatibility with the n8n platform. This project is not for commercial use - see Commons Clause license restrictions.
π€ AI-Generated Code Notice: Large portions of this codebase were developed with AI assistance. Code may contain errors or unexpected behavior. Use at your own risk.
π Personal Project: This fork was created for personal, non-commercial use and educational purposes. The maintainer assumes no responsibility for any use by third parties.
π READ FULL AI DISCLAIMER: For comprehensive details about AI-generated code risks, see AI_DISCLAIMER.md
A community fork of naskio/n8n-nodes-python for executing Python scripts with raw output control, advanced file processing capabilities, and comprehensive testing infrastructure.
Original Project: https://github.com/naskio/n8n-nodes-python by naskio - Thank you for the foundation!
This fork includes significant enhancements for raw Python script execution, structured output parsing, comprehensive file processing, and robust error handling.
β οΈ Not for Commercial Use: This software is licensed under Apache 2.0 with Commons Clause - commercial use is prohibited.
In n8n, go to Settings β Community Nodes and install:
n8n-nodes-python-raw
Input data from previous node:
[{"title": "My Video", "duration": 120, "author": "John"}]
Your Python code:
# Variables automatically extracted from input:
print(f"Processing: {title}") # "My Video"
print(f"Duration: {duration} sec") # 120 sec
print(f"Author: {author}") # "John"
# Generate a report file:
import os
report_content = f"Video: {title}\nDuration: {duration}s\nAuthor: {author}"
with open(os.path.join(output_dir, expected_filename), 'w') as f:
f.write(report_content)
print("Report generated successfully!")
Result: Direct access to your data AND automatic file processing!
The node has two outputs for better workflow control:
exitCode = 0 (successful execution)exitCode β 0 (errors or failures)Benefits: No need for conditional logic based on exitCode in your workflow!
Python Function β Output 1 (Success) β Continue Processing
β Output 2 (Error) β Error Handling/Notification
This eliminates the need to check exitCode in subsequent nodes - the routing is automatic!
Generate files in your Python scripts and automatically include them in n8n workflow output. Perfect for reports, images, data exports, and more.
output_file_path variable with complete file path (recommended)import os
import json
# Method 1: Use the ready-made path (recommended)
with open(output_file_path, 'w') as f:
json.dump({"results": "processed_data"}, f)
# Method 2: Build path manually using provided variables
file_path = os.path.join(output_dir, expected_filename)
with open(file_path, 'w') as f:
f.write("Report content")
# Create file with the exact filename specified
# n8n will automatically find it after script execution
with open(expected_filename, 'w') as f:
f.write("Generated content")
# Or in a subdirectory - n8n will find it recursively
os.makedirs("reports", exist_ok=True)
with open(os.path.join("reports", expected_filename), 'w') as f:
f.write("Report in subfolder")
import os
from reportlab.pdfgen import canvas
# Create PDF report
pdf_path = output_file_path # or os.path.join(output_dir, expected_filename)
c = canvas.Canvas(pdf_path)
c.drawString(100, 750, f"Report for {title}")
c.drawString(100, 730, f"Duration: {duration} seconds")
c.save()
print("PDF report generated!")
import csv
import os
# Process data and export to CSV
csv_path = output_file_path
with open(csv_path, 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['Title', 'Duration', 'Author'])
for item in input_items:
writer.writerow([item.get('title'), item.get('duration'), item.get('author')])
print(f"Exported {len(input_items)} items to CSV")
import subprocess
import os
from shutil import which
# Ensure ffmpeg is available
if which("ffmpeg") is None:
print("Error: ffmpeg not found")
exit(1)
# Generate test video
cmd = [
"ffmpeg", "-y",
"-f", "lavfi", "-i", "testsrc=size=1280x720:rate=30:duration=5",
"-f", "lavfi", "-i", "sine=frequency=1000:sample_rate=44100:duration=5",
"-c:v", "libx264", "-pix_fmt", "yuv420p",
"-c:a", "aac", "-b:a", "128k",
output_file_path
]
subprocess.run(cmd, check=True)
print("Test video generated successfully!")
Comprehensive debugging system for troubleshooting file processing issues with detailed diagnostics.
{
"fileDebugInfo": {
"input_files": {
"count": 2,
"total_size_mb": 5.47,
"files_by_type": {"image/jpeg": 1, "application/pdf": 1},
"files_details": [...],
"processing_errors": []
},
"output_files": {
"processing_enabled": true,
"output_directory": "/tmp/n8n_output_xyz",
"directory_exists": true,
"directory_writable": true,
"found_files": [...],
"scan_errors": []
},
"system_info": {
"python_executable": "/usr/bin/python3",
"working_directory": "/app",
"user_permissions": {"can_write_temp": true},
"environment_variables": {"output_dir_available": true}
},
"directory_listings": {
"working_directory": [...],
"output_directory": [...]
}
}
}
output_dir variable not available β Solution: Check system_info.environment_variablesChoose export format for generated scripts in "Export Script" debug mode to comply with security policies.
output_file_path variable (recommended)When Debug Mode is set to "Export Script", the node generates two downloadable files:
.py or .txt (configurable via Script Export Format)python_script_TIMESTAMP.py (or python_script_error_TIMESTAMP.py for errors)output_TIMESTAMP.json{
"timestamp": "2025-06-05T12:00:00.000Z",
"execution_results": {
"exitCode": 0,
"success": true,
"stdout": "script output",
"stderr": "",
"parsed_stdout": {...},
"parsing_success": true,
"executedAt": "2025-06-05T12:00:00.000Z",
"inputItemsCount": 1,
"executionMode": "once"
},
"export_info": {
"description": "Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ Python ΡΠΊΡΠΈΠΏΡΠ° ΠΈΠ· n8n",
"format_version": "1.0",
"exported_at": "2025-06-05T12:00:00.000Z",
"node_type": "n8n-nodes-python.pythonFunction"
}
}
The node returns a comprehensive result object:
{
"exitCode": 0, // 0 = success (routes to Output 1), non-zero = error (routes to Output 2), -2 = timeout
"stdout": "raw output string",
"stderr": "error messages",
"success": true,
"error": null,
"inputItemsCount": 1,
"executedAt": "2025-06-02T12:00:00.000Z",
"injectVariables": true,
"parseOutput": "none",
"executionMode": "once",
// Output File Processing results (when enabled)
"outputFiles": [
{
"filename": "report.pdf",
"size": 1024000,
"mimetype": "application/pdf",
"extension": "pdf",
"binaryKey": "report.pdf",
"createdAt": "2025-06-02T12:00:00.000Z"
}
],
"outputFilesCount": 1,
// Parsing results (when enabled)
"parsed_stdout": {"key": "value"},
"parsing_success": true,
"parsing_error": null,
"output_format": "json",
"parsing_method": "json",
// File Debug Information (when enabled)
"fileDebugInfo": {
"input_files": {...},
"output_files": {...},
"system_info": {...},
"directory_listings": {...}
},
// Error details (on failure)
"pythonError": {
"errorType": "ImportError",
"errorMessage": "No module named 'requests'",
"missingModules": ["requests"],
"traceback": "full traceback",
"lineNumber": 5
},
"detailedError": "comprehensive error description"
}
import os
import json
# Create a data analysis report
report_data = {
"total_items": len(input_items),
"analysis": "completed",
"timestamp": "2025-06-02T12:00:00Z"
}
# Save as JSON file using the ready-made path
with open(output_file_path, 'w') as f:
json.dump(report_data, f, indent=2)
print(f"Report saved to {expected_filename}")
Configuration:
Result: JSON file automatically included as binary data in n8n output
import os
import json
import csv
# Create multiple output files
os.makedirs(output_dir, exist_ok=True)
# 1. Generate summary JSON
summary = {"processed": len(input_items), "status": "complete"}
with open(os.path.join(output_dir, "summary.json"), 'w') as f:
json.dump(summary, f)
# 2. Generate detailed CSV
with open(os.path.join(output_dir, expected_filename), 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(['Title', 'Duration'])
for item in input_items:
writer.writerow([item.get('title'), item.get('duration')])
print("Multiple files generated")
Configuration:
Result: Both summary.json and details.csv files are automatically detected and included
import os
from PIL import Image, ImageDraw
# Create a simple chart image
img = Image.new('RGB', (800, 600), color='white')
draw = ImageDraw.Draw(img)
# Draw title
draw.text((50, 50), f"Analysis Results", fill='black')
draw.text((50, 100), f"Total Items: {len(input_items)}", fill='blue')
# Save image
img.save(output_file_path)
print(f"Chart saved as {expected_filename}")
Configuration:
Result: PNG image automatically included as binary data
# Using multiple API credentials to fetch data and generate report
import requests
import json
import os
# Access different service credentials
api1_data = requests.get(f"https://api1.com/data",
headers={"Authorization": f"Bearer {API1_TOKEN}"}).json()
api2_data = requests.get(f"https://api2.com/stats",
headers={"X-API-Key": API2_KEY}).json()
# Combine data and generate report
combined_report = {
"api1_results": api1_data,
"api2_results": api2_data,
"generated_at": "2025-06-02T12:00:00Z",
"total_items": len(input_items)
}
# Save comprehensive report
with open(output_file_path, 'w') as f:
json.dump(combined_report, f, indent=2)
print("Multi-API report generated")
Configuration:
Result: Report with data from multiple APIs saved as downloadable file
When "Inject Variables" is enabled (default), the node automatically injects powerful variables into your Python script:
title, duration, author)output_dir: Unique temporary directory for file generation (when Output File Processing enabled)expected_filename: Filename specified in configuration (when Output File Processing enabled)output_file_path: Complete file path for output file (when using Ready Variable Path mode)env_vars: Dictionary of environment variables (from credentials or system, optional)# Input from previous n8n node:
[
{"title": "Video 1", "duration": 120, "author": "John"},
{"title": "Video 2", "duration": 90, "author": "Jane"}
]
# Automatically available variables (from first item):
title = "Video 1" # Direct access
duration = 120 # No indexing needed
author = "John" # Clean variable names
# File processing variables (when enabled):
output_dir = "/tmp/n8n_python_output_12345"
expected_filename = "report.pdf"
output_file_path = "/tmp/n8n_python_output_12345/report.pdf"
# Legacy compatibility (optional):
env_vars = {...} # Environment variables dictionary
Benefits:
title instead of input_items[0]['title']video-name β video_name)import json
import os
print(f"Processing {len(input_items)} items total")
print(f"First item: {title}") # From first item
# Generate summary report
summary = {
"total_items": len(input_items),
"first_title": title,
"all_titles": [item.get('title') for item in input_items]
}
# Save to file
with open(output_file_path, 'w') as f:
json.dump(summary, f, indent=2)
import json
import os
# Variables extracted from current item
print(f"Processing: {title}") # Current item's title
print(f"Duration: {duration}") # Current item's duration
# Generate individual report file
report = {
"item_title": title,
"item_duration": duration,
"processed_at": "2025-06-02T12:00:00Z"
}
# Each item gets its own output file
with open(output_file_path, 'w') as f:
json.dump(report, f, indent=2)
output_dir variable not availableSolution:
Solution:
Solution:
Solution:
Problem: NameError: name 'os' is not defined
Solution: Add required imports to your script:
import os
import subprocess
import json
from shutil import which
# ... your code
expected_filename variable and enhanced output file processing instructionsComprehensive documentation is available in the docs/ directory:
Apache License 2.0 with Commons Clause - see LICENSE.md
β οΈ IMPORTANT DISCLAIMERS:
THE MAINTAINER ACCEPTS NO RESPONSIBILITY WHATSOEVER FOR:
Large portions of this codebase were developed with AI assistance. CRITICAL RISKS:
MANDATORY REQUIREMENTS BEFORE USE:
This software is licensed under Apache License 2.0 with Commons Clause, which prohibits commercial use. You may not sell, distribute for a fee, or use this software in any commercial product or service.
This software is provided "AS IS" without warranty of any kind. The maintainer assumes NO RESPONSIBILITY for:
Use at your own risk and thoroughly test before any production use.
This fork was created for personal, non-commercial use and educational purposes only. It is not intended for:
This is a community-maintained fork. Contributions welcome!
FAQs
π¨ WARNING: 100% AI-GENERATED EXPERIMENTAL CODE - HIGH RISK! Use at own risk, not for production. Python execution for n8n with file processing, multiple credentials, debug tools. Personal use only - Commons Clause license.
The npm package n8n-nodes-python-raw receives a total of 45 weekly downloads. As such, n8n-nodes-python-raw popularity was classified as not popular.
We found that n8n-nodes-python-raw demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago.Β It has 1 open source maintainer collaborating on the project.
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