🚀 30-Second Transformation

Your existing function (unchanged!):

def analyze_sales():
    total_revenue = 50000
    best_product = "widgets"
    return total_revenue, best_product

Make it runnable everywhere:

from runnable import PythonJob

def main():
    PythonJob(function=analyze_sales).execute()

if __name__ == "__main__":
    main()

🎉 Success! Your function now runs the same on laptop, containers, and Kubernetes with automatic tracking and reproducibility.

🔗 Chain Functions Without Glue Code

def load_customer_data():
    return {"count": 1500, "segments": ["premium", "standard"]}

def analyze_segments(customer_data):  # Name matches = automatic connection
    return {"premium_pct": 30, "growth_potential": "high"}

# What Runnable needs (same logic, no glue):
from runnable import Pipeline, PythonTask

def main():
    Pipeline(steps=[
        PythonTask(function=load_customer_data, returns=["customer_data"]),
        PythonTask(function=analyze_segments, returns=["analysis"])
    ]).execute()

if __name__ == "__main__":
    main()

Same pipeline runs unchanged on laptop, containers, and Kubernetes.

⚡ Installation

pip install runnable

For development:

uv sync --all-extras --dev

Run examples:

uv run examples/01-tasks/python_tasks.py

📊 Why Choose Runnable?

• 🎯 Easy to adopt: Your code remains as-is, no decorators or imposed structure
• 🏗️ Bring your infrastructure: Works with your platforms, not a replacement
• 📝 Reproducibility: Automatic tracking without additional code
• 🔁 Retry failures: Debug anywhere, retry from failure points
• 🧪 Testing: Mock/patch pipeline steps, test functions normally
• 💔 Move on: Easy removal - just delete runnable files, your code stays

🎯 Core Strengths & Extensibility

Runnable excels at data pipeline orchestration:

• Data processing pipelines
• ML model training workflows
• ETL operations
• Batch job orchestration
• Scientific computing reproducibility

🧪 Extensible Architecture & Experimental Features:

Runnable's plugin-based architecture demonstrates its extensibility through experimental features like:

• Async streaming capabilities - Proof of concept for real-time processing (local execution)
• Loop workflows - Dynamic iteration patterns for complex data processing
• Custom executors - Extensible to any infrastructure via plugins