pravaha 1.0.0

A lightweight Python task orchestration library for executing dependency-based workflows using a synchronous DAG executor

Pravaha (a sanskrit word which means flow) is a Python library for defining and executing dependency-aware workflows, allowing developers to automate complex workflows declaratively using Python decorators.

Features

• Define tasks using the @Task decorator

• Automatic execution order based on task dependencies

• Built-in logging and execution context for task outputs

• Task states tracking: PENDING, SUCCESS, FAILED, SKIPPED

• Fail-fast error handling for dependent tasks

• Lightweight and extensible for workflow automation

• Conditional Task Execution on the basis of environment variables, previous task output and success failure of previous task.

• Retry Policy

• Task Output Sharing

• Priority Based Task Execution

• Tagging of tasks

• Task Group Execution

• HTML Report Generation

How It Works

You define functions as tasks using the @Task decorator. The engine automatically:

1. Resolves task dependencies and enforces execution order

2. Passes outputs from one task to dependent tasks

3. Tracks execution states (SUCCESS, FAILED, SKIPPED)

4. Handles errors and optionally retries failed tasks (planned feature)

Example

from pravaha.core.task import Task
from pravaha.core.executor import TaskExecutor

@Task(name="fetch_data")
def fetch_data():
    """Fetch initial data."""
    return 42

@Task(name="process_data", depends_on=["fetch_data"])
def process_data(data):
    """Process data returned by fetch_data."""
    return data * 2

@Task(name="print_result", depends_on=["process_data"])
def print_result(result):
    """Print the final result."""
    print(f"Final Result: {result}")

# Execute all tasks in the correct order
TaskExecutor.execute()

Expected Output

Final Result: 84

Local Setup

Since the library is not yet published, you can set it up locally:

1. Clone the repository:

   git clone <repository-url>
   cd task-orchestrator

2. Install development dependencies:

   pip install -e .[dev]

Running Tests

The library includes unit tests for all core components:

• Task decorator

• Registry

• Executor logic

Run the tests:

pytest

Notes:

• Tests reset global state to avoid cross-test contamination

• DAG validation and dependency execution are fully covered

• CI/CD workflows can run tests automatically on every commit

Contributing

Contributions are welcome! Please follow these guidelines:

• Follow existing code style and naming conventions

• Add unit tests for new features or bug fixes

• Keep commits focused and descriptive (use conventional commits)

• Ensure tests pass before submitting a pull request