rtj.dev/content/posts/falyx.md

author	title	date	description	tags	ShowToc	ShowBreadCrumbs
Roland Thomas Jr	Falyx	2025-04-15		post falyx cli	false	true

🛡️ Introducing Falyx: A Resilient CLI Framework for Modern Workflows

In the ever-evolving landscape of software development, tools that offer resilience, clarity, and introspection are invaluable.

Enter Falyx—a robust, asynchronous command-line interface (CLI) framework designed to streamline and fortify your workflows.

⚙️ What is Falyx?

Falyx is a Python-based CLI framework that empowers developers to build modular, fault-tolerant command-line applications. Inspired by the discipline and strength of a phalanx, Falyx ensures that each component of your workflow stands firm — even in the face of failure.

🚀 Key Features

• Modular Action Chaining Compose complex workflows by chaining together discrete actions, each with its own context and lifecycle.
• Built-in Retry Mechanism – Handle flaky or transient failures with configurable retry policies, including exponential backoff.
• Lifecycle Hooks – Inject logic at every stage: before, after, on_success, on_error, and on_teardown.
• Execution Tracing – Built-in logging, result tracking, and timing for complete visibility.
• Async-First Design – Built on asyncio, with optional multiprocessing via ProcessAction.
• Extensible CLI Menus – Create interactive or headless menus using prompt_toolkit and rich, with support for history, tags, toggle states, and more.

🧪 Getting Started

Here's a simple example to illustrate how Falyx can be used to build a resilient CLI application:

import asyncio
import random
from falyx import Falyx, Action, ChainedAction

# Define a flaky asynchronous step
async def flaky_step():
    await asyncio.sleep(0.2)
    if random.random() < 0.5:
        raise RuntimeError("Random failure!")
    return "ok"

# Create actions with retry enabled
step1 = Action(name="step_1", action=flaky_step, retry=True)
step2 = Action(name="step_2", action=flaky_step, retry=True)

# Chain the actions together
chain = ChainedAction(name="my_pipeline", actions=[step1, step2])

# Set up the CLI menu
falyx = Falyx("🚀 Falyx Demo")
falyx.add_command(
    key="R",
    description="Run My Pipeline",
    action=chain,
    logging_hooks=True,
    preview_before_confirm=True,
    confirm=True,
)

# Entry point
if __name__ == "__main__":
    asyncio.run(falyx.run())

When run, this script presents an interactive CLI menu with built-in preview, confirmation, and retry handling.

❯ python simple.py
                                🚀 Falyx Demo

  [R] Run My Pipeline
  [Y] History                                         [Q] Exit

>

Or run headlessly using intuitive CLI arguments:

❯ python simple.py run R
Command: 'R' — Run My Pipeline
└── ⛓ ChainedAction 'my_pipeline'
    ├── ⚙ Action 'step_1'
    │   ↻ Retries: 3x, delay 1.0s, backoff 2.0x
    └── ⚙ Action 'step_2'
        ↻ Retries: 3x, delay 1.0s, backoff 2.0x
Confirm execution of R — Run My Pipeline (calls `my_pipeline`)  [Y/n] y
[2025-04-15 22:03:57] WARNING   ⚠️ Retry attempt 1/3 failed due to 'Random failure!'.
✅ Result: ['ok', 'ok']

📈 Real-World Applications

Falyx is particularly well-suited for:

• Automation Script: Build resilient tools that self-heal from transient errors.
• Data Pipelines: Orchestrate complex data flows with retry logic and full observability.
• Deployment Tooling: Create safe deploys with preview, rollback, and confirmation baked in.

📚 Learn More

• 📖 Source Code on GitHub

---

Falyx was designed for developers who don’t just want CLI tools to run — they want them to recover, report, and adapt.

If that sounds like you, give it a spin and start building resilient command flows today.

"Like a phalanx: organized, resilient, and reliable." ⚔️

---