📚 Full Documentation | Installation | API Reference

MageFlow

Manage Graph Execution Flow - A unified interface for task orchestration across different task managers.

Why MageFlow?

Instead of spreading workflow logic throughout your codebase, MageFlow centralizes task orchestration with a clean, unified API. Switch between task managers (Hatchet, Taskiq, etc.) without rewriting your orchestration code.

Key Features

🔗 Task Chaining - Sequential workflows where tasks depend on previous completions
🐝 Task Swarms - Parallel execution with intelligent coordination
📞 Callback System - Robust success/error handling
🎯 Task Signatures - Flexible task definition with validation
⏯️ Lifecycle Control - Pause, resume, and monitor task execution
💾 Persistent State - Redis-backed state management with recovery

Installation

pip install mageflow[hatchet]  # For Hatchet backend

Quick Setup

import asyncio
import redis
from hatchet_sdk import Hatchet, ClientConfig
import mageflow

# Configure backend and Redis
config = ClientConfig(token="your-hatchet-token")
redis_client = redis.asyncio.from_url("redis://localhost", decode_responses=True)
hatchet_client = Hatchet(config=config)

# Create MageFlow instance
mf = mageflow.Mageflow(hatchet_client, redis_client=redis_client)

Example Usage

Define Tasks

from pydantic import BaseModel

class ProcessData(BaseModel):
    data: str

@mf.task(name="process-data", input_validator=ProcessData)
async def process_data(msg: ProcessData):
    return {"processed": msg.data}

@mf.task(name="send-notification") 
async def send_notification(msg):
    print(f"Notification sent: {msg}")
    return {"status": "sent"}

Chain Tasks

# Sequential execution
workflow = await mageflow.chain([
    process_data_task,
    send_notification_task
], name="data-pipeline")

Parallel Swarms

# Parallel execution
swarm = await mageflow.swarm([
    process_user_task,
    update_cache_task,
    send_email_task
], task_name="user-onboarding")

Task Signatures with Callbacks

task_signature = await mageflow.sign(
    task_name="process-order",
    task_identifiers={"order_id": "12345"},
    success_callbacks=[send_confirmation_task],
    error_callbacks=[handle_error_task]
)

Use Cases

• Data Pipelines - ETL operations with error handling
• Microservice Coordination - Orchestrate distributed service calls
• Batch Processing - Parallel processing of large datasets
• User Workflows - Multi-step onboarding and registration
• Content Processing - Media processing with multiple stages

MageFlow Viewer

A desktop app for visualizing your workflows as interactive task graphs. See the full docs for details.

Homebrew (macOS):

brew install imaginary-cherry/mageflow/mage-voyance        # stable
brew install imaginary-cherry/mageflow/mage-voyance-beta   # beta

Direct download: GitHub Releases

Documentation

• Setup Guide
• API Reference
• Task Lifecycle
• Callbacks

License

MIT