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durable-task-python

Connect Python applications to the Durable Task Scheduler for stateful workflows.

60|28|Updated Mar 13, 2025
Search Tags:#python#patterns#orchestration#Azure#workflows#durabletask
One-click install
npx skills add https://github.com/Azure-Samples/Durable-Task-Scheduler --skill durable-task-python
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: durable-task-python
Source: https://github.com/Azure-Samples/Durable-Task-Scheduler/tree/main/.github/skills/durable-task-python
Command: npx skills add https://github.com/Azure-Samples/Durable-Task-Scheduler --skill durable-task-python

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill enables Python developers to implement durable, stateful workflows by connecting Python applications to the Durable Task Scheduler, reducing complexity and providing reliable orchestration across services.

Core Features & Use Cases

  • Durable orchestrations: create, manage, and replay stateful workflows with deterministic steps.
  • Activities and entities: define activities and stateful entities to model real-world processes.
  • Patterns support: implement function chaining, fan-out/fan-in, human interaction, and sub-orchestrations in Python.
  • Cloud and local development: leverage the local emulator for testing or deploy to Azure with DefaultAzureCredential.

Quick Start

Install the required Python packages (durabletask, durabletask-azuremanaged, azure-identity), then configure a worker and client to run and test orchestrations with the DTS emulator or a live scheduler.

Frequently Asked Questions about durable-task-python

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build stateful Python workflows with Azure orchestration?▼

To build stateful Python workflows, you connect Python applications to the Durable Task Scheduler using the durabletask and durabletask-azuremanaged packages. This setup provides reliable orchestration across services and supports deterministic replay for resilient execution.

What orchestration patterns can I implement in Python using the Durable Task Scheduler?▼

The Durable Task Scheduler supports function chaining, fan-out/fan-in, human interaction, and sub-orchestrations in Python. These patterns allow developers to model complex real-world processes by defining activities and stateful entities.

Can I test durable Python workflows locally before deploying to Azure?▼

Yes, you can test durable Python workflows locally using the DTS emulator. For production or cloud environments, you configure the worker and client to connect to a live Azure deployment using DefaultAzureCredential.

Do I need azure-identity to run Python orchestrations with the Durable Task Scheduler?▼

Yes, the azure-identity package is required for authenticating Python applications against a live Azure deployment. Along with the durabletask and durabletask-azuremanaged packages, it enables secure workflow execution in the cloud.

Why use durabletask for Python workflow orchestration instead of a generic task queue?▼

Durabletask solves the complexity of building durable, stateful workflows by supporting orchestration replay and stateful entities. Unlike generic task queues, it reliably manages long-running processes, human interaction, and sub-orchestrations across Python services.

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