resonate-saga-pattern-python

Coordinate long-running Python workflows with saga steps and compensations.

6|Updated Jan 8, 2026

Search Tags:#python #compensation #generator #saga #retry #durable-execution

Ready to run

npx skills add https://github.com/resonatehq/resonate-skills --skill resonate-saga-pattern-python

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Please help me install this Agent Skill.
Skill: resonate-saga-pattern-python
Source: https://github.com/resonatehq/resonate-skills/tree/main/resonate-saga-pattern-python
Command: npx skills add https://github.com/resonatehq/resonate-skills --skill resonate-saga-pattern-python

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Saga patterns enable durable, multi-step Python workflows by splitting processes into forward steps with compensating actions, ensuring consistency across failures.

Core Features & Use Cases

Saga orchestration with forward steps and compensations to restore state after partial failures.
Explicit step modeling using SagaStep objects to map forward and backward actions.
Robust error handling using try/except and the ability to retry compensations safely.
Support for sub-sagas and parallel fan-out while preserving correct compensation sequencing.

Quick Start

Register a saga with @resonate.register and yield ctx.run to execute forward steps while collecting completed steps for compensations.

Frequently Asked Questions about resonate-saga-pattern-python

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

FAQPage Schema

How do I handle partial failures in Python workflows without distributed locks?▼

A saga pattern coordinates durable Python workflows by pairing forward steps with compensating actions, restoring consistent state after partial failures without requiring distributed locks or two-phase commits.

How do I implement a saga with compensations in Python?▼

You implement a saga by registering a function with @resonate.register, yielding ctx.run to execute forward steps, and defining explicit SagaStep objects to map backward compensation actions for robust try/except error handling.

Can I retry failed compensations in a durable Python saga?▼

Yes, durable Python sagas support retryable compensations, allowing you to safely retry backward actions when restoring state after a partial failure in multi-step processes like payments or inventory adjustments.

Does this saga pattern support parallel fan-out and sub-sagas in Python?▼

Yes, the saga pattern supports sub-sagas and parallel fan-out in Python while preserving correct compensation sequencing, ensuring that backward actions execute in the proper order across nested workflows.

When should I use a saga pattern instead of two-phase commits for multi-step processes?▼

Use a saga pattern for multi-step processes like order processing requiring eventual consistency across failures, avoiding the distributed locks and blocking coordination overhead inherent to two-phase commits.