What is time-skipping in Temporal testing?

Time-skipping is a feature of the Temporal testing environment that allows tests to skip ahead in time. This enables you to test workflows that normally take days or months in just a few seconds.

How do I mock activities in Temporal Python tests?

You can use the Temporal worker integration to register mocked activity implementations or use Python's unittest.mock within the activity environment to isolate your workflow logic.

Why is replay testing necessary for Temporal?

Temporal workflows must be deterministic. Replay testing validates that your code changes haven't introduced non-deterministic behavior that would cause failures when running against existing workflow histories.

What is the recommended test coverage for workflows?

According to Temporal best practices, you should aim for at least 80% logic coverage for both workflows and activities to ensure production stability.

Temporal Python Testing

Name: Temporal Python Testing
Author: HermeticOrmus

byHermeticOrmus

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Security & Testing

Implements comprehensive testing strategies for Temporal workflows in Python using pytest, time-skipping, and advanced mocking.

About

This skill provides specialized guidance for testing Temporal applications in Python, ensuring workflow reliability and determinism through structured testing patterns. It equips developers with the tools to perform fast unit testing with time-skipping, integration testing with activity mocking, and replay testing to validate logic against production histories. By incorporating these best practices into your development cycle, you can maintain high code coverage, debug complex failures effectively, and ensure safe deployments of distributed orchestrations.

Key Features

Comprehensive activity mocking and error injection patterns
2 GitHub stars
Ready-to-use pytest fixtures for Temporal environments
Detailed guidance for local development and CI/CD integration
Workflow replay testing to ensure determinism and compatibility
Fast execution of long-running workflows using time-skipping

Use Cases

Testing month-long business processes in seconds via time-skipping
Verifying workflow versioning and determinism before production release
Isolating workflow logic from external API dependencies through mocking

About

Key Features

Comprehensive activity mocking and error injection patterns
2 GitHub stars
Ready-to-use pytest fixtures for Temporal environments
Detailed guidance for local development and CI/CD integration
Workflow replay testing to ensure determinism and compatibility
Fast execution of long-running workflows using time-skipping

Use Cases

Testing month-long business processes in seconds via time-skipping
Verifying workflow versioning and determinism before production release
Isolating workflow logic from external API dependencies through mocking