What is the recommended test coverage for Temporal applications?

Temporal best practices suggest maintaining at least 80% logic coverage for both workflows and activities, supplemented by integration tests for critical execution paths.

Can I test activities in isolation from workflows?

Yes, you can use the ActivityEnvironment provided by the Temporal Python SDK to run and test individual activities locally without the overhead of a full workflow or worker.

When should I use replay testing?

Replay testing is essential when modifying existing workflow code. It validates that your changes don't violate determinism by replaying old execution histories against the new code version.

How does time-skipping work in Temporal Python tests?

Time-skipping uses the WorkflowEnvironment.start_time_skipping() method to automatically advance the test clock whenever the workflow is blocked, allowing tests for long-running processes to complete in milliseconds.

Temporal Python Testing

Name: Temporal Python Testing
Author: HermeticOrmus

byHermeticOrmus

Security & Testing

Implements comprehensive testing strategies for Temporal workflows and activities using pytest and advanced time-skipping techniques.

About

This skill provides specialized guidance for testing Temporal applications in Python, ensuring workflow determinism and logic reliability through structured patterns. It enables developers to leverage time-skipping for testing long-running processes in seconds, implement robust activity mocking, and perform replay testing to prevent non-determinism errors in production. By integrating with pytest and providing local development configurations, this skill streamlines the creation of high-coverage test suites for distributed systems.

Key Features

Integration-ready pytest fixtures and async patterns
Local development setup with Docker Compose and coverage tools
Determinism validation through production history replay
Time-skipping for rapid execution of long-running workflows
Activity mocking strategies for isolated workflow logic testing
0 GitHub stars

Use Cases

Validating workflow determinism before deploying logic updates
Testing multi-month business processes in seconds via time-skipping
Setting up automated CI/CD pipelines for Temporal Python services

About

Key Features

Integration-ready pytest fixtures and async patterns
Local development setup with Docker Compose and coverage tools
Determinism validation through production history replay
Time-skipping for rapid execution of long-running workflows
Activity mocking strategies for isolated workflow logic testing
0 GitHub stars

Use Cases

Validating workflow determinism before deploying logic updates
Testing multi-month business processes in seconds via time-skipping
Setting up automated CI/CD pipelines for Temporal Python services