Temporal Python Testing

Temporal Python Testing Strategies

Comprehensive testing approaches for Temporal workflows using pytest, progressive disclosure resources for specific testing scenarios.

Do not use this skill when

• The task is unrelated to temporal python testing strategies
• You need a different domain or tool outside this scope

Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open resources/implementation-playbook.md.

Use this skill when

• Unit testing workflows - Fast tests with time-skipping
• Integration testing - Workflows with mocked activities
• Replay testing - Validate determinism against production histories
• Local development - Set up Temporal server and pytest
• CI/CD integration - Automated testing pipelines
• Coverage strategies - Achieve ≥80% test coverage

Testing Philosophy

Recommended Approach (Source: docs.temporal.io/develop/python/testing-suite):

• Write majority as integration tests
• Use pytest with async fixtures
• Time-skipping enables fast feedback (month-long workflows → seconds)
• Mock activities to isolate workflow logic
• Validate determinism with replay testing

Three Test Types:

1. Unit: Workflows with time-skipping, activities with ActivityEnvironment
2. Integration: Workers with mocked activities
3. End-to-end: Full Temporal server with real activities (use sparingly)

Available Resources

This skill provides detailed guidance through progressive disclosure. Load specific resources based on your testing needs:

Unit Testing Resources

File: resources/unit-testing.md When to load: Testing individual workflows or activities in isolation Contains:

• WorkflowEnvironment with time-skipping
• ActivityEnvironment for activity testing
• Fast execution of long-running workflows
• Manual time advancement patterns
• pytest fixtures and patterns

Integration Testing Resources

File: resources/integration-testing.md When to load: Testing workflows with mocked external dependencies Contains:

• Activity mocking strategies
• Error injection patterns
• Multi-activity workflow testing
• Signal and query testing
• Coverage strategies

Replay Testing Resources

File: resources/replay-testing.md When to load: Validating determinism or deploying workflow changes Contains:

• Determinism validation
• Production history replay
• CI/CD integration patterns
• Version compatibility testing

Local Development Resources

File: resources/local-setup.md When to load: Setting up development environment Contains:

• Docker Compose configuration
• pytest setup and configuration
• Coverage tool integration
• Development workflow

Quick Start Guide

Basic Workflow Test

import pytest
from temporalio.testing import WorkflowEnvironment
from temporalio.worker import Worker

@pytest.fixture
async def workflow_env():
    env = await WorkflowEnvironment.start_time_skipping()
    yield env
    await env.shutdown()

@pytest.mark.asyncio
async def test_workflow(workflow_env):
    async with Worker(
        workflow_env.client,
        task_queue="test-queue",
        workflows=[YourWorkflow],
        activities=[your_activity],
    ):
        result = await workflow_env.client.execute_workflow(
            YourWorkflow.run,
            args,
            id="test-wf-id",
            task_queue="test-queue",
        )
        assert result == expected

Basic Activity Test

from temporalio.testing import ActivityEnvironment

async def test_activity():
    env = ActivityEnvironment()
    result = await env.run(your_activity, "test-input")
    assert result == expected_output

Coverage Targets

Recommended Coverage (Source: docs.temporal.io best practices):

• Workflows: ≥80% logic coverage
• Activities: ≥80% logic coverage
• Integration: Critical paths with mocked activities
• Replay: All workflow versions before deployment

Key Testing Principles

1. Time-Skipping - Month-long workflows test in seconds
2. Mock Activities - Isolate workflow logic from external dependencies
3. Replay Testing - Validate determinism before deployment
4. High Coverage - ≥80% target for production workflows
5. Fast Feedback - Unit tests run in milliseconds

How to Use Resources

Load specific resource when needed:

• "Show me unit testing patterns" → Load resources/unit-testing.md
• "How do I mock activities?" → Load resources/integration-testing.md
• "Setup local Temporal server" → Load resources/local-setup.md
• "Validate determinism" → Load resources/replay-testing.md

Additional References

• Python SDK Testing: docs.temporal.io/develop/python/testing-suite
• Testing Patterns: github.com/temporalio/temporal/blob/main/docs/development/testing.md
• Python Samples: github.com/temporalio/samples-python

AGI Framework Integration

Adapted for @techwavedev/agi-agent-kit Original source: antigravity-awesome-skills

Memory-First Protocol

Retrieve prior test strategies, known flaky tests, and coverage gaps. Cache test infrastructure setup to avoid re-configuring test environments.

# Check for prior testing/QA context before starting
python3 execution/memory_manager.py auto --query "test patterns and coverage strategies for Temporal Python Testing"

Storing Results

After completing work, store testing/QA decisions for future sessions:

python3 execution/memory_manager.py store \
  --content "Testing strategy: integration tests hit real DB (no mocks), 85% line coverage, mutation testing on critical paths" \
  --type technical --project <project> \
  --tags temporal-python-testing testing

Multi-Agent Collaboration

Share test results and coverage reports with code review agents so they can verify adequate coverage on changed code.

python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "QA complete — test suite expanded with 12 new integration tests, all passing" \
  --project <project>

TDD Enforcement

This skill integrates with the framework's iron-law RED-GREEN-REFACTOR cycle. No production code without a failing test first.

Agent Team: QA

Dispatch qa_team to generate tests and verify they pass before marking implementation complete.

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