temporal-python-testing

Use WorkflowEnvironment.start_time_skipping() from temporalio.testing to create a test environment where workflow sleeps complete instantly. Run a Worker with your workflows and activities, then execute the workflow through the environment client and assert on the result.

Pass a mock function or a test double in place of the real activity when constructing the Worker in your test. This isolates workflow orchestration logic from external services and lets you simulate success, transient failures, and non-retryable ApplicationError cases.

Replay testing re-executes workflow code against recorded event histories using the Replayer class. If the new code makes the same decisions as the recorded history, the change is deterministic and safe to deploy; mismatches indicate breaking changes.

Non-determinism comes from using random.randint, datetime.now, or direct external API calls inside workflow code. Replace these with workflow.random(), workflow.now(), and activities so replayed executions produce identical decisions.

Use Docker Compose with the temporalio/auto-setup image and a PostgreSQL container, exposing port 7233 for the server and 8080 for the Web UI. Verify connectivity with a health check script that connects a client and executes a simple workflow.

Temporal best practices recommend at least 80 percent coverage for workflow and activity logic. Combine unit tests with time-skipping, integration tests with mocked activities, and replay tests against production histories before every deployment.