I am developing with the Temporal Python SDK and trying to make use of the workflow test support. For context, I am using WorkflowEnvironment.start_time_skipping() and when needing to patch other functions/methods, I use configured Worker with UnsandboxedWorkflowRunner(). I am also using mocked activities and registering them to the Workflow env (and Worker).
An Issue I am having is debugging workflow tests. Often times, I am met with infinite buffering as a result of a misconfigured Workflow. However, sometimes the root cause is something minor like a missing param, a missing activity, or a small typo.
Is there anyway to get more precise error traces and feedback when a test fails? I want to avoid this buffering behavior since it is nondescript and results in a simple debug turning into hours of wasted time.
I ran into the same issue when working with the Temporal Python SDK—those “infinite buffering” situations can be super frustrating, especially when it turns out to be something small.
A couple of things that helped me get better visibility into what’s going wrong:
Enable more verbose logging: Setting the logging level to DEBUG for Temporal internals can sometimes surface where the workflow is getting stuck.
Validate activity registration explicitly: Before running tests, I added small assertions/logs to confirm all expected activities are registered. Missing activities are a common silent failure point.
Wrap workflow execution with timeouts: Even in test environments, adding a timeout around workflow execution can at least fail fast instead of buffering forever.
Use smaller isolated tests: When debugging, I try to run the workflow with minimal inputs and mocked dependencies to narrow down where the issue starts.
Check sandbox vs unsandboxed differences: I’ve noticed subtle behavior differences when using UnsandboxedWorkflowRunner(), especially with patched functions—worth double-checking those patches are actually applied.
Also, for small mistakes like typos, missing params, or naming mismatches, I’ve found tools like a name generator helpful for quickly validating consistent naming patterns across workflows, activities, and mocks—it reduces those tiny errors that can cause big debugging headaches.
Would definitely love if Temporal adds better error surfacing here, but for now these workarounds made things a bit more manageable.