Temporal Applications Python

Temporal Applications in Python

Guidance for designing and implementing Temporal workflows and activities in Python so they are testable, type-safe, reliable, and keep orchestration separate from business logic. Use this skill when designing new workflows or activities, refactoring or extending existing ones, or when you need patterns for determinism, timeouts, retries, idempotency, or testing.

Load the skill first; then use the Resource index below to load only the resource(s) relevant to your task.

When to use this skill

• You are building or changing Temporal Python applications (workflows and activities on the official Python SDK).
• Tasks include: defining activity classes and workflow logic; structuring params and results; implementing long-running or unbounded workflows (e.g. processing a growing list with Continue-As-New); configuring timeouts, retries, and idempotency; ensuring workflow determinism; or testing workflows and activities.

Next step: Infer your task from the user request, then use the Resource index table below to choose which resources/ file(s) to load.

Boundaries

1. Workflows — Orchestrate only. No I/O or non-deterministic APIs in workflow code. Use SDK APIs for time/random/UUID. State that must survive Continue-As-New lives in a serialisable params model; copy from params into workflow instance at the start of run. Details and examples → resources/workflows-continue-as-new.md, resources/determinism.md.

2. Activities — Thin wrappers. Dependencies (DB, HTTP client, browser, etc.) are injected at construction (e.g. in worker bootstrap). Each activity method has one Pydantic params type and one result type; no tuples or multiple return values. Business logic lives in a separate, testable module or function that the activity calls. Details and examples → resources/activities.md.

3. Child workflows vs activities — Use child workflows for per-item orchestration (e.g. one child per item in a batch); use activities for side effects (I/O, external APIs). Keep history small by extracting loops into child workflows and passing state via params when using Continue-As-New.

Success criteria

Before considering the work done, check:

• Boundaries — Workflows orchestrate only; activities are thin wrappers with injected deps and single params/result models; business logic is in separate, testable code.
• Contracts — Pydantic params and result models; shared types between workflows and activities; serialisable workflow state in params for Continue-As-New.
• Determinism — No I/O or non-deterministic APIs in workflow code; use workflow.now(), workflow.random(), workflow.uuid4(); version workflow changes with patching when needed. See resources/determinism.md.
• Bounded history — Long-running or unbounded list/queue workflows use Continue-As-New with state in params; drain in-flight work before continue_as_new when using N-in-flight. See resources/workflows-continue-as-new.md.
• Reliability — Start-to-close timeout set for activities; schedule-to-close and retry policy as needed; heartbeats for long activities; idempotent design for side-effecting activities. See resources/reliability.md.
• Testability — Business logic testable without Temporal; activity/workflow tests use ActivityEnvironment and time-skipping WorkflowEnvironment as appropriate. See resources/testing.md.

Resource index

Load only the resource(s) that match your task. SKILL.md (this file) is the entry point; resources hold the detailed guidance and examples.

Cross-references: Resources may point to other resources (e.g. “For idempotency see reliability.md”). Load those when the task requires it.

Out of scope

• Worker bootstrap — Wiring task queues, registering workflows/activities, and process lifecycle are not covered here; see SDK and deployment docs.
• Signals — Pushing data into running workflows via signals is not detailed; use SDK docs when needed.
• Saga/compensation, search attributes, local activities — Out of scope for this skill; refer to Temporal docs for advanced patterns.