temporalio · temporal-cicd · Jul 24, 2026 · github-actions · Jul 17, 2026 · github-actions
@@ -214,4 +214,4 @@ A Workflow Cache is an in-memory LRU (least recently used) cache maintained by W
 
 When a Worker picks up a Workflow Task, it caches the Workflow's state in memory. This allows the Worker to continue processing subsequent Tasks for that Workflow without having to fetch the full Event History from the server and replay it from scratch. 
 
-This caching mechanism is closely tied to [Sticky Execution](/sticky-execution). The Temporal Service directs future Workflow Tasks to the same Worker that cached the Workflow, via a dedicated "Sticky Queue". If the cached Workflow is evicted, to make room for another for example, the Worker must replay the Event History to restore its state before continuing.
+This caching mechanism is closely tied to [Sticky Execution](/sticky-execution). The Temporal Service directs future Workflow Tasks to the same Worker that cached the Workflow, via a dedicated "Sticky Queue". If the cached Workflow is evicted, to make room for another for example, the Worker must replay the Event History to restore its state before continuing.
@@ -1299,4 +1299,4 @@ The approach isn't limited to games. Any long-lived entity that has to take acti
 - [Search Attributes](https://docs.temporal.io/visibility/search-attributes)
 - [Temporal Python SDK API Reference](https://python.temporal.io)
 - [Temporal Python SDK samples](https://github.com/temporalio/samples-python)
-- Companion article: [Entity Workflow Pattern](/guides/entity-pattern-loyalty-points)
+- Companion article: [Entity Workflow Pattern](/guides/entity-pattern-loyalty-points)
@@ -1127,4 +1127,4 @@ You've built a loyalty account system where each customer is an independent, sel
 - [Failure Detection — Timeouts, Activity Heartbeating, and Retry Policies](https://docs.temporal.io/develop/python/failure-detection)
 - [Temporal Python SDK samples](https://github.com/temporalio/samples-python)
 
-<!-- - [Companion article: Actor Workflow Pattern](ActorWorkflow_PlayerSessions.md) — covers the related Actor Workflow pattern for autonomous agents that take action -->
+<!-- - [Companion article: Actor Workflow Pattern](ActorWorkflow_PlayerSessions.md) — covers the related Actor Workflow pattern for autonomous agents that take action -->
@@ -463,4 +463,4 @@ You now have the capability to:
 - [Temporal Python SDK Documentation](https://docs.temporal.io/develop/python). SDK reference for Workflows, Signals, child Workflows, and Worker setup.
 - [Child Workflow Executions](https://docs.temporal.io/encyclopedia/child-workflows). How parent and child Workflows interact, including parent close policies.
 - [Workflow Message Passing](https://docs.temporal.io/encyclopedia/workflow-message-passing). Reference for Signals and Queries.
-- [Reference implementation source repository](https://github.com/temporal-sa/temporal-workflow-throttler). The upstream repository bundles `PermitSlotWorkflow`, the `Semaphore` helper, demo Workers, a FastAPI service, and a React UI. The package directory is named `throttler/` in the source.
+- [Reference implementation source repository](https://github.com/temporal-sa/temporal-workflow-throttler). The upstream repository bundles `PermitSlotWorkflow`, the `Semaphore` helper, demo Workers, a FastAPI service, and a React UI. The package directory is named `throttler/` in the source.
@@ -784,4 +784,4 @@ Your application now safely integrates with rate-limited external APIs, ensuring
 ### API documentation
 - [SendGrid Rate Limits](https://docs.sendgrid.com/api-reference/rate-limits)
 - [Stripe Rate Limits](https://stripe.com/docs/rate-limits)
-- [OpenAI Rate Limits](https://platform.openai.com/docs/guides/rate-limits)
+- [OpenAI Rate Limits](https://platform.openai.com/docs/guides/rate-limits)
@@ -1353,4 +1353,4 @@ You've built a document approval system that holds up under real conditions —
 - [Temporal Python SDK API Reference](https://python.temporal.io)
 - [Temporal Python SDK GitHub repository](https://github.com/temporalio/sdk-python)
 - [Temporal Python SDK samples](https://github.com/temporalio/samples-python)
-- [Entity Workflow Pattern](entity-pattern-loyalty-points) — covers the related Entity Workflow pattern for modeling long-lived domain objects
+- [Entity Workflow Pattern](entity-pattern-loyalty-points) — covers the related Entity Workflow pattern for modeling long-lived domain objects
@@ -683,4 +683,4 @@ Your application now intelligently routes work based on resource requirements, e
 ### Community resources
 - [Blog: AI, ML, and Data Engineering Workflows with Temporal](https://temporal.io/blog/ai-ml-and-data-engineering-workflows-with-temporal)
 - [Forum: Task Queue Best Practices](https://community.temporal.io/t/in-what-situation-should-we-use-multiple-separated-task-queues/1254)
-- [Temporal ML/AI Use Cases](https://temporal.io/use-cases/machine-learning)
+- [Temporal ML/AI Use Cases](https://temporal.io/use-cases/machine-learning)
@@ -1060,4 +1060,4 @@ By following this guide, you have implemented a recoverable and compensatable pi
 - [Temporal Queries](https://docs.temporal.io/develop/typescript/message-passing#queries) — Guide to defining and handling synchronous read-only Queries on Workflows.
 - [Temporal Search Attributes](https://docs.temporal.io/visibility#search-attribute) — Reference for creating and using custom Search Attributes for Workflow visibility.
 - [Temporal Visibility](https://docs.temporal.io/visibility) — Overview of the Visibility subsystem and List Filter Query syntax.
-- [ApplicationFailure Reference](https://docs.temporal.io/references/failures#application-failure) — API reference for `ApplicationFailure` including retryable and non-retryable error handling.
+- [ApplicationFailure Reference](https://docs.temporal.io/references/failures#application-failure) — API reference for `ApplicationFailure` including retryable and non-retryable error handling.
@@ -726,4 +726,4 @@ By following this guide, you have implemented a time-bound capacity provisioning
 - [Temporal Capacity Modes](https://docs.temporal.io/cloud/capacity-modes)
 - [Temporal Best Practices](https://docs.temporal.io/best-practices)
 - [Python Capacity Mode Sample](https://github.com/lainecsmith/temporal-cloud-ops-capacity-modes)
-- [Go Capacity Mode Sample](https://github.com/temporal-sa/temporary-rate-limit-increases)
+- [Go Capacity Mode Sample](https://github.com/temporal-sa/temporary-rate-limit-increases)
@@ -499,4 +499,4 @@ Your file processing Workflows now guarantee that all Activities execute on the
 
 ### Community resources
 - [Forum: When to Use Multiple Task Queues](https://community.temporal.io/t/in-what-situation-should-we-use-multiple-separated-task-queues/1254)
-- [Slack: Using Dynamic Task Queues for Traffic Routing](https://community.temporal.io/t/using-dynamic-task-queues-for-traffic-routing/3045)
+- [Slack: Using Dynamic Task Queues for Traffic Routing](https://community.temporal.io/t/using-dynamic-task-queues-for-traffic-routing/3045)
Original file line number	Diff line number	Diff line change
Expand Up		@@ -214,4 +214,4 @@ A Workflow Cache is an in-memory LRU (least recently used) cache maintained by W

		When a Worker picks up a Workflow Task, it caches the Workflow's state in memory. This allows the Worker to continue processing subsequent Tasks for that Workflow without having to fetch the full Event History from the server and replay it from scratch.

		This caching mechanism is closely tied to [Sticky Execution](/sticky-execution). The Temporal Service directs future Workflow Tasks to the same Worker that cached the Workflow, via a dedicated "Sticky Queue". If the cached Workflow is evicted, to make room for another for example, the Worker must replay the Event History to restore its state before continuing.
		This caching mechanism is closely tied to [Sticky Execution](/sticky-execution). The Temporal Service directs future Workflow Tasks to the same Worker that cached the Workflow, via a dedicated "Sticky Queue". If the cached Workflow is evicted, to make room for another for example, the Worker must replay the Event History to restore its state before continuing.