Unverified50% confidenceFactExact time
The result returned by get_task contains an execution object that separates workflow information from runtime information.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/16/2026
First Seen
Valid until: 1/14/2027
Sources
Related Entities
Related Claims
UnverifiedThe caller specifies the target sub-workflow through the Execute Workflow node, and can pass structured input parameters and receive its returned results.77% similarUnverifiedHeavy tasks such as document summarisation and code generation are suited to being offloaded to a background task queue, where a worker picks up the task, calls the LLM API, and writes back the result, thereby decoupling user requests from long inference.73% similarUnverifieddatasette.add_background_task() technically relies on Python's asyncio event loop to register coroutines as background-running tasks.71% similarUnverifiedUsing scheduler.yield(), setTimeout, or requestIdleCallback can split long tasks into multiple smaller tasks, yielding main thread control between tasks.71% similarUnverifiedThe Split Out node receives the list path output[0].content[0].text.tasks and can split each task into an independent entry.68% similar
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