Workers
Workers control how much AI work OpenVibely can run at once. In the UI, worker settings help teams avoid overwhelming a model provider, a local machine, or a single project.
What Users Configure
Open Workers from the System section of the sidebar.
| Capacity Layer | What It Protects |
|---|---|
| Global capacity | Overall execution slots across the instance. |
| Project limits | Prevent one selected project from consuming all execution capacity. |
| Model limits | Prevent one provider or model config from being overloaded. |
| Worker timeout | Stops stalled executions from holding capacity forever. |
When To Change Worker Settings
- Lower limits when using expensive hosted models.
- Lower limits when a provider rate-limits heavily.
- Raise limits when you have enough machine capacity and provider quota.
- Set project limits when one repository should not block work in others.
- Use model limits when local Ollama or a specific hosted provider is slower than the rest.
How It Feels In The App
When capacity is available, tasks can move from queued to running. When capacity is constrained, tasks wait in the queue until a worker slot is available. Alerts and task statuses help users notice stuck or failed work.