Docs · Features
Engines and providers
Engines are the models that do the work, your local server, a cloud LLM, or a machine-translation service, and every step, judge and extraction resolves one.
What it does#
Nothing in Localization OS translates anything by itself, it orchestrates models. This is where you tell it which model to use, and how to reach it. A built-in provider slot goes live the moment its key, or for a local server its base URL, is configured. You can also create a named, fully configured engine profile with its own base URL, key, model and settings, referenced by any workflow step that wants it.
Three states matter. Mock: no key or base URL configured, everything runs but nothing is real, useful for trying the product with no setup at all. Local: your own model server, no per-call cost, works offline. Cloud: a configured provider, with real spend tracked per call and optionally capped with a monthly ceiling.
Getting started#
- Start your local model server and load a model.
- In the web UI, go to Setup AI, Local / self-hosted engine, and set the base URL.
- Use Test connection, then Fetch models, and pick one as the default.
- Leave workflow steps' provider as local with a blank model, so they all use that default, and changing it later changes them all at once.
config set local_base_url http://localhost:1234/v1
providers key set claude
engine create --name "DeepL prod" --kind deepl --secret api_key=YOUR_KEY
engine listWorks with#
- Workflows: every translate and check step resolves an engine, its own provider and model if set, otherwise the run default.
- Documents: translating a document dispatches per-segment engine calls, bounded by a concurrency setting; translation-memory hits skip the engine entirely.
- Evaluations: candidates and the LLM judge each resolve an engine, which is how two setups are compared side by side.
- Glossaries: term extraction is a real engine call and is spend-capped; offline it still works against the deterministic mock.
- Memory: memory only ever appears inside a prompt an engine receives.
Current limits#
The offline mock is selected explicitly: choose Mock in Setup AI, or set the default provider to mock. On a configured install, a built-in provider with no key refuses to translate rather than silently degrading to placeholder output. A named engine profile that is misconfigured is refused loudly too, since it was an explicit choice.
Translation output that looks like placeholder text means the mock engine is active; the provider indicators in the interface show live versus mock at a glance.
A stored cloud key that no longer decrypts is treated as a dead engine rather than an empty one, and needs to be re-entered.
A spend cap, where configured, is a brake rather than a budget: it refuses new paid calls once reached. Local and mock calls are exempt.
Model output limits are real: a very long output is retried automatically with a larger budget up to the model's own ceiling, and then it fails honestly rather than truncating silently.
Operator note for local reasoning models. Some reasoning models, for example a Gemma reasoning model, can spend their whole output budget on hidden reasoning and return no translation text. Localization OS recognizes that case rather than mistaking it for a cut-off translation: it retries once with reasoning turned off at the same budget instead of enlarging the budget, and for known reasoning models it turns reasoning off on the first call. If it still returns nothing, the run fails with a plain message that the model's reasoning consumed the output budget, rather than advising you to raise the output ceiling.
An optional, admin-only record of exactly what was sent to a model and what came back writes nothing at all while it is switched off. When it is on, its full request and reply bodies stay readable only by an administrator on the host, but a bodies-withheld summary, call counts, statuses, error classes, finish reasons, spend, and timings, with no prompt or reply text, is readable with a docs-scoped key over the API, the command line's remote mode, and the MCP server, scoped to your own organization.