The new Lingo.dev CLI now runs the localization pipeline pre-edit, human review, AI review, rephrase, back-translation, each toggled on the engine.

Toggle stages on the engine's Pipeline tab, or override per job through pipelineConfig. Pre-edit cleans typos in the source before they reach any locale. The core localization step applies your glossary, brand voice, and instructions. Human review pauses the run until your team or an external provider signs off. AI review reconciles a reviewer's edits back to engine config. Rephrase rewrites for marketing copy. Back-translation flags semantic drift.

A run that includes a human-review stage can wait days before it lands, so the CLI splits a job into lingo push and lingo pull. pull re-attaches to the most recent push and downloads outputs from any machine, including after the original terminal was closed.

lingo push --estimate reports what a translation run will cost before it runs.

The CLI uploads the source bytes so the server plans the exact delta only the keys and locales that are new or changed against what's already localized then prints the estimate and exits without translating anything.

Run it before a large or first-time translation to size the job, then run lingo push to translate for real. The number is an estimate, not a quote: it reflects the planned delta at the time it runs.

The Lingo.dev GitHub App runs continuous localization on any repository, straight from git.

Install it from your organization settings, commit a .lingo/config.json that points at your localization engine, and every push translates changed source files through your engine and opens a pull request with the localized files. Each run executes on the async localization pipeline, so translations pass through the stages configured on your engine pre-edit, AI review, human review, back-translation. It localizes JSON, JSONC, Markdown, MDX, Markdoc, and OpenAPI files, and you can scope runs from a pull request with /lingo translate.

Also this week:

API keys now come in two types: Personal and Service.

Personal keys behave like before they inherit their owner's role and per-engine grants. Service keys carry their own role and per-engine scope, so a production worker isn't tied to any one teammate. A service key can be roleless and reach only the engines listed on it.

Also this week:

Localization Engine Suggestions are concrete edits to a localization engine a glossary entry, an instruction, or a brand-voice change proposed by the platform from its own AI Reviewer verdicts.

When reviews on a translation come back low, the platform suggests a specific edit to the localization engine's glossary, instructions, or brand voice, with the reasoning attached. Apply writes the edit into the localization engine and the next translation runs with the fix in place. An auto-suggestions toggle on the localization engine's Reviews tab runs the analysis in the background, so a run of low scores quietly produces suggestions waiting in the Suggestions tab. Suggestions are also addressable from code through the Localization Engine Suggestions API https://lingo.dev/docs/api/engin... free-text feedback like the German copy sounds too stiff goes in the same way and returns the same kind of structured suggestion.

Two new built-in AI Reviewers ship: a glossary reviewer and an instructions reviewer. Toggle on the glossary reviewer and every configured glossary term is automatically checked on every translation. Toggle on the instructions reviewer and every configured instruction is checked the same way. Open any translation in the dashboard log and see pass/fail per term, pass/fail per instruction, with the reviewer's reasoning on failure. One toggle replaces authoring an AI Reviewer for every single criterion.

Also shipped

• Per-engine AI Reviewer assignment. Attach different reviewers to different engines instead of one set for the org.

• Organization-level timezone setting. Reports display in the timezone you configure for your org.

Full changelog: lingo.dev/changelog

Rephrase is a new stage in the localization pipeline that rewrites translations to read like native, idiomatic copy in the target locale.

The async localization pipeline runs rephrase right after the core localization step. An AI agent rewrites the translation preferring local equivalents over literal renderings, keeping placeholders, variables, tags, and formatting exactly as-is, and applying your engine's glossary, brand voice, and instructions the same way the core localization step does. Downstream stages human review, post-edit, back-translation see the rephrased output and continue from there.

Toggle it on the engine's Pipeline tab, or override per job through `pipelineConfig.rephrase`.

RBAC is now available. Define roles, assign them to users, and grant per-engine access independently. Ownership transfer is supported, so the original creator is no longer a permanent dependency.

Also shipped

• Claude Desktop logs into your MCP server with a click. The server now speaks browser-redirect OAuth the client opens a tab, you confirm in the dashboard, you're in. No more copy/pasting API keys into client configs.

• lockedKeys on the async API. Slugs, IDs, URL parameters, and any value that's structural metadata rather than translatable content stay verbatim across target locales and bypass the LLM entirely.

• Logs show the context that produced each translation. Open any request in the Log tab and you can see exactly which glossary terms, instructions, and brand-voice rules the engine retrieved plus the glossary review reasoning.

• Cost-by-model breakdown. See how much you spent on each model, in tokens and in dollars.

Full changelog: lingo.dev/changelog

Human-in-the-loop for in-house AI translation review.

The async localization pipeline includes human-in-the-loop review an optional stage where the job pauses until a human edits or approves the AI translation. Until now, that stage routed to an external network of professional translators. Now you can set the review provider to Internal reviewers on an engine, and pending translations land in a review queue inside the dashboard instead of leaving your organization.

Auto top-up replenishes your credit balance automatically. Opt in once, set a threshold and an amount, and credits get purchased the moment your balance dips below the threshold translation CI runs stop failing on empty balances.

Also shipped

• New Reports charts: instruction adherence and terminology coverage. Track over time how often translations follow your configured instructions and apply your glossary terms.

• Credit balance alerts. Dashboard indicator shows current balance; emails fire at 20% remaining and at $0.

• ISO 5060 classification visible in triage. Each triaged job surfaces the standardized error class alongside the agent's suggestions.