AI Translate tool
The AI Translate tool translates untranslated block content with a large language model. It supports several providers (Anthropic, OpenAI, Gemini, Azure OpenAI, Ollama) and writes the result as the target text, recording an alternate-translation annotation with the originating provider. A target language is required, and provider credentials are required for hosted providers.
Blocks that contain inline codes are sent through a placeholder-preserving prompt so tags survive translation, then reconstructed from the response. Blocks can be translated one at a time or grouped into batches sent in a single structured call; batches can run concurrently. When run with a session cache, already-translated targets are reused and fresh translations are written back, so repeat runs skip work already done.
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
apiKey | string | API key for the AI provider | |
batchConcurrency | integer | 1 | Number of concurrent batch calls (0 or 1 = sequential) |
batchSize | integer | 100 | Number of blocks per LLM call |
model | string | AI model name | |
provider | string | anthropic | AI provider |
skipMatched | boolean | false | Skip blocks that already have a target translation |
Configure these parameters interactively and copy the flow-step YAML on the Tool Reference.
Examples
Translate with Anthropic
Translate untranslated blocks using an Anthropic model.
provider: anthropic model: claude-sonnet-4-5
Translate only new content
Skip blocks that already have a translation.
provider: openai skipMatched: true
Processing notes
Operates on translatable blocks with non-empty source; other parts pass through unchanged.
Blocks with inline codes are translated through a placeholder-preserving prompt and reconstructed from the response.
Each translation is recorded as an alternate-translation annotation tagged with the provider.
Limitations
Requires a target language and, for hosted providers, valid credentials.
Hosted providers make network API calls; cost and latency scale with content volume.
Session-cache reuse applies on the block-by-block path; the batched path writes overlays on output but does not pre-skip via the per-block cache.
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