LLM-Conductor

CLI pipeline for localizing nested JSON string resources with language models (Ollama or any OpenAI-compatible API). Optional second stage proofreads with a separate endpoint.

This is not a local machine-translation product, offline dictionary, or GUI translator. There is no built-in MT engine — you supply the model and the prompts; quality follows them. Think “batch orchestration for LLM localization,” not “install and translate offline.”

Language pair = prompt only. The engine does not hard-code source or target languages. Name them in llm.toml. The default init profile is one working example (often English→another language), not a product limit.

Features

• Recursive JSON of any depth; non-strings left untouched
• Per-string isolation (no batching unrelated keys into one prompt)
• Key-name linguistic hints (_male / _female / _plural / …)
• Glossary injection, formatting-tag masking, disk cache + in-run dedup
• Reply validation, consecutive-failure breaker, OS locks, progress heartbeat
• Independent transports for translation vs verification

Quick start

1. Install or grab a binary

pip install -e ".[dev]"          # from a clone
# or download llm-conductor.exe from GitHub Releases (when published)

PyPI upload is prepared (packaging metadata is in place) but may not be live yet — prefer a Release .exe, pip install from a clone, or pip install git+https://github.com/Necrofox/llm-conductor.git once the repo is public.

2. Configs next to the program

llm-conductor init               # conductor.toml + llm.toml
# portable checkout:
python llm-conductor.py init

Configs resolve beside the entry point (exe / wrapper / installed shim), not cwd. Edit llm.toml for your endpoint and prompts; set [validation].allowed_scripts for the target writing system (e.g. ["cyrillic"], ["latin"], ["cjk"]). Prefer key_env over literal keys.

3. Run

llm-conductor run --target path/to/strings.json
llm-conductor run --target path/to/strings.json --stage translate --strict
llm-conductor run --target project/ui.list --result2target

Results land under result/<target-name>/ (translated/, redacted/, final/) unless --result2target. See doc/cli.md.

Ollama vs OpenAI-compatible

Point translation and verification at different hosts if you want.

Example

Input (see examples/sample.json):

{
  "npc": {
    "greeting_male": "Welcome, traveler.",
    "greeting_female": "Welcome, traveler."
  }
}

Same English text, different key suffixes → separate requests and (usually) different grammatical forms in the target language. Full prompt rules: doc/prompts.md.

Key-name conventions

Documentation

Cache note

Rejected model replies are not cached. A warm-cache rerun that only hits residual misses can exit with code 5 even if an earlier full run was 0. Fix prompts/model or remeasure with --no-cache (doc/cli.md).

Roadmap (high level)

• Richer input formats (e.g. subtitle adapters)
• Optional in-stage parallelism with single-flight on the full cache key
• Stage-local resume orthogonal to the LLM cache (concept)

License

MIT — see LICENSE.

Кратко (RU)

LLM-Conductor — CLI-конвейер: гоняет строковые листья JSON через LLM (Ollama / OpenAI-совместимые API), опционально второй моделью вычитывает перевод. Это не локальный переводчик и не движок MT — модель и промпты ваши. Пара языков задаётся только текстом промпта в llm.toml, не схемой движка. Быстрый старт: init → правки профиля → run --target …. Документация на английском в doc/.