torque

We’ve spent a while trying to generate a very specific, complex, multi‑turn conversation dataset. Most tools pushed us toward glue scripts and one‑off pipelines that were hard to review or reuse. Torque is our attempt to make this boring and predictable: a small, MIT‑licensed framework that treats dataset generation like building a UI. You define small, declarative “components” and compose them into pipelines. The goal is clear code and repeatable runs, not another heavy DSL. It’s early and open source. We’d love feedback on the API design, examples you’d like to see, and rough edges we should fix first. Docs and code: https://github.com/qforge-dev/torque

We were frustrated with dataset generation DX: ad‑hoc scripts and JSON templates break as flows branch, tool‑calls drift, and reproducibility is hard. So we built Torque: a schema‑first, declarative, fully typesafe DSL to generate conversational datasets. What it is: Declarative DSL - Compose conversations like React components Fully Typesafe - Zod schemas with complete type inference Provider Agnostic - Generate with any AI SDK provider (OpenAI, Anthropic, DeepSeek, vLLM, LLaMA.cpp etc.) AI-Powered Content - Generate realistic varied datasets automatically without complicated scripts Faker Integration - Built-in Faker.js with automatic seed synchronization for reproducible fake data Cache Optimized - Reuses context across generations to reduce costs Prompt Optimized - Concise, optimized structures, prompts and generation workflow