Lmql

LMQL is a programming language and platform designed specifically for querying and controlling large language models (LLMs) using natural language and logical constraints. Its core value proposition lies in making LLM interactions more predictable, efficient, and cost-effective by allowing developers to specify not just prompts, but also the desired output structure, validation rules, and computational logic in a single, integrated script. This shifts the paradigm from simple prompt engineering to a more robust form of 'language model programming,' where the model's reasoning is guided and constrained to produce reliable, structured results.

Key features: LMQL introduces a declarative query syntax that blends natural language prompts with Python-like control structures. For example, you can define a template where the model must generate a list of items, and the script will automatically handle token budgeting and re-prompting if the output is too long. It supports advanced constraints like 'the answer must be a number between 1 and 10' or 'the generated JSON must have these specific keys,' which are enforced during the generation process itself, not just in post-processing. This enables complex interactions like multi-step reasoning chains, guided data extraction, and controlled text generation where the output format is guaranteed.

What sets LMQL apart is its compiler-based approach; it translates high-level constraints into an optimized sequence of inference calls and token-level guidance, which can significantly reduce API costs and latency by avoiding generation of invalid tokens. Unlike wrapper libraries that only format prompts, LMQL operates closer to the model's token generation loop, offering fine-grained control. It integrates with popular backends like OpenAI, Hugging Face Transformers, and llama.cpp, making it versatile across different model providers and local setups. This technical foundation makes it a powerful tool for building deterministic applications on top of inherently non-deterministic LLMs.

Ideal for developers and researchers building applications that require structured, reliable outputs from LLMs, such as automated data annotation, content moderation systems, legal document analysis, and educational tutoring bots. It is particularly valuable in industries like finance, healthcare, and legal tech, where output consistency and adherence to schema are critical. Use cases include generating API call parameters from natural language, creating strict Q&A systems, and developing agents that follow precise interaction protocols.

LMQL is an open-source project and is free to use forever. There are no tiered pricing plans for the core language and runtime. Users only incur costs from the underlying LLM APIs or inference infrastructure they choose to connect it to, such as credits for OpenAI or compute for running local models.