LMQL

LMQL is a query language that lets you describe prompts, constraints, and control flow as code, rather than as ad-hoc text. Compared to plain prompt engineering, LMQL provides structure, reusability, and programmatic control over how an LLM is called and how its output is validated, making complex applications easier to maintain and debug.

LMQL is designed to be backend-agnostic and can work with popular LLM providers such as OpenAI and others, depending on the current connectors and APIs supported by the project. You typically configure the model endpoint and API keys, then write LMQL queries that run on top of that backend.

LMQL introduces a concise query syntax, but it is designed to feel familiar to developers and integrates closely with Python. You can gradually adopt LMQL by embedding queries inside existing Python code, keeping most of your application logic in your preferred language.

LMQL itself is a query layer and does not charge per token; you primarily pay for the underlying LLM provider according to their pricing. Check the official LMQL website and documentation for the current licensing terms and any additional service offerings.

LMQL cannot eliminate hallucinations entirely, but its constraint and validation features allow you to restrict output formats, check intermediate results, and reject or regenerate invalid responses. This helps improve reliability compared to unstructured prompting alone.