LMQL

LMQL, or Language Model Query Language, is a domain-specific programming language created to optimize interactions with large language models through structured and modular prompt design. Developed by researchers at ETH Zurich, LMQL blends imperative control flow with declarative constraints, enabling developers to enforce typed output structures, validate syntax, and filter model generations in real time. The language treats prompts as query functions, allowing users to define typed variables, execute nested control structures, and enforce exact formatting rules across complex tasks. By applying token masking and early stopping during generation runtime, LMQL significantly reduces unnecessary model API requests and lowers inference latency. The framework offers broad backend portability, enabling developers to switch seamlessly between proprietary APIs like OpenAI and open-source models such as LLaMA using minimal code modifications. Integrated directly into Python environments, LMQL streamlines prompt engineering workflows for data extraction, text transformation, and automated reasoning tasks. However, developers may encounter a steep initial learning curve due to specialized query syntax and limited documentation covering advanced conversational context management.