LLM prompts as first-class Python citizens

What it does

LMQL is a Python superset that treats top-level strings as LLM queries. You write ordinary Python control flow—if statements, loops, and variables—interleaved with prompts containing bracketed placeholders like [GREETINGS] that the model fills in during execution. A where clause lets you enforce constraints such as stopping phrases, token limits, or data types directly on the generated output, so the model stays on script without post-hoc filtering.

The interesting bit

Instead of calling an LLM as an external black box, LMQL makes inference a native language construct. The runtime uses logit masking and speculative execution to enforce constraints during generation rather than after, and it supports branching decoders like beam search and best_k that operate across the program’s prompt structure.

Key highlights

• Native Python syntax: use classes, variables, and control flow without switching languages.
• Constraint language via where: restrict output with stopping conditions, character-level rules, and data types enforced at the token level.
• Advanced decoding: argmax, sample, beam search, and best_k are built in.
• Multi-model backends: OpenAI, Azure OpenAI, and Hugging Face Transformers are supported.
• Async API with cross-query batching, plus integrations for LangChain and LlamaIndex.

Caveats

• The browser-based Playground IDE requires Node.js, and local Hugging Face models rely on a separate inference API process.
• GPU-accelerated local inference is tested only on specific Ubuntu/CUDA setups and Windows WSL2; macOS users are limited to API-only mode.
• Installation currently targets Python 3.10 exactly.

Verdict

Try it if you want to orchestrate multi-step LLM workflows with real program logic rather than template spaghetti. Skip it if your needs are limited to simple, stateless API calls.