LMQL

A query language for LLMs that bolts types, templates, and constraints onto prompting.

Free· Free and open source (Apache-style); self-host or use with your own model API keysCodingMulti-model (OpenAI, Hugging Face Transformers, llama.cpp)

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Best for

Pick LMQL if you want a principled, constraint-driven query language for LLMs and are comfortable adopting a small DSL on top of Python.

Skip if

Skip it if you want a managed platform, drag-and-drop prompt tooling, or a large commercial ecosystem with enterprise support.

LMQL (Language Model Query Language) is an open-source programming language for interacting with large language models, developed by ETH Zurich's Secure, Reliable, and Intelligent Systems Lab. It treats prompting as a first-class programming problem: you write Python-flavored queries with typed variables, control flow, and `where` constraints, and an optimizing runtime enforces those constraints on the model's output (regex, type, length, stop conditions, and more). The result is structured, validated generations without the usual hand-rolled parsing and retry logic.

It's aimed at developers who are tired of stringly-typed prompt engineering and want portable, composable LLM code. LMQL is backend-agnostic, running against OpenAI, Hugging Face Transformers, and llama.cpp with the same query, and it ships nested queries, multi-part prompting, and a `@lmql.query` decorator so calls look like native Python functions. The project is free and open source on GitHub (eth-sri/lmql); there is no hosted SaaS or paid tier.

Because it's a research-led language rather than a commercial product, expect a learning curve, a smaller ecosystem than LangChain or DSPy, and a release cadence tied to academic priorities rather than enterprise SLAs. For anyone building constrained-decoding pipelines or agent loops where output shape matters, it remains one of the more rigorous tools in the category.

Editor's take

LMQL is the rare prompt tool that takes correctness seriously, modeling generation as a constrained search problem instead of string interpolation. It's a sharp pick for engineers who care about structured outputs and portability across model backends, but the DSL tax is real and you're betting on a research project, not a vendor.

— The AI Tool Bible editorial team

Pros

✅ Type and regex constraints enforced by the runtime, not after-the-fact parsing
✅ Backend-agnostic across OpenAI, HF Transformers, and llama.cpp
✅ Nested queries and Python control flow make prompts composable and reusable
✅ Fully open source with an academic research pedigree (ETH Zurich SRI Lab)