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LMQL

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LMQL: A Programming Language for Large Language Models

Last updated Dec 1, 2025

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What is LMQL?

LMQL is a programming language tailored for large language models (LLMs). It offers robust and modular LLM prompting through the use of types, templates, constraints, and an optimizing runtime. It simplifies the creation of complex prompts by allowing procedural programming techniques in a query-like syntax. Created by the SRI Lab at ETH Zurich, LMQL supports features such as nested queries, scripted prompting, and custom constraints. It also provides a Playground IDE for ease of use.

LMQL's Top Features

Key capabilities that make LMQL stand out.

Nested Queries

Scripted Prompting

Custom Constraints

Optimizing Runtime

Playground IDE

Local Model Support

Tool Augmentation

High-level Constraint Management

Sequential Query Execution

Integration with Popular Libraries

Use Cases

Who benefits most from this tool.

Developers

Creating complex and modular prompt structures for LLMs.

Researchers

Implementing advanced prompting techniques and optimization strategies.

Data Scientists

Utilizing structured prompting for data analysis and interpretation.

AI Practitioners

Building intelligent chatbots and interactive systems.

Educators

Teaching and demonstrating advanced prompting and LLM capabilities.

Hobbyists

Experimenting with LLMs and custom prompt designs.

Enterprises

Optimizing internal and customer-facing interactions through advanced LLM prompting.

AI Enthusiasts

Exploring the potential of procedural programming in LLM prompting.

Tech Startups

Innovating new applications and solutions using optimized LLM prompts.

Community Members

Participating in the development and documentation of LMQL features.

Tags

programming languagelarge language modelstypestemplatesconstraintsoptimizing runtimequeriesSRI LabETH Zurichnested queriesscripted promptingcustom constraintsPlayground IDE

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