M4A

LMQL

Query language for programming large language models

open-sourcebetapythonquery-languagestructured-outputsopen-sourceresearch
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Memory Types

Integrations

openai, azure, huggingface, replicate


Overview


LMQL (Language Model Query Language) is a programming language designed specifically for working with large language models. Developed at ETH Zurich, LMQL combines SQL-like syntax with Python to provide declarative constraints on LLM outputs. It allows developers to specify what they want from an LLM using familiar query patterns rather than imperative code.


The language supports constraints, control flow, and scripting capabilities, making it possible to express complex LLM interactions concisely. LMQL's runtime optimizes queries to minimize token usage while ensuring constraints are satisfied, making it both expressive and efficient.


Key Features


  • **SQL-Like Syntax**: Familiar query language for LLMs
  • **Declarative Constraints**: Specify what you want, not how to get it
  • **Type Constraints**: Ensure outputs match types and patterns
  • **Control Flow**: Conditionals and loops in query language
  • **Python Integration**: Embed Python expressions seamlessly
  • **Multi-Model Support**: Works with various LLM backends
  • **Optimized Execution**: Minimizes token usage automatically
  • **Playground**: Interactive web-based development environment

    • When to Use LMQL


    LMQL is ideal for:

  • Research projects exploring LLM capabilities
  • Applications with complex output constraints
  • Teams preferring declarative programming
  • Projects requiring token-efficient generation
  • Developers familiar with SQL
  • Rapid prototyping of LLM interactions

    • Pros


  • Novel declarative approach to LLM programming
  • Concise expression of complex constraints
  • Automatic optimization of token usage
  • Strong research foundation from ETH
  • Interactive playground for development
  • Works across multiple models
  • Type safety for outputs
  • Good for exploratory development

    • Cons


  • New language with learning curve
  • Smaller community and ecosystem
  • Still in research/beta stage
  • Less production adoption
  • Limited compared to full frameworks
  • Documentation still evolving
  • May not integrate with existing codebases easily
  • Tooling and IDE support limited

    • Pricing


  • **Open Source**: Free, Apache 2.0 license
  • **Self-Hosted**: Free to use anywhere
  • **No Commercial Offering**: Pure research project

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