LMQL

LMQL is a programming language for language models that uses types, templates, and constraints to control LLM interaction and output generation.

LMQL is a programming language designed for interacting with Large Language Models. It allows developers to define prompts using types, templates, and constraints, providing structure to model outputs. The system uses an optimizing runtime to execute these queries, which can be embedded directly within Python code. Developers can modularize prompt components using nested queries to reuse local instructions. The language works across multiple backends, allowing users to switch between models with a single line of code. It was created by the SRI Lab at ETH Zurich and contributors.

LMQL at a glance

Best for: Software engineer, Researcher
Runs on: Open source
Pricing: Free
Released: April 2023
Category: AI coding tool
Website: lmql.ai

Key features

Constrained generation for specific output formats
Nested queries for modularized prompt components
Typed variables for guaranteed output structure
Python-based control flow for prompt construction
Portability across different LLM backends
Optimizing runtime for execution
String interpolation for prompt generation

Use it for

Building structured LLM prompts with custom constraints
Developing modular and reusable prompt components
Executing LLM queries from within Python programs
Switching between different LLM backends

Alternatives to LMQL

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LangChain4j — Integrates LLMs and vector stores directly into Java applications.
LLM CLI — It enables command-line interaction with remote APIs and local machine models.
llmware — Build private, local RAG pipelines with 300+ prepackaged models and integrated document parsing.
Langfa.st — Compare model responses side-by-side to validate prompts before deployment.
Llmll — The compiler proves each function against its contract with Z3 before merging.
vLLM — Maximizes throughput with PagedAttention and continuous batching.
LightLLM — It delivers high-speed inference and serving performance for large language models.
MLX LM — Runs LLMs and fine-tuning natively on Apple silicon hardware.
MLC LLM — Enables native optimization and deployment of AI models across platforms.
AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
llm-d — Deploys production-grade LLM serving on existing hardware clusters.

All LMQL alternatives