LMQL

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LMQL (Language Model Query Language) is a high-level query language designed specifically for working with large language models such as OpenAI GPT or other LLM backends. Instead of managing long prompts, ad‑hoc templates, and scattered post-processing logic, developers can define structured queries that combine natural language prompts with programming constructs, constraints, and control flow in a single place. LMQL brings familiar ideas from traditional query and programming languages—like variables, filters, scoring, and modular composition—into the LLM world. With LMQL, you can specify what the model should produce, how it should behave, and which constraints it must obey, all inside one concise script. The runtime takes care of prompt construction, token streaming, partial validation, and efficient interaction with the underlying model API. This makes LLM applications easier to debug, test, version, and reuse across projects. LMQL fits naturally into modern development workflows: you can embed queries in Python, integrate them into existing backends, and combine them with external tools, APIs, and data sources. Whether you are building complex agents, structured data extractors, code assistants, or evaluation pipelines, LMQL helps you move from ad‑hoc prompting to robust, maintainable, and testable LLM programs, while keeping full control over latency, cost, and output quality.

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Features

Declarative LLM query language
Integrated prompt and control flow
Token-level constraints and validation

Use Cases

Build robust conversational agents where dialog flows, constraints, and tool calls are defined in clear LMQL scripts instead of fragile prompt chains.
Implement structured information extraction from unstructured text, enforcing formats like JSON, SQL, or domain-specific schemas at generation time.
Create LLM-powered code assistants that combine natural-language instructions with programmatic checks, testing, and repository context.
Design evaluation and red-teaming pipelines that query models with consistent prompts, capture intermediate reasoning, and enforce safety filters.
Prototype and iterate on complex LLM workflows quickly, while keeping the full history of prompt and logic changes under version control.

LMQL

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Frequently Asked Questions

What is LMQL and how is it different from plain prompt engineering?

Which language models and providers can I use with LMQL?

Do I have to learn a new programming language to use LMQL?

Is LMQL free to use and how does pricing work?

Can LMQL help reduce hallucinations and enforce output formats?

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