LMQL - 2026 Pricing, Features, Reviews & Alternatives

LMQL is a Python-based programming language for structured, programmatic interaction with large language models (LLMs). Designed for developers and researchers, it combines a query-style syntax with Python scripting to enable fine-grained control over prompt construction, output constraints, and model behavior. Constraint types include value sets, regex patterns, integer types, and stopping conditions, all enforced at token generation time rather than post-hoc filtering.

The platform supports multiple LLM backends, including OpenAI models and locally hosted models via Hugging Face Transformers. Developers can define typed output variables, branch on intermediate model outputs, and compose multi-step reasoning pipelines within a single query. Scripted control flow allows conditional logic, loops, and nested prompts, making it suited for complex agentic workflows, structured data extraction, and automated evaluation tasks.

LMQL targets software engineering teams, AI researchers, and data science practitioners who require reproducible, auditable LLM interactions. Deployment options include local Python environments and cloud-connected API configurations. The constraint-guided decoding architecture reduces reliance on post-processing by shaping outputs during generation, improving structural reliability across batch and interactive workloads.