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LMQL
LLM Orchestrators
Overview
A declarative programming language for Large Language Models. It combines the power of natural language prompting with the precision of Python-like control flow, allowing for constrained generation and efficient token usage.
LMQL is a declarative query language for LLMs that blends natural-language prompting with Python-like control flow and constraints, enabling efficient, constrained generation. It lets you express what valid output looks like and let the runtime enforce it. It targets developers who want high-level, reliable LLM programming.
Key Features
- Declarative LLM query language
- Constrained generation
- Python-like control flow
- Token-efficient execution
- Open-source
Best For
Developers who want a high-level, constraint-driven way to program LLMs.
Pros & Cons
Pros
- Expressive constraints
- Efficient token usage
- Open-source
Cons
- Learning a new language
- Smaller ecosystem
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Pulse Verdict
“The SQL for LLMs. LMQL provides the high-level abstractions and constraints needed to build reliable, high-performance AI applications without the usual token waste.”
Pricing
Open-source and free; you supply the model.
Pricing changes often — confirm current plans on the official site.