LMQL
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LMQL: A Programming Language for Large Language Models
Listing updated Dec 1, 2025
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What is LMQL?
LMQL's Top Features
Key capabilities that make LMQL stand out.
Nested Queries
Scripted Prompting
Custom Constraints
Optimizing Runtime
Playground IDE
Local Model Support
Tool Augmentation
High-level Constraint Management
Sequential Query Execution
Integration with Popular Libraries
Use Cases
Who benefits most from this tool.
Developers
Creating complex and modular prompt structures for LLMs.
Researchers
Implementing advanced prompting techniques and optimization strategies.
Data Scientists
Utilizing structured prompting for data analysis and interpretation.
AI Practitioners
Building intelligent chatbots and interactive systems.
Educators
Teaching and demonstrating advanced prompting and LLM capabilities.
Hobbyists
Experimenting with LLMs and custom prompt designs.
Enterprises
Optimizing internal and customer-facing interactions through advanced LLM prompting.
AI Enthusiasts
Exploring the potential of procedural programming in LLM prompting.
Tech Startups
Innovating new applications and solutions using optimized LLM prompts.
Community Members
Participating in the development and documentation of LMQL features.
Tags
programming languagelarge language modelstypestemplatesconstraintsoptimizing runtimequeriesSRI LabETH Zurichnested queriesscripted promptingcustom constraintsPlayground IDE
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Frequently Asked Questions
What is LMQL?
LMQL is a programming language designed for large language models, enabling robust and modular prompting through types, templates, and constraints.
Who developed LMQL?
LMQL was developed by the SRI Lab at ETH Zurich along with various contributors.
Can LMQL handle nested queries?
Yes, LMQL supports nested queries, which allow for modularized local instructions and the re-use of prompt components.
What is scripted prompting in LMQL?
Scripted prompting in LMQL allows for control flow and branching behavior in prompt construction, enabling more sophisticated prompt designs.
What constraints can be applied in LMQL?
LMQL allows for high-level constraints such as length restrictions and logical conditions to be applied to prompt outputs.
Is there an IDE available for LMQL?
Yes, LMQL provides a Playground IDE which can be used for writing and testing queries.
Can I use local models with LMQL?
Yes, LMQL supports self-hosted models via frameworks like 🤗 Transformers and llama.cpp.
What are some use cases of LMQL?
LMQL can be used for complex prompt generation, chatbots, interactive systems, and any scenario requiring structured prompting and constrained generation.
Does LMQL support integration with other tools?
Yes, LMQL supports tool augmentation and has integrations with LangChain, LlamaIndex, and Pandas among others.
How does LMQL optimize LLM queries?
LMQL uses an optimizing runtime to efficiently handle constraints and improve the performance of large language model queries.