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LMQL

Free#30 of 49 in LLM Application Development Frameworks

LMQL: Build prompt programs with typed outputs, explicit control flow and constrained decoding. Ranked #30 of 49 in LLM Application Development Frameworks by our editors (6.4/10); pricing: Free plan; best for constrained prompt programs with explicit control flow.

6.4/10Editor score
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LMQL6.4 Visit LMQL

At a glance

  • Editor score
    6.4 / 10
  • Pricing
    Free plan
  • Best for
    Constrained prompt programs with explicit control flow
  • Free plan
    Yes
  • Paid from
    None
  • RAG support
    Yes
  • Facts checked
    28 Sep 2026

  • Where it wins

    • Constrain generated outputs with logical rules and typed variables.
    • Use branching, loops, nested queries and multiple decoder algorithms.
    • Connect Python tools and model backends including OpenAI and Transformers.

  • Where it doesn't

    • Its specialized prompt-programming approach may not suit general app development.
    • JavaScript SDK support is not included.
    • Working with LMQL requires a Python-based workflow.

Our verdict on LMQL

LMQL is an open-source programming language and runtime for applications that interact with large language models. It is aimed at developers who want prompts to behave like structured programs, with typed variables, explicit control flow and constraints on generated output. The project includes a Python library, command-line tool, browser-based Playground IDE and chat-serving utilities. Its focus is a fit for constrained prompt programs rather than a general-purpose framework for every kind of LLM application.

Control over generation is the defining strength. LMQL supports logical output constraints, structured outputs, branching and loops, along with decoder algorithms such as argmax, sample and beam. Nested queries let developers reuse prompt components, while Python tool and function integration supports augmented reasoning. The chatbot library adds streaming and WebSocket serving. These capabilities suit workflows where outputs need defined structure or prompt logic needs to make decisions; teams seeking a simpler prompt wrapper may find the programming model more than they need.

LMQL can run with cloud or self-hosted deployment, and supports model backends including OpenAI, Azure OpenAI, Hugging Face Transformers, llama.cpp and Replicate. Integrations with LangChain, LlamaIndex and Pandas provide options for connecting it to adjacent Python workflows, including retrieval-based applications. It is open source under the Apache-2.0 license, with documentation, community and email support channels. Choose LMQL when explicit constraints and program flow matter more than a broad, language-agnostic application framework. Teams that need a JavaScript SDK or a less specialized approach should look elsewhere.

LMQL pricing

Plans Free planFree Free to use — no paid tier required for the core job.
See plans on lmql.ai

LMQL fact sheet

Free planYes
Paid fromNone
Primary languagePython
RAG supportYes
Agent and tool useYes
Workflow graphsNot verified
Prompt optimizationNot verified
Open-source licenseApache-2.0
JavaScript/TypeScript SDKNo
DeploymentCloud, Self-hosted
PlatformsWeb, Windows, macOS, Linux
SupportEmail, Community, Docs
Built forSolo, Small business, Mid-market (editorial estimate)
Integrations8 integrations: OpenAI, Azure OpenAI, Replicate, Hugging Face Transformers, llama.cpp, LangChain …
PricingFree plan
Websitelmql.ai
Facts checked28 Sep 2026

LMQL integrations

LMQL lists 8 integrations on its own site.

  • OpenAI
  • Azure OpenAI
  • Replicate
  • Hugging Face Transformers
  • llama.cpp
  • LangChain
  • LlamaIndex
  • Pandas

Alternatives to LMQL

See all LMQL alternatives →

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Featured on iTechGuides

Featured on iTechGuides — LMQL 6.4/10

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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