MCP简体中文Upgrade
Market/Categories/Developer & AI Platform/LMQL

Independent product intelligence · Worldwide · 2026-05-01 → 2026-07-31

LMQLlmql.ai

LMQL is a query language for large language models.

Developer & AI Platformlmql.aiTracked since 2026-08-193 discovery sources
3.7KBacklinks
530Referring domains
25Domain authority
+0.8%Referring domains · 90 days

Authority & distribution

How strong is LMQL’s web footprint?

Backlinks measure accumulated distribution; referring domains show how broadly that authority is spread. Both are observable on the open web, so both are published for every product here.

Backlinks

3.7KCurrent snapshot

Referring domains

530Current snapshot

Authority

25Domain authority · category median 22

90-day move

+0.8%Referring domains, last 90 days
At 25, LMQL sits above the 22 median for its category.

Trajectory · Pro

The counts above are the latest of 370 daily observations. The curve through them is on the full profile.

See the trajectoryOpen the full profile

Traffic & engagement

Where LMQL ranks

Measured · Pro

Measured for this product: global rank, country rank and category rank.

Unlock this profileOpen the full profile

AI referral visibility

Which AI assistants are already sending LMQL traffic?

Measured · Pro

Measured for this product: AI referral traffic.

Unlock this profileOpen the full profile

Product profile

What LMQL is selling.

Traffic makes more sense when you can connect it to the actual product promise.

“LMQL”

LMQL is a query language for large language models.

Developer & AI PlatformSeen 2026-08-19 → 2026-08-21Coverage tier C

What we have measured

4 of 8 signal groups are available for this product: identity, authority and backlinks, AI referrals, search demand.

Not measured for this domain: traffic and engagement, audience demographics, country distribution, acquisition mix. Those sections are left out of the page rather than filled with estimates.

Canonical website: https://lmql.ai/

Observed across 3 discovery sources.

Research questions

What the evidence answers about LMQL.

Every answer is tied to a source we collected, and the questions with no evidence behind them are left unanswered rather than guessed.

What features and capabilities does LMQL offer?

LMQL offers nested queries for modular prompt reuse, execution trace visualization, and works across multiple backends including llama.cpp, OpenAI, and Transformers. Key features include constrained LLMs, typed variables and regex, multi-part prompts, distribution measurement, Python support, meta prompting, and tool augmentation.

How does LMQL handle constraints in LLM outputs?

LMQL enforces hard constraints on LLM outputs using 'where' clauses in its syntax. This ensures generated outputs satisfy specific criteria, such as length limits or stopping at certain tokens, and allows for typed variables to guarantee output format.

What is LMQL and what problem does it solve?

LMQL is a programming and query language for interacting with large language models. It provides robust and modular prompting using types, templates, constraints, and an optimizing runtime. It was created by the SRI Lab at ETH Zurich and contributors.

What is LMQL used for and in what situations?

LMQL is used for prompt engineering and constrained generation with LLMs. It allows developers to write modular, reusable prompt components and use Python control flow for prompt construction. It supports use cases like chatbots, tool augmentation, and multi-part prompts.

Who is LMQL for?

LMQL is designed for developers working with large language models. It is categorized as a developer and AI platform tool, providing a programming language interface for LLM interaction.

What is LMQL?

LMQL is a query language for large language models.

Behind the lock

Unlock full LMQL intelligence.

  • Backlink trajectoryThe figures above are the latest observation. The daily curve behind them holds 370.
  • AI referral detailHow much traffic assistants send.
  • Search structure and ranking depthOrganic against paid, brand against non-brand, and how many keywords rank in the top three against the long tail — which is what decides whether the demand is portable or tied to the name.
  • The rest of the marketPage through every leaderboard 100 rows at a time, put any five products side by side, and query the whole dataset over API / MCP.

Worldwide estimates · Data period 2026-05-01 → 2026-07-31. Figures are measured estimates intended for market research. Signals we have not measured for this domain are omitted, never estimated; a measured zero is still reported as zero. How this was measured