lmql
LMQL (Language Model Query Language) is a Python superset and runtime that lets you embed LLM calls directly inside code, using templated variables plus decoding algorithms and constraints (e.g., logit masking, datatype/format constraints, stopping conditions). It supports sync/async execution, multi-model backends (e.g., OpenAI, Azure OpenAI, HuggingFace Transformers), and includes tooling such as a playground and an inference API for serving models.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Secrets are expected to be provided via environment variables or an api.env file, which is generally safer than hardcoding; however, the provided content does not describe how secrets are redacted in logs, nor does it describe TLS or rate limiting for LMQL endpoints in detail. Scope/permission granularity is not applicable because auth appears to be for upstream model providers rather than an LMQL-hosted API with scoped access.
⚡ Reliability
Best When
You want to write LLM-assisted programs with Python-like control flow, and you need strong control over outputs via constraints and decoding strategies across OpenAI/Azure/Transformers backends.
Avoid When
You require an opinionated SaaS with turnkey authentication, billing, and HTTP-based APIs as the main contract; LMQL is primarily a developer library/runtime plus optional local inference/streaming endpoints.
Use Cases
- • Constraint-guided text generation (format/datatype/length/stopping constraints)
- • Schema-safe structured output / controlled decoding
- • Programmatic LLM workflows with Python control flow and variables
- • Parallel/async LLM execution and batching
- • Interactive chat or tool-use patterns integrated into code
- • Local or hosted model inference using a consistent programming model
Not For
- • Use as a lightweight templating engine without programmatic logic
- • Projects needing strict REST CRUD-style APIs and stable HTTP semantics as the primary interface
- • Environments where running arbitrary model prompts is disallowed by policy (it executes LLM calls driven by code)
Interface
Authentication
Auth guidance is described for OpenAI credentials via environment variables or an api.env file; auth to LMQL itself (library/runtime) is not described as a hosted service.
Pricing
No pricing model for LMQL is described in the provided content; costs depend on the chosen upstream model provider (e.g., OpenAI/Azure) and/or local hardware.
Agent Metadata
Known Gotchas
- ⚠ LMQL is a Python-superset language/runtime; agent integration typically involves embedding/compiling and executing .lmql/Python-embedded queries, not calling a conventional REST CRUD API.
- ⚠ When using the Playground or local Transformers via lmql run, an inference API instance may need to be started (lmql serve-model).
- ⚠ Auth is handled via environment variables/api.env for upstream providers; misconfiguration can fail at runtime when the model backend is contacted.
- ⚠ Rate limits are not described in the provided content; limits will depend on the upstream provider and any local inference settings.
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Scores are editorial opinions as of 2026-03-29.