{"id":"eth-sri-lmql","name":"lmql","af_score":58.8,"security_score":64.8,"reliability_score":36.2,"what_it_does":"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.","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.","last_evaluated":"2026-03-29T15:01:42.116450+00:00","has_mcp":false,"has_api":true,"auth_methods":["Environment variable configuration (OPENAI_API_KEY or LMQL_OPENAI_SECRET/LMQL_OPENAI_ORG)","api.env file with openai-org/openai-secret"],"has_free_tier":false,"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."],"error_quality":0.0}