{"id":"eth-sri-lmql","name":"lmql","homepage":"https://lmql.ai","repo_url":"https://github.com/eth-sri/lmql","category":"ai-ml","subcategories":[],"tags":["ai-ml","llm","python","constrained-decoding","tooling","inference-api","async","openai","transformers"],"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.","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)"],"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.","alternatives":["LangChain/LangGraph","DSPy","Guidance","Prompt templates with JSON schema validation (e.g., pydantic + constrained decoding)","Hugging Face Transformers generation with custom logits processors/constraint libraries"],"af_score":58.8,"security_score":64.8,"reliability_score":36.2,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T15:01:42.116450+00:00","interface":{"has_rest_api":true,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":true,"sdk_languages":["Python"],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":["Environment variable configuration (OPENAI_API_KEY or LMQL_OPENAI_SECRET/LMQL_OPENAI_ORG)","api.env file with openai-org/openai-secret"],"oauth":false,"scopes":false,"notes":"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":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"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."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":58.8,"security_score":64.8,"reliability_score":36.2,"mcp_server_quality":0.0,"documentation_accuracy":85.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":85.0,"rate_limit_clarity":20.0,"tls_enforcement":90.0,"auth_strength":70.0,"scope_granularity":30.0,"dependency_hygiene":55.0,"secret_handling":75.0,"security_notes":"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.","uptime_documented":0.0,"version_stability":60.0,"breaking_changes_history":50.0,"error_recovery":35.0,"idempotency_support":"false","idempotency_notes":"Not assessable from the provided README; LMQL programs are generally prompt/execution-driven where retries may re-run generation unless the application is designed to be idempotent.","pagination_style":"none","retry_guidance_documented":false,"known_agent_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."]}}