LMQL

LMQL provides a domain-specific language that allows developers to write LLM interactions declaratively using constraint syntax rather than imperative Python/JavaScript. The language compiles prompt templates, variable bindings, and logical constraints into optimized execution plans that manage context windows, token budgets, and conditional branching. Constraints are evaluated against LLM outputs in real-time, enabling early stopping, validation, and dynamic prompt adaptation without manual parsing or post-processing logic.

LMQL abstracts away provider-specific API differences (OpenAI, Anthropic, Llama, etc.) through a unified query interface that compiles to the appropriate backend calls. The abstraction layer handles parameter mapping, token counting, context window management, and response formatting across heterogeneous providers without requiring developers to write provider-specific code paths. This enables seamless model swapping and cost optimization by routing queries to different providers based on constraints or cost thresholds.

LMQL tracks costs across queries by integrating provider-specific pricing models (per-token rates for OpenAI, Anthropic, etc.) and aggregating costs across batch executions. The runtime provides cost estimates before query execution and detailed cost breakdowns after execution, enabling data-driven optimization decisions. This is particularly useful for cost-sensitive applications or teams managing budgets across multiple LLM providers.

LMQL tracks token consumption across prompt templates, variable bindings, and LLM outputs, enforcing hard limits on context window usage through declarative budget constraints. The runtime automatically truncates or summarizes inputs when approaching token limits, and provides visibility into token allocation across prompt components. This prevents context overflow errors and enables predictable cost and latency behavior without manual token counting or prompt engineering iterations.

LMQL enables conditional logic within prompt definitions that branches based on LLM outputs, variable values, or constraint satisfaction without explicit if-else statements. The language supports pattern matching, logical predicates, and state transitions that adapt subsequent prompts based on prior responses. This is compiled into an execution graph that manages state and control flow, enabling complex multi-step interactions (e.g., clarification loops, fallback strategies) to be expressed concisely as declarative constraints.

LMQL enforces structured output formats (JSON, YAML, key-value pairs) through declarative schema constraints that validate LLM responses in real-time. The language supports type checking, field validation, and format constraints that are evaluated against LLM outputs before returning results. If validation fails, the runtime can automatically re-prompt with corrected instructions or constraint hints, eliminating manual JSON parsing and error handling code.

LMQL compiles prompt templates into optimized execution plans that pre-compute static portions, manage variable substitution, and apply constraint-aware optimizations (e.g., reordering constraints for early termination). The compiler analyzes template structure, identifies opportunities for caching or batching, and generates efficient code that minimizes redundant computation. This enables faster execution and lower token usage compared to naive string interpolation approaches.

LMQL provides execution traces that show constraint evaluation, variable bindings, LLM outputs, and branching decisions at each step of query execution. Developers can inspect traces to understand why constraints succeeded or failed, how variables were bound, and which branches were taken. This enables interactive debugging of complex multi-step prompts without manual logging or print statements, accelerating iteration and troubleshooting.

LMQL supports batch execution of multiple queries with shared context or variable bindings, enabling efficient parallel processing across multiple LLM calls. The runtime manages batching, request pooling, and response aggregation to minimize latency and maximize throughput. This is particularly useful for processing large datasets or running multiple prompt variants simultaneously for A/B testing or ensemble approaches.

LMQL abstracts execution across local models (via Ollama, vLLM, or other inference servers) and remote APIs (OpenAI, Anthropic, etc.) through a unified query interface. The runtime handles model loading, inference, and response formatting transparently, enabling seamless switching between local and remote execution for cost optimization, latency reduction, or privacy compliance. This is particularly useful for hybrid deployments where some queries run locally and others use cloud APIs.

LMQL supports defining reusable prompt templates as functions or modules that can be composed into larger workflows. Templates can accept parameters, return structured outputs, and be combined with other templates through function calls or composition operators. This enables building libraries of domain-specific prompts that can be versioned, tested, and reused across projects without code duplication.