Outlines

Enforces LLM outputs to strictly conform to JSON schemas by integrating with the model's token generation loop. Uses a finite-state machine (FSM) built from the schema to mask invalid tokens at each generation step, ensuring the output is always valid JSON matching the provided schema structure. This eliminates post-generation parsing failures and guarantees structural correctness without requiring output validation.

Constrains LLM token generation to match a regular expression pattern by converting the regex into a finite automaton and masking invalid tokens at each step. The regex is compiled into a state machine that tracks which tokens are valid continuations from the current state, ensuring outputs strictly adhere to the pattern without post-generation filtering.

Enables combining multiple constraints into a single generation pass by composing constraint state machines. The framework applies all constraints simultaneously, masking tokens that violate any constraint. This allows complex requirements like 'JSON schema AND matches regex pattern' to be enforced without multiple generation passes or post-processing.

Provides built-in profiling tools to measure constraint overhead and identify bottlenecks. The framework tracks time spent in constraint state updates, token masking, and sampling, allowing users to optimize constraint definitions or switch to faster constraint types. Includes suggestions for constraint simplification based on profiling data.

Provides utilities to validate constraint definitions before deployment and test constraints against sample inputs. The framework checks constraint syntax, detects unreachable states in constraint state machines, and runs constraints against test cases to ensure they behave as expected. This prevents constraint errors from reaching production.

Caches compiled constraint state machines to avoid recompilation on repeated use. When the same constraint is used multiple times (e.g., in a batch or across multiple requests), the framework reuses the cached state machine instead of recompiling it. This significantly reduces initialization overhead for repeated constraints.

Enforces LLM outputs to conform to context-free grammars (CFGs) by building a parser that tracks valid tokens at each generation step. The grammar is parsed into a state machine that knows which tokens can legally follow the current parse state, enabling generation of syntactically valid code, markup, or domain-specific languages without post-generation validation.

Provides a unified interface for applying structured generation constraints across multiple LLM backends (transformers, vLLM, llama.cpp, Ollama, OpenAI API) by abstracting the token generation loop. The framework detects the backend type and applies token masking at the appropriate level — either by intercepting the model's forward pass (local models) or by post-processing logits (API-based models) — ensuring constraints work consistently regardless of deployment.

Implements low-level token generation control by intercepting the model's logits (raw output scores) and zeroing out invalid tokens before sampling. The framework applies constraint-specific masking functions that set logits to negative infinity for tokens that violate the constraint, forcing the model to sample only from valid continuations. This happens at the token level during generation, not after.

For LLM backends that do not support logits access or token masking (e.g., OpenAI API, Claude), applies constraints through prompt engineering and post-generation validation. The framework generates a detailed prompt that instructs the model to follow the constraint, then validates the output against the constraint and retries if necessary. This provides constraint guarantees across all backends, though with higher latency due to potential retries.

Enables real-time token-by-token generation while maintaining constraint guarantees by applying masking at each step of the generation stream. The framework buffers tokens as they are generated, applies constraints incrementally, and yields valid tokens to the caller. This allows streaming output to users while ensuring the final result conforms to the constraint, without waiting for full generation to complete.

Processes multiple prompts with the same constraint in parallel, applying token masking to all sequences in a batch simultaneously. The framework batches constraint state updates and logits masking operations, leveraging GPU parallelism to generate multiple constrained outputs efficiently. This is significantly faster than sequential generation for large batches.

Allows users to define custom constraint types by implementing a simple interface (e.g., a function that takes current state and returns valid next tokens). The framework integrates custom constraints into the generation pipeline, applying them alongside built-in constraints. This enables domain-specific constraints that are not covered by JSON schema, regex, or grammar approaches.

Provides native integration with vLLM's batching and scheduling engine to apply constraints at the vLLM level, enabling high-throughput constrained generation with minimal overhead. The framework hooks into vLLM's token generation loop to apply masking, leveraging vLLM's optimizations for batching, paging, and scheduling. This is the fastest approach for production deployments.