Outlines Structured Generation

• You are running local models (Transformers, llama.cpp, vLLM) and need structured outputs without API round-trips or prompt hacking.
• You have Pydantic schemas already defined and want the model to fill them directly, including nested models, enums, and field constraints.
• You are building a batch extraction or classification pipeline where output shape must be deterministic and downstream code cannot tolerate parsing errors.
• You need maximum inference speed and cannot afford the latency of generation-then-validation retry loops.

• You are exclusively using hosted API models (e.g., OpenAI, Anthropic) and need automatic retry/healing on failures — Instructor is a better fit there.
• You prefer a declarative query syntax over Python schema definitions — LMQL may suit you better.
• Your use case requires token healing or complex multi-step prompt workflows — consider Guidance instead.

Outlines compiles your schema into a Finite State Machine and masks invalid tokens at every sampling step. This makes malformed outputs impossible by construction, not just unlikely.

Pass any BaseModel subclass directly to outlines.generate.json(). Nested models, Enum fields, Literal types, and Field validators all translate automatically into generation constraints.

Beyond JSON, Outlines provides generate.regex() for pattern-matched strings, generate.choice() for fixed label sets, and generate.integer() / generate.float() for numeric types.

Supports Hugging Face Transformers, llama.cpp (GGUF files), and vLLM for high-throughput multi-GPU deployments. Basic OpenAI API support is also available.

The FSM is compiled once per schema and cached. When only one valid token exists at a position, Outlines fast-forwards through it automatically — making structured generation as fast or faster than unconstrained sampling.

Because invalid outputs cannot be produced, there is no need for generation-then-validation cycles. This simplifies application logic and reduces latency in production pipelines.

Extract typed fields — names, dates, prices, enums — from unstructured text into Pydantic model instances with guaranteed schema compliance, ready for direct use in application code.

Use generate.choice() to constrain the model to a fixed label set. No need for output parsing or prompt tricks to prevent the model from inventing new categories.

Process large document sets where every output must conform to the same shape. The deterministic output structure integrates cleanly with databases, APIs, and downstream ML steps.

Generate structurally valid synthetic records — user profiles, product listings, application forms — for testing, seeding databases, or prototyping data pipelines.