outlines

Outlines uses FSM-constrained token sampling to enforce valid JSON structure directly during generation. It accepts a JSON schema or Pydantic model, then filters tokens at each step so the model can only produce outputs matching your schema, eliminating post-generation validation.

Yes. Outlines automatically converts Pydantic models into JSON schemas and applies FSM-based constraints during sampling. This ensures generated outputs are type-safe and conform to your model's field definitions without manual schema writing.

Outlines supports Transformers, vLLM, llama.cpp, and OpenAI. You can deploy the same structured generation logic across local and remote backends, letting you choose based on latency, cost, and infrastructure requirements.

Outlines uses context-free grammars (CFGs) and finite-state machines to define valid token sequences. At each generation step, it filters the model's next-token distribution to only allow tokens that keep the output on a valid path, guaranteeing grammar conformance.

Use FSM constraints when you need guaranteed validity without re-sampling rejected outputs, want faster inference by avoiding wasted tokens, or require production-grade reliability. Post-processing works for non-critical tasks; constraints are better for schema-dependent workflows at scale.

Yes. Beyond JSON, Outlines supports constrained generation for XML, code, and custom formats using grammar-based rules. You define the structure via CFG or FSM, and it enforces validity during sampling across all supported backends.