Guidance Review 2026

Guidance review 2026: token-level constrained LLM generation with grammars, regex, and JSON schema — MIT open source — features, pros, cons, use cases.

Guidance is an open-source library that gives developers token-level control over LLM output. Instead of asking a model nicely for JSON and praying, Guidance lets you interleave normal Python with select statements, regex patterns, context-free grammars, and JSON schema constraints — and the library uses logit biasing during generation to make the model physically incapable of emitting tokens that would violate the constraint. The result is faster, cheaper, and bulletproof structured output: a JSON-schema-constrained generation never produces invalid JSON, a regex constraint always matches, a grammar constraint always parses. Guidance also supports stateful programs that branch on what the model produced, multi-turn role messages, tool calls, image inputs (where supported), and partial caching of shared prefixes for big speedups. It works with local models via transformers, llama.cpp, and vLLM, as well as hosted OpenAI and Anthropic APIs (with reduced constraint enforcement on hosted models that don't expose logits). Originally launched inside Microsoft Research, the project now lives at github.com/guidance-ai/guidance under MIT and is actively maintained by an independent community. There is no managed service or pricing — it's a pure Python library you install and use with your own compute.