Developer Adds Native GBNF Grammar Support to Local LLM Structured-Output Library

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A developer building a local, low-latency text classification pipeline found that existing schema types in the shapecraft library could not directly apply GBNF grammars to constrain llama.cpp model output. GBNF (Grammar-Based Normalization Format) forces a language model to produce only tokens valid under a defined grammar, making malformed outputs structurally impossible rather than merely unlikely. To fill the gap, the developer added a new schema-input type accepting a raw GBNF string, returning a plain string result rather than a parsed object. When paired with a llama.cpp backend, the grammar is enforced at the token level with a "constrained" guarantee; for cloud APIs like OpenAI or Anthropic that lack a grammar parameter, the grammar is injected into the prompt and outputs are validated post-generation with a "best-effort" guarantee. The implementation also includes a bundled GBNF interpreter that handles recursive rules and avoids catastrophic backtracking by deduplicating parser states by position rather than path.

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