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TypeLLM·Sep 25, 2026·v0.1.6·open source
TypeLLM
TypeLLM is a framework that enables type-safe generation for autoregressive large language models without requiring changes to their architecture or weights. It allows models to retain native thinking and free-form generation capabilities while ensuring outputs strictly adhere to defined JSON schemas.
LLMType-SafeSGLangStructured Output
overview
TypeLLM is a framework that enables type-safe generation for autoregressive large language models without requiring changes to their architecture or weights. It allows models to retain native thinking and free-form generation capabilities while ensuring outputs strictly adhere to defined JSON schemas.
key features
- 01No out-of-schema hallucinations with JSON Schema enforcement
- 02Dependency-aware execution using DAGs and incremental prefix reuse
- 03Optional thinking mode with per-field budget for complex reasoning
- 04Permutation averaging to reduce option-order bias in enum tasks
- 05Vision-language model support for image inputs
- 06Negligible output-token cost with efficient batch processing
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