mlc-ai/xgrammar

Fast, Flexible and Portable Structured Generation

What it solves

Large Language Models (LLMs) often struggle to produce output in a specific, machine-readable format. XGrammar solves this by ensuring 100% structural correctness of the generated text, preventing the model from producing malformed JSON or invalid syntax.

How it works

It uses constrained decoding to restrict the model's output to follow a specific set of rules. It supports general context-free grammar (CFG), allowing it to handle a wide range of structures including JSON, regular expressions (regex), and custom grammars.

Who it’s for

Developers building LLM-powered applications that require reliable structured data output, as well as developers of LLM inference engines who want to integrate a high-performance structured generation backend.

Highlights

  • Near-zero overhead: Optimized for extreme efficiency, particularly in JSON generation.
  • Universal deployment: Works across Linux, macOS, and Windows, and supports a wide range of hardware (NVIDIA GPU, AMD GPU, Apple Silicon, CPU, TPU).
  • Broad model support: Compatible with major models like Llama, Qwen, DeepSeek, Phi, and Gemma.
  • Broad integration: Integrated as the default backend for vLLM, SGLang, TensorRT-LLM, and MLC-LLM.

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