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With AI, Everyone is a Dev. EveryDev.ai © 2026
    1. Home
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    3. XGrammar
    XGrammar icon

    XGrammar

    AI Development Libraries
    Featured

    An open-source library for efficient, flexible, and portable structured generation for LLMs, enabling near-zero overhead constrained decoding with support for JSON, regex, and custom context-free grammars.

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    At a Glance

    Pricing
    Open Source

    Fully free and open-source under the Apache 2.0 license. Install via pip and use in any project.

    Engagement

    Available On

    Linux
    macOS
    Windows
    API
    CLI

    Resources

    WebsiteDocsGitHubllms.txt

    Topics

    AI Development LibrariesLLM OrchestrationLocal Inference

    Alternatives

    WebLLMOllamaMiniMind
    Developer
    MLC AIEst. 2023

    Listed Sep 2026

    About XGrammar

    XGrammar is an open-source library developed under the mlc-ai organization that brings fast, flexible structured generation to large language model inference. Released under the Apache 2.0 license, it is installable via pip and integrates directly into LLM inference engines as a backend for constrained decoding. The project publishes versioned releases on GitHub and PyPI, with active development continuing through 2026.

    What It Is

    XGrammar is a structured generation engine that uses constrained decoding to guarantee 100% structural correctness in LLM outputs. It supports general context-free grammar, enabling a broad range of output formats including JSON, regex, and custom EBNF or Lark grammars. The library is built around a minimal, portable C++ backend that can be embedded into diverse runtime environments, and it exposes APIs for Python, C++, JavaScript, and Swift. The project's stated mission is to bring "flexible zero-overhead structure generation everywhere."

    How Constrained Decoding Works

    XGrammar intercepts the token generation process and applies grammar-based bitmask operations to filter the model's logit distribution at each decoding step, ensuring only tokens that conform to the target structure are sampled. Key components in the API include GrammarCompiler, CompiledGrammar, and GrammarMatcher, which together compile a grammar definition and then match it token-by-token during inference. The library also supports a "Structural Tag" system for tool calling and reasoning workflows, allowing structured outputs to be interleaved with free-form text.

    Platform and Hardware Coverage

    The library is designed for universal deployment across a wide range of environments:

    • Operating systems: Linux, macOS, Windows
    • Hardware: CPU, NVIDIA GPU, AMD GPU, Apple Silicon (MPS), TPU
    • Languages: Python, C++, JavaScript, Swift
    • Models: Qwen, Llama, DeepSeek, Phi, Gemma, and others

    For Apple Silicon users, a Metal-accelerated variant is available via pip install "xgrammar[metal]". Community Rust bindings are also available through the third-party xgrammar-rs project.

    LLM Engine Integration

    XGrammar is the default structured generation backend for several major open-source LLM inference engines. According to the project's README, it has been officially integrated into vLLM (December 2024), SGLang (November 2024), TensorRT-LLM (January 2025), MLC-LLM (November 2024), Modular's MAX (February 2025), OpenVINO GenAI (September 2025), and Mirai (December 2025). The library is co-designed with LLM inference engines to enable what the project describes as "zero-overhead structured generation."

    Update: XGrammar-2 and v0.2.8

    In May 2026, the team released XGrammar-2, described in a blog post as a "fast, customizable structured generation" engine targeting agentic LLM workloads. A corresponding paper, "XGrammar-2: Dynamic and Efficient Structured Generation Engine for Agentic LLMs," was published in the Proceedings of the ACM Conference on AI and Agentic Systems (2026). The latest stable release on GitHub is v0.2.8, published September 24, 2026, with a development version (0.2.9.dev) visible in the documentation. The project maintains an active release cadence without a fixed calendar schedule, publishing as features and fixes are ready.

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    Pricing

    OPEN SOURCE

    Open Source

    Fully free and open-source under the Apache 2.0 license. Install via pip and use in any project.

    • Constrained decoding
    • JSON, regex, and custom grammar support
    • Python, C++, JavaScript, Swift APIs
    • LLM engine integration
    • Structural Tag system

    Capabilities

    Key Features

    • Constrained decoding for 100% structural correctness
    • JSON generation support
    • Regex-based structured generation
    • Custom EBNF grammar support
    • Lark grammar support
    • Structural Tag system for tool calling and reasoning
    • Near-zero overhead structured generation
    • Python, C++, JavaScript, and Swift APIs
    • Portable C++ backend
    • LLM inference engine integration
    • Bitmask operations for token filtering
    • Grammar serialization
    • Runtime safeguards
    • OpenAI tool call schema support
    • JavaScript API support

    Integrations

    vLLM
    SGLang
    TensorRT-LLM
    MLC-LLM
    Modular MAX
    OpenVINO GenAI
    Mirai/uzu
    PyPI
    WebLLM
    API Available
    View Docs

    Ratings & Reviews

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    Developer

    MLC AI

    MLC AI builds open-source machine learning compilation and deployment tools, with a focus on running LLMs universally across hardware environments. The organization is backed by contributors from CMU Catalyst, UW SAMPL, SJTU, OctoML, and the broader MLC community. Their flagship projects include MLC LLM for native runtime deployment and WebLLM for in-browser inference, both built on Apache TVM. MLC AI prioritizes privacy, cost reduction, and hardware diversity by enabling client-side AI execution without server dependencies.

    Founded 2023

    Used by

    vLLM (XGrammar integration)
    SGLang (XGrammar integration)
    NVIDIA TensorRT-LLM (XGrammar integrati…
    Modular MAX (XGrammar integration)
    +2 more
    Read more about MLC AI
    WebsiteGitHub
    2 tools in directory

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