{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Enforces structured output from large language models by computing token masks that constrain decoding to valid context-free grammars, JSON schemas, or regular expressions with minimal per-token overhead.","skillfed_tags":["llm-inference","structured-output","grammar-constraints"],"use_cases":["Enforce JSON schema compliance in LLM outputs for API integrations or data pipelines requiring structured responses.","Constrain LLM generation to valid regular expressions or domain-specific grammars in real-time inference servers.","Integrate structured output constraints into vLLM, llama.cpp, or SGLang deployments without significant latency overhead.","Use within the Guidance Python library to build multi-turn LLM workflows with guaranteed output format compliance.","Embed in Chromium-based browsers to enforce JSON Schema for the Prompt API's structured output feature."],"what_it_does":"llguidance is a Rust library with Python bindings that implements constrained decoding for large language models. It computes token masks\u2014sets of valid next tokens\u2014that ensure LLM output conforms to a specified grammar, JSON schema, or regular expression. The library supports multiple grammar formats including JSON schemas, regular expressions, and context-free grammars in Lark-like syntax, and can be used directly from Python, Rust, C, or C++.\n\nThe library is designed for performance: mask computation takes approximately 50\u03bcs per token for a 128k-token vocabulary with negligible startup cost. It has been integrated into major LLM inference frameworks including vLLM, llama.cpp, SGLang, and Chromium, and powers OpenAI's Structured Output feature. It uses Earley's algorithm for parsing combined with regex derivatives and trie-based token traversal to avoid the startup overhead and memory costs of pre-computed automata approaches.","worth_installing":"Yes. The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and solves a critical problem in LLM inference\u2014ensuring structured output without sacrificing speed. It is production-ready, widely integrated into major frameworks, and backed by peer-reviewed research. Install if you need deterministic structured output from LLMs in any inference context."},"id":"llguidance","links":{"html":"https://skillfed.io/packages/llguidance","md":"https://skillfed.io/packages/llguidance.md","pypi":"https://pypi.org/project/llguidance/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":"MIT","license_treatment":"permissive","name":"llguidance","python_support":"supports_current","summary":"Bindings for the Low-level Guidance (llguidance) Rust library for use within Guidance"},"popularity":{"monthly_downloads":5546840,"position":2077,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.8.0"}