{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/5"}],"enrichment":{"capability":"Constrains language model token generation to enforce JSON Schema, regex, or other structured output formats, ensuring models produce valid formatted output at every generation step.","skillfed_tags":["llm-inference","structured-output","token-filtering"],"use_cases":["Extract structured data (names, dates, classifications) from language model responses as valid JSON matching a Pydantic schema","Enforce regex patterns (e.g., phone numbers, email addresses, sentiment labels) in model outputs without post-processing","Generate valid JSON arrays or objects from language models in batch or beam-search scenarios with different constraints per input","Integrate format enforcement into vLLM or other inference servers via guided decoding without custom inference code","Reduce hallucinations by constraining optional fields and allowing the model to control JSON formatting within valid bounds"],"what_it_does":"lm-format-enforcer is a token-filtering library that constrains language model generation to produce valid output matching a specified format. Rather than post-processing or prompt engineering, it operates at the token level during inference, filtering which tokens the model is allowed to emit at each step. This ensures the output strictly conforms to JSON Schema, regular expressions, or other structured formats while giving the model freedom in whitespace and field ordering to reduce hallucinations.\n\nThe library works by combining a character-level parser (for the target format) with a tokenizer prefix tree (representing all possible token sequences). It integrates into existing inference pipelines\u2014transformers, vLLM, LangChain, llama.cpp, and others\u2014without modifying the model or high-level loop. It supports batched generation, beam search, optional JSON fields, and nested structures, making it practical for production use cases where strict output format is required.","worth_installing":"Yes. The package solves a real problem\u2014ensuring language models produce valid structured output\u2014with low install friction, active maintenance, no security vulnerabilities, and permissive licensing. It integrates cleanly into existing inference pipelines and supports multiple format types and model frameworks. Install if you need deterministic output format from language models; skip if you only need post-processing validation."},"id":"lm-format-enforcer","links":{"html":"https://skillfed.io/packages/lm-format-enforcer","md":"https://skillfed.io/packages/lm-format-enforcer.md","pypi":"https://pypi.org/project/lm-format-enforcer/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-08-24","license_spdx":null,"license_treatment":"permissive","name":"lm-format-enforcer","python_support":"supports_current","summary":"Enforce the output format (JSON Schema, Regex etc) of a language model"},"popularity":{"monthly_downloads":3555892,"position":2578,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.11.3"}