{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/13"}],"enrichment":{"capability":"Adds configurable safety and compliance guardrails to LLM applications by wrapping OpenAI's Python client to automatically validate and moderate inputs and outputs against a wide range of built-in checks.","skillfed_tags":["llm-safety","content-moderation","compliance"],"use_cases":["Protect customer-facing chatbots from jailbreak attempts and inappropriate content without modifying core chat logic.","Detect and mask personally identifiable information in LLM inputs and outputs to meet data privacy compliance.","Evaluate guardrail effectiveness on labeled datasets and benchmark different model configurations for safety performance.","Integrate safety checks into OpenAI Agents SDK workflows to ensure agent outputs meet compliance requirements.","Filter hallucinated or off-topic responses to keep LLM outputs within business scope and factual accuracy."],"what_it_does":"OpenAI Guardrails is a Python wrapper around OpenAI's client that intercepts and validates LLM requests and responses against configurable safety rules. It provides a drop-in replacement for the standard OpenAI client, letting you add guardrails without rewriting your application code. The package includes built-in checks for content moderation, PII detection, jailbreak attempts, hallucination detection, NSFW content, off-topic prompts, and URL filtering, all configured via JSON.\n\nThe package depends on openai, openai-agents, presidio-analyzer, pydantic, and thinc. It integrates with OpenAI's Agents SDK via GuardrailAgent for agent-based workflows and includes an evaluation framework to benchmark guardrail performance on labeled datasets. When a guardrail is triggered, it raises GuardrailTripwireTriggered, allowing you to handle violations gracefully. The spaCy model for PII detection is an optional but documented prerequisite if that guardrail is enabled.","worth_installing":"Yes, if you need to add safety and compliance checks to an OpenAI-based LLM application. The package is actively maintained, has low install friction, uses a permissive MIT license, and offers a drop-in integration pattern that minimizes code changes. It is still in preview (version 0.3.0), so expect potential API changes; verify stability for production use cases before committing to it."},"id":"openai-guardrails","links":{"html":"https://skillfed.io/packages/openai-guardrails","md":"https://skillfed.io/packages/openai-guardrails.md","pypi":"https://pypi.org/project/openai-guardrails/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-21","license_spdx":"MIT","license_treatment":"permissive","name":"openai-guardrails","python_support":"supports_current","summary":"OpenAI Guardrails: A framework for building safe and reliable AI systems."},"popularity":{"monthly_downloads":315200,"position":7689,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.0"}