Guardrails AI
Open-source Python framework with the largest community-driven validator hub for LLM input/output guards.
What it does
An open-source Python framework for adding structural, type, quality, and security guarantees to LLM outputs. Features the Guardrails Hub — the largest community-driven collection of pre-built validators covering toxicity, PII detection, hallucination, jailbreak prevention, prompt injection detection, RAG relevance, and more. 19k+ GitHub stars. Also launched the Guardrails Index — the first benchmark comparing 24 guardrails across 6 categories. Snowglobe is the enterprise product.
Security relevance
Guardrails AI sits in the request/response path and applies composable validators at both input and output. Validators can be combined into Guards — input guards intercept prompts for injection and sensitive data, output guards check for PII leakage, toxicity, hallucination, and format compliance. The modular design means you deploy only the validators you need. Supports any LLM provider via LiteLLM integration.
When to use it
Use when you need flexible, composable guardrails for LLM applications. Python library that integrates into application code or runs as a standalone server via Flask/Docker. Lower barrier than NeMo Guardrails (no Colang to learn) while offering more security-focused validators than most alternatives. Start with the Hub to find validators matching your risk profile.
OWASP coverage
Risks addressed — mapped to both OWASP Top 10 standards. 4 in LLM, 2 in Agentic.
The raw record
What Yuntona stores. Single source of truth — fork it on GitHub.
name: Guardrails AI slug: guardrails-ai type: Mixed category: AI Guardrails & Firewalls url: https://guardrailsai.com reviewed: 2026-04 added: 2026-04 updated: 2026-04 risks: llm: [LLM01, LLM02, LLM06, LLM09] asi: [ASI01, ASI02] complexity: Guided Setup pricing: — audience: Builder lifecycle: [deploy] tags: [Framework, Guardrails, Open Source, Python, Validators]