Guardrails AI

What does Guardrails AI do?

Guardrails AI combines simulation, evaluation, and runtime protection so teams can test and govern GenAI before users see failures. Snowglobe generates realistic personas and hundreds of conversations in minutes, then turns those runs into judge-labeled datasets for evals and fine-tuning. The Guardrails Hub adds a searchable library of validators, while the open-source framework wraps LLM apps with policy checks for hallucinations, PII leaks, jailbreaks, and custom rules. At scale, the platform supports thousands of realistic scenarios automatically and a 65+ community-built guardrails library. It works across any LLM and any deployment option, including cloud and on-prem, and the site points to integrations and ecosystem links such as Meta, Anthropic, Microsoft Presidio, Arize AI, and Zoom. Customer examples include Masterclass, AI Verify, Meta Superintelligence, and Changi Airport Group.

Why use Guardrails AI?

• It combines pre-release simulation and live runtime protection, so teams can catch failures before and after deployment.
• The open-source framework and hub give teams a large community-built guardrail library instead of starting from scratch.
• It supports any LLM and any deployment option, which reduces lock-in when stacks change.
• Snowglobe turns simulated conversations into judge-labeled datasets, helping teams reuse testing work for evals and fine-tuning.
• The platform is built for high-volume testing, with hundreds of conversations in minutes and thousands of scenarios automatically.

Who is Guardrails AI for?

• AI product teams who need realistic scenario coverage before shipping chatbot changes.
• ML engineers who want eval datasets and runtime checks for LLM workflows.
• Safety and compliance teams who need policy enforcement across AI outputs.
• Platform teams who need guardrails that fit cloud or on-prem deployments.
• Research teams who need to simulate failures and inspect edge cases at scale.

What are Guardrails AI's key features?

What does Guardrails AI integrate with?

• Zoom
• DeepLearning.AI
• Arize AI
• BespokeLabs.AI
• Microsoft Presidio
• Wikipedia
• Meta
• Anthropic

What are Guardrails AI's use cases?

How does Guardrails AI work?

1. Connect your first agent with Connect Your Agent, then point Guardrails AI at the workflow you want to evaluate or protect. Choose the LLM you already use, since Any LLM support keeps the setup flexible.
2. Build or import Eval Sets for Chatbots and Fine-tuning Datasets to represent the conversations and edge cases you care about. Use Train on Data You Don't Have Yet to simulate scenarios before real traffic arrives.
3. Run Search and explore, plus lightning-fast search, to inspect failures across thousands of realistic scenarios automatically. Use Find Where Your Agent Breaks to pinpoint the prompts, responses, or branches that need attention.
4. Add Validators and AI-powered validation to enforce policies and detect risks at runtime. Then use Low-latency, real-time protection to control every interaction without adding noticeable delay.
5. Ship with Control What Ships to Production, then keep iterating as new cases appear. Use Any Deployment Options to move the same guardrails across cloud or self-hosted environments as your system grows.

Frequently asked questions