AI GuardrailsOpen Source✦ Free Tier

Guardrails AI

Output validation and structured generation for LLMs

⭐ 4,500 stars● Health 85/100 — Active· commit recency (40 pts) · star momentum (30 pts) · issue ratio (20 pts) · forks (10 pts)App Infrastructure

Open in Builder →Website ↗GitHub ↗

About

Open-source framework for adding input/output validation to LLM applications. Define validators (Pydantic-style) for LLM responses to catch hallucinations, PII leaks, bias, and schema violations. Supports automatic retry and reask on validation failure, with any LLM provider.

Choose Guardrails AI when…

•You need LLM outputs to conform to a specific schema or policy
•You want automatic PII detection and redaction in LLM responses
•You're building production AI that requires structured, validated output

Builder Slot

How do you keep your AI safe?Optional for most stacks

Input/output validation, topic controls, and safety rails that ensure LLM responses stay within defined policies

Dev Tools

Not applicable

App Infra

Optional

Hybrid

Optional

Other tools in this slot:

NeMo Guardrails Lakera Guard LLM Guard OpenAI Guardrails

Stack Genome Detection

AIchitect's Genome scanner detects Guardrails AI in your project via these signals:

pip packages

guardrails-ai

env vars

GUARDRAILS_API_KEY

Integrates with (2)

LiteLLMLLM Infrastructure

Guardrails AI plugs into LiteLLM-routed calls as a validation step, applying structured-output and policy validators across any model LiteLLM supports.

→ Add validator-driven guardrails to any LiteLLM-backed application without changing the model provider.

Compare →

OpenAI APILLM Infrastructure

Guardrails AI ships a drop-in wrapper around the OpenAI client that runs validators against structured outputs and re-prompts on validation failure.

→ Get validated, schema-conformant outputs from OpenAI without writing retry logic by hand.

Compare →