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guardrails-ai

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Guardrails AI — LLM output validation and guardrails. Define guardrails as XML/JSON specs, validate outputs against structural and semantic constraints, correct/retry on failure, and audit model behavior.

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How to use this skill

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Source SKILL.md: https://github.com/mkurman/zorai/blob/HEAD/skills/scientific-skills/guardrails-ai/SKILL.md

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Overview

Guardrails AI provides a guardrails framework for LLM applications with structured output validation, type safety, retry/reprompt logic, and risk management. Uses RAIL (Reliable AI Markup Language) specs or Pydantic models.

Installation

uv pip install guardrails-ai

Basic Guard

import guardrails as gd

rail_spec = (
    '<rail version="0.1">'
    '<output>'
    '  <string name="summary" description="Brief summary" format="length: 1-100"/>'
    '  <integer name="sentiment" format="valid-choices: {1, 0, -1}"/>'
    '</output>'
    '<prompt>'
    'Summarize this text: {{text}}'
    '</prompt>'
    '</rail>'
)

guard = gd.Guard.from_rail_string(rail_spec)
raw, validated = guard(text="I loved this movie!")
print(validated)  # {"summary": "...", "sentiment": 1}

Pydantic Guard

from pydantic import BaseModel
from guardrails import Guard

class Extraction(BaseModel):
    name: str
    age: int = 0

guard = Guard.from_pydantic(Extraction)
result = guard("John is 25 years old")

References