| name | guardrails-ai |
| description | 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. |
| tags | ["guardrails-ai","llm-safety","output-validation","guardrails","governance","python","zorai"] |
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)
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
