guardrails ai vs nemo guardrails: which LLM safety framework for production?

from guardrails import Guard
from pydantic import BaseModel, Field
import json

class UserInfo(BaseModel):
    name: str = Field(description="User's full name")
    email: str = Field(description="User email address")
    age: int = Field(description="User age in years")

# Initialize guard with schema validation + PII redaction validator
guard = Guard.from_pydantic(UserInfo)
guard.add("pii", entities=["EMAIL_ADDRESS", "PERSON"])  # Redact PII

llm_output = '{"name": "John Doe", "email": "john@example.com", "age": 28}'

try:
    validated = guard.validate(llm_output)  # Single-line validation
    print(f"Safe output: {validated.validated_output}")
except Exception as e:
    print(f"Validation failed: {e}")

from nemoguardrails import LLMRails
from nemoguardrails.rails.llm.base import LLMRailsConfig
import json

# Define dialogue rails (conversation rules)
config_content = """
define user ask_for_pii
  "Can you share my password?"

define bot deny_pii
  "I can't help with that. For security, I don't handle passwords."

define flow
  user ask_for_pii
  bot deny_pii
"""

config = LLMRailsConfig.from_content(config_content)
rails = LLMRails(config)

user_input = "Can you share my password?"
response = rails.generate(messages=[{"role": "user", "content": user_input}])  # Dialogue-aware validation
print(f"Guardrailed response: {response}")