Guardrails AI — Validate LLM Outputs in Production

What is Guardrails AI?

Guardrails AI is a framework for adding validation, safety checks, and structural constraints to LLM outputs. It provides 50+ pre-built validators from the Guardrails Hub — covering hallucination detection, PII filtering, toxicity checking, format validation, and more. Wrap any LLM call with a Guard to automatically validate and fix outputs before they reach users.

Answer-Ready: Guardrails AI validates LLM outputs with 50+ validators from Guardrails Hub. Checks hallucination, PII, toxicity, and format compliance. Auto-retries on validation failure. Works with OpenAI, Claude, any LLM. Production-ready with Guardrails Server. 4k+ GitHub stars.

Best for: AI teams deploying LLMs in production needing output safety. Works with: OpenAI, Anthropic Claude, LangChain, any LLM. Setup time: Under 5 minutes.

Core Features

1. Guardrails Hub (50+ Validators)

2. LLM Integration

from guardrails import Guard
from guardrails.hub import ToxicLanguage, DetectPII

guard = Guard().use_many(
    ToxicLanguage(on_fail="filter"),
    DetectPII(on_fail="fix"),
)

# Wrap any LLM call
result = guard(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Summarize this customer complaint"}],
)
print(result.validated_output)  # PII removed, toxicity filtered

3. Auto-Retry on Failure

guard = Guard().use(ValidJSON(on_fail="reask"))

# If LLM returns invalid JSON, automatically retries with corrective prompt
result = guard(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Return user data as JSON"}],
    max_reasks=3,
)

4. Structured Output

from pydantic import BaseModel

class UserProfile(BaseModel):
    name: str
    age: int
    email: str

guard = Guard.for_pydantic(output_class=UserProfile)
result = guard(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Extract user info from: John, 30, john@example.com"}],
)
print(result.validated_output)  # UserProfile(name="John", age=30, ...)

5. Guardrails Server

# Deploy as API server for production
guardrails start --config guard_config.py
# POST /guards/{guard_name}/validate

FAQ

Q: Does it work with Claude? A: Yes, pass model="anthropic/claude-sonnet-4-20250514" to the guard call.

Q: What happens when validation fails? A: Configurable per validator — filter (remove), fix (correct), reask (retry with LLM), or raise (throw error).

Q: Can I write custom validators? A: Yes, extend the Validator base class. Custom validators can use LLMs, APIs, or rule-based logic.