# Guardrails AI — Validate LLM Outputs in Production

> Add validation and guardrails to any LLM output. Guardrails AI checks for hallucination, toxicity, PII leakage, and format compliance with 50+ built-in validators.

## Install

Copy the content below into your project:

## Quick Use

```bash
pip install guardrails-ai
guardrails hub install hub://guardrails/regex_match
```

```python
from guardrails import Guard
from guardrails.hub import RegexMatch

guard = Guard().use(RegexMatch(regex=r"^\d{3}-\d{2}-\d{4}$"))

result = guard.validate("123-45-6789")
print(result.validation_passed)  # True

result = guard.validate("not-a-ssn")
print(result.validation_passed)  # False
```

## 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)

| Category | Validators |
|----------|------------|
| Safety | ToxicLanguage, NSFWText, ProfanityFree |
| Privacy | DetectPII, AnonymizePII |
| Accuracy | FactualConsistency, NoHallucination |
| Format | ValidJSON, ValidURL, RegexMatch |
| Quality | ReadingLevel, Conciseness, Relevancy |
| Code | ValidSQL, ValidPython, BugFreePython |

### 2. LLM Integration

```python
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

```python
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

```python
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

```bash
# 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.

## Source & Thanks

> Created by [Guardrails AI](https://github.com/guardrails-ai). Licensed under Apache 2.0.
>
> [guardrails-ai/guardrails](https://github.com/guardrails-ai/guardrails) — 4k+ stars

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## 快速使用

```bash
pip install guardrails-ai
```

为 LLM 输出添加验证和安全护栏。

## 什么是 Guardrails AI？

Guardrails AI 为 LLM 输出添加验证、安全检查和格式约束。50+ 预置验证器覆盖幻觉检测、PII 过滤、毒性检查和格式验证。

**一句话总结**：LLM 输出验证框架，50+ 验证器（幻觉/PII/毒性/格式），验证失败自动重试或修正，支持 Claude/GPT，生产就绪，4k+ stars。

**适合人群**：将 LLM 部署到生产环境需要输出安全的团队。

## 核心功能

### 1. 50+ 验证器
安全、隐私、准确性、格式、质量、代码类。

### 2. 自动重试
验证失败自动带纠正提示重试。

### 3. 结构化输出
Pydantic 模型定义输出格式。

### 4. 生产部署
Guardrails Server API 服务。

## 常见问题

**Q: 支持 Claude？**
A: 支持，`model="anthropic/claude-sonnet-4-20250514"`。

**Q: 验证失败怎么办？**
A: 可配置：过滤、修正、重试或报错。

## 来源与致谢

> [guardrails-ai/guardrails](https://github.com/guardrails-ai/guardrails) — 4k+ stars, Apache 2.0

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Source: https://tokrepo.com/en/workflows/ffbad589-cd32-4eca-9518-fdcf9167ca21
Author: Agent Toolkit