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ScriptsMar 31, 2026·2 min de lecture

Guardrails — Validate & Secure LLM Outputs

Guardrails is a Python framework for validating LLM inputs/outputs to detect risks and generate structured data. 6.6K+ GitHub stars. Pre-built validators, Pydantic models. Apache 2.0.

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Introduction

Guardrails is a Python framework for building reliable AI applications by validating LLM inputs and outputs to detect and mitigate risks, and generate structured data. With 6,600+ GitHub stars and Apache 2.0 license, it provides pre-built validators through Guardrails Hub covering common risk categories (PII, toxicity, hallucination, SQL injection), input/output guards that intercept LLM interactions, structured data generation using Pydantic models, standalone server deployment via REST API, and support for both proprietary and open-source LLMs.

Best for: Teams building production AI apps who need output validation, safety guardrails, and structured outputs Works with: Claude Code, OpenAI Codex, Cursor, Gemini CLI, Windsurf LLMs: OpenAI, Anthropic, Cohere, HuggingFace, and any LLM

Key Features

  • Pre-built validators: PII detection, toxicity, hallucination, SQL injection via Hub
  • Input/output guards: Intercept and validate every LLM interaction
  • Structured outputs: Generate typed data using Pydantic models
  • REST API server: Deploy guards as a standalone service
  • Custom validators: Create domain-specific validation rules
  • Any LLM: Works with proprietary and open-source models

FAQ

Q: What is Guardrails? A: Guardrails is a Python framework with 6.6K+ stars for validating LLM inputs/outputs. Pre-built validators for PII, toxicity, hallucination. Structured outputs via Pydantic. Apache 2.0.

Q: How do I install Guardrails? A: pip install guardrails-ai. Use Guard.from_pydantic(Model) to create validated LLM calls.

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Source et remerciements

Created by Guardrails AI. Licensed under Apache 2.0. guardrails-ai/guardrails — 6,600+ GitHub stars

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