Guardrails AI

library active open-source

A Python framework for validating LLM outputs. Guardrails AI provides validators for checking response quality, safety, and structure. It can automatically fix invalid outputs through re-asking or programmatic correction, ensuring LLM responses meet specified requirements.

Implements

Concepts this tool claims to implement:

  • Guardrails primary

    Validator library for output checking. Built-in validators for toxicity, PII, profanity, relevance. Custom validator support. On-fail actions (reask, fix, filter, refrain).

  • Structured Output primary

    Pydantic model validation for LLM outputs. RAIL (RAIL AI Language) for defining output schemas. Automatic retry on validation failure.

  • Hallucination secondary

    Provenance validators to check if outputs are grounded. Citation validators. Fact-checking integrations.

Integration Surfaces

  • Python SDK
  • RAIL specification (XML)
  • Pydantic integration
  • LangChain integration
  • Guardrails Hub (validator marketplace)

Details

Vendor
Guardrails AI Inc.
License
Apache-2.0
Runs On
local, cloud
Used By
system

Links

Notes

Guardrails AI focuses on output validation rather than input filtering (though it can do both). The Hub provides community validators. Good for ensuring structured outputs and quality control. Simpler to adopt than NeMo Guardrails but less sophisticated for conversation control.

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