Guardrails AI
used in
0
stacks
Open-source Python framework for validating and correcting LLM inputs and outputs using composable validators.
Wraps LLM calls in Guard objects that run input/output validators for risks such as PII leakage, toxicity, and format violations, with configurable on-fail actions including re-asking the model. A community Validator Hub provides 100+ prebuilt validators, and structured output validation supports Pydantic models. The Apache 2.0 core is maintained by Guardrails AI, which also sells Guardrails Pro, a managed enterprise offering.
Strengths
- Validator Hub with 100+ prebuilt, composable validators
- Structured output validation against Pydantic schemas
- Configurable on-fail actions, including automatic re-ask of the model
- Apache 2.0 licensed core, self-hostable
Tradeoffs
- Many validators pull in heavy ML model dependencies and add per-call latency
- Validator quality is uneven since much of the hub is community-contributed